Recent supply chain disruptions are forcing organizations to challenge the prevailing wisdom and look for newer approaches to decision making. The simultaneous shocks to demand and supply, and the magnitude of these shocks are not something the world ever experienced. The demand patterns significantly shifted in terms of the product and channel mix. On the supply side, material shortages, staffing challenges, and shipping capacity constraints are all happening at the same time. The supply chain nodes which were once deemed to be relatively static have become far more dynamic in the recent past. The rapid shifts to eCommerce during the pandemic caused retailers and brand owners alike to flex their network nodes (where goods are made and inventories are stocked) significantly. Pop up warehouses, micro fulfillment centers, and warehousing-on-demand are all examples of how the nodes are becoming increasingly dynamic. The product flow paths and modes of transportation altered radically as well even without accounting for any network node changes, owing to port closures or shortages in various transportation lanes. Some companies have taken extreme measures such as chartering private cargo ships to sail around port delays.
For the longest time modeling and designing such nodes, modes, and flows has been the realm of Supply Chain Design. While Supply Chain Planning and Execution garnered much attention over the years, Design remained very episodic and project-based. Just a handful of optimization and operations research experts ran models of the network and made recommendations. Organizations did not see much of a need to relook at their supply chain design unless pressed for. Barring significant changes such as mergers and acquisitions, organizations looked at their supply chain design once every year or two. Planning and Execution systems in turn consider the rules, policies, and flow path assumptions recommended by Supply Chain Design as key inputs and assumptions guiding the S&OP and S&OE decisions. Decisions such as what should be the order minimums and multiples, what should be the replenishment frequency to my downstream locations, what should be the splits between different sources available, and how they should be prioritized, are all the types of policies that need increasingly frequent revision.
Several organizations are now realizing that such inputs and assumptions, if not tested regularly, become stale and lead to erroneous planning and execution decisions. Amongst the companies we engage with, there is an increasing acknowledgement that the clock speeds of the design and planning processes will need to be more tightly aligned, i.e., the testing of the policies and product flow paths along with implementation of any changes that become increasingly closer to the S&OP cadence. This is leading these organizations to move from Design being an episodic activity to a continuous process. For example, if a retailer stands up a number of micro fulfillment centers to support a spike in eComm demand, this translates into changes to the flows and possibly modes of transportation, and accordingly master data changes. Design can help test such ideas before implementing changes to the master data.
From a technological perspective, Design remained the realm of desktop modeling techniques for close to two decades since its advent, leveraged by individuals trained in advanced analytics and optimization techniques. However, with the new generation technologies, it now is practical to keep the data feeds fresh to make the modeling exercises a continuous, ongoing discipline. Thanks to cloud computing – solve speeds, scale, and granularity at which the algorithms can be applied have also grown tremendously. Taking advantage of these advances provides much sharper and refined inputs into planning and execution processes. New generation no-code platforms allow for the ability to deploy this algorithmic intelligence to planners and business users through apps. This is making the process of matching the Design and Planning clock speeds feasible.
No longer does one need to be confined by static and stale rules and assumptions guiding the planning and execution processes but can test these more dynamically. In fact, with the advances in AI, one no longer needs to test different Design parameters manually thinking through them. Instead, AI powered prescriptive analytics can proactively start recommending scenarios for users to consider. The technology is reaching such an inflection point. This capability will be game changing. Considering the number of permutations and combinations involved in decisions such as network node skips or transportation mode switches or volume consolidation opportunities, AI scales well to find those value creation opportunities and surface them to the users to act on.
As organizations prioritize resiliency initiatives, Design helps them be deliberate about where to build optionality in their supply chains. Sustainability initiatives can benefit through optimizing the carbon footprint. Taxes and tariffs can be modeled, and tax efficient supply chains can be designed. These are all capabilities that are typically beyond the reach of planning systems and fall in the realm of advanced analytics. Once supply chains are designed for these capabilities, planning and execution systems accept such design as input.
Leading organizations are recognizing the need for Design to be continuous and process driven as opposed to episodic and project-based. They are establishing Design Centers of Excellence and aligning these functions closely to Planning and / or data science, analytics practices. They are designing career paths to retain and grow such talent.
As Winston Churchill said, “Never let a good crisis go to waste”. For those who are building their careers in the discipline of Supply Chain Design and Modeling, the current disruptions present an opportunity to showcase the difference they can make and rise to the occasion. However, for this to happen, one has to lay the technology foundation to ensure Design exercises can be performed in a repeatable and timely manner.
Supply Chain Design has crossed the chasm after all!
Dr. Madhav Durbha is the Vice President of Supply Chain Strategy Coupa Software, where his team helps customers and prospects solve various supply chain challenges. Prior to Coupa, Dr. Durbha held positions at LLamasoft, Kinaxis, JDA Software and i2 Technologies, Inc. With more than 20 years in the supply chain industry, Dr. Durbha has broad experience in strategy & process consulting, supply chain software, program management, software application development & deployment, machine learning and data science. He received his Ph.D. in chemical engineering from the University of Florida and his bachelor’s degree in chemical engineering from the Indian Institute of Technology at Madras.
