A Conceptual Reference Framework for Data-driven Supply Chain

Collaboration

Anna-Maria Nitsche

1,2 a

, Christian-Andreas Schumann

and Bogdan Franczyk

1,3 b

Faculty of Economics and Management Science, University of Leipzig, Leipzig, Germany

Faculty of Business and Economics, University of Applied Sciences Zwickau, Zwickau, Germany

Department of Information Systems, Wrocław University of Economics, Wrocław, Poland

Keywords: Empirically Grounded Reference Modelling, Supply Chain Collaboration, Digitalisation, Collaborative

Enterprise Architecture.

Abstract: This paper presents the preliminary results of the systematic empirically based development of a conceptual

reference framework for data-driven supply chain collaboration based on the process model for empirically

grounded reference modelling by Ahlemann and Gastl. The wider application of collaborative supply chain

management is a requirement of increasingly competitive and global supply networks. Thus, the different

aspects of supply chain collaboration, such as inter-organisational exchange of data and knowledge as well as

sharing are considered to be essential factors for organisational growth. The paper attempts to fill the gap of

a missing overview of this field by providing the initial results of the development of a comprehensive

framework of data-driven supply chain collaboration. It contributes to the academic debate on collaborative

enterprise architecture within collaborative supply chain management by providing a conceptualisation and

categorisation of supply chain collaboration. Furthermore, this paper presents a valuable contribution to

supply chain processes in organisations of all sectors by both providing a macro level perspective on the topic

of collaborative supply chain management and by delivering a practical contribution in the form of an

adaptable reference framework for application in the information technology sector.

1 INTRODUCTION

The global integration of supply chains, continuous

population growth and urbanisation put city

ecosystems and logistics networks under increasing

pressure (Hölderich et al., 2020; Schönberg, Wunder,

& Huster, 2018; Witten & Schmidt, 2019) while other

mega trends such as the digitalisation and automation

of business processes also drive comprehensive

changes in the logistics sector, where approximately

half of the companies consider themselves to be

trendsetters or innovators (Kohl & Pfretzschner,

2018). Thus, cross-industry logistics cooperation for

digitalisation (Kohl & Pfretzschner, 2018) and supply

chain transparency (Kersten, Seiter, von See,

Hackius, & Maurer, 2017; Kersten, von See, S, &

Grotemeier, 2020; Zanker, 2018) drive the need for

stronger interconnection of and cooperation between

companies. Tremendous changes and potential

https://orcid.org/0000-0003-3164-5066

https://orcid.org/0000-0002-5740-2946

paradigm shifts are expected within the logistics and

supply chain sector over the next decade, for instance

concerning the influence of technology on physical

and information or data flows, new models of

cooperation in connected value networks, and

autonomous decision-making (Backhaus et al., 2020;

Junge, Verhoeven, Reipert, & Mansfeld, 2019). The

wider application of collaborative supply chain

management (SCM) is driven by multiple factors

(Cao, Vonderembse, Zhang, & Ragu-Nathan, 2010)

and a high heterogeneity of systems and processes

(Glöckner, 2019). Inter-organisational exchange of

data and knowledge and sharing are well-known

problems and considered to be crucial competitive

factors (Backhaus et al., 2020; Gesing, 2017; Junge et

al., 2019).

While the relevance of supply chain collaboration

(SCC) within logistics and SCM is frequently

highlighted in the literature (Cao & Zhang, 2013;

Nitsche, A., Schumann, C. and Franczyk, B.

A Conceptual Reference Framework for Data-driven Supply Chain Collaboration.

DOI: 10.5220/0010474107510758

In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 2, pages 751-758

ISBN: 978-989-758-509-8

751

Glöckner, 2019; Schönberg et al., 2018; Soosay &

Hyland, 2015), a uniform orientation framework for

SCC is not available. This position paper thus aims to

provide a comprehensive overview of data-driven

SCC to facilitate the implementation of inter-

organisational data and knowledge exchange. It

contributes to the academic debate on collaborative

enterprise architecture (EA) within collaborative

SCM by providing a conceptualisation and

categorisation of data-driven SCC. Furthermore, this

paper presents a valuable contribution to supply chain

processes in organisations of all sectors by providing

a macro level perspective on the topic of collaborative

SCM, and by delivering an adaptable reference

framework for application in the information

technology (IT) sector. The remainder of the position

paper presents the research approach and methods,

the intended reference modelling approach, and

preliminary results.

