Conference Agenda

In this study, we propose novel multicriteria product prioritization and classification methods that use criteria from different functions of a manufacturing firm. We transform the scores for each criterion using a quantile transformer. In the prioritization study, the goal is to choose products for pre-build inventory and cycle-stock inventory. Criteria weights are determined with the Analytic Hierarchy Process (AHP) approach. In the classification study, the goal is to develop customized production and inventory policies for different groups of products. To this end, we use Principal Component Analysis (PCA) and Fuzzy C-Means (FCM) clustering. We illustrate the use of the methods with two case studies from a leading tire manufacturer. The methods are transparent and easily understood by company managers. Being structured, data-based as well as flexible, these methods can ease the decision burden on managers and contribute to quick and well-tailored operational decisions.

This paper proposes a flexible two-stage pricing optimization methodology for nondurable products such as electronics considering the fact that their price only reduces at certain time points in the lifecycle. The proposed method incorporates different key factors of electronics such as manufacturers’ learning effect and customers’ word-of-mouth (WOM), which influence the average production cost and future demand, respectively. We theoretically prove the existence and uniqueness of our proposed pricing strategy based on these two factors. Interestingly, the results indicate that the primary reason for manufacturers to decrease the price of electronic products is the learning effect; WOM influences manufacturers’ profits by impacting sales volume rather than pricing decisions. Only the overestimation type of WOM may contribute to the potential improvement of profits, but it is not a sufficient condition.

The executional efficiency in omnichannel retailing requires a high level of accuracy in store inventory records. This research focuses on exploring the factors that impact the accuracy of inventory records and develops an inventory classification scheme that would identify products that are prone to such errors. We analyze product attributes such as price, sales, and popularity, as well as store policies on inventory and replenishments at a large retail store location. Using weekly store inventory records matched with physical store inventory counts, we identify different inventory error profiles of the store inventory, and the associated product attributes and store policies. The proposed approach will help retail firms identify error-prone SKUs that would help focus monitoring and/or remediation actions to select SKUs rather than the entire store inventory. We use the inventory error classification scheme in a simulation study to compare different mitigation approaches including inventory audits and error-based safety stocks.

A Systematic Literature Review confirmed that trends in globalization are changing and highlighted the role of geo-politics and geo-economics in shaping supply chain design. Specifically the findings identified Populism and Nationalism as emergent theme's with the potential to influence supply chain design. A qualitative research study has been designed to understand how do perceptions of nationalism and populism influence decision making in global supply chains. The researchers will conduct 25-30 semi-structured interviews with supply chain decision makers across multiple industries and countries. The interviews will be transcribed and analyzed for trends that could be identified as a presence of nationalist and populist influence on sourcing decisions. The results will inform a theoretical framework on recognizing the potential for this influence, which can be applied to supply chain decision making in practice.