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A Framework for Enhancing Life Cycle Inventory Data Availability | Kinh tế vĩ mô | Trường Đại học kinh tế Thành Phố Hồ Chí Minh

Conclusions The integrated LCI data collection process and strategy has demonstrated the potential role of a multinational company to quickly engage and act as a strong enabler to unlock latent data for various aspects of the confectionery supply chain. Overall, it is recommended that the research findings serve as the foundations to transition towards a standardised procedure which can practically guide other multinational companies to considerably increase the availability of LCI data. Tài liệu được sưu tầm và soạn thảo dưới dạng file PDF để gửi tới các bạn cùng tham khảo, ôn tập đầy đủ kiến thức, chuẩn bị cho các buổi học thật tốt. Mời bạn đọc đón xem!

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DATA AVAILABILITY, DATA QUALITY

A framework for increasing the availability of life cycle inventory

data based on the role of multinational companies

Jamal Hussain Miah

1,2

& Andrew Griffiths

& Ryan McNeill

& Sharla Halvorson

Urs Schenker

& Namy Espinoza-Orias

& Stephen Morse

& Aidong Yang

Jhuma Sadhukhan

Received: 20 December 2016 /Accepted: 23 August 2017 /Published online: 4 October 2017

The Author(s) 2017. This article is an open access publication

Abstract

Purpose The aim of the paper is to assess the role and effec-

tiveness of a proposed novel strategy for Life Cycle Inventory

(LCI) data collection in the food sector and associated supply

chains. The study represents one of the first of its type and

provides answers to some of the key questions regarding the

data collection process developed, managed and implemented

by a multinational food company across the supply chain.

Methods An integrated LCI data collection process for con-

fectionery products was developed and implemented by

Nestlé, a multinational food company. Some of the key

features includes (1) management and implementation by a

multinational food company; (2) types of roles to manage,

provide and facilitate data exchange; (3) procedures to identi-

fy key products, suppliers and customers; (4) LCI question-

naire and cover letter and (5) data quality management based

on the pedigree matrix. Overall, the combined features in an

integrated framework provide a new way of thinking about the

collection of LCI data from the perspective of a multinational

food company.

Results and discussion The integrated LCI collection frame-

work spanned across 5 months and resulted in 87 new LCI

datasets for confectionery products from raw material, prima-

ry resource use, emission and waste release data collected

from suppliers across 19 countries. The data collected was

found to be of medium to high quality compared with second-

ary data. However, for retailers and waste service companies,

only partially completed questionnaires were returned. Some

of the key challenges encountered during the collection and

creation of data included lack of experience, identifying key

actors, communication and technical language, commercial

compromise, confidentiality protection and complexity of

multi-tiered supplier systems. A range of recommendations

are proposed to reconcile these challenges which include

standardisation of environmental data from suppliers, concise

and targeted LCI questionnaires and visualising complexity

through drawings.

Conclusions The integrated LCI data collection process and

strategy has demonstrated the potential role of a multinational

company to quickly engage and act as a strong enabler to

unlock latent data for various aspects of the confectionery

supply chain. Overall, it is recommended that the research

findings serve as the foundations to transition towards a

standardised procedure which can practically guide other mul-

tinational companies to considerably increase the availability

of LCI data.

Responsible editor: Niels Jungbluth

Electronic supplementary material The online version of this article

(https://doi.org/10.1007/s11367-017-1391-y) contains supplementary

material, which is available to authorized users.

* Jamal Hussain Miah

j.miah@surrey.ac.uk

Nestlé UK Ltd, Rowan Drive, Fawdon, Newcastle Upon Tyne NE3

3TR, UK

Centre for Environment and Sustainability (CES), Faculty of

Engineering and Physical Sciences, University of Surrey,

Guildford GU2 7XH, UK

Nestlé UK Ltd, Group Technical and Production, Haxby Road,

York YO91 1XY, UK

Nestlé Confectionery Product & Technology Centre (PTC), Haxby

Road, York YO91 1XY, UK

Nestlé Research Centre (NRC), CT-Nutrition, Health, Wellness and

Sustainability, 1000 Lausanne 26, Switzerland

Nestlé Research Centre (NRC), Sustainability & Novel Packaging,

1000 Lausanne 26, Switzerland

Department of Engineering Science, University of Oxford, Parks

Road, Oxford OX1 3PJ, UK

Int J Life Cycle Assess (2018) 23:1744–1760

DOI 10.1007/s11367-017-1391-y

Keywords

Confectionery

Data collection

Food industry

Food products

Life cycle inventory

Multinational

1 Introduction

From the early days of life cycle assessment (LCA) over

40 years ago, the availability of Life Cycle Inventory (LCI)

data has been a continuing major problem—a bottleneck—for

the wide application of LCA (Testa et al. 2016; Ang et al.

2014; Finnveden et al. 2009; Pennington et al. 2007). As an

internationally recognised and standardised approach, the ap-

plication of LCA involves four phases which are (1) goal and

scope definition, (2) inventory analysis, (3) impact assessment

and (4) interpretation (ISO 2006). Overall, it is estimated that

70–80% of the time and cost involved in an LCA are related to

data collection in the inventory phase by an organisation, es-

pecially for complex products that have several components

and where the upstream and downstream supply chain struc-

tures are even more complex involving many actors (Testa

et al. 2016; Ang et al. 2014; Berkhout and Howes 1997).

Since the advent of LCA, there are many published LCA

studies where data collection is reported as a background ac-

tivity (Resta et al. 2016 2014; Meinrenken et al. ; Mila i Canals

et al. 2011; Rebitzer and Buxmann 2005). The collection of

data falls into two types: primary data and secondary data.

Primary data are defined as Bdirectly measured or collected

data representative of activities at a specific facility or set of

facilities^ (European Commission 2013). For example,

emissions/consumptions directly related to a specific process

(Kim et al. 2015; Kellens et al. 2011), otherwise known as

process LCI (Islam et al. 2016 2005; Suh and Huppes ).

Primary data tends to be highly specific and accurate. A variety

of techniques can be used to collect primary data such as invoice

bills, metered data, questionnaires, interviews and site visits

(UNEP 2011; BSI PAS 2050 2011; European Commission

2010; EPA 1993, 1995, 2014). Once primary data is collected,

the data is transformed into LCI for a range of environmental

impacts such as Global Warming Potenital (GWP), ozone de-

pletion and acidification (Bare 2011; Goedkoop et al. 2009;

IPCC 2006 2002; Guinée et al. ). In comparison, secondary data

are defined as Bdata that is not directly collected, measured, or

estimated, but rather sourced from a third-party life-cycle-

inventory database^ (European Commission 2013). This can

also include data from publications and reports. However, sec-

ondary data tends to be less specific and highly aggregated.

Some of the major LCI databases (DB) include Ecoinvent DB

(Ecoinvent 2016 2014), US LCI DB (NREL ), World Food LCA

DB (WFLDB) (Nemecek et al. 2014) and Plastics Europe DB

(PlasticsEurope 2015). For both primary and secondary data,

there are guidelines available to ensure completeness, quality

and transparency (Weidema et al. 2013; PEF World Forum

2013; UNEP 2011). Overall, for many LCAs, the common

strategy for data collection is to collect the highest proportion

of data from primary data sources which is carried out by an

LCA practitioner. However, a considerable amount of time and

cost is required by an LCA practitioner to physically collect

primary data and rationalise and interpret LCI data as defined

by the goal and scope of the LCA study (Testa et al. 2016; Jolliet

et al. 2015 2014; Ang et al. ).

In an effort to reduce cost and time of data collection,

several approaches have been developed that streamline and

simplify LCA methodology (Scanlon et al. 2013; Ning et al.

2013; Dowson et al. 2012) including reduction in LCA stages,

e.g. gate-to-gate (factory) (Jimenez-Gonzalez et al. );2000

meta-product-based accounting (Mila i Canals et al. 2011);

single impact categories, e.g. carbon dioxide or freshwater

consumption (Stoessel et al. 2012); cut-off rules, e.g. 95%

data coverage (Almeida et al. 2015); substitution of similar

data (Dong et al. 2015) and simplification of the whole supply

chain which are considered (Roches et al. 2010). However,

despite these efforts, the availability of LCI data continues to

be a consistent problem found in many LCA studies (Resta

et al. 2016; Meinrenken et al. 2014; Mila i Canals et al. 2011).

Over the past 20 years, the primary and secondary data

collected have been used to develop and populate LCI DBs

dedicated at the national level, e.g. the US LCI (NREL 2014);

Australian LCI (ALCAS 2011), Quebec LCI (Lesage and

Samson 2016) and also at the sectorial level, e.g. WFLDB

(Nemecek et al. 2014), Plastics Europe DB (PlasticsEurope

2015) and for agricultural products such as AgriBalyse DB in

France (Koch and Salou 2013; Colomb et al. 2015) or

Agrifootprint DB in the Netherlands (Agri-footprint gouda

2014). However, current LCI DBs are limited in available data

that is current and of high quality. In addition, another aspect

which is rarely discussed is the major gaps from the informa-

tion in the public domain and available LCI datasets given the

considerable rise in environmental reporting by companies

across the full supply chain (Corporate Register 2017).

Although such information may not be suitable as LCI data,

what they do demonstrate is the potential available data and

actors that can be harnessed to provide suitable data for LCA

applications.

Traditionally, the central vehicle to collect and compile LCI

has been by consultants (Ecodesk 2015). However, the effec-

tiveness of consultants to facilitate data exchange is limited as

shown by the availability of data in current LCI DBs. As such,

alternative strategies have emerged which involve single or

multiple actors to catalyse participation and encourage coop-

eration across the supply chain to increase data availability, as

shown in Fig. 1.

Due to the involvement of different actors, a range of dif-

ferent strategies have been developed to facilitate and collect

LCI. For example, web-based systems (Ramos et al. 2016;

BONSAI 2016 2015 2016; Recchioni et al. ; Mistry et al. ;

Bellon-Maurel et al. 2014), trade bodies/industry associations

Int J Life Cycle Assess (2018) 23:1744–1760 1745

(Jungbluth et al. 2016; Popp et al. 2013; Finkbeiner et al.

2003; Pomper 1998) and consultants (Credit 360 2015;

Ecodesk 2015). However, the collection of data by these

routes requires the strong involvement of actors across the

whole supply chain where the main strategy and implementa-

tion process in terms of collecting data and data quality checks

has been on a voluntary basis, promoted and instigated at a top

level by a third party, e.g. research institutes, universities,

governments, industry associations and consultants

(Recchioni et al. 2015; Skone and Curran 2005). Even so,

the ability of a third party to effectively engage and therefore

collect data in a reasonable and practical timeframe with actors

across the supply chain will be limited as they will not have

full knowledge of the supply chain or the limitations of inter-

nal processes adopted by actors across that chain (Lesage and

Samson 2016).

Another strategy that has received little attention is a

company-led approach, especially from the perspective of

powerful and influential actors such as manufacturing and

retail companies. This is an important, and perhaps surprising,

gap in the literature as due to the integration of manufacturing

and retail companies within supply chains, they offer the op-

portunities to engage, initiate, collect, influence and manage

LCI data directly through actors across the supply chain. As

such, our hypothesis is that a company-led approach to data

collection can provide an effective means to collect data. In

order to satisfy this hypothesis, this paper seeks to address

several research questions by presenting an effective and nov-

el LCI data collection process and the implementation experi-

ence by Nestlé, a multinational food company for confection-

ery products. The research questions are as follows:

1. What is the timeframe to collect inlet/outlet flow data

and can it be accelerated?

2. How much data should be collected and are their limita-

tions on quality?

3. What are the effective tools to collect data?

4. Who are the key actors in the supply chain and how to

identify them?

5. How effective is a company acting as the facilitator for

data exchange?

6. What are the motivations for data exchange?

7. What are the challenges of collecting LCI data?

8. Can the collection of inlet/outlet flow data be

standardised?

9. What is the resource required to collect inlet/outlet flow

data?

10. What are the quality controls required to ensure robust

datasets?

11. What company initiatives are recommended to promote

an efficient LCI data collection?

The paper begins by presenting the proposed LCI data

collection process employed by Nestlé in Sect. 2. This is

followed by a selection of results of the LCI data collection

process for confectionery products in Sect. 3. A discussion of

the implementation experience, key challenges encountered

and how the Nestlé LCI process compares to other initiatives

and—in particular—what were the major differences and

what we can learn from Nestlé’s experience that will help with

LCI collection is provided in Sect. 4. Lastly, the conclusions

are provided in Sect. 5.