2 KEY CONCEPTS

SCC is a broad term and can be characterised “as

seven interweaving components of information

sharing, goal congruence, decision synchronization,

incentive alignment, resources sharing, collaborative

communication, and joint knowledge creation” (Cao

& Zhang, 2013, p.55). Richey, Adams, and Dalela

(2012, p.35) define collaboration as “a mutually

shared process where two or more firms display

mutual understanding and a shared vision, and the

firms in question voluntarily agree to integrate

human, financial, or technical resources with the aim

of achieving collective goals”. Barratt (2004)

similarly states that a collaborative culture is based on

trust, mutuality, information exchange, openness, and

communication. Data-driven collaboration means

that the collaborative process is prescribed by

relevant data structures (D. Müller, Reichert, &

Herbst, 2007). In other words, it is determined by, or

dependent on, the collection or analysis of data as it

is “happening or done according to information that

has been collected” (CUP, 2021).

EA is defined as “the organizing logic for business

process and IT capabilities reflecting the integration

and standardization requirements of the firm’s

operating model” (CISR, 2016). It supports the

complexity management of organisations through the

structured description of organisations and their

relationships and through communication facilitation

for business and IT alignment (Niemi & Pekkola,

2017; Simon, Fischbach, & Schoder, 2014).

Collaborative EA attempts to fill the gap caused by

the existing lack of collaboration in EA development

(Banaeianjahromi & Smolander, 2017).

3 RESEARCH APPROACH &

METHODS

Design science research (DSR) has been part of

information systems (IS) research under varying

terms for at least 30 years (Peffers, Tuunanen, &

Niehaves, 2018). Its construction-oriented and

problem-solving approach aims at the creation and

evaluation of useful IT solutions for organisational

problems (Gregor & Hevner, 2013; Hevner, March,

Park, & Ram, 2004) and is thus suitable to depict

collaborative SCM. This paper intends to contribute a

DSR artefact in the form of a model. Based on the

typology of reference models (vom Brocke, 2003),

the characteristics of the Conceptual Reference

Framework for Data-Driven Supply Chain

Collaboration are illustrated in Table 1. Reference

modelling serves multiple purposes, thus addressing

all levels and business fields of enterprises,

for example strategic and organisational aspects, the

Table 1: Typology of reference models based on vom Brocke (2003, p.98) with the appropriate categories underlined.

Characteristic Description

Model-related

Aspect Aspect-specific Multi-aspect

Formality Not formal Semi-formal Formal

Subject Technical concept

Data processing

concept

Implementation

Objective Organisational system model Application System Model

Sector Industry Trade SCM Other sectors

Task Support Purpose Steering

Method-related

Fulfilment of

requirements

Reference model-unspecific

Reference model-specific

Technology-related

Representation Print

Computer-aided

Organisation-related

Availability Unpublished Published

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design of IS, the description of organisations,

business process (re-)engineering and knowledge

management, and is therefore suitable for this

research project (Becker & Delfmann, 2013; Fettke,

Loos, & Zwicker, 2007).

The construction of the Conceptual Reference

Framework for Data-Driven Supply Chain

Collaboration is developed based on the process

model for empirically grounded reference modelling

suggested by Ahlemann and Gastl (2007) as a

deductive approach (Rehse, Fettke, & Loos, 2015),

which consists of planning, model construction,

validation, practical testing, and documentation (see

Figure 1) and has been applied in various contexts

(e.g. de Kinderen & Kaczmarek-Hess, 2019). This

procedure is chosen to bridge the gap between

theoretical and empirical construction methods, as a

prevalence of analytical and theoretical concepts over

empirically developed reference models is

acknowledged (Fettke & Loos, 2004). As phrased by

Fettke and Loos (2004, p.338), the “wide gap between

theoretical and empirical research in a real science is

worrying”. Moreover, the process model matches

well if the DSR paradigm due to the similarity of the

phases and the underlying research principles.

