Prior discussions of predictive model development highlight advances in methods, but the value of tools that implement those methods has been understudied. In a field experiment, we study the importance of tools by restricting access to machine learning libraries in a prediction competition. We find that teams with unrestricted access perform 30% better, and teams with high general data-science skills are less affected by the intervention than teams with high tool-specific skills.

We investigate the remanufacturing strategy for the original equipment manufacturer (OEM) and independent remanufacturer (IR) in an innovative industry where the consumer valuation of the products increases with the innovation level and we consider the investments of an OEM to enhance innovation, in the face of a potential entry onto the market by an IR, together with two remanufacturing strategies: whether to remanufacture and whether to include innovative features in remanufactured products.

Optimization in sustainability is inherently multi-criteria and the underlying value function is usually unknown and difficult to elicit. Firms seeking to be more sustainable face difficult choices during material and supplier selection with no clear guidelines on how to make trade-offs between conflicting environmental impact categories. We propose an interactive optimization method to help decision makers, using an illustrative example of sustainable sourcing in the apparel industry.

This paper considers an industrial symbiosis system composed of a supplier and a manufacturer. The supplier produces product A with the output of by-product, which can be reused by the manufacturer as input to produce product B. Competition and cooperation are often juxtaposed in the same system. This paper examines the mode choice of the supplier, i.e., continue to cooperate to supply by-products to the manufacturer or generate direct competition by encroaching on the manufacturer's market.