On Leveraging Emerging Technologies and Big Data Analytics: Developing Supply Chain Leaders

Emerging technologies [e.g., 3D printing and manufacturing, Internet of Things (IoT) and artificial intelligence] and big data analytics applications have the potential to enhance the competitiveness of firms via improving supply chain decision making. Supply chains generate vast amounts of data that firms can turn into intelligence through analytics.  Success stories of firms that have harnessed the power of the emerging technologies and big data analytics abound. However, most firms have yet to fully and mindfully leverage these technologies and big data analytics to transform their supply chain operations. With the advancements in technologies and networking capabilities, firms are actively engaged in capturing “big” data related to their supply chains. While many firms are awash in data, they are unsure how to use it to drive their supply chains. Furthermore, many are engaging in fragmented utilization or implementation rather than a systematic and coordinated effort. The results are unclear benefits, including lack of real insight and competitiveness.

This session will feature a panel including faculty from the Carlson School and leaders from SPS Commerce, Stratasys and Epicor to showcase how technological and big data analytics capabilities can be developed. Specifically, we will discuss how to leverage emerging technologies, analyze and interpret big data – characterized by volume, variety, velocity, and veracity – that is fundamental to managing supply chains. Illustrative examples will be presented to: (i) Get exposed to these technologies and various facets of big data analytics such as data access, data aggregation, data analysis, data visualization, and data interpretation; (ii) Evaluate the appropriateness and inappropriateness of technologies and big data analytics for a particular supply chain decision context; (iii) Engage in big data analytics exercises relevant to strategically manage supply chains, and (iv) Interpret and communicate the results of big data analysis to the top management.

KK Sinha

Kingshuk K. Sinha

Chair, Supply Chain & Operations Department - Carlson School of Management

Kingshuk K. Sinha (KK) is a Professor and Chair of the Supply Chain and Operations Department, and is the holder of the Mosaic Company–Jim Prokopanko Professorship in Corporate Responsibility. He also serves as a Graduate Faculty in Bioinformatics and Computational Biology. KK's scholarly interests lie the intersection of Technology Management, Supply Chain Management and Big Data Analytics. Two of the MBA/MS electives he has designed and taught are: "Managing Technologies in the Supply Chain" and "Big Data Analytics in Supply Chains."

Karen Donohue

Board of Overseers Professor of Supply Chain and Operations & Academic Director, MS in Supply Chain Management - Carlson School of Management

Karen Donohue's research examines methods for coordinating inventory and distribution decisions across supply chains. She draws on a number of different methodologies in her research including stochastic modeling, game theory, and behavioral economics.

Her analytical work focuses on identifying and measuring the impact of different contractual schemes between supply chain partners and competitors.  Examples include using tiered pricing and buyback contracts to coordinate production decisions and using service-based competition schemes to incentivize suppliers to invest in higher service quality.  This research is normative in the sense that it prescribes how supply chain partners should behave, under a given set of rules, in order to maximize expected profit.  Her most recent normative research focuses on analyzing contracting schemes that a buyer can use to induce his suppliers to invest in service quality when these suppliers vary in their capacity levels and cost structures.  This research establishes a scoring rule for the buyer that can be used to incentivize custom service level targets for each supplier while maximizing profit for the buyer.