4 THE CONCEPTUAL

REFERENCE MODEL

4.1 Phase I: Planning

Phase I of the reference modelling approach based on

Ahlemann and Gastl (2007), as illustrated in Figure 1,

covers model-related planning, including the problem

identification and definition as well as method-

related, organisational, technological and project

planning. The steps within this phase are based on the

four design areas for reference modelling identified

by vom Brocke and Fettke (2019): organisation (i.e.

the analysis of the organisational environment),

model (i.e. the variability of requirements), method

(i.e. the selection of the design approach) and

technology (i.e. the selection of a technical platform

on which model creation, storage, exchange and

discourse can be realised).

The model-related planning is concerned with the

definition of the reference model domain, which is

referred to as the problem definition by Schütte

(1998). This step can be done in collaboration with

domain experts or potential model users, such as

supply chain managers (Ahlemann & Gastl, 2007).

Inter-model relationships in the form of compliance

with relevant standards and norms such as the SCOR

model should also be identified (Ahlemann & Gastl,

2007). Method-related planning is the second

component of phase I and is tasked with the selection

of appropriate problem-solving and model

representation techniques. Representation techniques

can be chosen from a large variety of modelling

languages and concepts such formal, semi-formal,

natural, and graphical languages. As stated above, the

modelling approach for the artefact is based on the

process model for empirically grounded reference

modelling suggested by Ahlemann and Gastl (2007)

while natural language is chosen as the representation

technique. Organisational planning covers the

definition and documentation of a research design,

the identification of the experts to be involved in the

modelling process as well as the coordination of these

activities. Technological planning is concerned

with the selection of appropriate technologies to

support the modelling process, including the model

Figure 1: Reference modelling approach based on Ahlemann and Gastl (2007).

A Conceptual Reference Framework for Data-driven Supply Chain Collaboration

753

construction (i.e. modelling tools or computer-aided

software engineering tools), the documentation of the

reference model (i.e. text processing systems) and the

recording and analysis of the expert interviews (i.e.

audio systems) (Ahlemann & Gastl, 2007). The last

step of phase I is project planning. A top-down

approach for complex tasks has long time been

established as suitable to achieve different levels of

abstraction (Schütte, 1998). The project planning

should include a time and work schedule, a resource

plan, and a risk analysis.

4.2 Phase II: Model Construction

The second phase is the model construction phase

which comprises capturing existing domain

knowledge, constructing the reference model frame,

preparing, and executing the first empirical enquiry,

and designing the initial reference model structure.

The first step is the analysis of relevant current

domain knowledge to ensure the model uniqueness

and to incorporate previous research. To capture

existing domain knowledge, appropriate sources such

as scientific publications or practice reports need to

be identified, catalogued, and prioritised (Ahlemann

& Gastl, 2007). The modelling approach for the

reference framework is based on thorough reviews of

the literature.

The subsequent construction of the reference

model frame is useful for structuring the expert

interviews and for constructing and documenting the

reference model. First, general domain knowledge of

logistics process and collaboration modelling is used.

Apart from the distinction of different levels of focus,

E. Müller and Ackermann (2010) propose the

modelling of logistics structures using 3-level models

and structure types such as production and

distribution system, transport system and

infrastructure system. Levels of focus are usually the

macro level, i.e. the external relations of the network,

the meso level, i.e. inter-organisational level, and the

micro level, i.e. intra-logistics. Villarreal, Salomone,

and Chiotti (2007) suggest that Business to Business

(B2B) collaboration necessitates integration at both a

business and a technological level. Thus,

collaborative business processes need to be specified

and modelled for decision-making, setting strategic

goals, coordinating actions, and exchanging

information. Furthermore, the requirements of inter-

organisational collaborations need to be incorporated.

These include a global view of the collaboration,

enterprise autonomy, the decentralised management

of collaborative processes through peer-to-peer

interactions and the use of suitable abstractions to

model complex communicative actions and

negotiations (Villarreal et al., 2007). This also

highlights the importance of data-driven

collaborative processes. While the model is intended

to also include business to customer (B2C) relations,

the focus is on business-level interactions.

The reference model frame is depicted in Figure 2

and consists of the agents of a basic 1-tier supply

chain: supplier, manufacturer, and customer.

Within the individual agents four layers of structure

types can be distinguished in the model frame.

These are business collaboration system, production

and distribution system, transport system,

and infrastructure system. Here, infrastructure system

Figure 2: Reference model frame.