2 Methods

2.1 Description of case company and food factory

The case company is Nestlé UK Ltd., a large food company in

the UK and a subsidiary of Nestlé SAwho are a global leading

nutrition, health and wellness food company. Across the

globe, Nestlé are active on addressing many sustainability

issues related to the Sustainable Development Goals (SDGs)

as part of their Creating Shared Value (CSV) strategy (Nestlé

2015a). For example, working with smallholder farmers

through the Nestlé Cocoa plan (Nestlé 2015b) and Nestlé

Nescafe plan (Nestlé 2015c), assessing and optimising the

environmental impact of Nestlé products by LCA-based ap-

proaches (Nestlé 2013) and contributing to the development

of environmental data across the supply chain such as the

World Food LCA database (WFLDB 2014). As an organisa-

tion, there is not only the potential but a broad array of expe-

rience which can contribute to supply chain engagement and

expedite data collection across the supply chain.

In the UK, Nestlé have 14 food factories that manufacture a

range of products that include coffee, cereals, pet food, water

and confectionery. The case factory is based in the North East

of England that manufactures a range of confectionery prod-

ucts that are sugar, chocolate and biscuit based by utilising a

diverse range of processing technologies. In total, there are

approximately 130 Stock Keeping Units (SKUs) which are a

variation of a brand product format, e.g. single bar pack and

Fig. 1 Different types of actors which can play a role to collect LCI data

1746 Int J Life Cycle Assess (2018) 23:1744–1760

multiple bars pack. The SKUs are sold to a range of customers

both in the UK and across the globe (Miah et al. 2015a). The

use of a case study in this way allows for an in-depth explo-

ration of the supply chain, and while it is acknowledged that

the findings are specific to that chain, it can be reasonably

surmised that the results are applicable for other multinational

food companies who manufacture and sell food products di-

rectly to retailers.

2.2 Overview of confectionery LCI data collection process

The LCI data collection process was initiated and developed

by a transdisciplinary process involving both Nestlé practi-

tioners and academics from the University of Surrey (Miah

et al. 2015b). The LCI data collection process presented here

(Fig. 2) is based on LCI guidelines (Nemecek et al. ;2014

ALCAS 2014; UNEP 2011; BSI PAS 2050 2011; European

Commission 2010) and the challenges faced by Rebitzer et al.

(2004) and Berkhout and Howes (1997). As a methodology,

the LCI data collection process displays features which are

found in approaches by different companies, e.g. data sources,

questionnaires, data quality management, etc. What distin-

guishes the approach presented here is the combined features

and, more importantly, the role of a multinational food com-

pany (e.g. Nestlé), rather than a third party, to initiate, moti-

vate, accelerate and manage the whole collection of inlet/

outlet flow data across the supply chain.

The goal of the LCI data collection process is to provide an

effective and efficient streamlined route to practically collect

data—on a voluntary basis—across different input intensities

such as electricity, natural gas, water and solid waste that is

both specific and general at different stages of the product

supply chain that can be used to conduct an LCA, e.g. envi-

ronmental hotspot analysis.

The scope of the primary data collection process includes

first-tier suppliers, factory, retailer, consumer and disposal.

The farm-level stage was not included due to the indirect

relationship with farmers and existing Nestlé initiatives such

as the Cocoa plan (Nestlé 2015b), Nescafe plan (Nestlé

2015c) and contributing partner to the World Food LCA da-

tabase (WFLDB 2014). The integrated LCI data collection

process begins at the food factory because food manufacturers

typically carry out the design of the product which sets forth

the product supply chain structure both upstream and down-

stream. From here onwards, the data collection strategy

branches both upstream and downstream of the product sup-

ply chain where the collected data is reviewed, analysed and

normalised, if required. The final stage involves a reconcilia-

tion and aggregation of LCI datasets.

The responsibility for the whole management and imple-

mentation (including analysis) of the LCI data collection pro-

cess is by a single person in Nestlé known as the data collec-‘

tor manager’. On occasion, internal and external LCA experts

are sought for advice. Overall, a range of people are involved

throughout whose role falls into two categories: (1) data pro-

vider and (2) data exchange facilitator. The ‘data provider’ are

people from different organisations across the stages of the life

of a food product which provide data. The data exchange‘

facilitator’ are people primarily from Nestlé who have

established relationships with data provider organisations to

facilitate data exchange. From Nestlé’s perspective, an indic-

ative level of resource required and expected data quality is

provided at each life cycle stage as guidance. The different

stages are explained in the following subsections.

2.3 Description of the potential available resource

The potential available resource is an indication of the differ-

ent people that could potentially be made available from the

food company to participate in the collection of inlet/outlet

flow data. The process to identify people is a continuing pro-

cess but starts during the goal and scope definition, before the

identification of SKUs, by developing a list/map of potential

available resource based on recommendations from the

decision-maker who commissioned the LCA. The decision-

maker is likely to be someone in a senior position responsible

for environmental sustainability improvements in the compa-

ny. Following this, further people can be identified as data

collection progresses. The types of people involved are pri-

marily internal to the food company from the environment/

sustainability department to provide further guidance and di-

rection towards data providers both internal and external to the

food company. For example, at the factory life cycle stage, the

food company is directly involved with the management and

operation of the food factory and will have several depart-

ments where various data is collected related to the environ-

ment. As such, there are a large number of people that could

be coordinated to collect inlet/outlet flow data at the factory

life cycle stage. In comparison to the farm-level life cycle

stage, the food company will not necessarily have a direct

involvement with the management and operation of the farm

as Nestlé does not own farms. Although, they do have direct

suppliers, where a strong relationship is established, through

which data collection is possible indirectly to the farmers. As

such, there will be a low number of people that could be

coordinated to collect inlet/outlet flow data at the farm-level

life cycle stage. Overall, the types of people involved internal-

ly to the food company will vary depending on the life cycle

stage as different departments or functions will have varying

knowledge based on their role, experience and the relation-

ships they have with people both internally and externally via

institutions. The degree of engagement of human resources in

LCI-related activities will vary for different food companies,

but a general description is provided in Table 1 to distinguish

between low, medium and high resources. The direct relation-

ship refers to a business/professional relationship. On the

Int J Life Cycle Assess (2018) 23:1744–1760 1747

Farm level

Raw material

processing

Food factory Customers Consumers Disposal

START

Identify key products

Identify ingredients and

packaging materials

Identify ingredients and

packaging suppliers

Develop Life Cycle

Inventory (LCI)

questionnaire

Email LCI questionnaire

to suppliers and invite to

a webinar

Follow-up suppliers with

emails and phon e calls

Extract factory

environmental data

Develop environmental

standards

Extract LCI data and

standardise

Develop customer

category map

Identify raw materials

from LCI data extraction

Identify major customers

Develop Life Cycle

Inventory (LCI)

questionnaire

Email LCI questionnaire

to customers and invite

to a telephone meeting

Follow-up suppliers with

emails and phone calls

Extract LCI data and

standardise

Search ge neral LCI

databases and literature

for LCI profiles on raw

materials

Determine consumer

profile

Extract environmental

impa ct of consumer

profile

Identify routes to

disposal

Extract environmental

impa ct of disposal

routes

Data Quality Management

LCI dataset compilation

Life cycle

stages

Types of

Actors

involved

Life Cycle

Inventory

collection

process

Output

Farmers

NGOs

Research institutes

Agricultural cooperatives

Food processors

NGOs

Research institutes

Trade associations

Food company

Retailers

Research institutes

Trade associations

Retailers

Research institutes

NGOs

Waste service

providers

National government

Local government

Potential

Available

resource

Expected

Data quality

LOW

MEDIUM / HIGH HIGH

MEDIUM / HIGH

LOW / MEDIUM MEDIUM / HIGH

Define Goal and Scope

Role of

Actors

Data provider

Data exchange

facilitator

Data collector

manager

Data provider

Data exchange

facilitator

Data provider

Data exchange

facilitator

Data provider

Data exchange

facilitator

Data provider

Data exchange

facilitator

Data provider

Data exchange

facilitator

Procurement

Logistics

Engineers

SHE

Production

Logistics

Sales / marketing

Procurement

Logistics

Sales / marketing

Logistics

Sales / marketing

Logistics

Engineers

SHE

Logistics

Fig. 2 Life Cycle Inventory (LCI) data collection process

1748 Int J Life Cycle Assess (2018) 23:1744–1760

other hand, the indirect relationship refers to the business/

professional relationship with an intermediary to collect data

from the life cycle stage.

2.4 Description of data quality management

The management of data quality primarily involves the vali-

dation of data from the various life cycle stages to ensure data

is robust, and thereby reduces the level of uncertainty in fur-

ther analysis. A semi-quantitative assessment method known

as the pedigree matrix is used which was originally developed

by Weidema and Wesnaes (1996) and has gained traction over

the course of 20 years to become the de facto quality assess-

ment method for several LCI DBs (Ecoinvent 2016; NREL

2014; ALCAS 2011). The pedigree matrix contains ratings for

different data quality indicators (DQIs) such as reliability (R),

completeness (C), temporal correlation (TC), geographical

correlation (GC) and technological correlation (TeC). The

DQIs are then assessed based on the judgement of experts

(e.g. LCA practitioners) and converted into a data quality

score (DQS) by Eq. (1). The score is rated into high

(DQS ≤ 1.6), medium (DQS ≥ 1.6 to <3) and low (DQS 3≥

to ≤ 5) quality.

DQS

R þ C þ TC þ GC þ TeC þ X

 4

þ 4

ð1Þ

where

DQS data quality score

R, C, TC, GC, TeC: see values found in Weidema and

Wesnaes (1996)

weakest quality level obtained (i.e. highest numerical

value) among the data quality indicators

i number of applicable data quality indicators

The data quality management process involves

reviewing the data provided to (1) screen for any data gaps,

(2) identify anomalies in datasets and (3) ascertain data

quality as described in Weidema and Wesnaes (1996) and

Eq. (1). Based on the review, a list of questions is

developed and sent to the data provider for clarification.

From here onwards, a two-way dialogue (via emails, phone

calls and physical meetings) continues with the aim to in-

crease the quality of data to the highest quality level which

is practical and economical to collect. Overall, throughout

the data analysis approach, internal and external LCA ex-

perts are sought to provide additional quality assurances on

the compiled dataset. For example, possible explanations

of anomalies in data and verification of expected results.

2.5 Description of food factory data collection: stages

A1–A3

After the goal and scope was defined, the next step was to

identify key products which can include distinct product cat-

egories and major products. The identification process was

carried out through engagement with the factory production

team who were able to provide production data split out into

product categories. For the list of SKUs in each key product

category, the major SKU was selected based on a Pareto anal-

ysis of the SKU production volumes which can be extracted

from production and sales records. The major SKU is thus the

reference product for the key product category throughout the

whole LCI data collection process.

At a factory level, the input intensity monitored will typi-

cally cover energy, water, solid waste and liquid waste. The

scale of available data will vary depending on the coverage of

utility meters across site and within processes, billed utility

invoices and systems to record physical materials, e.g. solid

waste. As such, a combination of the available data in con-

junction with reasonable estimates based on expert judgement

was needed to allocate the input intensity down to a key prod-

uct group based on mass allocation. A general rule for the

allocation process is not possible as this will depend on the

combination of available data and expert judgement.

Alternatively, an economic allocation approach can be used

if economic data is readily available. However, the major lim-

itation compared to a mass allocation approach is the repre-

sentation of input-output flows based on economic data rather

than physical dimensions based on mass; hence, this is subject

to price variability. As such, an economic allocation is recom-

mended when mass data is not available.

Table 1 The degree of

engagement of human resources

in LCI related activities

Human resources Description

Low • No involvement in the life cycle stage

• Indirect relationship with life cycle stage operator via an intermediary, e.g. co-operatives

Medium • No direct involvement in the life cycle stage management or operation

• A mix of direct and indirect relationships with life cycle stage operator

High • A direct involvement in the life cycle stage via management and/or operation

• A range of departments actively involved in environmental issues

Int J Life Cycle Assess (2018) 23:1744–1760 1749

2.6 Description of raw material processing data collection:

stages B1–B6

For the major SKU identified, a list of ingredients and pack-

aging materials was determined based on the product recipe

and packaging specification. The source of the data was ob-

tained from the production specialists at the food factory.

Following this, the identification of suppliers involved engag-

ing with the procurement team of the food manufacturing

company who has a business relationship with the suppliers

and is able to formally and more appropriately request infor-

mation. Prior to contacting the suppliers, an LCI questionnaire

and cover letter was developed to provide the suppliers with

the motivations of the request and the types of information

required. The design of the questionnaire contains a range of

information categories shown in Table 2. The questionnaire

template can be found in the Electronic Supplementary

Material.

