Over the years, supply chain professionals have been trying to improve supply chain efficiency so they can gain an edge over the competition. In this guest commentary, Thomas Roemer, Executive Director of the Leaders for Global Operations at MIT Sloan School of Management will show you where the problem lies and ways we can deal with this issue.
Companies seeking to further improve their supply chain efficiency will have to continue fighting their old foe – variability – albeit in a set of new clothes.
One emerging way to combat demand variability is to locate (some) manufacturing closer to the customer. By reducing lead times due to shorter delivery routes, inventory and waste in the system can be reduced without sacrificing service levels. Much of the recently observed reshoring efforts fall into this category.
Highly flexible manufacturing goes a step further by moving from a Built-to-Stock to a Built-to-Order system for the most erratic demand pattern. Amazon's printing and binding some of its demand at centers close to their customers while serving base demands from stock is one example of this approach.
If the final “manufacturing” of the product can be performed by the customer (such as downloading, storing, printing and, increasingly, 3D-printing of files), then this approach also solves the “final mile problem”. However, for less information intensive products, the sheer variety of final customer locations requires a more systemic approach. Amazon’s drone-based approach is perhaps the most illustrious and futuristic attempt to address this problem, while reception boxes and collection-and-delivery points are some of the current solutions. Companies that successfully overcome this challenge will not simply reduce cost and increase service levels, but also reduce their carbon footprint and open up revenue streams from selling carbon credits.
Data analytics or “Big Data” also bears huge potential for increasing supply chain efficiencies. Better detection of demand trends, and active control of demand variation by promotions and advertising bears the potential to curb variability at its source. Revenue management systems at airlines have successfully pursued this path for decades. With the omnipresence of data, this path is now open to other industries as well.
A less obvious benefit of “Big Data” lies in the effective management of quality variations in complex supply chains. Adulterated baby formulas, pet foods, medications or toys can be hugely costly to companies, while their sources can be difficult to trace. A better understanding of the types of products and supply chains that are susceptible to adulteration and ways to quickly detect sources can have a large impact on social welfare as well as the bottom line.