Real time supply chain event management for manufacturing enterprises

Supply Chain Event Management (SCEM) systems are becoming critical with the reduction of inventories and just-in-time deliveries in global supply chains. These systems augment supply chain management (SCM) systems by automatically identifying exceptions based on transaction data collected in the business. Advances in enterprise integration provide vast untapped potential for data driven approaches to automate identification of exceptions and trends in the transactional data. The past two decades have also seen significant improvements in mathematical modeling and control theoretic approaches for real time decision-making. Blending data driven techniques for sensing and diagnosis with control theory and mathematical modeling based algorithms for response in real time can be one approach to SCEM. The objective in this research is to augment the performance of supply chains by systematically reducing the possibilities of malfunctions and glitches, and reducing the damage incurred due to them. The proposed SCEM system will supplement the existing SCM systems by providing two additional functionalities: 1. Sense-imparting the capability to predict the possibility of systematic malfunctions as well as diagnose the cause for detected anomalies before they snowball into catastrophes. 2. Response-imparting the ability to trigger analytics for evaluated reactions to detected anomalies. SCEM can be viewed as means to improve the ROI in existing business processes, SCM, and IT assets by improving visibility and enabling managers to make better decisions. The main advantage of SCEM is to serve as a feed-forward control for supply chain by triggering corrective actions before an exception "snow-balls" into a catastrophe. We present some of our recent work in intelligent event filtering for adaptive demand conditioning along with adaptive production and inventory control.