With the advent of big data in supply chain information systems (SCIS), data compliance and consistency are becoming vital. Today, SC stakeholders need to pay more attention to data governance, which requires changing traditional management methods. These can be achieved by mastering a single repository through what is usually named master data management (MDM). However, accomplishing this objective is particularly challenging in the complex logistics networks of supply chains (SC). The volatile nature of the logistics flows that increase exponentially because of the facilitation of exchanges' interoperability in the information systems. In this chapter, the authors propose an MDM-based framework for the supply chain information systems as an enabler for strong collaboration and compliance. For proof of concept, a case study of a French hypermarket is examined through benchmarking scenarios. The outcomes of the case validate our approach as a hands-on solution when applied correctly. Finally, the chapter discusses the key findings and the limitations of our framework.
TopIntroduction
With the inundation of the Data records and the replications in the Big Data era, the distribution of networked Information Systems (IS) in Supply Chain Management (SCM) is facing the challenges of non-compliance and irregularities (Myung, 2016). The different Data lakes and Databases preserve the business objects' depictions in numerous ways with disparate features, even with the same real-world entities (Chehbi Gamoura, 2019). Ideally, managers must align the Data transactions related to these entities for entire business activities across the inter-connected applications (Meriton et al., 2020).
Today, the traditional Enterprise Resource Planning (ERP) systems cannot hold the complexity and heterogeneity in Big Data high-scaled systems (Kamble & Gunasekaran, 2020). In such cases, the Data accumulation without appropriate compliance tends to generate a set of new issues with unpredictable costs of the inconsistency risks in SCM (Chehbi-Gamoura et al., 2020). To address these new growing challenges, the owners of IS strive to invest the required efforts to implement standard repository-based systems such as Master Data Management (MDM) (Al-Shargabi et al, 2020; Aljawarneh, 2012; Aljawarneh et al, 2017; Chehbi-Gamoura et al, 2018; Esposito et al, 2018; Jaswal et al, 2019; Kalpana et al, 2018; Lizcano et al, 2020; Malhotra et al, 2019; Mohammed et al, 2019; Mouchili et al, 2018; Singh,2011).
More than a simple repository, the MDM system may be a real asset to help the network members build a real collaborative, and compliant environment. If it is set up and accomplished appropriately, it can provide reliable reporting, adjusted compliance, and accurate decisions (Han et al., 2017). Despite the awareness of these benefits for academics and industries, the literature in SCM's publications reveals a real research gap in this point (In et al., 2018). Consequently, in this document, the authors propose a new platform to provide a shared bridge for the SC partners to communicate, access, improve, and compliant the information across it. Moreover, this chapter presents a real-world case study as a validation frame to examine our architecture's plausibility and limits. The case study describes the Data compliance issues in the marketing/sales web-based applications of a French hypermarket.
The purposes of this paper are both industrial and academic. From the academic perspective, the objective is to provide a review of MDM research maturity in the interconnected Supply Chain systems and then to depict the landscape and gaps of the current researches in the Big Data era. The paper offers a new architecture to support a collaborative and compliant system for the Supply Chains partners from the industrial view.
The remainder of the paper is organized as follows. Section 2 explores the background and related researches. In section 3, researchers present the fundamental concepts of the proposed architecture. Section 4 describes the case study used to validate the proposed approach. Finally, section 5 discusses the key findings, limits, and open points.