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also comprises handling and storage processes. The

main flows within supply networks are shown as

summarised arrows between the individual agents,

they comprise information, financial and material

flows. The supply chain agents are communicating

and collaborating on different levels. A 3-level

structure, which is prevalent in logistics and SCM

models, is used to illustrate the different levels of

collaboration. The micro level is restricted to one

agent, in this example the supplier, and thus covers

intra-logistical collaborative aspects such as cross-

department coordination. The meso level on the other

hand describes the relations on an inter-organisational

level within the supply chain. For instance, the

supplier and manufacturer collaborate on topics such

as production planning harmonisation. The third level

is concerned with the external relations of the

network and takes on a macro perspective regarding

collaboration. This could include strategic network

decisions such as coopetition or sharing of

infrastructure.

During phase II, the first empirical enquiry is

prepared and executed to enable the first construction

cycle of the reference model based on the experts’

domain knowledge. The preparation comprises the

identification, examination and selection of interview

partners, and the creation of an interview guide.

To acquire experts for the interviews, different

sampling techniques are available (Saunders, Lewis,

& Thronhill, 2016). Due to the need for the

involvement of expert domain knowledge, Ahlemann

and Gastl (2007) propose two non-probability

techniques, namely chain sampling and anonymous

sampling using (mass) media. The approach for this

artefact uses homogenous purposeful sampling as

described by Saunders et al. (2016), which is similar

to chain sampling. To include different perspectives

and to achieve a meaningful model, it is

recommended to involve several experts (Saunders et

al., 2016; Schütte, 1998). While it is generally

advised to continue qualitative data collection until

data saturation is reached, Saunders et al. (2016)

propose minimum sample size relative to the nature

of the study. For the construction of the Conceptual

Reference Framework for Data-Driven Supply Chain

Collaboration, which uses semi-structured in-depth

interviews, Saunders et al. (2016) advice the

involvement a minimum of a total 10 interviewees

(i.e. 5 interviews per cycle). To incorporate both

academic and practice-oriented viewpoints and

experiences, the intended qualitative sample

comprises four scholarly experts and three experts

with a practical SCC background from different

industries in Germany and the UK. Thus, the

empirical inquiry is based on a total of 14 in-depth

interviews.

An interview guide can be used to structure the

interview. Ahlemann and Gastl (2007) recommend

dividing the interview guide into separate sections

which cover the model context, the interviewees

domain knowledge and specific experience regarding

the model as well as the problem domain and the

design of the reference model, and the interviewee’s

person. Here, elements of EA are incorporated into

the research project and the interview guide for the

first empirical inquiry is structured according to the

ARIS concept (Scheer, 1997, 2013) and the St. Gallen

approach to business engineering (Österle, 1995;

Österle & Blessing, 2005; Österle & Senger, 2011;

Winter, 2010). The ARIS concept is a process-

oriented framework concept for modelling and

implementing reference models that focuses on the

following views of a business process: functional,

organisational, data and control view. The views’

relationships are specified in the control segment. The

St. Gallen approach to business engineering was

developed in the early 1990s at the Institute for

Information Systems at the University of St. Gallen

(Österle, 1995) and has been continuously developed

(e.g. Österle & Blessing, 2005). It includes principles

and methods for the transformation of organisations

in the information era and distinguishes between three

design levels, namely strategy, organisation, and

information system.

Currently, the status of the research project is a

finalised preparation of the first empirical enquiry,

including the acquisition of experts. All subsequent

steps are intended for the months before and directly

following the conference as the interviews are

scheduled for February/March (first empirical

enquiry) and May/June (second empirical enquiry).

Following the first empirical enquiry, the initial

reference model structure is designed. Here,

established problem solution and representation

techniques can be used. The model construction is

generally based on five sources of data, namely the

interview results, relevant standards and norms,

existing research results identified through a literature

review, own domain knowledge and other appropriate

data sources. As the interview guide is based on EA

concepts, the construction of the initial reference

model structure is similarly based on these elements.

The Conceptual Reference Framework for Data-

Driven Supply Chain Collaboration factors in both

natural and artificial intelligence as it is intended to

incorporate an artificial intelligence toolbox based on

findings of literature reviews. This enables supply

chain managers and other stakeholders to choose and

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compare the available artificial intelligence and

machine learning tools regarding their specific use

context.

