Supply chain lifecycle decision analytics

Abstract

The globalization of today's supply chains (e.g., information and communication technologies, military systems, etc.) has created an emerging security threat that could degrade the integrity and availability of sensitive and critical government data, control systems, and infrastructures. Commercial-off-theshelf (COTS) and even government-off-the-self (GOTS) products often are designed, developed, and manufactured overseas. Counterfeit items, from individual chips to entire systems, have been found in commercial and government sectors. Supply chain attacks can be initiated at any point during the product or system lifecycle, and can have detrimental effects to mission success. To date, there is a lack of analytics and decision support tools used to analyze supply chain security holistically, and to perform tradeoff analyses to determine how to invest in or deploy possible mitigation options for supply chain security such that the return on investment is optimal with respect to cost, efficiency, and security. This paper discusses the development of a supply chain decision analytics framework that will assist decision makers and stakeholders in performing risk-based cost-benefit prioritization of security investments to manage supply chain risk. Key aspects of our framework include the hierarchical supply chain representation, vulnerability and mitigation modeling, risk assessment and optimization. This work is a part of a long term research effort on supply chain decision analytics for trusted systems and communications research challenge.

Strategies on Establishing Supply Chain Resilience in the Era of VUCA

“…When a small probability risk occurs, in order to ensure uninterrupted supply chain resilience, adding some redundancy, such as additional inventory and capacity buffer, is an important strategy to improve supply chain resilience. However, excessive redundancy means higher costs, and the resulting "cow-whip effect", and the redundancy design should not only focus on the level of elasticity, but also need a trade-off between the level of elasticity and the cost it causes [5]. In order to overcome the "bullwhip effect", the supply chain members can jointly build an appropriate amount of redundancy in the inventory, and share the cost through the contract design among the members to realize the coordination of the supply chain.…”

A Review on Data Analytics for Supply Chain Management: A Case study

“…BDA is important in creating an integrated view of operational performance and customer satisfaction of both sender and recipient in the SCM [16]. It is really challenging to meet fully the Supply chain susceptibility because of the complexity in Supply chain components, process, supplier and services [25]. Supply chain analytics has been developed its own identity in supply chain management by using the Business Intelligence tools for the analysis of customer behavior, optimization of upstream and downstream operations and also insight on advanced routing solutions.…”

“…Supply chain analytics has been developed its own identity in supply chain management by using the Business Intelligence tools for the analysis of customer behavior, optimization of upstream and downstream operations and also insight on advanced routing solutions. Examples are component complexity, supplier complexity, process complexity and service complexity [3].…”