eBooks & Briefs

Why You Should be Using Demand Sensing to React Faster to Market Changes

Put machine learning to work on your data to foresee—and act on—demand changes before your competitors know what hit them
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Introduction

Why use demand sensing? Because all the sophisticated tools to influence demand with pricing, new product introductions and promotions have pushed demand volatility and consumer expectations to unprecedented levels. Internet-fed trends are changing at hyper-speed. To thrive in this complex, fast-paced world, you need to be ready for whatever tomorrow brings. Here’s why you should use demand sensing to reduce demand variability by extracting signal out of the noisy demand and seeing and reacting to downstream demand faster than ever before.

Demand sensing companies have “an ability to see trends sooner and, combined with an agile supply chain response, the ability to react sooner to changing demand.”

Gartner3

Constrained by a [Sub-Optimal] Monthly Forecast?

Feel like you’re always behind the eight-ball? If it seems like you’re constantly chasing the elusive accurate forecast, adjusting on the fly but never fast enough, and wasting precious expense expediting items to avoid service failures, you’re not alone.

/ Yesterday’s forecast and inventory models are no match for today’s long tail demand

Items with intermittent, unpredictable or “long tail” demand are proliferating, making demand forecasting and inventory management a headache for businesses. Inventory mixes are wrong. The wrong products are being over-served, locking up precious working capital, while others are being under-served, and causing erosion of margin and market share.

/ Traditional approaches can’t deliver a truly responsive and reliable forecast

Today’s fast-movers become slow-movers much quicker than in the past. All this movement across SKUlocations increases heterogeneous demand behavior and makes the use of traditional forecast techniques extremely difficult. Many companies follow the traditional approach of investing most of their resources and talent in using historical sales data to establish a reliable baseline forecast. The problem is, they can’t enable a truly responsive, data-backed forecast without accounting for external demand variables that impact or indicate additional demand variation.

“As the long tail (small orders shipped with low-frequency) of the supply chain grows, demand latency increases and there is a greater need for demand sensing technologies.”

Lora Cecere, founder of SupplyChain Insights