Knowledge Risks in Digital Supply Chains: A Literature Review

Abstract

The digital transformation changes the way how organizations exchange data in supply chains. Data traditionally shared, is enriched by detailed data sets captured by sensors in the production itself. In addition to the promised benefits, also new risks arise. Advanced data analytic approaches make it possible to extract knowledge from such data sets and thus increase the risk that competitive knowledge unintentionally spills over. From a knowledge management perspective, little attention is paid to such knowledge risks arising from data-centric collaborations. Hence, this paper has the goal to investigate knowledge risks in data-centric collaborations by conducting a structured literature review. Thereby, we focus on digital supply chains, as data-centric collaborations play a central role within them. Based on our review, we identify four characteristics of digital supply chains relevant for managing knowledge risks. Based on these characteristics, we present causes, risks and countermeasures from an organizational, technical and legal perspective.

Knowledge sharing and protection in data-centric collaborations: An exploratory study

“…Using current data-science approaches, a reengineering of knowledge from such datasets becomes possible, establishing a new type of knowledge risk (Ilvonen et al, 2018). Hence, knowledge protection should pay attention to data-centric collaborations arising in the context of digital supply chains (SC) (Zeiringer & Thalmann, 2020).…”

“…The datasets are exchanged to optimise the SC operations and are thus intentionally shared with SC partners. However, as organisations are unaware of the knowledge that can be extracted from the datasets, this exchange is not yet covered by (knowledge) risk management (Fruhwirth et al, 2021;Ilvonen et al, 2019;Kaiser et al, 2020;Zeiringer & Thalmann, 2020;Birkel & Hartmann, 2019).…”

A Network-based Tool for Identifying Knowledge Risks in Data-Driven Business Models

“…We contribute to the literature on business model innovation [2,7,8] by suggesting the consideration of knowledge risks already in the business model design supported by our artifact. Further it complements existing research on technical and organizational measures to manage knowledge risks in data-centric collaborations [27,49] as well as methods for managing knowledge risks in general [24,35].…”

“…Knowledge risks can arise from shared data sets in data-centric collaborations or digital supply chains [27,49]. It is challenging for organizations to be aware of which knowledge could be extracted out of shared data sets via data analytics methods by other actors leading to unintended knowledge leakage [25,49]. Managing such knowledge risks require, legal, organizational or technical measures [49].…”

“…It is challenging for organizations to be aware of which knowledge could be extracted out of shared data sets via data analytics methods by other actors leading to unintended knowledge leakage [25,49]. Managing such knowledge risks require, legal, organizational or technical measures [49]. For instance, grey-box modelling together with a variety of more fine-granular knowledge protection practices have been found as potential solution to deal with knowledge risks in such data-centric collaboration in an in-depth investigation of knowledge protection practices in an industrial case study [27].…”