The cover letter developed was contained to a single page

to keep the communication concise. It included the purpose of

the data request, contact details and a deadline of 4 weeks

from receipt. The cover letter was signed off by the procure-

ment contact who managed the business relationship with

suppliers and by the head of sustainability and head of pro-

curement in the food manufacturing company. This was to

ensure the request was supported at a high level in the food

manufacturing company.

Both the inlet/outlet flow questionnaire and cover letter

were sent via e-mail to the business contact in the supplier

company. The option to follow-up with a webinar or phone

call was provided. Any further communications took place

through e-mails to discuss and clarify the request in more

detail. When the inlet/outlet flow questionnaire was returned,

the data were reviewed to gauge the sensibility of the infor-

mation. The LCI data for each inlet/outlet flow obtained for a

specific geographic location were then converted to a range of

life cycle impacts per tonne of material manufactured based

on different life cycle impact assessment (LCIA) methodolo-

gies discussed in Sadhukhan et al. (2014) using GaBi 6.0.

2.7 Description of farm level data collection: stages C1 C2–

The raw materials required to manufacture the ingredients and

packaging materials can be found in the information extracted

from the inlet/outlet flow questionnaires sent to the food

manufacturing company suppliers. For the raw materials that

could not be extracted or were not available due to incomplete

or unreturned inlet/outlet flow questionnaires, literature

searches were carried out on the general manufacture of in-

gredients and packaging materials to create a list of raw ma-

terials. Once a list of raw materials was made, they were

categorised into similar groups (e.g. dairy includes milk, whey

etc) adopting the approach by Mila i Canals et al. (2011) for

modular ‘builidng blocks’. Afterwards, the raw material

groups were cross-referenced with commercial LCI databases

to find similar LCI profiles (Ecoinvent 2016; Quantis 2014;

ALCAS 2011).

2.8 Description of customers’ data collection: stages

D1–D6

The portfolio of customers for confectionery products can be

highly diverse depending on how developed the market where

the products are sold, e.g. high street retailers to convenience

stores to cinema outlets to snacks on an aeroplane. As such,

the development of the range of customer categories was

based on the literature (Spencer and Kneebone 2007;

Spencer and Kneebone 2012) and from the food manufactur-

ing company logistics team. For the major SKUs, it was pos-

sible to extract delivery orders over a 1-year time period to

identify major customer categories and specifically the cus-

tomers per se. From the identification of the major customer, it

was possible to identify the key account manager inside the

food manufacturing company who manages the business re-

lationship with an equivalent in the customer company. At the

same time, the sustainability/environmental contact in the

food manufacturing company was able to provide an equiva-

lent contact in the major customer category from previous and

ongoing relationships. Before contacting the customer, a tai-

lored LCI questionnaire was developed to include the same

information categories as for customers. The processing of the

returned inlet/outlet flow questionnaire is the same as

discussed in Sect. 2.6.

2.9 Description of consumers’ data collection: stages

E1–E2

As most confectionery products are ‘ready to eat’, they do

have short shelf-lives, and consumers are not expected to carry

out any processes before consuming them. In this particular

scenario, consumer behaviour regarding transportation from

point of purchase (customer store location) to consumption,

storage of product, food waste and disposal of packaging are

the relevant parameters to be evaluated. Therefore, the process

to collect data for the major SKUs was largely based on liter-

ature supported by the marketing and sales team in the food

manufacturing company and retailers.

2.10 Description of disposal data collection: stages F1–F2

For the major SKUs and based on the consumption behaviour

of the consumer, the waste materials can be identified. The

process to determine the routes to disposal should in principle

follow the waste hierarchy (European Commission 2008), but

in practice, this can differ where there are national averages

that can be taken that provide recycling rates and disposal to

1750 Int J Life Cycle Assess (2018) 23:1744–1760

landfill (EA 2014). For more specific environmental impact of

different waste treatment options, the engagement with waste

service providers that operate on a local or national level can

provide data on a kilogram basis.

3 Results

3.1 Amount of data collected

The amount of data collected from both primary and second-

ary data sources are shown in Table 3. Overall, 183 LCI

datasets were targeted for specific ingredients of which 129

were collected from primary and secondary sources. The total

primary data collected was 100 whereas secondary data rep-

resented 29.

3.2 Types of data collected

A range of primary data was collected for the factory, raw

material processing and retailer shown in Tables 4 and 5 and

Electronic Supplementary Material. For the conversion of pri-

mary data to environmental impacts, this was based on the

energy data collected. The collection of emissions data was

not found to be available across the majority of data providers

as this was not measured and/or was confidential.

For the confectionery factory, the input intensity data is

provided at the factory and product category level, shown in

Table 4. Overall, the sugar product category has the highest

natural resources consumption.

The LCI questionnaire and cover letter developed were

sent via e-mail to 67 ingredients and packaging suppliers

requesting 2013 data only. In total, only 55% returned ques-

tionnaires that went through a review process with the sup-

pliers over a series of e-mails before being converted on a

relative basis, e.g. per ton of bulk product delivered to the

confectionery factory. The LCI data were then converted to

a range of environmental impacts to widen the application

depending on the preference of LCA practitioner, see

Electronic Supplementary Material for full LCIA data.

Similar to raw material processing, an LCI questionnaire

and cover letter was sent to two major food retailers in the UK.

However, only one retailer was able to provide some informa-

tion which was not in the correct format, as shown in Table 5.

3.3 Quality of data collected

3.3.1 DQSs for both primary and secondary data

The data collected was assessed based on the pedigree data

quality matrix. A comparison of the calculated data quality

score (DQS) for 123 LCI datasets is shown in Fig. 3. The

orange bars represent secondary data whereas the blue bars

represent primary data.

Overall, the DQS were then categorised into high-,

medium- and low-quality groups, as shown in Table 6.

Table 2 An overview of LCI

questionnaire categories and

general content

Information

category

412

701

4.16

0.020

Sugar product category 8

642

1570

3.64

0.057

Chocolate biscuit

product category

714

1081

2.22

0.070

Major SKUs

Estimated based on average SKU

1752 Int J Life Cycle Assess (2018) 23:1744–1760

point to collect data was from retailers but it was unclear if this

was the right choice given the diversity of customers. In order

to navigate through the complexity, a literature review was

carried out to find different distribution channels for food

products. The information collected was combined with

Nestlé data based on discussions with the marketing and sales

teams to create a full and general representation of the entire

customer portfolio for confectionery; see Fig. 6.

3.5 Challenges encountered during primary data

collection

In the course of the LCI data collection process, Nestlé en-

countered several challenges which are shown in Table 8 with

a range of recommendations proposed to resolve.

4 Discussion

4.1 Comparison with other data collection approaches

A critical assessment of a novel integrated LCI data collection

process and strategy has been presented from the perspective

of a multinational food company. It is the first of its kind and

helps to fill an important gap in the existing knowledge on

alternative strategies for LCI data collection. One of the key

features of the process is the ability to leverage the resources

of a manufacturing company to efficiently collect environ-

mental data across the whole supply chain. For example, it

was found that Nestlé—a multinational food company—was

able to harness the perceived power of the organisation and

translate into a supply chain leadership role to collect data. For

example, the involvement of more than 50 different people

across many divisions within the company was engaged to

identify actors across the supply chain and to facilitate data

exchange. In comparison to existing approaches (Ramos et al.

2016; Jungbluth et al. 2016; Recchioni et al. 2015; Mistry

et al. 2016; Bellon-Maurel et al. 2014; Popp et al. 2013;

Finkbeiner et al. 2003; Pomper 1998), the collection of data

has been primarily on a voluntary basis where the implemen-

tation process is managed by a third party to drive the collec-

tion of data from different actors across the supply chain.

Another major benefit is the speed to collect primary data.

The application of the LCI process at a confectionery factory

in the UK found that a company-led approach was able to

collect a portfolio of new environmental data in a relatively

short period of 5 months. In total, 100 primary LCI datasets

were collected from 67 ingredients and packaging suppliers

across 13 countries. In comparison to other data collection

approaches in the food industry (Ramos et al. 2016;

Jungbluth et al. 2016; Milà i Canals et al. 2011), they do not

provide an indication of the time involved to carry out data

collection, especially for large amounts of data at different

scales of problems, e.g. single product, multi-products, factory

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

101

103

105

107

109

111

113

115

117

119

121

123

DataQualityScores(DQS)

LCIdataset

Fig. 3 A comparison the DQS for 129 LCI datasets collected

Table 5 Environmental aspects

of retail in ambient conditions, for

different scales

Scale Electricity

(kWh/m

day)

Natural gas

(kWh/m

day)

Water

day)

Solid waste

(ton/m

day)

Superstore 0.0944 0.0267 1.53 × 10

−4

6.02 × 10

−6

Supermarket 0.0419 0.0113 7.12 × 10

−5

5.21 × 10

−6

Warehouse 0.021 0.00877 8.77 × 10

−5

1.42 × 10

−5

Int J Life Cycle Assess (2018) 23:1744–1760 1753

or even company level. Based on the experience gained, it is

expected that a second round of data collection could result in

a shorter timeframe of a few months. For example, with a

projected timeframe of 3 months, this could result in 400

LCI datasets per year. Therefore, in addition to existing routes

of data collection (Ramos et al. 2016; Mistry et al. 2016; Popp

et al. 2013; Finkbeiner et al. 2003; Ecodesk 2015), the role of

companies can significantly create more LCI data which can

benefit both companies, supply chain actors and wider

industry.

One other major benefit is the ability to create up-to-date

and high-quality data. For example, the primary data collected

has resulted in 100 new LCI datasets where the majority of the

LCI datasets are not found anywhere in the literature (NREL

2014; Quantis 2014). In addition, the LCI datasets are rela-

tively new where at the time of collecting, data were no more

than 1 year old. Such data will be particularly useful for envi-

ronmental analysis in the confectionery industry which in the

EU alone comprises of over 11,000 confectionery manufac-

turers (CAOBISCO 2015).

Furthermore, another major benefit is the transparency in

data collection to encourage high quality and reproducibility.

For example, novel processes have been developed to visually

describe the rationalisation and identification of ingredients,

suppliers and customers across the supply chain compared to

previous approaches (Ramos et al. 2016; Jungbluth et al.

2016; Recchioni et al. 2015; Mistry et al. 2016; Bellon-

Maurel et al. 2014).

4.2 Quality of data collected and gaps in data

The quality of data collected was found to vary considerably

from both primary and secondary sources based on the pedi-

gree matrix (Weidema and Wesnaes 1996). For example, the

majority of primary data collected was found to be medium

quality whereas the secondary data varied from high-to-low

quality. However, the primary data had the potential to be high

quality but was limited due to the representativeness criteria

since the data only represented one site and not the whole

market/country. Further statistical analysis showed the prima-

ry data had the lowest variation based on the standard devia-

tions of DQS whereas secondary data for disposal and farm

stage had the highest variation. For the retail and disposal

level, only partially completed LCI questionnaires were

returned. As such, data was sourced from the Ecoinvent data-

base (Ecoinvent 2016). However, due to the generalised na-

ture of LCI profiles in Ecoinvent, the quality was found to be

low based on the ratings assigned on the pedigree matrix.

Overall, the highest data quality was obtained for those com-

panies that operate closer along the food supply chain to the

multinational food company leading the data collection pro-

cess. Hence, the critical stages of the supply chain requiring

further research would be agricultural production (farm level)

on one side, and retailers and waste treatment companies on

the other side.

One of the major limitations found in practice was the

collection of primary data at farm level. For the farm level, it

was found few suppliers manage and operate vertically inte-

grated operations from farm level to ingredient/packaging

manufacture. However, due to the complex nature of farms,

they can perform multiple services/functions over various pe-

riods of time creating multiple outputs. As such, the collection

of primary data was out of scope as the timeframe to compile

an inventory of all the materials and energy consumed at a

farm level is much longer (e.g. months to years) compared to

the other life cycle stages. In addition, the infrastructure and

technology required to collect data is less advanced for

farmers and have limited resources in terms of knowledge

and expertise. Therefore, data was sourced from secondary

LCI DBs such as WFLDB (Nemecek et al. 2014) and

AgriBalyse (Koch and Salou 2013; Colomb et al. 2015). To

this extent, the pursuit of specific farm level data should only

be for significant raw materials as there is a trade-off between

the volume of LCI data collected and resources expended in

terms of people’s time. Despite the inclusion of secondary

data, the LCI data collection process has shown that a multi-

national company can potentially engage and facilitate LCI

data collection directly with farmers or indirectly through

first-tier suppliers. Although, longer-term initiatives are

Table 6 DQSs for data collected categorised into high, medium and

low data quality

Data

quality

group

Farm Raw

materials

processing

Factory Distribution Retail Disposal

High 2 0 0 0 0 0

Medium 1 96 4 0 0 0

Low 10 0 0 1 1 8

Table 7 Statistical analysis of

DQS

Farm Raw materials

processing

Factory Distribution

Retail

Disposal

Average 3.23 2.74 2.78 n/a n/a 3.97

Standard deviation 0.95 0.08 Negligible n/a n/a 0.17

One dataset only available

1754 Int J Life Cycle Assess (2018) 23:1744–1760

required to establish environmental training through formal

partnerships (e.g. TESCO 2015; Nestlé 2015a b, , c) to support

and reduce the environmental impact at farms.

