Over the past decade, my research team has been working to understand best practices in the implementation of supply chain analytical and managerial technology. Our work was initially funded during the birth of the era of Big Data analytics, when very little attention was being paid to the field of study.
Since then, new analytical tools continue to make claims of increased effectiveness in the management of complexity and the enhancement of business value. Nevertheless, the implementation of analytics is expensive, staffing is difficult, and the field is constantly and increasingly changing. Additionally, our research encourages supply chain professionals to measure their company’s technological readiness. Enhanced levels of technological insecurity and discomfort, coupled with low levels of optimism and innovation, will likely doom any supply chain analytics initiative from the start.
But, if you have the money,
can find the talent,
stay abreast of technological changes
and have an optimistic and innovative team…
You certainly should be working to develop your supply chain analytical capabilities. In fact, successful integration could result in four major overlooked improvements that can be noted as TIP-C.
Top line growth with a cost savings
Analytics can help specifically identify the activities and processes that add value for partners, customers, and consumers. That specificity notes key activities for focus and other activities that your business should reduce or eliminate. The goal here is to produce the strongest ROQ {return on (service) quality}. Companies are then able to scale up the core competencies in their supply chains and develop the knowledge needed to grow across international borders. The reality is that analytics can help identify what functions to outsource, offshore, nearsource, and/or insource. Updated real-time supply chain metrics will help companies reduce inventory and avoid distribution issues.
Intangible Liability Reduction
Our world is fraught with greater risk. Having materials, factories, and customers all over the world means exposure to what we call intangible liabilities. Ethical dilemmas, natural disasters, port closures, political uprisings, etc. can be considered liabilities in analytical models. Noting these massive disruptions early will help supply chains to develop contingency planning and provide opportunities to estimate the risk involved in sourcing from different locations around the world. The growing global supplier portfolio—that many firms consider as they evaluate partners— creates a level of strategic complexity resulting in more opportunities for managers to overlook important disruptive events. Operating across borders means operating in countries that very likely have different laws and regulations. Supply chain managers can use analytics to develop detailed risk management scenarios preparing them for both man-made and natural liabilities.
Process Efficiency
Analytical solutions that support collaborative business planning are currently helping supply chains orchestrate responsive strategy as they develop a better understanding of trends and preferences. Employing IoT tracking and RFID, managers are able to break down processes and rebuild them with insight into where the bottlenecks exist across the extended enterprise. Machine learning algorithms can now more accurately predict equipment failure, uncover opportunities for waste reduction, and track quality at operational and strategic levels. Supply chain analytical solutions are already helping reduce cycle-time/delivery days by analyzing GPS data in addition to traffic, weather, and trends.
Global Competitiveness
Our Center for Supply Chain Innovation is currently facilitating my master’s level supply chain consulting project. Collaborating with Michael Rubin’s RueGiltGroupe (RGG), our advanced students from multiple colleges across the Auburn campus are using supply chain analytics to enhance consumer/user experience.
RGG has increasing domestic and international competition in the fashion retail space. They also have product sourced from nearly the entire globe. Our students are currently crunching sales and operational data to examine the value creation process with a goal of improving order fulfillment and customer loyalty.
Globalization of the ecommerce fashion market necessitates that supply chains are highly efficient and effective in order to stay competitive. Tools like R, Tableau, Artificial Intelligence, and others present exciting opportunities that prepare students for new challenges in an increasingly global and data driven supply chain. Real-world challenges that cannot be learned from a textbook.
Glenn Richey
Raymond J. Harbert Eminent Scholar and Professor in Supply Chain Management
Research Director, Center for Supply Chain Innovation
