Supply chain environmental investments in dynamic industries: Comparing investment and performance differences with static industries

Research linking investments in environmental practices to firm performance has matured over the past years. However, empirical research is still ambiguous on how and whether investments in environmental practices improve a plant's performance. We believe that contingency factors, especially the industry in which plants act has a significant role in the success of environmental investments. Using empirical data collected across a wide range of industries our results indicate that plants competing in dynamic industries such as apparel do on average invest less in supply chain environmental practices compared to plants in static industries. In addition, these environmental investments do not significantly improve operational performance in dynamic industries in terms of cost, quality, delivery and flexibility. However, in static industries environmental investments do significantly improve a plant's operational performance in terms of cost, quality and flexibility.     

Setting operating policies for supply hubs

This paper deals with the joint management of operations at the supply hub for the customer and the upstream supplier. Different operating conditions are considered, namely, backordering, minimum and maximum specified inventory levels. Some analytical insights on better managing suppliers operating under a vendor managed inventory program are presented. Essentially, we show that the penalty cost imposed on over- and under-stocking, and the min–max policy for hub inventory reside in the power of the hub operator. The relationship between supply hub policy and performance measures is quite complex and non-linear in nature. We suggest a structured hierarchical approach which can help supply hub in achieving balance between various parties involved in chain. A numerical example and an algorithm are included to highlight this result.     

A continuous model for supply planning of assembly systems with stochastic component procurement times

This paper studies a customized product assembly scenario where some components cannot be stocked due to high component cost and risk. We consider the case where these key components are ordered after a demand has been registered with a promised delivery date. Component lead-times are stochastic and associated distribution function is known in advance. The objective is to determine the ordering time for each component such as to minimize the sum of expected holding and backlogging costs. An approach to solve this problem is proposed and the algorithm is tested on a randomly generated data set.     

Buyer perceptions of supply disruption risk: A behavioral view and empirical assessment

As supply chains become more complex, firms face increasing risks of supply disruptions. The process through which buyers make decisions in the face of these risks, however, has not been explored. Despite research highlighting the importance of behavioral approaches to risk, there is limited research that applies these views of risk in the supply chain literature. This paper addresses this gap by drawing on behavioral risk theory to investigate the causal relationships amongst situation, representations of risk, and decision-making within the purchasing domain. We operationalize and explore the relationship between three representations of supply disruption risk: magnitude of supply disruption, probability of supply disruption, and overall supply disruption risk. Additionally, we draw on exchange theories to identify product and market factors that impact buyers’ perceptions of the probability and magnitude of supply disruption. Finally, we look at how representations of risk affect the decision to seek alternative sources of supply. We test our model using data collected from 223 purchasing managers and buyers of direct materials. Our results show that both the probability and the magnitude of supply disruption are important to buyers’ overall perceptions of supply disruption risk. We also find that product and market situational factors impact perceptions of risk, but they are best understood through their impact on perceptions of probability and magnitude. Finally, we find that decisions are based on assessments of overall risk. These findings provide insight into the decision-making process and show that all three representations of risk are necessary for fully understanding risky decision-making with respect to supply disruptions.