Supply Chain Complexity

Die Globalisierung und die dadurch omnipräsente Konkurrenz zwingt Unternehmen in gnadenlose Konkurrenzkämpfe. Eine Möglichkeit sich einen Wettbewerbsvorteil zu verschaffen, ist das Anbieten eines sehr breiten Produktportfolios und die Anpassung dessen auf Kundenwünsche. Dies kann in der Folge zu einer Variantenexplosion und zu einer enormen Komplexität innerhalb der Supply Chain führen.

Stephen Hawking commented that this century will be the century of complexity. This demonstrates the timeliness and relevance of this knowing supply chain complexity project (SCCknow). Most managers view complexity as a necessary evil, something to be tolerated as a fact of life. Anecdotal evidence, however, suggests companies can leverage complexity for competitive advantage. SC complexity (SCC) is defined as the number of SC nodes and the dynamic interaction between them. Until recently, SCC was discussed as having a negative impact on performance. This view is myopic and a more nuanced understanding of SCC is required. For example, some complexity is absolutely necessary as an additional product variant or another supplier could be needed to deliver requisite customer value and secure performance. Existing research neglects the positive effects of SCC and falls short to give guidance on how to identify a desired level of complexity and whether more complexity can be value-adding too – not just value-dissipating.

Therefore, SCCknow aims to answers the following research questions by applying a multi-method research approach.

RQ1: How can complexity be measured from a node-, edge-, and SC level perspective?

Based on a literature review, an interview series with SC experts in the US and Austria will be conducted, to develop a more nuanced understanding and measure of SCC.

RQ2: How can be differentiated between value-added (= good) and value-dissipating (= bad) complexity?

To better understand why and how companies pursue distinct complexity strategies, we employ an inductive study of ~ten leading manufacturers.

RQ3: How can complexity effectively be monitored and visualized in a specific SC in order to manage a desirable level of SCC?

Social network analysis will be applied to cover structural SC embeddedness. We will gather superior data sets about SCs, and visualize and monitor SCC. Superior data sets, because Dr. Gerschberger i) is logistics expert in the COVID crisis team of the Austrian government monitoring the entire Austrian grocery network, and ii) heads the Josef Ressel Centre for Real-Time Value Network Visibility and has access to SC data of 10,000 suppliers as well as suppliers of suppliers and 40,000 interrelations between them. Bearing in mind that companies have rarely data on their suppliers beyond their direct suppliers, these data set is extraordinary and very large.

SCCknow tries to operationalize a more nuanced understanding of SCC – allowing to differentiate between value-adding and value-dissipating complexity on a node-, edge-, and supply chain level, and will be the first attempt in SC research aiming to monitor and visualize SCC by developing generally applicable tools.

The two main partners in SCCknow are Dr. Gerschberger and Dr. Montreuil.