An analytic infrastructure for harvesting big data to enhance supply chain performance

Big data has already received a tremendous amount of attention from managers in every industry, policy and decision makers in governments, and researchers in many different areas. However, the current big data analytics have conspicuous limitations, especially when dealing with information silos. In this paper, we synthesise existing researches on big data analytics and propose an integrated infrastructure for breaking down the information silos, in order to enhance supply chain performance. The analytic infrastructure effectively leverages rich big data sources (i.e. databases, social media, mobile and sensor data) and quantifies the related information using various big data analytics. The information generated can be used to identify a required competence set (which refers to a collection of skills and knowledge used for specific problem solving) and to provide roadmaps to firms and managers in generating actionable supply chain strategies, facilitating collaboration between departments, and generating fact-based operational decisions. We showcase the usefulness of the analytic infrastructure by conducting a case study in a world-leading company that produces sports equipment. The results indicate that it enabled managers: (a) to integrate information silos in big data analytics to serve as inputs for new product ideas; (b) to capture and interrelate different competence sets to provide an integrated perspective of the firm’s operations capabilities; and (c) to generate a visual decision path that facilitated decision making regarding how to expand competence sets to support new product development

The acquisition of quality information in a supply chain with voluntary vs. mandatory disclosure

Quality information acquisition and disclosure have significant ramifications for supply chain members. This paper investigates the interaction between a manufacturer's product quality information acquisition and different product quality information disclosure systems in a supply chain wherein the manufacturer can privately acquire the precise quality information of its product by affordable means initially. We consider two different quality information disclosure systems for the quality information acquisition: voluntary disclosure (i.e., the manufacturer determines whether to disclose the quality information that he has acquired), and mandatory disclosure (i.e., the manufacturer is mandated to disclose the quality information that he has acquired). We examine the effects of voluntary disclosure and mandatory disclosure on the equilibrium strategies and payoffs of the manufacturer and the retailer and on the consumer surplus. It is shown that mandatory disclosure significantly reduces the manufacturer's incentive to acquire the precise product quality information and leads to a reduction in the product quality information that the retailer and the consumers can receive. Interestingly, although the manufacturer is ex‐ante better off, the retailer's ex‐ante payoff and the expected consumer surplus become lower under mandatory disclosure, as opposed to voluntary disclosure of product quality information