Overall, several recommendations are proposed to resolve

data gaps and ensure the highest quality level for incomplete

datasets, outdated data and data using proxies (Sadhukhan

et al. 2014). For incomplete datasets, only sections which

are completed and provide meaningful information are recom-

mended. For outdated data, the pursuit of recent data is en-

couraged. For data using proxies, an investigation of the rep-

resentation in terms of correlation and relevance should be

assessed. In addition, the role of external LCA experts should

be sought to provide additional quality assurances on the com-

piled dataset. For example, possible explanations of anomalies

in data and verification of expected results.

4.3 Effectiveness of tools and processes

An assessment of the different tools and processes applied dur-

ing the LCI data collection is shown in Fig. 4. Although such

tools and processes are found in the general literature (UNEP

2011; European Commission 2010; EPA 1993, ,1995 2014), we

have presented for the first time an assessment of what was

effective from the implementation experience on a two-axis

graph showing the effectiveness to collect data with the effort

required. It was found that the most effective process was the

follow-up calls/e-mails with read receipts to data providers.

However, such an approach was rather intensive and repetitive

as records were kept to track communications.

Another effective process was the regular meetings with

Nestlé personnel to review progress, identify any problems

Fig. 5 Two ingredients map showing how suppliers were reduced from start to end

Fig. 4 Assessment of the

effectiveness of tools deployed

Int J Life Cycle Assess (2018) 23:1744–1760 1755

and provide support. In comparison, Ramos et al. (2016)

found a web-based tool was effective to bring large numbers

of companies together (e.g. 23 food small medium enterprises

(SMEs)) on a single digital platform. Such a tool could be

integrated within a company-led approach but would require

an initial capital expenditure to develop.

4.4 Challenges encountered during implementation

Over the course of the data collection process, several chal-

lenges were encountered as listed in Table 8. In compari-

son to the challenges found in the literature (UNEP 2011;

European Commission 2010; EPA 2014), the major differ-

ence is the comprehensive overview with recommenda-

tions to resolve in the context of implementing a

company-led LCI data collection process. For example,

the data collected within the company, there was a major

challenge of conflict of interest since the data collector

manager, data provider and data exchange facilitator all

work for the same company. Although this may be the

case, the process to ensure robust datasets still remains

by keeping records of data at different stages of transfor-

mation, validating data with experts in the company and

pursuing data which is of high quality based on the pedi-

gree data quality matrix. Further checks on the quality of

data can be carried out by comparing data with similar

materials/products and independently reviewing the data

by a third party.

Another major challenge was the lack of engagement from

supply chain actors, in particular ingredients and packaging

Fig. 6 Customer distribution channels and customer categories for confectionery products

1756 Int J Life Cycle Assess (2018) 23:1744–1760

suppliers. For example, a total of 45 of suppliers did not return

the LCI questionnaires. Based on further discussion with sup-

pliers, it was found that there were several reasons for either

participating partially or not at all. For example, lack of re-

sources and LCA experience, commercial compromise, sen-

sitivity of data disclosure and confidentiality protection. It was

found for the majority of suppliers, in particular the SMEs,

that they did not have experience in completing a LCI ques-

tionnaire to a high level of detail where for some, it was

completely new and for others, they had previously

experienced multiple environmental data requests for various

formats where their LCA teams employ LCA tools.

Furthermore, the commercial implications were a topic that

came up often in the engagement process with suppliers both

SME and large. Despite reassurance measures such as confi-

dentiality protection through NDAs and anonymisation of da-

ta if shared in the public domain, the resistance to participate

with some suppliers still remained. It was found that the level

of participation depended largely on trust and relationships in

terms of the people involved and the length of relationships.

Table 8 List of challenges

encountered by Nestlé

Challenges Recommendations

1. Lack of engagement from supply chain actors 1. Supplier engagement events to raise awareness and

discuss challenges

2. Circulate company sustainability reports and policies

2. Lack of experience by Nestlé and suppliers 3. Standardise environmental data received from

suppliers

4. Educate suppliers with webinars and short PowerPoint

presentation

5. Develop concise and targeted LCI questionnaires

6. Provide 4–8 weeks to return completed LCI

questionnaires

3. Lack of resources 7. Offer assistance to complete (remotely or physically

present)

4. Identifying key actors within Nestlé and across

the supply chain

8. Start building initial contact list from company network

and expand

9. Search for environment/sustainability contacts on

sustainability reports/websites

5. Engaging with actors with no direct business

relationship

10. Contact people with direct business relationship to

pursue request with indirect relationship

11. Integrate environmental considerations in the audit

criteria of multi-tiered suppliers

6. Language barriers from non-UK data providers 12. Reduce communication to e-mails

13. Check with data providers preferred language

14. Provide translated questionnaire

7. Different technical language to express

environmental, engineering and supply chain

information

15. Align technical language to SI units/terminology

8. Commercial compromise 15. Anonymise data sources

16. Be transparent and clear on the application of data to

reassure data provider on minimal compromise

17. Intermediary agent to host the data

9. Sensitivity of data disclosure 18. Aggregate data

19. Expand location point e.g. city to country

10. Confidentiality protection 20. Non-disclosure agreements (NDAs)

21. Respect environmental disclosure policy of suppliers

11. Conflict of interest with same person as data

collector manager, provider and reviewer

22. Independently review data by third party

12. Navigating through a complex multi-tiered

supplier systems

23. Engage with different suppliers and company supply

chain/procurement function personnel to build

common knowledge of supply chain structure

13. Visualising complexity 24. Create basic diagrams verified by supply chain actors

14. Modelling production processes 25. Engage with engineers to verify modelling

Int J Life Cycle Assess (2018) 23:1744–1760 1757

4.5 Motivations for companies to participate

Despite the challenges, several motivating factors were

found for encouraging data providers to participate in the

company-led LCI data collection process as part of their

overarching Corporate Social Responsibility (CSR) strategy

(Dahlsrud 2008). For example, the opportunity to collabo-

rate with Nestlé (e.g. strengthen relationship, ways of work-

ing and partnerships), opportunity to learn about the envi-

ronmental impact of their organisation/product in Nestlé’s

products and opportunity to develop learning experience of

LCI data request. However, in total, only 55% of suppliers

returned the LCI questionnaire. A surprising finding was the

lack of implementation from some companies who publical-

ly advocated sustainability improvements and supplier en-

gagement both at an SME and (multinational corporation)

MNC level as part of their CSR strategy. Despite this, the

role of CSR can be a strong motivator for companies to

participate as it was generally found that the sustainability

commitments by different companies helped companies ini-

tially participate. As such, it is recommended that a range of

initiatives are developed to encourage efficient LCI data col-

lection by the company (i.e. Nestle). Such initiatives will

aim to bring supply chain actors together to develop a mu-

tual understanding on promoting sustainable supply chains.

For example, workshops to discuss strategies to improve

supply chain sustainability, specific partnerships with sup-

pliers on key ingredients and LCA/environmental awareness

training in the food industry.

4.6 Towards a standardised procedure in the food sector

The LCI data collection process has the potential to transition

towards a standardised procedure for the food sector subject to

further application and consensus of stakeholders across the

food industry. However, not all food companies have the abil-

ity to lead an LCI data collection process across the supply

chain since these companies are either SMEs or do not man-

ufacture finished products. As such, in this context, these com-

panies can have a different role in which they can support

organisations seeking to lead an LCI data collection process

across the supply chain. Alternatively, such companies can

group together to initiate an LCI data collection process for

common materials shared between the companies. Despite

this, the LCI data collection process and strategy provides an

initial basis for other companies to further design their respec-

tive data collection strategies.

5 Conclusions

This paper has presented a novel LCI data collection process

developed, managed and implemented by a multinational

food company. It represents one of the very first such studies

of its type to critically assess the role and effectiveness of a

multinational food company on collecting LCI data across

the supply chain. For example, the application at a multi-

product confectionery factory in the UK has resulted in a

portfolio of 100 new environmental LCI datasets from the

interaction with 67 ingredients and packaging suppliers

across the globe and several food retailers. However, the

majority of primary data collected was from ingredients

and packaging suppliers, food factory and partial data for

retailers and waste disposal providers with no data at the

farm level. In addition, several challenges were encountered

during implementation from the lack of experience, identi-

fying key actors, confidentiality protection and complexity

of multi-tiered supplier systems. Despite this, by using the

internal resources, business relationships and influence of a

multinational food company, it was found that a multination-

al company can play a critical role, especially in engagement

and facilitation by transforming latent data found within

companies or reported publically across the supply chain

towards expansion of LCI data.

Furthermore, in order to encourage the reproducibility for

other multinational companies, it is recommended the pro-

posed LCI data collection process serves as a foundation to

contribute towards a standardised procedure, in particular for

food products. The specific features which can contribute to-

wards a standardised procedure includes (1) process flow di-

agram of LCI data collection, (2) identification and role of

actors in the company and across the supply chain, (3) supply

chain maps, (4) processes to manage gaps in data and data

quality and (5) LCI questionnaire. Overall, the key benefits

of the proposed LCI data collection process includes (1) the

ability to leverage the resources of a manufacturing company

to efficiently collect environmental data across the whole sup-

ply chain, (2) the speed to collect primary data, (3) the ability

to create up to date and medium to high quality data and (4)

the increased transparency in data collection. However, further

engagement with different food companies and applications

across food categories would be required to develop a robust

standardised procedure, especially supported by research in-

stitutes and NGOs.

Acknowledgements We wish to gratefully acknowledge and thank the

EPSRC and Nestlé UK Ltd. for their assistance and support in funding

this research as part of the Engineering Doctorate on Sustainability for

Engineering and Energy Systems (EngD SEES) at the University of

Surrey. We would also like to thank two anonymous reviewers for their

suggestions and comments on previous versions.

Open Access This article is distributed under the terms of the Creative

Co m m o ns A ttr i b u tio n 4. 0 I n t e rna t i o nal Li c e n se ( htt p : / /

creativecommons.org/licenses/by/4.0/), which permits unrestricted use,

distribution, and reproduction in any medium, provided you give

appropriate credit to the original author(s) and the source, provide a link

to the Creative Commons license, and indicate if changes were made.

