Recent disruptions in the supply chain are forcing organizations to challenge prevailing wisdom and seek new approaches to decision-making. Simultaneous shocks to demand and supply, and the magnitude of these shocks is not something the world has ever seen. Demand patterns shifted dramatically in terms of product and channel mix. On the supply side, material shortages, staffing challenges and shipping capacity limitations occur at the same time. The supply chain contract that was once considered relatively static has become more dynamic in the recent past. Rapid shifts to e-commerce during the pandemic have led retailers and brand owners alike to dramatically browse the nodes of their networks (where merchandise is manufactured and inventories are stored). Pop-up repositories, mini fulfillment centers, and on-demand warehousing are all examples of how to increase contract dynamism. Product flow paths and transport patterns have been radically changed even without accounting for any network node changes, due to port closures or shortages of various transport lanes. Some companies have taken extreme measures such as chartering private cargo ships to sail around delays in ports.
For the longest time, the modeling and design of these nodes, modes, and flows has been the realm of supply chain design. While supply chain planning and execution has received a lot of attention over the years, the design has remained very casual and project-based. A few optimization and operations research experts have run network models and made recommendations. Organizations have seen little need to reconsider the design of their supply chain unless they are pressed. Barring big changes like mergers and acquisitions, organizations have looked at the design of their supply chain once every one or two years. Planning and execution systems in turn consider rules, policies, and flow path assumptions recommended by supply chain design as key inputs and assumptions that guide S&OP and S&OE decisions. Decisions such as what should be minimum demand and its multiples, what should be the frequency of replenishment for downstream sites, what should be the divisions between the different sources available, and how they should be prioritized, are all kinds of policies that need increasing frequency of review.
Many organizations now realize that these inputs and assumptions, if not regularly tested, become outdated and lead to wrong planning and implementation decisions. Among the companies we deal with, there is a growing recognition that the clock speeds of the design and planning processes will need to be more consistent, i.e. testing policies and product flow paths along with implementing any changes that grow increasingly closer to the cadence of S&OP. This leads these organizations to move from being an episodic activity to an ongoing process. For example, if a retailer builds a number of small fulfillment centers to support a high demand for eComm, this translates into changes in flows and possibly transmission routes, and thus changes to master data. Design can help test such ideas before implementing changes to master data.
From a technical perspective, design has been a domain of desktop modeling techniques for nearly two decades since its emergence, enhanced by individuals trained in advanced analytics and optimization techniques. However, with new generation technologies, it is now more practical to keep the data feeds up-to-date in order to make the modeling exercises a continuous and continuous system. Thanks to cloud computing – solution speeds, scaling, and the accuracy with which algorithms can be applied have grown exponentially. Taking advantage of these developments provides clearer and more refined input into the planning and implementation processes. Tokenless next-generation platforms enable the ability to deploy this algorithmic intelligence to planners and business users through applications. This makes matching design and layout clock speeds possible.
One no longer needs to be constrained by static and outdated rules and assumptions that guide planning and execution, but can test them more dynamically. In fact, with the advancements in artificial intelligence, one no longer needs to test different design parameters by manually thinking through them. Instead, AI-powered meta-analytics can proactively begin recommending scenarios for users to consider. Technology is reaching this inflection point. This ability will change the rules of the game. Given the number of permutations and combinations involved in decisions such as network node skipping, transfer mode switches, or volume merging opportunities, AI expands well to find these value creation opportunities and offer them to users to work on.
As organizations prioritize resilience initiatives, the design helps them think about where to build discretion in their supply chains. Sustainability initiatives can benefit by improving the carbon footprint. Taxes and tariffs can be modeled, and tax-efficient supply chains can be designed. These are all capabilities that are usually out of reach of planning systems and fall within the realm of advanced analytics. Once supply chains are designed for these capabilities, planning and execution systems accept such design as input.
Leading organizations recognize the need for design to be continuous and process-driven rather than episodic and project-based. They establish design centers of excellence and align these functions closely with planning and/or data science and analytics practices. They design career paths to retain and develop these talents.
As Winston Churchill said, “Never let a good crisis go to waste.” For those who are building their careers in supply chain design and modelling, the current turmoil presents an opportunity to showcase the difference they can make and rise to the occasion. However, for this to happen, one has to lay the technological foundation to ensure that design exercises can be carried out in a timely and repeatable manner.
Supply chain design has crossed the chasm after all!
Dr. Madhav Durpa is the Vice President of the Coupa Program for Supply Chain Strategy, where his team assists clients and prospects in solving various supply chain challenges. Prior to joining Coupa, Dr. Durba held positions at LLamasoft, Kinaxis, JDA Software, and i2 Technologies, Inc. With more than 20 years in the supply chain industry, Dr. Dorba has extensive experience in strategic and business consulting, supply chain software, software management, software application development and deployment, machine learning and data science. He received his Ph.D. He holds a Bachelor’s degree in Chemical Engineering from the University of Florida and a Bachelor’s degree in Chemical Engineering from the Indian Institute of Technology, Madras.