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Since ages, Demand Forecasting has always been a mix of the art and the science. No matter how sophisticated the models and the data analysis, there is an underlying ingenuity that helped to read subtle cues and forecast future demand.

In the olden times, for example- a traveling merchant with goods laden on his cart, used to visit village after village to sell at a profit. He would sense the demand by factoring in-

• Size of villages (Population)
• Size of agricultural fields
• Festivals or Special events happening
• Town gossip of impending event
• Harvest (If the villager's harvest is poor trader will probably sell more essentials, and if harvest is bountiful then more of luxuries)
• Weather (Rain)
• Competition from other traders
• War and Conflict etc.

Noting much have changed except that the trader is now a big retailer, selling 100000s of items of every imaginable shape and price.

Going by the same Traveling Merchant's analogy – I believe ingenuity and experience still play a significant role, and is valuable, even alongside sophisticated data analytics and modeling.

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While we engage with CPG and Retailers, there is no doubt that Demand forecasting is the bedrock of supply chain planning and inventory management. And how predictable a product's demand is, therefore plays a crucial role in forecast accuracy and, ultimately, supply chain success.

?? Understanding forecastability is the key.

Ready to upgrade your forecasts? I would be happy to discuss further on our Machine Learning driven Demand Forecasting Approach!

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