1758 Int J Life Cycle Assess (2018) 23:1744–1760

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Int J Life Cycle Assess (2018) 23:1744–1760 DOI 10.1007/s11367-017-1391-y
DATA AVAILABILITY, DATA QUALITY
A framework for increasing the availability of life cycle inventory
data based on the role of multinational companies
Jamal Hussain Miah 1,2 & Andrew Griffiths3 & Ryan McNeill4 & Sharla Halvorson5 &
Urs Schenker 6 & Namy Espinoza-Orias6 & Stephen Morse2 & Aidong Yang7 & Jhuma Sadhukhan 2
Received: 20 December 2016 / Accepted: 23 August 2017 / Published online: 4 October 2017
# The Author(s) 2017. This article is an open access publication Abstract
features includes (1) management and implementation by a
Purpose The aim of the paper is to assess the role and effec
m-ultinational food company; (2) types of roles to manage,
tiveness of a proposed novel strategy for Life Cycle Inventory
provide and facilitate data exchange; (3) procedures to identi-
(LCI) data collection in the food sector and associated supply
f key products, suppliers and customers; (4) LCI question-
chains. The study represents one of the first of its type andnaire and cover letter and (5) data quality management based
provides answers to some of the key questions regarding the
on the pedigree matrix. Overall, the combined features in an
data collection process developed, managed and implemented integrated framework provide a new way of thinking about the
by a multinational food company across the supply chain. collection of LCI data from the perspective of a multinational
Methods An integrated LCI data collection process for con-food company.
fectionery products was developed and implemented byResults and discussion The integrated LCI collection frame-
Nestlé, a multinational food company. Some of the keywork spanned across 5 months and resulted in 87 new LCI
datasets for confectionery products from raw material, prima-
ry resource use, emission and waste release data collected
Responsible editor: Niels Jungbluth
from suppliers across 19 countries. The data collected was
Electronic supplementary material The online version of this article
found to be of medium to high quality compared with second-
(https://doi.org/10.1007/s11367-017-1391-y) contains supplementary
material, which is available to authorized users.
ary data. However, for retailers and waste service companies,
only partially completed questionnaires were returned. Some * Jamal Hussain Miah
of the key challenges encountered during the collection and j.miah@surrey.ac.uk
creation of data included lack of experience, identifying key
actors, communication and technical language, commercial 1
compromise, confidentiality protection and complexity of
Nestlé UK Ltd, Rowan Drive, Fawdon, Newcastle Upon Tyne NE3multi-tiered supplier systems. A range of recommendations 3TR, UK 2
are proposed to reconcile these challenges which include
Centre for Environment and Sustainability (CES), Faculty of
Engineering and Physical Sciences, University of Surrey,
standardisation of environmental data from suppliers, concise Guildford GU2 7XH, UK
and targeted LCI questionnaires and visualising complexity 3
Nestlé UK Ltd, Group Technical and Production, Haxby Road, through drawings. York YO91 1XY, UK
Conclusions The integrated LCI data collection process and 4
Nestlé Confectionery Product & Technology Centre (PTC), Haxby strategy has demonstrated the potential role of a multinational Road, York YO91 1XY, UK
company to quickly engage and act as a strong enabler to 5
Nestlé Research Centre (NRC), CT-Nutrition, Health, Wellness andunlock latent data for various aspects of the confectionery
Sustainability, 1000 Lausanne 26, Switzerland
supply chain. Overall, it is recommended that the research 6
findings serve as the foundations to transition towards a
Nestlé Research Centre (NRC), Sustainability & Novel Packaging, 1000 Lausanne 26, Switzerland
standardised procedure which can practically guide other mul- 7
tinational companies to considerably increase the availability
Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK of LCI data.
Int J Life Cycle Assess (2018) 23:1744–1760 1745
Keywords Confectionery . Data collection . Food industry .
strategy for data collection is to collect the highest proportion
Food products . Life cycle inventory . Multinational
of data from primary data sources which is carried out by an
LCA practitioner. However, a considerable amount of time and
cost is required by an LCA practitioner to physically collect 1 Introduction
primary data and rationalise and interpret LCI data as defined
by the goal and scope of the LCA study (Testa et al. 2016; Jolliet
From the early days of life cycle assessment (LCA) overt al. 2015; Ang et al. 2014).
40 years ago, the availability of Life Cycle Inventory (LCI) In an effort to reduce cost and time of data collection,
data has been a continuing major problem—a bottleneck—forseveral approaches have been developed that streamline and
the wide application of LCA (Testa et al. 2016; Ang et a sil.
mplify LCA methodology (Scanlon et al. 2013; Ning et al.
2014; Finnveden et al. 2009; Pennington et al. 2007). As an
2013; Dowson et al. 2012) including reduction in LCA stages,
internationally recognised and standardised approach, the ap-e.g. gate-to-gate (factory) (Jimenez-Gonzalez et al. 2000);
plication of LCA involves four phases which are (1) goal and
meta-product-based accounting (Mila i Canals et al. 2011);
scope definition, (2) inventory analysis, (3) impact assessmentsingle impact categories, e.g. carbon dioxide or freshwater
and (4) interpretation (ISO 2006). Overall, it is estimated tha
c tonsumption (Stoessel et al. 2012); cut-off rules, e.g. 95%
70–80% of the time and cost involved in an LCA are related tao
ta coverage (Almeida et al. 2015); substitution of similar
data collection in the inventory phase by an organisation, es-
data (Dong et al. 2015) and simplification of the whole supply
pecially for complex products that have several componentschain which are considered (Roches et al. 2010). However,
and where the upstream and downstream supply chain struc-despite these efforts, the availability of LCI data continues to
tures are even more complex involving many actors (Testabe a consistent problem found in many LCA studies (Resta
et al. 2016; Ang et al. 2014; Berkhout and Howes 1997).
et al. 2016; Meinrenken et al. 2014; Mila i Canals et al. 2011).
Since the advent of LCA, there are many published LCA Over the past 20 years, the primary and secondary data
studies where data collection is reported as a background ac- collected have been used to develop and populate LCI DBs
tivity (Resta et al. 2016; Meinrenken et al. 2014; Mila i Canalsdedicated at the national level, e.g. the US LCI (NREL 2014);
et al. 2011; Rebitzer and Buxmann 2005). The collection of
Australian LCI (ALCAS 2011), Quebec LCI (Lesage and
data falls into two types: primary data and secondary data
S.amson 2016) and also at the sectorial level, e.g. WFLDB
Primary data are defined as Bdirectly measured or collected(Nemecek et al. 2014), Plastics Europe DB (PlasticsEurope
data representative of activities at a specific facility or set o
2f015) and for agricultural products such as AgriBalyse DB in
facilities^ (European Commission 2013). For example, France (Koch and Salou 2013; Colomb et al. 2015) or
emissions/consumptions directly related to a specific process Agrifootprint DB in the Netherlands (Agri-footprint gouda
(Kim et al. 2015; Kellens et al. 2011), otherwise known a
2 s014). However, current LCI DBs are limited in available data
process LCI (Islam et al. 2016; Suh and Huppes 2005)t.hat is current and of high quality. In addition, another aspect
Primary data tends to be highly specific and accurate. A variety
which is rarely discussed is the major gaps from the informa-
of techniques can be used to collect primary data such as invoitc i e
on in the public domain and available LCI datasets given the
bills, metered data, questionnaires, interviews and site visitsconsiderable rise in environmental reporting by companies
(UNEP 2011; BSI PAS 2050 2011; European Commission across the full supply chain (Corporate Register 2017).
2010; EPA 1993, 1995, 2014). Once primary data is collected, Although such information may not be suitable as LCI data,
the data is transformed into LCI for a range of environmental
what they do demonstrate is the potential available data and
impacts such as Global Warming Potenital (GWP), ozone de-actors that can be harnessed to provide suitable data for LCA
pletion and acidification (Bare 2011; Goedkoop et al. 2009;applications.
IPCC 2006; Guinée et al. 2002). In comparison, secondary data Traditionally, the central vehicle to collect and compile LCI
are defined as Bdata that is not directly collected, measured, o
h ras been by consultants (Ecodesk 2015). However, the effec-
estimated, but rather sourced from a third-party life-cycle-tiveness of consultants to facilitate data exchange is limited as
inventory database^ (European Commission 2013). This can shown by the availability of data in current LCI DBs. As such,
also include data from publications and reports. However, sec-alternative strategies have emerged which involve single or
ondary data tends to be less specific and highly aggregated.
multiple actors to catalyse participation and encourage coop-
Some of the major LCI databases (DB) include Ecoinvent DBeration across the supply chain to increase data availability, as
(Ecoinvent 2016), US LCI DB (NREL 2014), World Food LCA shown in Fig. 1.
DB (WFLDB) (Nemecek et al. 2014) and Plastics Europe DB Due to the involvement of different actors, a range of dif-
(PlasticsEurope 2015). For both primary and secondary data,ferent strategies have been developed to facilitate and collect
there are guidelines available to ensure completeness, qualityLCI. For example, web-based systems (Ramos et al. 2016;
and transparency (Weidema et al. 2013; PEF World ForumBONSAI 2016; Recchioni et al. 2015; Mistry et al. 2016;
2013; UNEP 2011). Overall, for many LCAs, the commonBellon-Maurel et al. 2014), trade bodies/industry associations 1746
Int J Life Cycle Assess (2018) 23:1744–1760 5.
How effective is a company acting as the facilitator for data exchange? 6.
What are the motivations for data exchange? 7.
What are the challenges of collecting LCI data? 8.
Can the collection of inlet/outlet flow data be standardised? 9.
What is the resource required to collect inlet/outlet flow data? 10.
What are the quality controls required to ensure robust datasets? 11.
What company initiatives are recommended to promote
Fig. 1 Different types of actors which can play a role to collect LCI data an efficient LCI data collection?
The paper begins by presenting the proposed LCI data
(Jungbluth et al. 2016; Popp et al. 2013; Finkbeiner et al
c .ollection process employed by Nestlé in Sect. 2. This is
2003; Pomper 1998) and consultants (Credit 360 2015; followed by a selection of results of the LCI data collection
Ecodesk 2015). However, the collection of data by theseprocess for confectionery products in Sect. 3. A discussion of
routes requires the strong involvement of actors across the
the implementation experience, key challenges encountered
whole supply chain where the main strategy and implementa-and how the Nestlé LCI process compares to other initiatives
tion process in terms of collecting data and data quality check a s
nd—in particular—what were the major differences and
has been on a voluntary basis, promoted and instigated at a to
wphat we can learn from Nestlé’s experience that will help with
level by a third party, e.g. research institutes, universities,LCI collection is provided in Sect. 4. Lastly, the conclusions
governments, industry associations and consultants are provided in Sect. 5.
(Recchioni et al. 2015; Skone and Curran 2005). Even so,
the ability of a third party to effectively engage and therefore
collect data in a reasonable and practical timeframe with actors 2 Methods
across the supply chain will be limited as they will not have
full knowledge of the supply chain or the limitations of inter 2 -
.1 Description of case company and food factory
nal processes adopted by actors across that chain (Lesage and Samson 2016).
The case company is Nestlé UK Ltd., a large food company in
Another strategy that has received little attention is athe UK and a subsidiary of Nestlé SAwho are a global leading
company-led approach, especially from the perspective of nutrition, health and wellness food company. Across the
powerful and influential actors such as manufacturing andglobe, Nestlé are active on addressing many sustainability
retail companies. This is an important, and perhaps surprising,issues related to the Sustainable Development Goals (SDGs)
gap in the literature as due to the integration of manufacturin a g
s part of their Creating Shared Value (CSV) strategy (Nestlé
and retail companies within supply chains, they offer the op-
2015a). For example, working with smallholder farmers
portunities to engage, initiate, collect, influence and managethrough the Nestlé Cocoa plan (Nestlé 2015b) and Nestlé
LCI data directly through actors across the supply chain. As
Nescafe plan (Nestlé 2015c), assessing and optimising the
such, our hypothesis is that a company-led approach to dataenvironmental impact of Nestlé products by LCA-based ap-
collection can provide an effective means to collect data. In
proaches (Nestlé 2013) and contributing to the development
order to satisfy this hypothesis, this paper seeks to address
of environmental data across the supply chain such as the
several research questions by presenting an effective and nov-
World Food LCA database (WFLDB 2014). As an organisa-
el LCI data collection process and the implementation experi-
tion, there is not only the potential but a broad array of expe-
ence by Nestlé, a multinational food company for confection-rience which can contribute to supply chain engagement and
ery products. The research questions are as follows:
expedite data collection across the supply chain.
In the UK, Nestlé have 14 food factories that manufacture a 1.
What is the timeframe to collect inlet/outlet flow datrange of products that include coffee, cereals, pet food, water and can it be accelerated?
and confectionery. The case factory is based in the North East 2.
How much data should be collected and are their limita o -
f England that manufactures a range of confectionery prod- tions on quality?
ucts that are sugar, chocolate and biscuit based by utilising a 3.
What are the effective tools to collect data?
diverse range of processing technologies. In total, there are 4.
Who are the key actors in the supply chain and howato
pproximately 130 Stock Keeping Units (SKUs) which are a identify them?
variation of a brand product format, e.g. single bar pack and
Int J Life Cycle Assess (2018) 23:1744–1760 1747
multiple bars pack. The SKUs are sold to a range of custome
a rse sought for advice. Overall, a range of people are involved
both in the UK and across the globe (Miah et al. 2015a). Ttheroughout whose role falls into two categories: (1) data pro-
use of a case study in this way allows for an in-depth explo
vi -der and (2) data exchange facilitator. The ‘data provider’ are
ration of the supply chain, and while it is acknowledged tha
p teople from different organisations across the stages of the life
the findings are specific to that chain, it can be reasonably
of a food product which provide data. The ‘data exchange
surmised that the results are applicable for other multinationaflacilitator’ are people primarily from Nestlé who have
