Warehouse shelves
By Vinay Chaturvedi | February 06, 2023

Struggling to Manage CPG Supply Chain Variability? This is What You’re Probably Missing

Intelligent Order Promising isn’t just a buzzy tech term. It’s the only AI-based method proven effective at staving off the traditional consequences of disruptive events, such as supply and demand imbalances.

In years to come, the extreme product shortages of the pandemic may seem like just another blip in the history of marketplace behavior. Yet, they leave behind a permanent realization that near-term events can rapidly cause consumer demand to explode or supply to be interrupted. What was once considered a possibility is now a very real probability. Furthermore, these events seem to be happening more frequently than ever before, and often defying market logic. Case in point – the recent unbowed demand for lumber despite prices that were more than four times the norm. 

In a survey of 150 manufacturing executives, law firm Foley & Lardner found that 70% agreed that their companies will, as a result of the pandemic, lessen their focus on sourcing from the lowest-cost supplier in favor of higher supply chain resiliency, and only 7% are not undertaking contingency planning efforts to prepare for future disruptions. 

However, major modifications to supply chain inter-dependencies and production output take time to engineer. Unless management feels that the gap will be of longer duration, companies are unlikely to immediately invest millions in building new manufacturing facilities or make radical supply chain changes. In the end, the near-term job of managing inventory through times of tight supply falls to the planners and warehouse managers in charge of allocation and order fulfillment. 

In the article, “Who Gets What When Supply Chains Are Disrupted?”, Yossi Sheffi, the Elisha Gray II Professor of Engineering Systems at MIT, makes the point that these fulfillment decisions must be guided by an explicit corporate strategy that considers post-shortage objectives and impact. Choice of tactics can have different timeframe and marketplace repercussions. For example, manufacturers deciding to respond on a first-come first serve basis during a shortage, treating everyone equally to avoid the appearance of favoritism, can run into significant challenges if customers try to game the system by artificially inflating their orders. 

In addition, increasing on time, in full (OTIF) penalties from big-box retailers are pushing the responsibility for fixing those supply shortages back onto the manufacturer, making unintended shortfalls a very costly mistake. Consumer packaged goods (CPG) manufacturers, juggling different revenue strategies across their major retailer customers and direct-to-consumer (DTC) channels, have worked hard to establish preferred relationships to maximize consumer retention and minimize OTIF penalties. These strategic priorities can be undone if they are not reflected at the end of the supply chain as well as the beginning. 

Intelligent Order Promising (IOP), coupled with highly accurate short-term demand sensing, ensures that top-down strategy is captured in bottom-up execution. Advantages of this easy-to-deploy capability to supply chain planning and execution include: 

  • maintenance of allocations or logical pools of supply at different levels of the customer hierarchy to counterbalance first come, first served fulfillment in the absence of more direction when unexpected shortages occur, 

  • assignment of limited supply to orders based on currently available and incoming supply, allocated amounts and order priorities, and 

  • determination of those priorities by a set of business rules aligned with strategically determined segments, based on rigorous analysis. These rules are established from the beginning but can be easily refreshed as business conditions evolve. 

Why does IOP differ from Available to Promise (ATP) bolt-on functionality in an ERP system? ERP-based ATP and Order Promising systems have limited capability and typically require 3-4 months to recalibrate – this is no longer the timeframe in which business is expected to respond. IOP, on the other hand, enables a streamlined handling of order fulfillment adjustments for complex supply chain situations. IOP also builds a direct bridge between strategic segments, business goals, and day-to-day execution and can adapt immediately when those strategic goals change. 

By automating the process of supply reservation and tie-breaking across channels, accounts and orders, IOP eliminates time-consuming, manual, and sometimes irrational order quantity manipulation for both the CPG company and its retail partners. It also leads to low- to no-touch planning at the warehouse by letting the system do the heavy lifting. 

In the end, there is an upside to the supply interruption of the past few years. Leading CPG manufacturers are learning from these disruptions and implementing technologies that better prepare them for a world of greater variability and tighter constraints. IOP is the answer for those who wish to face the next marketplace surprise with the confidence that it can be absorbed without undue business impact. 


Editor’s Note: 

If you’d like to learn more about how you can leverage IOP or about the AI technology underpinning IOP, start here

Best Practices, Automation, Retail, Hospitality, Manufacturing,
Vinay Chaturvedi
Vinay Chaturvedi

Vinay Chaturvedi manages the retail replenishment and supply chain products at antuit.ai. 

Prior to joining antuit.ai, Vinay worked for over fifteen years with the SAS institute in retail and supply chain product management. During that time, he helped develop SAS’ Demand-Driven Planning and Optimization suite of products. 

Vinay has hands-on experience with manufacturing, automation, production planning, and supply chain management in automotive and consumer electronics industries. At antuit.ai, he has helped develop expertise in Demand Planning, Direct Store Delivery Order management, CPG Order Promising, and Retail Replenishment. 

An ardent customer advocate, he is a recognized expert in Demand Planning and Replenishment Optimization.

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