Supply Chain Visibility: Impact and Value of Real-time Resource Allocation
Dr. Guodong LYU
Research Fellow in the Institute of Operations Research & Analytics
National University of Singapore
In recent years, we have seen a surge of interest in supply chain visibility. Under this paradigm, decision-makers are able to trace the real-time data (e.g., stock level, resource allocation flow) along the entire supply chain so that they can identify the decision-making bottlenecks and take actions more efficiently. Motivated by the Gaze Heuristic, we propose a target-based online planning framework to deal with real-time resource allocation problems in both stationary and nonstationary environments. Leveraging on the Blackwell’s Approachability Theorem and Online Convex Optimization tools, we characterize the near-optimal performance guarantee of our online solution in comparison with the offline optimal solution, and explore the properties of different allocation policies.
We use synthetic and real data from various industries, from supply chain planning in manufacturing, to resource deployment in ride-sharing markets, to examine the impact and value of these real-time solutions in practice: (1) we present a new insight into the impact of supply chain visibility on the capacity configuration in the capacity pooling system. Our results show that the pooling system does not need to hold any safety stock to deliver the required demand fulfillment service if real-time allocation with full visibility is utilized, when the number of customers is sufficiently large in the system; (2) we study a real-time ride-matching problem in the ride-sourcing context, with multi-objectives (e.g., service quality, revenue) to be considered. We develop a new technique that can be used to choose the weight adaptively over time, based on real-time tracking of the gaps in attained performance and a set of performance targets. Our results show that the real-time matching policy could potentially contribute to the long-term sustainability and reputation of the ride-sourcing platform by dispatching more orders to drivers with higher service quality, without sacrificing the short-term platform revenue.