food companies who manufacture and sell food products di-established relationships with data provider organisations to rectly to retailers.
facilitate data exchange. From Nestlé’s perspective, an indic-
ative level of resource required and expected data quality is
2.2 Overview of confectionery LCI data collection process
provided at each life cycle stage as guidance. The different
stages are explained in the following subsections.
The LCI data collection process was initiated and developed
by a transdisciplinary process involving both Nestlé practi-2.3 Description of the potential available resource
tioners and academics from the University of Surrey (Miah
et al. 2015b). The LCI data collection process presented here
The potential available resource is an indication of the differ-
(Fig. 2) is based on LCI guidelines (Nemecek et al. 2014 e ;
nt people that could potentially be made available from the
ALCAS 2014; UNEP 2011; BSI PAS 2050 2011; European food company to participate in the collection of inlet/outlet
Commission 2010) and the challenges faced by Rebitzer et alf.low data. The process to identify people is a continuing pro-
(2004) and Berkhout and Howes (1997). As a methodology,cess but starts during the goal and scope definition, before the
the LCI data collection process displays features which are
identification of SKUs, by developing a list/map of potential
found in approaches by different companies, e.g. data sources,available resource based on recommendations from the
questionnaires, data quality management, etc. What distin-decision-maker who commissioned the LCA. The decision-
guishes the approach presented here is the combined features
maker is likely to be someone in a senior position responsible
and, more importantly, the role of a multinational food com-
for environmental sustainability improvements in the compa-
pany (e.g. Nestlé), rather than a third party, to initiate, moti-
ny. Following this, further people can be identified as data
vate, accelerate and manage the whole collection of inlet/collection progresses. The types of people involved are pri-
outlet flow data across the supply chain.
marily internal to the food company from the environment/
The goal of the LCI data collection process is to provide a s n
ustainability department to provide further guidance and di-
effective and efficient streamlined route to practically collectrection towards data providers both internal and external to the
data—on a voluntary basis—across different input intensitiesfood company. For example, at the factory life cycle stage, the
such as electricity, natural gas, water and solid waste that is
food company is directly involved with the management and
both specific and general at different stages of the producotperation of the food factory and will have several depart-
supply chain that can be used to conduct an LCA, e.g. envi
m -ents where various data is collected related to the environ- ronmental hotspot analysis.
ment. As such, there are a large number of people that could
The scope of the primary data collection process includesbe coordinated to collect inlet/outlet flow data at the factory
first-tier suppliers, factory, retailer, consumer and disposal.life cycle stage. In comparison to the farm-level life cycle
The farm-level stage was not included due to the indirects age, the food company will not necessarily have a direct
relationship with farmers and existing Nestlé initiatives suchinvolvement with the management and operation of the farm
as the Cocoa plan (Nestlé 2015b), Nescafe plan (Nestléas Nestlé does not own farms. Although, they do have direct
2015c) and contributing partner to the World Food LCA da-suppliers, where a strong relationship is established, through
tabase (WFLDB 2014). The integrated LCI data collectionwhich data collection is possible indirectly to the farmers. As
process begins at the food factory because food manufacturersuch, there will be a low number of people that could be
typically carry out the design of the product which sets forth
coordinated to collect inlet/outlet flow data at the farm-level
the product supply chain structure both upstream and down-life cycle stage. Overall, the types of people involved internal-
stream. From here onwards, the data collection strategyly to the food company will vary depending on the life cycle
branches both upstream and downstream of the product sup-stage as different departments or functions will have varying
ply chain where the collected data is reviewed, analysed and
knowledge based on their role, experience and the relation-
normalised, if required. The final stage involves a reconcilia- ships they have with people both internally and externally via
tion and aggregation of LCI datasets.
institutions. The degree of engagement of human resources in
The responsibility for the whole management and imple-LCI-related activities will vary for different food companies,
mentation (including analysis) of the LCI data collection pro-but a general description is provided in Table 1 to distinguish
cess is by a single person in Nestlé known as the ‘data colle
bce-tween low, medium and high resources. The direct relation-
tor manager’. On occasion, internal and external LCA expertsship refers to a business/professional relationship. On the 1748
Int J Life Cycle Assess (2018) 23:1744–1760 Raw material Life cycle Farm level Food factory Customers Consumers Disposal stages processing Food processors Retailers Waste service Types of Farmers Food company Retailers NGOs NGOs Research institutes Research institutes providers Actors Research institutes Trade associations NGOs National government involved Research institutes Agricultural cooperatives Trade associations Local government Data provider Data provider Data collector Data provider Data provider Data provider Data exchange Role of Data exchange Data exchange manager Data exchange Data exchange facilitator Actors facilitator facilitator Data provider facilitator facilitator Data exchange facilitator Procurement Procurement Engineers Sales / marketing Sales / marketing Engineers Potential Logistics Logistics SHE Logistics Logistics SHE Available Production Logistics resource Logistics Sales / marketing Expected Data quality LOW MEDIUM / HIGH HIGH MEDIUM / HIGH LOW / MEDIUM MEDIUM / HIGH START Define Goal and Scope A1 Identify key products Identify ingredients and Extract factory Develop customer Determine consumer B1 packaging m aterials A2 environmental data D1 category map E1 profile Identify ingredients and Develop environmental Extract environmental Identify routes to packaging suppliers B2 A3 standards Identify major customers E2 impa ct of consumer D2 F1 Life Cycle profile disposal Inventory collection process Develop Life Cycle Develop Life Cycle Extract environmental Inventory (LCI) B3 impa ct of disposal questionnaire Inventory (LCI) D3 F2 questionnaire routes Email LCI questionnaire Email LCI questionnaire to suppliers and invite to B4 to customer s and invite a webinar D4 to a telephone meeting Follow-up suppliers with B5 emails and phon e calls Follow-up suppliers with D5 emails and phone calls Identify raw materials Extract LCI data and B6 Extract LCI data and C1 from LCI data extraction standardise D6 standardise Search general LCI databases a nd literature C2 for LCI profiles on raw materials Data Quality Management G Output LCI dataset compilation H
Fig. 2 Life Cycle Inventory (LCI) data collection process
Int J Life Cycle Assess (2018) 23:1744–1760 1749 Table 1 The degree of engagement of human resources Human resources Description in LCI related activities Low
• No involvement in the life cycle stage
• Indirect relationship with life cycle stage operator via an intermediary, e.g. co-operatives Medium
• No direct involvement in the life cycle stage management or operation
• A mix of direct and indirect relationships with life cycle stage operator High
• A direct involvement in the life cycle stage via management and/or operation
• A range of departments actively involved in environmental issues
other hand, the indirect relationship refers to the business/developed and sent to the data provider for clarification.
professional relationship with an intermediary to collect dataFrom here onwards, a two-way dialogue (via emails, phone from the life cycle stage.
calls and physical meetings) continues with the aim to in-
crease the quality of data to the highest quality level which
2.4 Description of data quality management
is practical and economical to collect. Overall, throughout
the data analysis approach, internal and external LCA ex-
The management of data quality primarily involves the vali-
perts are sought to provide additional quality assurances on
dation of data from the various life cycle stages to ensure datta
he compiled dataset. For example, possible explanations
is robust, and thereby reduces the level of uncertainty in fur-
of anomalies in data and verification of expected results.
ther analysis. A semi-quantitative assessment method known
as the pedigree matrix is used which was originally developed
2.5 Description of food factory data collection: stages
by Weidema and Wesnaes (1996) and has gained traction overA1–A3
the course of 20 years to become the de facto quality assess-
ment method for several LCI DBs (Ecoinvent 2016; NRELAfter the goal and scope was defined, the next step was to
2014; ALCAS 2011). The pedigree matrix contains ratings foridentify key products which can include distinct product cat-
different data quality indicators (DQIs) such as reliability (R),
egories and major products. The identification process was
completeness (C), temporal correlation (TC), geographical carried out through engagement with the factory production
correlation (GC) and technological correlation (TeC). Theteam who were able to provide production data split out into
DQIs are then assessed based on the judgement of experts
product categories. For the list of SKUs in each key product
(e.g. LCA practitioners) and converted into a data quality category, the major SKU was selected based on a Pareto anal-
score (DQS) by Eq. (1). The score is rated into higyhsis of the SKU production volumes which can be extracted
(DQS ≤ 1.6), medium (DQS ≥ 1.6 to <3) and low (DQS ≥f 3
rom production and sales records. The major SKU is thus the to ≤ 5) quality.
reference product for the key product category throughout the
whole LCI data collection process. R þ C þ TC þ GC þ TeC þ X
At a factory level, the input intensity monitored will typi- DQS ¼ W 4 ð1Þ i þ 4
cally cover energy, water, solid waste and liquid waste. The
scale of available data will vary depending on the coverage of
utility meters across site and within processes, billed utility where
invoices and systems to record physical materials, e.g. solid DQS data quality score
waste. As such, a combination of the available data in con-
junction with reasonable estimates based on expert judgement
R, C, TC, GC, TeC: see values found in Weidema and
was needed to allocate the input intensity down to a key prod- Wesnaes (1996)
uct group based on mass allocation. A general rule for the X
allocation process is not possible as this will depend on the W
weakest quality level obtained (i.e. highest numerical
value) among the data quality indicators
combination of available data and expert judgement. i
number of applicable data quality indicators
Alternatively, an economic allocation approach can be used
if economic data is readily available. However, the major lim-
The data quality management proce ss involves itation compared to a mass allocation approach is the repre-
reviewing the data provided to (1) screen for any data gaps,entation of input-output flows based on economic data rather
(2) identify anomalies in datasets and (3) ascertain data
than physical dimensions based on mass; hence, this is subject
quality as described in Weidema and Wesnaes (1996) andto price variability. As such, an economic allocation is recom-
Eq. (1). Based on the review, a list of questions is
mended when mass data is not available. 1750
Int J Life Cycle Assess (2018) 23:1744–1760
2.6 Description of raw material processing data collection:
modular ‘builidng blocks’. Afterwards, the raw material stages B1–B6
groups were cross-referenced with commercial LCI databases
to find similar LCI profiles (Ecoinvent 2016; Quantis 2014;
For the major SKU identified, a list of ingredients and pack A-LCAS 2011).
aging materials was determined based on the product recipe
and packaging specification. The source of the data was ob-
2.8 Description of customers’ data collection: stages
tained from the production specialists at the food factory. D1–D6
Following this, the identification of suppliers involved engag-
ing with the procurement team of the food manufacturing The portfolio of customers for confectionery products can be
company who has a business relationship with the suppliershighly diverse depending on how developed the market where
and is able to formally and more appropriately request infort-he products are sold, e.g. high street retailers to convenience
mation. Prior to contacting the suppliers, an LCI questionnaire
stores to cinema outlets to snacks on an aeroplane. As such,
and cover letter was developed to provide the suppliers withe development of the range of customer categories was
the motivations of the request and the types of information
based on the literature (Spencer and Kneebone 2007;
required. The design of the questionnaire contains a range ofSpencer and Kneebone 2012) and from the food manufactur-
information categories shown in Table 2. The questionnaireing company logistics team. For the major SKUs, it was pos-
template can be found in the Electronic Supplementarysible to extract delivery orders over a 1-year time period to Material.
identify major customer categories and specifically the cus-
The cover letter developed was contained to a single pagetomers per se. From the identification of the major customer, it
to keep the communication concise. It included the purpose of
was possible to identify the key account manager inside the
the data request, contact details and a deadline of 4 weeksfood manufacturing company who manages the business re-
from receipt. The cover letter was signed off by the procurel-
ationship with an equivalent in the customer company. At the
ment contact who managed the business relationship with same time, the sustainability/environmental contact in the
suppliers and by the head of sustainability and head of pro f -
ood manufacturing company was able to provide an equiva-
curement in the food manufacturing company. This was tolent contact in the major customer category from previous and
ensure the request was supported at a high level in the foo o d
ngoing relationships. Before contacting the customer, a tai- manufacturing company.
lored LCI questionnaire was developed to include the same
Both the inlet/outlet flow questionnaire and cover letterinformation categories as for customers. The processing of the
were sent via e-mail to the business contact in the supplie r r
eturned inlet/outlet flow questionnaire is the same as
company. The option to follow-up with a webinar or phonediscussed in Sect. 2.6.
call was provided. Any further communications took place
through e-mails to discuss and clarify the request in more
2.9 Description of consumers’ data collection: stages
detail. When the inlet/outlet flow questionnaire was returned,E1–E2
the data were reviewed to gauge the sensibility of the infor-
mation. The LCI data for each inlet/outlet flow obtained for a
