by Gregory Sabin – Visiting Lecturer, The Ohio State University Fisher College of Business
A 2012 Supply Chain Insights survey asked supply chain managers to name their top 10 pain points. Three out of four respondents listed demand volatility, which made it one of the most painful aspects of supply chain management, second only to supply chain visibility. Firms can reduce demand volatility and the associated risks by incorporating economic and demographic data to create simple and more accurate business models.
Risks associated with demand volatility include both risks of overestimating and underestimating demand. Overestimation of demand will cause declines in the firm’s return on assets (ROA) because of the overcommitment of assets and unnecessary expenditures that will be incurred in anticipation of surplus demand that does not materialize. Underestimating demand is associated with increased production costs, lower quality levels and decreased customer satisfaction.
These risks affect every part of the business, including customer service, financial planning and analysis, supplier development, new product development, human resource management, product/process engineering and investor relations. As such, firms need to approach forecasting and planning from a cross-functional perspective.
Why are most businesses not already doing this? As recently as five or six years ago, businesses lacked not only easy access to the detailed information needed to add analytical models to their forecasting process, but also the ability to process that information in a cost-effective manner. Traditionally, this meant firms focused primarily on internal marketing and supply chain information such as distributor estimates, sales projections, product lead times, inventory levels, production capacity and workforce head counts.
Now we are seeing the amount of readily available information exploding in the public domain. As “big data” and tools to access the information has grown to a point of critical mass, firms cannot only access customer, product and competitor information, but also macroeconomic data that is more detailed and forward-looking than what has been available in the past. Combining this economic data with proprietary firm specific information is creating a new proactive approach to balancing the risk associated with forecasting and demand management.
Early adopters of this new approach are utilizing data-driven analytical tools to enhance the planning and forecasting processes and to give significantly more accurate information to all business units involved in their company’s planning process. The pain associated with demand volatility can be reduced because a firm has armed itself not only with better information, but also with an integrated cross-functional perspective.
The Risk Institute Executive Education Series will continue on April 30, 2015 when Professor Sabin will co-lead a half-day session on Demand Uncertainty, Data Analytics and Risk Management. For more information or to sign up for the session, visit FISHER.OSU.EDU/RISK
One thought on “Data Analytics and Managing the Risk of Demand Uncertainty”
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