Enhancing supply chain performance using data analytics

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This joint study between WMG, University of Warwick and the management analytics firm Concentra set out to research the capacity of modern data analytics to optimise supply chain performance. The research base comprises five European manufacturing businesses, all of different sizes and operating in unrelated industries. Using SupplyVue, Concentra’s supply chain analytics tool, the researchers were able to clearly demonstrate the ability of modern data analytics to create visibility across complex supply chains and to identify areas of sub-optimal performance. These insights informed improvements in the firms’ supply chain management processes and policies, which led clear business benefits in every case. The study indicates that the potential for analytics in supply chain management is high, but largely untapped. Our findings suggest that the easiest wins centre around basic good management measures, such as demand profiling, forecasting and the systematisation of supply processes.


Between 60%-90% of company costs occur in the supply chain. As a consequence, even small efficiency improvements in supply chain management can result in major cost-saving gains. However, research indicates that the drive for cost cutting increases the risk of reduced service quality.

The guiding hypothesis of this paper is that data analytics helps break the management silos that commonly occur in modern supply chains and replace them with a more holistic approach to supply chain management.

This joint research project set out to analyse the capacity of modern data analytics to optimise supply chain performance. Adopting a case study approach, we tested a best practice supply chain analytics tool on five separate companies from a variety of different sectors and geographies.

The tool adheres to a consistent set of core management principles, which are systematically applied in the supply chain evaluation process. The tool contains a suite of data collection and analysis technologies through which this evaluation process is operationalised.

The primary goal of the research was to evaluate whether this generic tool could be employed to identify and consequently resolve the root causes of specific supply chain problems.

As well as testing our central hypothesis, this research exercise also sought to draw out practical observations concerning the application of data analytics in supply chain-related problem solving.

In this regard, our investigation centred on three principle questions:

  • What types of data and information deliver the most value in supply chain optimisation processes?
  • How can data analytics use performance data to best effect?
  • And, how should data analytics be integrated into business decision-making so as to capture its fullest value?

Research design

A four-stage research design was deployed for this study.

Stage 1
Case study identification

Five companies were selected to cover a broad scope of supply chain activities (e.g. retail, manufacturing, distribution).

Stage 2
Scoping study

Through semi-structured interviews and site visits, the supply chain context, project scope, supply chain challenge and data requirements were defined.

Stage 3
Data download, analysis and review of current state

Data was extracted, cleaned and uploaded into SupplyVue for analysis and review of the current state supply chain with the case study partner.

Stage 4
Modelling and review of future state

Based on the analysis of the current state, an opportunity for supply chain improvement was modelled to demonstrate the potential benefits. This future state was then reviewed with each case study company.