Commentary: Disruptive storms are no match for progressive supply & demand planning

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Commentary: Disruptive storms are no match for progressive supply & demand planning

By Chad Schumacher - 04/22/2020
Chad Schumacher
Chad Schumacher

The everyday task of balancing supply and demand is an enormous challenge at the heart of every thriving business. 

Throw a global pandemic with massive supply and demand disruptions into the mix, and the task seems insurmountable. Our hope for a safe end to the COVID-19 crisis creates food for thought on why some organizations seem to thrive during disruptions better than others. 

Not all disruptions we experience reach the scale of COVID-19. Still, regional weather is unpredictable, manufacturing lines go down unexpectedly, and raw materials become unavailable at the worst possible times. Executing business plans despite a massive disruption is difficult regardless of the cause.  

The business problems we encounter during a major business disruption are varied and many: 

•    Quickly increasing, decreasing or shifting demand  
•    New labor staffing challenges
•    Concerns with raw material scarcity
•    New production or transportation constraints  

Business as unusual?
Like it or not, many organizations are set up to run demand and supply analytics that represent normal business with reasonable demand projections and long-term supply assumptions.  In a disruption, all this is thrown out the window as the organization needs to quickly assess the new reality. Those behind the curve with adopting the best planning technologies and business practices tend to stumble and fall when consumers need them the most. 

The organizations that do this well have full end-to-end visibility of demand and supply and can quickly pivot with new information. They usefully integrated demand and supply modeling techniques to understand the impact of a disruption. Today’s best forecasting and demand planning practices incorporate a wide variety of causal factors that provide insights to shifting demand. 

These factors might include changes in promotional or marketing strategies that would apply to specific brands or customers. Or they may represent economic or competitive activity that would affect an entire product category.  Leading forecasting technologies leverage automated machine learning and artificial intelligence (AI) to integrate and reconcile these forecasts up and down a business hierarchy. 

The organization with these practices in place has instant visibility on which products, plants, and customers are most significantly affected by the disruption. If the event is unprecedented like the COVID-19 pandemic, progressive demand planners quickly go into high-level “best, middle, and worse case” scenario mode to understand the range of potential effects while letting the models reconcile the hierarchies.  As the company progresses through the event, the models will start to pick up on the latest trends and the range of likely outcomes will start to shrink.

After creating solid forecasts, best-in-class retailers and CPG manufacturers immediately run mathematical optimization models identifying the best supply chain response. The goal is to minimize costs while satisfying as much demand as possible. This model normally produces a plan for procurement, production, transportation, and inventory. 

A sales plan identifies which demand can and cannot be met identifying commercial risks and opportunities. And with estimates of supplier capacities, the analysis may even determine how best to source materials from suppliers. Included in these models are disruption questions for which planners should seek answers: 

•    What should the labor planning response be? 
•    What are the inherent risks on the production lines with limited operators?
•    What is the impact to transportation capacity, and what adjustments need to be made?

Companies that excel in times like this have figured out how to reduce the cycle time between demand and supply modeling by using analytics. They are quickly able to assess new demand realities and create essential supply models that accurately reflect the disruption. Because the speed to taking action from insights is fast, an organization can make immediate decisions and course-correct over time by re-running the models as new information comes in.  

How does an organization get there?  
Most companies would honestly claim that the time required to create new demand projections, prepare data for accurate network models, and get insights into the hands of decision-makers would be weeks or months.  Leading organizations are investing in both the people skills and technology to create a single data model feeding both demand and supply analytics along with the reporting environment. They understand the discipline required to sustain it. 

They pair business savvy analytical talent with technology that flexibly automates the modeling process. And don’t expect to stare at slides or spreadsheets in planning meetings. These organizations use powerful data visualization tools that sit on top of the data model ensuring little time is lost transferring insights to reports.  

When a disruption happens, leaders gather along with their analytical teams to quickly run models representing the new situation. There is little time spent on data collection and model building because that’s been happening all along. As partners, they approach the problem with appropriate mathematical models. They trust that model output identifies likely outcomes given everything known about the current disruption. 

The business knows how to work with the model to identify risks and opportunities that ultimately create the best possible strategy.  And they do all this quickly and iteratively as the situation develops.

COVID-19 underscores the need to establish progressive demand and supply planning practices now so future disruptions are more manageable.  Companies that can quickly integrate demand and supply modeling have the best chance of weathering any disruptive storm.

Chad Schumacher is a customer advisor at SAS.
 

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