How Transdisciplinarity Can Help to Improve Operations Research on Sustainable Supply Chains—A Transdisciplinary Modeling Framework

• Author: Christian Nuss,Dennis Stindt,Axel Tuma,Ramin Sahamie
• Date: 01 June 2016
• Published date: 01 June 2016
• DOI: http://doi.org/10.1111/jbl.12127

How Transdisciplinarity Can Help to Improve Operations Research

on Sustainable Supply Chains—A Transdisciplinary Modeling

Framework

Dennis Stindt, Ramin Sahamie, Christian Nuss, and Axel Tuma

University of Augsburg

We present a transdisciplinary modeling framework that enhances collaborative research on sustainable supply chain management (SSCM).

Decision support concerning such systems is commonly provided using operations research (OR) methodologies. The quality of respec-

tive models depends on the appropriateness of both mathematical representation of the focal system and data input. Concerning this matter, OR

faces severe criticism as groundwork is commonly neglected. This results in a lack of holistic understanding and in insufficient modeling of

real-world problems. Crucial characteristics of the underlying system are often over simplified due to single-discipline assessments. Particularly,

in the context of complex sustainability challenges, multiple nonacademic competencies and expertise are required. Although latest research

indicates that collaborative research settings are highly beneficial regarding SSCM, a dearth of integration between disciplines exists. Therefore,

we develop a conceptual framework that helps to overcome these shortcomings based on the paradigm of transdisciplinary research (TDR),

which needs substantiation to enhance collaboration and to ensure applicability. Accordingly, we propose appropriate methodologies for each

step within the framework. Overall, the framework enables holistic analysis of a focal system by providing a sound approach for SSCM-

oriented TDR projects. The value of the framework is eventually demonstrated by two cases that deal with SSCM issues.

Keywords: interdisciplinarity; collaboration; operations research; closed-loop supply chains; sustainability; energy systems

INTRODUCTION

The production and distribution of goods as well as the sour-

cing of raw materials negatively impact the environment in

many ways. In order to address these ecological effects under

consideration of both economic necessities and societal pres-

sure, increasing attention is paid to the concept of sustainable

supply chain management (SSCM). Decision problems in this

area are commonly analyzed and assessed using operations

research (OR) methods. This toolset aims on translating

unstructured real-world problems into quantitative models. The

quality of the solution depends on the appropriateness of both

mathematical description and data input. In this context, OR on

SSCM is subject to criticism based on the observation that it is

diverging from reality and lacks holistic, real-world approaches

(van Wassenhove and Besiou 2013). Complementary perspec-

tives are needed for proper assessment of such systems (Saha-

mie et al. 2013) and “researchers must transcend disciplinary

boundaries and adopt a more holistic approach”(Sanders and

Wagner 2011, 321). Hence, the shortcomings of OR may be

tackled by the paradigm of transdisciplinary research (TDR) as

it integrates researchers from different academic backgrounds

as well as nonacademic stakeholders, like practitioners, politi-

cians, or nongovernmental organizations (NGOs) (Baumg€

artner

et al. 2008).

TDR requires a process of knowledge integration, which is

subject to various obstacles that endanger the project’s success.

Thus, structured approaches that address these challenges and

steer collaboration are required but are not extant for issues of

SSCM. Based on this observation, we develop a framework enti-

tled “transdisciplinary modeling framework”(TMF) that supports

analyses of SSCM issues relying on transdisciplinarity. Accord-

ingly, we state the research question as follows:

Which approaches are appropriate to effectively improve

OR-based analysis of SSCM issues by means of a TDR

process?

Considering the OR-oriented generic problem-solving process

(see Figure 1), the TMF emphasizes and improves the step of

verbal modeling. This step is crucial as it defines goals, charac-

teristics of a focal system, cause-and-effect relationships, decision

alternatives, and information, which are eventually transferred to

the subsequent step of mathematical modeling. In common OR

literature verbal modeling is often underdeveloped, which causes

a dominant part of shortcomings in mathematical modeling.

Addressing this, the TMF provides a conceptual framework for

SSCM-oriented TDR, which eventually supports the development

of holistic OR models. As the TMF is designed to support rela-

tively complex TDR projects, it may also prove to be beneficial

for interdisciplinary collaboration or may assist corporate pro-

jects, where employees from different backgrounds and divisions

are involved.

Finally, we apply the TMF to two cases dealing with SSCM

issues. First, we demonstrate the application of the TMF in the

context of a TDR group that tackles the challenge of evaluating

sustainable energy systems. Second, we exemplify the framework

regarding management and assessment of a closed-loop supply

chain (CLSC) in the plastics and polymer industry. These

demonstration cases deepen the understanding of the TMF and

highlight typical pitfalls that are experienced in common OR

studies.

