The EPIC framework was developed by Srinivasan, Stank, Dornier, and Peterson (2014) to assess supply chain readiness from Economic, Political, Infrastructural and Competence perspectives and was applied to 55 countries, a monumental undertaking. However, the framework needs further validation by applying it to other countries and using other raters. This research does both. Guatemala, for example, was not included in the 2014 EPIC ratings yet Guatemala is ideal for studying supply chain readiness given their participation and an anticipative role in CAFTA-DR (Dominican Republic-Central America Free Trade Agreement). Using Guatemala as a case, professionals in teams of three to six individuals in Germany and the United States applied the EPIC framework to judge supply chain readiness. The findings may serve as a model for businesses entering these sometimes operationally challenging markets. Areas for future research are included.

Extant research on lean manufacturing has focused on the relationship between systemic inventory and performance. However, limited research, if any, has focused on dynamics between three main types of inventory – raw material, work-in-process, and finished goods – and firm performance. Drawing on the interdependence framework and a sample of 1,286 firms representing 41,067 firm-quarter observations from 2000 to 2013, when considering two types of inventory efficiencies jointly, higher efficiency in the subsequent inventory type must be accompanied by lower efficiency in the previous inventory type. In other words, lower raw material inventory efficiency and higher work-in-process inventory efficiency, or lower work-in-process inventory efficiency and higher finished goods inventory efficiency are associated with higher performance. High performance is also realized only when inventory efficiency is increasing across all three inventory types. The findings show that pursuing efficiency in one type of inventory requires lower efficiency in the previous inventory stage, and benefits of lean manufacturing could only be realized when all three inventory efficiencies are simultaneously high.

Competition on price alone is no longer sustainable. Increasingly, firms are competing on multiple dimensions such as service quality, performance quality, process technology, and product variety. In this paper we study how firms might make some of these decisions in a high margin and fragmented Indian auto component replacement market. Using game theoretic principles, we analyze the replacement market when the non-OES (original equipment suppliers, national/regional) makes decisions in presence of an OES. Along with this, we analyze the market when the OES and non-OES enter simultaneously and make decisions based on their risk appetite. Our results indicate that the investment in a more capable technology doesn’t necessarily lead to higher optimal price. More capable technology decreases the marginal cost which allows the firm to decrease the price but more capable technology also increases the share of market demand which allows the firm to price higher.

Food security is the principal outcome of any given food system and it can be defined in terms of a sustainable food system where the core goal is to feed everyone sustainably, equitably and healthily. A sustainable food system addresses needs for availability, affordability and accessibility, is diverse, ecologically-sound and resilient, and builds the capabilities and skills necessary for future generations. This paper identifies the essential elements of food supply chain management in support of a sustainable food system, which ultimately enhances food security. The existing food supply chain and food system literature is synthesized in order to study the correspondence between public interests towards sustainable food and corporate interests. Giant food retailers, food processors and manufacturers, and food service supply chains in particular, extend their global reach, influence food culture and may be more important in shaping food systems than governments. Thus, the paper proposes food security frameworks for both upstream and downstream supply chain management. It concludes that sustainable food system thinking and societal orientation towards food security can hold the key to unlocking the next wave of food supply chain innovation and growth, and offers implications and suggestions for future research.

A mathematical model is proposed to plan production system resilience in a supply chain to overcome production related disruptions using appropriate operational factors at optimum cost. The research considers internally generated disruption risks due to supply, quality management, and plant reliability failures; and externally generated disruption risks from natural calamities. The production system of a supply chain has several options to utilize controllable operational factors to inhibit or mitigate the risks it faces. The operational factors are planned to mitigate the natural calamity disruptions and to inhibit the internally generated risks to create resilience. Since supply chain outcomes may be considered to be the net effect of complex interactions among several operational factors and resources, appropriate linkage of the operational factors is the key to select the right option(s) to create system resilience to contain risks and disruptions. Each operational factor can influence more than one supply chain outcome or relevant risk; on the other hand, containment of each risk may need contributions from several operational factors. Selection of the suitable controllable operational factors would be the most viable option for resilience creation. The analysis of the model outcomes establishes the effectiveness of the proposed model based procedure in creating production system resilience within an optimum cost using controllable operational factors. The operational factors used are: supplier flexibility; plant capacity flexibility; designating suppliers by a quality metric based evaluation procedure to have ensured quality inputs; quality metrics based plant capability determination and allocating production to capable plants to have ensured quality products. A numerical example illustrates the applicability of the model.