As most confectionery products are ‘ready to eat’, they do
specific geographic location were then converted to a range of
have short shelf-lives, and consumers are not expected to carry
life cycle impacts per tonne of material manufactured based
out any processes before consuming them. In this particular
on different life cycle impact assessment (LCIA) methodolo-scenario, consumer behaviour regarding transportation from
gies discussed in Sadhukhan et al. (2014) using GaBi 6.0. point of purchase (customer store location) to consumption,
storage of product, food waste and disposal of packaging are
2.7 Description of farm level data collection: stages C1–C2 the relevant parameters to be evaluated. Therefore, the process
to collect data for the major SKUs was largely based on liter-
The raw materials required to manufacture the ingredients andature supported by the marketing and sales team in the food
packaging materials can be found in the information extractedmanufacturing company and retailers.
from the inlet/outlet flow questionnaires sent to the food
manufacturing company suppliers. For the raw materials that2.10 Description of disposal data collection: stages F1–F2
could not be extracted or were not available due to incomplete
or unreturned inlet/outlet flow questionnaires, literature For the major SKUs and based on the consumption behaviour
searches were carried out on the general manufacture of in-
of the consumer, the waste materials can be identified. The
gredients and packaging materials to create a list of raw ma-
process to determine the routes to disposal should in principle
terials. Once a list of raw materials was made, they werfeollow the waste hierarchy (European Commission 2008), but
categorised into similar groups (e.g. dairy includes milk, wheyin practice, this can differ where there are national averages
etc) adopting the approach by Mila i Canals et al. (2011) fo t r
hat can be taken that provide recycling rates and disposal to
Int J Life Cycle Assess (2018) 23:1744–1760 1751 Table 2 An overview of LCI questionnaire categories and Information Description of content Data provider general content category Supplier/customer
• Basic information on supplier to include material names and Supplier overview manufacturing site locations.
• Confirmation of environmental management systems
• Confirmation of previous LCAs in the company
• Confirmation of willingness to collect further data down the supply chain Production
Production volumes of the factory and raw materials required to Suppliers
manufacture ingredients/packaging Land footprint
The area space occupied by the total site and factory Suppliers and customers Store volume
The volume space occupied by the retail site and/or warehouse Customers Energy
Includes the different energy types: electricity, natural gas, fuel oil Suppliers and
consumed at a factory and if possible at a product level customers Water
Includes the different water types: main, ground, river, and recycledSuppliers and
consumed at a factory and if possible at a product level customers Atmospheric
Includes the release of pollutants (if measured) to the atmosphere Suppliers emissions including particulate matter Solid waste
Includes solid materials that are discarded off-site and not recycledSuppliers and on-site customers Liquid waste
Includes liquid process water sent to a wastewater treatment plant a S n u d ppliers
any liquids that are siphoned into tanks to be treated off-site Transportation
Includes a general breakdown of the transportation route from the Suppliers and
location of manufacturing to the customer location Customers
landfill (EA 2014). For more specific environmental impact of
Table 4. Overall, the sugar product category has the highest
different waste treatment options, the engagement with wastenatural resources consumption.
service providers that operate on a local or national level canThe LCI questionnaire and cover letter developed were
provide data on a kilogram basis.
sent via e-mail to 67 ingredients and packaging suppliers
requesting 2013 data only. In total, only 55% returned ques-
tionnaires that went through a review process with the sup- 3 Results
pliers over a series of e-mails before being converted on a
relative basis, e.g. per ton of bulk product delivered to the 3.1 Amount of data collected
confectionery factory. The LCI data were then converted to
a range of environmental impacts to widen the application
The amount of data collected from both primary and second-depending on the preference of LCA practitioner, see
ary data sources are shown in Table 3. Overall, 183 LC
EIlectronic Supplementary Material for full LCIA data.
datasets were targeted for specific ingredients of which 129 Similar to raw material processing, an LCI questionnaire
were collected from primary and secondary sources. The totaland cover letter was sent to two major food retailers in the UK.
primary data collected was 100 whereas secondary data rep-However, only one retailer was able to provide some informa- resented 29.
tion which was not in the correct format, as shown in Table 5. 3.2 Types of data collected 3.3 Quality of data collected
A range of primary data was collected for the factory, raw
3.3.1 DQSs for both primary and secondary data
material processing and retailer shown in Tables 4 and 5 and
Electronic Supplementary Material. For the conversion of pri-The data collected was assessed based on the pedigree data
mary data to environmental impacts, this was based on thequality matrix. A comparison of the calculated data quality
energy data collected. The collection of emissions data wascore (DQS) for 123 LCI datasets is shown in Fig. 3. The
not found to be available across the majority of data provider
o srange bars represent secondary data whereas the blue bars
as this was not measured and/or was confidential. represent primary data.
For the confectionery factory, the input intensity data is Overall, the DQS were then categorised into high-,
provided at the factory and product category level, shown inmedium- and low-quality groups, as shown in Table 6. 1752
Int J Life Cycle Assess (2018) 23:1744–1760 Table 3
Amount of LCI datasets collected from both primary and secondary data sources Life cycle stages
Farm/raw materials Raw material Factory Distribution Retail Use Disposal (C1–C2) processing (B1–B6) (A1–A3) (D1–D6) (D1–D6) (E1–E2) (F1–F2) Target amount of LCI datasets 22 147 4 1 1 n/a 8 Total number of primary LCI 0 96 4 0 0 n/a 0 datasets collected
Total number of secondary LCI 13 0 0 1 1 n/a 8 datasets collected
Total number of no data collected 9 51 0 0 0 n/a 0
Percentage of total primary data 0% 65% 100% 0% 0% n/a 0% collected (%)
Percentage of total secondary data59% 0% 0% 100% 100% n/a 100% collected (%)
Percentage of no data collected 41% 35% 0% 0% 0% n/a 0% (%)
For the DQS, two statistical analysis techniques are used to
effectiveness to collect data and effort required to implement,
determine variability by calculating the average and standardas shown in Fig. 4. A comparison of the effectiveness of tools
deviation, as shown in Table 7. The average DQS shows tha a t
nd processes are discussed in Sect. 4.
the raw materials processing data has on average the best A range of visual diagrams were also created which im-
quality compared to data collected for the other life cycle
proved understanding of different aspects of the confectionery
stages. However, caution must be taken in the interpretationsupply chain. For example, the identification of raw materials
as the sample size for the different life cycle stages are coan-d their associated suppliers was strongly aided by the de-
siderably different and will influence the final results. Despitevelopment of an ingredient map for 20 major SKUs (see Fig. 5
this, the rank of highest to lowest quality based on the averafge
or one SKU). In Fig. 5, the inner circle represents ingredients
is raw material processing, factory, farm and disposal.(coded from A to R), and the percentages shown are the share
Furthermore, when investigating the variability of data withinof their contribution to material supply. The outer circle shows
each life cycle stage, the calculated standard deviation shows
the origins of the materials. Initially, the maps were generated
the factory has the lowest variability whereas the farm stage
for all suppliers for each ingredient but over the course of time,
has the highest. The rank of lowest to highest variability base t d
hey were narrowed down to single supplier for each ingredi-
on the standard deviation is factory, raw material processing,
ent based on highest percentage procured. In total, 147 ingre-
disposal and farm. Overall, the statistical analysis shows the
dients and packaging materials purchased from 67 suppliers in
primary data collected for the factory and raw material pro1-9 countries were identified.
cessing has the highest quality.
Another aspect of the supply chain which was visually
represented was the upstream confectionery supply chain in
3.4 Effectiveness of tools and processes deployed
terms of distribution and retail. Due to the history of Nestlé in
the UK, they have developed mature channels to a range of
A subjective assessment is made of the tools and processes
customers in the UK. As such, the upstream section of the
deployed through the data collection process in terms of the
supply chain is complex and diverse. Initially, the starting Table 4 Factory and product category level environmental Scale Number of Electricity Natural gas Water Solid waste resource consumption SKUs (kWh/ton) (kWh/ton) (m3/ton) (ton/ton) Confectionery factory 130 539 1045 3.55 0.041 Chocolate product 9a 412b 701b 4.16b 0.020b category Sugar product category 8 a 642b 1570b 3.64b 0.057b Chocolate biscuit 3a 714b 1081b 2.22b 0.070b product category a Major SKUs
b Estimated based on average SKU
Int J Life Cycle Assess (2018) 23:1744–1760 1753 Table 5 Environmental aspects
of retail in ambient conditions, for Scale Electricity Natural gas Water Solid waste different scales (kWh/m2 day) (kWh/m2 day) (m3/m2 day) (ton/m2 day) Superstore 0.0944 0.0267 1.53 × 10 −4 6.02 × 10−6 Supermarket 0.0419 0.0113 7.12 × 10 −5 5.21 × 10−6 Warehouse 0.021 0.00877 8.77 × 10 −5 1.42 × 10−5
point to collect data was from retailers but it was unclear if th
o ifsa manufacturing company to efficiently collect environ-
was the right choice given the diversity of customers. In order
mental data across the whole supply chain. For example, it
to navigate through the complexity, a literature review waswas found that Nestlé—a multinational food company—was
carried out to find different distribution channels for food able to harness the perceived power of the organisation and
products. The information collected was combined withtranslate into a supply chain leadership role to collect data. For
Nestlé data based on discussions with the marketing and salesexample, the involvement of more than 50 different people
teams to create a full and general representation of the entir a e
cross many divisions within the company was engaged to
customer portfolio for confectionery; see Fig. 6.
identify actors across the supply chain and to facilitate data
exchange. In comparison to existing approaches (Ramos et al.
3.5 Challenges encountered during primary data
2016; Jungbluth et al. 2016; Recchioni et al. 2015; Mistry collection
et al. 2016; Bellon-Maurel et al. 2014; Popp et al. 2013;
Finkbeiner et al. 2003; Pomper 1998), the collection of data
In the course of the LCI data collection process, Nestlé en h -
as been primarily on a voluntary basis where the implemen-
countered several challenges which are shown in Table 8 with
tation process is managed by a third party to drive the collec-
a range of recommendations proposed to resolve.
tion of data from different actors across the supply chain.
Another major benefit is the speed to collect primary data.
The application of the LCI process at a confectionery factory 4 Discussion
in the UK found that a company-led approach was able to
collect a portfolio of new environmental data in a relatively
4.1 Comparison with other data collection approaches
short period of 5 months. In total, 100 primary LCI datasets
were collected from 67 ingredients and packaging suppliers
A critical assessment of a novel integrated LCI data collection
across 13 countries. In comparison to other data collection
process and strategy has been presented from the perspective
approaches in the food industry (Ramos et al. 2016;
of a multinational food company. It is the first of its kind a J n u d
ngbluth et al. 2016; Milà i Canals et al. 2011), they do not
helps to fill an important gap in the existing knowledge on
provide an indication of the time involved to carry out data
alternative strategies for LCI data collection. One of the key
collection, especially for large amounts of data at different
features of the process is the ability to leverage the resources
scales of problems, e.g. single product, multi-products, factory 4.50 4.00 3.50 3.00 ) S 2.50 Q 2.00 cores(D 1.50 ualityS 1.00 ataQ D 0.50 0.00 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99 101 103 105 107 109 111 113 115 117 119 121 123 LCIdataset
Fig. 3 A comparison the DQS for 129 LCI datasets collected 1754
Int J Life Cycle Assess (2018) 23:1744–1760 Table 6
DQSs for data collected categorised into high, medium an m
d ajority of primary data collected was found to be medium low data quality
quality whereas the secondary data varied from high-to-low Data Farm Raw
Factory Distribution Retail Disposal quality. However, the primary data had the potential to be high quality materials
quality but was limited due to the representativeness criteria group processing
since the data only represented one site and not the whole
market/country. Further statistical analysis showed the prima- High 2 0 0 0 0 0
ry data had the lowest variation based on the standard devia- Medium 1 96 4 0 0 0
tions of DQS whereas secondary data for disposal and farm Low 10 0 0 1 1 8
stage had the highest variation. For the retail and disposal
level, only partially completed LCI questionnaires were
returned. As such, data was sourced from the Ecoinvent data-
or even company level. Based on the experience gained, it i
b sase (Ecoinvent 2016). However, due to the generalised na-
expected that a second round of data collection could result itn
ure of LCI profiles in Ecoinvent, the quality was found to be
a shorter timeframe of a few months. For example, withl a
ow based on the ratings assigned on the pedigree matrix.
projected timeframe of 3 months, this could result in 400
Overall, the highest data quality was obtained for those com-
LCI datasets per year. Therefore, in addition to existing routes
panies that operate closer along the food supply chain to the
of data collection (Ramos et al. 2016; Mistry et al. 2016; Poppmultinational food company leading the data collection pro-
et al. 2013; Finkbeiner et al. 2003; Ecodesk 2015), the role ofcess. Hence, the critical stages of the supply chain requiring
companies can significantly create more LCI data which can
further research would be agricultural production (farm level)
benefit both companies, supply chain actors and wideron one side, and retailers and waste treatment companies on industry. the other side.
One other major benefit is the ability to create up-to-date One of the major limitations found in practice was the
and high-quality data. For example, the primary data collectedcollection of primary data at farm level. For the farm level, it
has resulted in 100 new LCI datasets where the majority of th w e