4.3 Phase III: Validation

Phase III is the validation phase which consists of the

preparation and execution of the second empirical

enquiry and the model refinement. In contrast to

phases I and II, which are concerned with the model

construction, phase III and onwards have the purpose

of stabilisation, discussion, and refinement through

empirical research (Ahlemann & Gastl, 2007). The

lists of correction proposals gathered during each

expert interview form the basis for the further model

refinement. The integration of conflicting suggestions

is a critical aspect of this step and all decisions made

in relation to this need to be documented in sufficient

detail (Ahlemann & Gastl, 2007).

This research project intends to apply a formative-

summative design-evaluate-construct-evaluate

pattern based on Sonnenberg and vom Brocke (2012)

containing elements of both ex-ante and ex-post

evaluation. The evaluation of the model is based on

the principles of proper reference modelling, the

recommendations for DSR evaluation (Peffers,

Rothenberger, Tuunanen, & Vaezi, 2012) and the

framework for evaluation in design sciences

(Venable, Pries-Heje, & Baskerville, 2016) and uses

expert evaluation (Peffers et al., 2012) to judge

accuracy, completeness, consistency, generality,

level of detail, reliability/robustness, usability/ease of

use and effectiveness. For instance, usefulness may

be assessed based on the scale of Davis (Prat, Comyn-

Wattiau, & Akoka, 2015).

4.4 Phase IV: Practical Testing

Phase IV is tasked with the application or practical

testing and the subsequent model refinement and

completion. The reference model should ideally be

used in an information or organisational system

project to increase its acceptance, to further refine and

improve the model and to confirm its applicability

and practical benefits. The results from the practical

application can be used to improve and complete the

conceptual model. This phase is intended to apply the

Conceptual Reference Framework for Data-Driven

Supply Chain Collaboration to a last mile supply

chain and logistics network context.

4.5 Phase V: Documentation

A complete documentation is carried out in the fifth

and last phase to ensure increased comprehension and

validity. Ahlemann and Gastl (2007) recommend a

documentation structure comprising a description of

the construction process, annotations of model

elements, the documentation of empirical elements

and a table of model elements. Further publications

are planned to realise this phase.

5 CONCLUSIONS

Despite the comprehensive perspective on the

research problem provided by the design-oriented

approach of this paper, the limitations that relate to

the related choices need to be acknowledged.

Although the DSR approach contains several useful

suggestions, it suffers from significant weaknesses

(Zelewski, 2007). While the adopted DSR guidelines

necessitate rigorous testing and validation, they

themselves do not necessarily meet these

requirements. Furthermore, the process model for

empirically grounded reference modelling

(Ahlemann & Gastl, 2007) is limited regarding its

focus on qualitative methods for data generation.

The paper presents the preliminary results of the

systematic empirically based development of a

Conceptual Reference Framework for Data-Driven

Supply Chain Collaboration. The authors intend to

complete and evaluate the reference framework

following the data collection (phase II). The wider

application of collaborative SCM is a requirement of

increasingly competitive and global supply networks.

Thus, the different aspects of SCC, such as inter-

organisational exchange of data and knowledge and

sharing can be considered as essential factors for

organisational growth. This paper and further

development of the research project attempt to fill the

gap of a missing overview of this field by providing a

comprehensive framework of data-driven SCC. It

contributes to the academic debate on collaborative

EA within collaborative SCM by providing a

conceptualisation and categorisation of SCC.

Furthermore, this paper presents a valuable

contribution to supply chain processes in

organisations of all sectors by both providing a macro

level perspective on the topic of collaborative SCM

and by delivering a practical contribution in the form

of an adaptable reference framework for application

in the IT sector.

Future research avenues include the completion,

evaluation, and application of the Conceptual

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Reference Framework for Data-Driven Supply Chain

Collaboration. In addition, quantitative approaches as

well as case studies can be used to further develop and

refine the framework. Potentially, it could be adapted

to other areas where data-driven collaboration could

have a positive impact.

ACKNOWLEDGEMENTS

This work was supported by the tax revenues on the

basis of the budget adopted by the Saxon State

Parliament under Grant SAB/100379142.

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