Corresponding author:

Axel Tuma, University of Augsburg—Chair of Production & Supply

Chain Management, Universitaetsstr. 16, Augsburg 86159, Germany;

E-mail: axel.tuma@wiwi.uni-augsburg.de

Journal of Business Logistics, 2016, 37(2): 113–131 doi: 10.1111/jbl.12127

© Council of Supply Chain Management Professionals

SHORTCOMINGS OF OR IN SSCM

SSCM is defined as “creating goods by using processes and sys-

tems that are nonpolluting, that conserve energy and natural

resources in economically viable, safe, and healthy ways”(Gla-

vic and Lukman 2007, 1883). In accordance to the given defini-

tion, these kinds of challenges regularly comprise decision

problems, like energy-oriented production scheduling (Rager

et al. 2015), emission and waste reduction (Tan 2007), utilization

of raw materials (Majozi and Gouws 2009), and reverse logistics

and CLSC management (Carter and Ellram 1998; Rubio et al.

2008). Generally, such problems require multiple expertise as the

focus on economic factors is extended by ecologic and social cri-

teria, which are commonly barely understood by management

scientists (Sahamie et al. 2013). A majority of these decision

problems is tackled by OR methods.

By reviewing according OR studies, it becomes obvious that

an integration of multiple knowledge carriers from academia and

practice does not happen sufficiently although it would be benefi-

cial in many cases (Sanders and Wagner 2011). For instance,

“technical peculiarities of unit operations are often over-simpli-

fied or are even neglected”(Schultmann et al. 2004, 737) or the

central process of goal formation and defining an objective func-

tion is widely under developed (Eden and Ackermann 2013).

Furthermore, assumptions are not realistic (Jayant et al. 2011). In

line with that, van Wassenhove and Besiou (2013) as well as

Sodhi and Tang (2008) state that OR becomes disintegrated

from practice. It seems that recent OR studies focus on “mathe-

matical optimization in the first place”(Ulrich 2012, 1229)

instead of generating system understanding upfront. Although

this “research-practice gap”is widely recognized in manage-

ment sciences, there is little guidance to bridge it (Bansal et al.

2012, 73).

Summing up, OR is suffering from shortcomings regarding

groundwork in the field of verbal model development and a lack

of knowledge integration regarding expertise that is required for

sufficient analysis of SSCM problems. To address the stated

shortcomings, traditional OR methods need to be complemented

with participative and interactive problem structuring methods

that enable elicitation and convergence of divergent knowledge

(Rosenhead 1996). A promising approach is provided by the

research paradigms of inter- and transdisciplinarity.

COLLABORATIVE RESEARCH—APPROACHES AND

CHALLENGES

Collaborative research, namely inter- and transdisciplinarity, goes

beyond the scope of isolated traditional disciplinary boundaries.

Interdisciplinary research represents an approach that transcends

the narrow scope of disciplinary views by breaking down disci-

plinary boundaries. It occurs through coordination by a higher

level concept, mutual understanding of terms, and knowledge

integration among academic disciplines (Jantsch 1972). The para-

digm of TDR is even more comprehensive than interdisciplinar-

ity “involving not only scientists but also practitioners from

beyond the realm of science (e.g., the users) in the research

work”(Defila and Di Giulio 1999, 13). Regarding industry-aca-

demia collaborations, academia may benefit from such collabora-

tions through higher transferability of research results to the

economy, while corporate players profit from an increased com-

petitiveness and responsiveness on the market (Kaufmann and

T€

odtling 2001).

Most TDR contributions are directly deduced from real-world

projects. Table 1 summarizes transdisciplinary case studies and

research papers clustered in accordance to their epistemic back-

ground. Sustainable landscaping of rural or urban areas is a

major topic of investigation. In these studies, the transdisci-

plinary team regularly comprises academic experts from disci-

plines like biology, chemistry, geology, and societal as well as

political stakeholders. Corporate involvement is rather scarce.

Research on water management issues is similarly structured.

Studies that evaluate the effects of environmental legislation,

support green policy setting, or work on measures for climate

protection are explicitly located at the interface between aca-

demics and politics. Research groups that examine sociological

dynamics and behavioral patterns are less common. Studies on

socio-technical issues conduct technical assessments considering

ethical or social aspects of innovative technologies. Summing up,

the spectrum for applying TDR is wide. Concerning the scope of

our research, we can conclude that articles developing best prac-

tice models for TDR within the broad field of SSCM or other

OR problems are scarce.

In general, frameworks for structuring and steering TDR are

needed as such research organizations cause substantial chal-

lenges. These emanate from the involvement of divergent and

domain-specific mindsets, different values and beliefs, as well as

different modes and cultures of communication. Apart from the

participants’attitude toward transdisciplinarity and their “willing-

ness to learn, to listen, to cooperate, and to accept other interests

and values”(Scholz et al. 2000, p. 485), challenging situations

within such multiple-stakeholder projects mainly emerge from

two causes:

•A lack of clarity about focus and objectives of the project

(Lang et al. 2011)

•A high level of complexity (Rosenhead 1989, 343).

In the first case, methodologies for mutual learning and con-

vergence of understanding are required. In the latter case, there

is a need for methodologies that structure and reduce complexity.

Complexity challenges in TDR projects mainly arise along two

Figure 1: Generic problem-solving process (inspired by Wern-

ers 2008).

114 D. Stindt et al.