as found few suppliers manage and operate vertically inte-
LCI datasets are not found anywhere in the literature (NREL
grated operations from farm level to ingredient/packaging
2014; Quantis 2014). In addition, the LCI datasets are rela-manufacture. However, due to the complex nature of farms,
tively new where at the time of collecting, data were no mo t r
hey can perform multiple services/functions over various pe-
than 1 year old. Such data will be particularly useful for envi-
riods of time creating multiple outputs. As such, the collection
ronmental analysis in the confectionery industry which in theof primary data was out of scope as the timeframe to compile
EU alone comprises of over 11,000 confectionery manufac-an inventory of all the materials and energy consumed at a turers (CAOBISCO 2015).
farm level is much longer (e.g. months to years) compared to
Furthermore, another major benefit is the transparency inthe other life cycle stages. In addition, the infrastructure and
data collection to encourage high quality and reproducibility.technology required to collect data is less advanced for
For example, novel processes have been developed to visuallyfarmers and have limited resources in terms of knowledge
describe the rationalisation and identification of ingredients, and expertise. Therefore, data was sourced from secondary
suppliers and customers across the supply chain compared toLCI DBs such as WFLDB (Nemecek et al. 2014) and
previous approaches (Ramos et al. 2016; Jungbluth et al.AgriBalyse (Koch and Salou 2013; Colomb et al. 2015). To
2016; Recchioni et al. 2015; Mistry et al. 2016; Bellon- this extent, the pursuit of specific farm level data should only Maurel et al. 2014).
be for significant raw materials as there is a trade-off between
the volume of LCI data collected and resources expended in
4.2 Quality of data collected and gaps in data
terms of people’s time. Despite the inclusion of secondary
data, the LCI data collection process has shown that a multi-
The quality of data collected was found to vary considerably
national company can potentially engage and facilitate LCI
from both primary and secondary sources based on the pedi-data collection directly with farmers or indirectly through
gree matrix (Weidema and Wesnaes 1996). For example, thefirst-tier suppliers. Although, longer-term initiatives are
Table 7 Statistical analysis of DQS Farm Raw materials Factory Distributiona Retaila Disposal processing Average 3.23 2.74 2.78 n/a n/a 3.97 Standard deviation 0.95 0.08 Negligible n/a n/a 0.17 a One dataset only available
Int J Life Cycle Assess (2018) 23:1744–1760 1755 Fig. 4 Assessment of the
effectiveness of tools deployed
required to establish environmental training through formal4.3 Effectiveness of tools and processes
partnerships (e.g. TESCO 2015; Nestlé 2015a, b, c) to support
and reduce the environmental impact at farms.
An assessment of the different tools and processes applied dur-
Overall, several recommendations are proposed to resolveing the LCI data collection is shown in Fig. 4. Although such
data gaps and ensure the highest quality level for incomplete
ools and processes are found in the general literature (UNEP
datasets, outdated data and data using proxies (Sadhukhan2011; European Commission 2010; EPA 1993, 199 , 5 2014), we
et al. 2014). For incomplete datasets, only sections whichave presented for the first time an assessment of what was
are completed and provide meaningful information are recom-effective from the implementation experience on a two-axis
mended. For outdated data, the pursuit of recent data is en
g -raph showing the effectiveness to collect data with the effort
couraged. For data using proxies, an investigation of the repr-equired. It was found that the most effective process was the
resentation in terms of correlation and relevance should befollow-up calls/e-mails with read receipts to data providers.
assessed. In addition, the role of external LCA experts shouldHowever, such an approach was rather intensive and repetitive
be sought to provide additional quality assurances on the coma-s records were kept to track communications.
piled dataset. For example, possible explanations of anomalies Another effective process was the regular meetings with
in data and verification of expected results.
Nestlé personnel to review progress, identify any problems
Fig. 5 Two ingredients map showing how suppliers were reduced from start to end 1756
Int J Life Cycle Assess (2018) 23:1744–1760
Fig. 6 Customer distribution channels and customer categories for confectionery products
and provide support. In comparison, Ramos et al. (2016)company-led LCI data collection process. For example,
found a web-based tool was effective to bring large numbertshe data collected within the company, there was a major
of companies together (e.g. 23 food small medium enterprises
challenge of conflict of interest since the data collector
(SMEs)) on a single digital platform. Such a tool could be
manager, data provider and data exchange facilitator all
integrated within a company-led approach but would requirework for the same company. Although this may be the
an initial capital expenditure to develop.
case, the process to ensure robust datasets still remains
by keeping records of data at different stages of transfor-
4.4 Challenges encountered during implementation
mation, validating data with experts in the company and
pursuing data which is of high quality based on the pedi-
Over the course of the data collection process, several chal-
gree data quality matrix. Further checks on the quality of
lenges were encountered as listed in Table 8. In compari-data can be carried out by comparing data with similar
son to the challenges found in the literature (UNEP 2011;
materials/products and independently reviewing the data
European Commission 2010; EPA 2014), the major differ- by a third party.
ence is the comprehensive overview with recommenda-
Another major challenge was the lack of engagement from
tions to resolve in the context of implementing asupply chain actors, in particular ingredients and packaging
Int J Life Cycle Assess (2018) 23:1744–1760 1757 Table 8 List of challenges encountered by Nestlé Challenges Recommendations
1. Lack of engagement from supply chain actors
1. Supplier engagement events to raise awareness and discuss challenges
2. Circulate company sustainability reports and policies
2. Lack of experience by Nestlé and suppliers
3. Standardise environmental data received from suppliers
4. Educate suppliers with webinars and short PowerPoint presentation
5. Develop concise and targeted LCI questionnaires
6. Provide 4–8 weeks to return completed LCI questionnaires 3. Lack of resources
7. Offer assistance to complete (remotely or physically present)
4. Identifying key actors within Nestlé and across8. Start building initial contact list from company network the supply chain and expand
9. Search for environment/sustainability contacts on
sustainability reports/websites
5. Engaging with actors with no direct business 10. Contact people with direct business relationship to relationship
pursue request with indirect relationship
11. Integrate environmental considerations in the audit
criteria of multi-tiered suppliers
6. Language barriers from non-UK data providers
12. Reduce communication to e-mails
13. Check with data providers preferred language
14. Provide translated questionnaire
7. Different technical language to express
15. Align technical language to SI units/terminology
environmental, engineering and supply chain information 8. Commercial compromise 15. Anonymise data sources
16. Be transparent and clear on the application of data to
reassure data provider on minimal compromise
17. Intermediary agent to host the data
9. Sensitivity of data disclosure 18. Aggregate data
19. Expand location point e.g. city to country 10. Confidentiality protection
20. Non-disclosure agreements (NDAs)
21. Respect environmental disclosure policy of suppliers
11. Conflict of interest with same person as data
22. Independently review data by third party
collector manager, provider and reviewer
12. Navigating through a complex multi-tiered
23. Engage with different suppliers and company supply supplier systems
chain/procurement function personnel to build
common knowledge of supply chain structure 13. Visualising complexity
24. Create basic diagrams verified by supply chain actors
14. Modelling production processes
25. Engage with engineers to verify modelling
suppliers. For example, a total of 45 of suppliers did not retuern
xperienced multiple environmental data requests for various
the LCI questionnaires. Based on further discussion with sup-
formats where their LCA teams employ LCA tools.
pliers, it was found that there were several reasons for eitherFurthermore, the commercial implications were a topic that
participating partially or not at all. For example, lack of rec-ame up often in the engagement process with suppliers both
sources and LCA experience, commercial compromise, sen- SME and large. Despite reassurance measures such as confi-
sitivity of data disclosure and confidentiality protection. It was
dentiality protection through NDAs and anonymisation of da-
found for the majority of suppliers, in particular the SMEs
t ,a if shared in the public domain, the resistance to participate
that they did not have experience in completing a LCI ques-
with some suppliers still remained. It was found that the level
tionnaire to a high level of detail where for some, it wa
ofsparticipation depended largely on trust and relationships in
completely new and for others, they had previouslyterms of the people involved and the length of relationships. 1758
Int J Life Cycle Assess (2018) 23:1744–1760
4.5 Motivations for companies to participate
food company. It represents one of the very first such studies
of its type to critically assess the role and effectiveness of a
Despite the challenges, several motivating factors were multinational food company on collecting LCI data across
found for encouraging data providers to participate in thethe supply chain. For example, the application at a multi-
company-led LCI data collection process as part of theirproduct confectionery factory in the UK has resulted in a
overarching Corporate Social Responsibility (CSR) strategy portfolio of 100 new environmental LCI datasets from the
(Dahlsrud 2008). For example, the opportunity to collabo-interaction with 67 ingredients and packaging suppliers
rate with Nestlé (e.g. strengthen relationship, ways of work-
across the globe and several food retailers. However, the
ing and partnerships), opportunity to learn about the envi-majority of primary data collected was from ingredients
ronmental impact of their organisation/product in Nestlé’s and packaging suppliers, food factory and partial data for
products and opportunity to develop learning experience ofretailers and waste disposal providers with no data at the
LCI data request. However, in total, only 55% of suppliersfarm level. In addition, several challenges were encountered
returned the LCI questionnaire. A surprising finding was the
during implementation from the lack of experience, identi-
lack of implementation from some companies who publical-fying key actors, confidentiality protection and complexity
ly advocated sustainability improvements and supplier en-of multi-tiered supplier systems. Despite this, by using the
gagement both at an SME and (multinational corporation)internal resources, business relationships and influence of a
MNC level as part of their CSR strategy. Despite this, the
multinational food company, it was found that a multination-
role of CSR can be a strong motivator for companies to
al company can play a critical role, especially in engagement
participate as it was generally found that the sustainabilityand facilitation by transforming latent data found within
commitments by different companies helped companies ini-companies or reported publically across the supply chain
tially participate. As such, it is recommended that a range o t f owards expansion of LCI data.
initiatives are developed to encourage efficient LCI data col- Furthermore, in order to encourage the reproducibility for
lection by the company (i.e. Nestle). Such initiatives willother multinational companies, it is recommended the pro-
aim to bring supply chain actors together to develop a mu-
posed LCI data collection process serves as a foundation to
tual understanding on promoting sustainable supply chains.contribute towards a standardised procedure, in particular for
For example, workshops to discuss strategies to improvefood products. The specific features which can contribute to-
supply chain sustainability, specific partnerships with sup-wards a standardised procedure includes (1) process flow di-
pliers on key ingredients and LCA/environmental awarenessagram of LCI data collection, (2) identification and role of training in the food industry.
actors in the company and across the supply chain, (3) supply
chain maps, (4) processes to manage gaps in data and data
4.6 Towards a standardised procedure in the food sector
quality and (5) LCI questionnaire. Overall, the key benefits
of the proposed LCI data collection process includes (1) the
The LCI data collection process has the potential to transition
ability to leverage the resources of a manufacturing company
towards a standardised procedure for the food sector subject to
t efficiently collect environmental data across the whole sup-
further application and consensus of stakeholders across theply chain, (2) the speed to collect primary data, (3) the ability
food industry. However, not all food companies have the abil-to create up to date and medium to high quality data and (4)
ity to lead an LCI data collection process across the supplty
he increased transparency in data collection. However, further
chain since these companies are either SMEs or do not mane-ngagement with different food companies and applications
ufacture finished products. As such, in this context, these coma-cross food categories would be required to develop a robust
panies can have a different role in which they can supposrttandardised procedure, especially supported by research in-
organisations seeking to lead an LCI data collection processtitutes and NGOs.
across the supply chain. Alternatively, such companies can
group together to initiate an LCI data collection process for Acknowledgements
We wish to gratefully acknowledge and thank the
common materials shared between the companies. DespiteEPSRC and Nestlé UK Ltd. for their assistance and support in funding
this, the LCI data collection process and strategy provides an
this research as part of the Engineering Doctorate on Sustainability for
initial basis for other companies to further design their respec
E-ngineering and Energy Systems (EngD SEES) at the University of
tive data collection strategies.
Surrey. We would also like to thank two anonymous reviewers for their
suggestions and comments on previous versions.
Open Access This article is distributed under the terms of the Creative 5 Conclusions
Commons Attribution 4.0 International License (htt p://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give
This paper has presented a novel LCI data collection process
appropriate credit to the original author(s) and the source, provide a link
developed, managed and implemented by a multinationalto the Creative Commons license, and indicate if changes were made.
Int J Life Cycle Assess (2018) 23:1744–1760 1759 References
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