Graph Database to Enhance Supply Chain Resilience for Industry 4.0

Introduction

Globalization have stimulated automotive industry to develop globally interconnected and complex supply chain networks with greater physical distances. Henry Ford's supply chain was integrated and conceptually simple at the beginning of the twentieth century (The Economist, 2009). However, by the modern globalized economy, Ford accepted that they could not be the best in every field and began to develop interconnected and complex structures like supply web than supply chain (CSCMP's Supply Chain Quarterly, 2010). Thus, supply chain distribution risks increase with number of involved organizations in the network (Raghunath K. M., 2018). Supply chain data relationships become interconnected, and multi-tier rather than hierarchical or one-to-one. As the volume and complexity of data grow, supply chain managers require greater data transparency to analyze complex network behaviors to understand and support strategic decision makings. To achieve this, automotive industry companies need to digitize their supply chains to visualize better and understand how they work. However, due to its rapidly growing size and complexity of data, few companies have been able to apply big analytics techniques to manage their supply chains. We developed a graph database framework to integrate multiple complex levels within an automotive supply chain network to enhance supply chain transparency.

One of the significant challenges for supply chain management is to mitigate risk by creating resilient supply chains. The literature has produced many definitions of supply chain resilience by several disciplines. Resilient supply chains require strong traceability systems for effective and timely decision making for external and internal changes. However, a recent survey shows that most supply chain companies still rely on spreadsheets to plan their supply chain process, making them less responsive and fragile (Supply Chain 247, 2018; Reuters Events, 2018). Recent advances in big analytics and AI support fast and effective decisions in many areas. In this paper, we evaluate one of the advanced databases in big data for supply chain management by testing its computational performance for big data analysis. Especially, we design a supply chain data model for graph database which tracks all the flow of raw materials from tier suppliers to finished products and their holistic interrelationships. Also, we introduce Time-to-Stockout analysis for supply chain resilience. The proposed Time-to-Stockout (TTS) performance metric could simplify the dynamic nature of the supply chain environment with respect to both market-side demand and supply-side inventory. It will offer a deeper knowledge of resilience and provide tools for managers to track and monitor the inventory risk, which can be propagated through supply chains. Several previous papers have evaluated graph databases and developed benchmarks. However, most of them used synthetic data or social network data. A few recent papers start to use real data to evaluate graph databases. As far as we know, this is the first paper to use a graph database for supply chain data. Especially, we evaluate it based on real data of Ford supply chain.

The remainder of the paper consists of five other sections. First, we introduce literature papers for the supply chain and graph database. Second, we present a way to model interconnected, multiple-depth supply chain data with a graph database. Third, we suggest the Time-to-Stockout performance metric and introduce the concept of Time-to-Stockout analysis. Fourth, we show the computational results obtained by applying a graph database to Ford supply chain data, and how do we support decisions for supply chain managers. Finally, we present conclusions and discussion.