Top-down or bottom-up? How to approach forecasting in a data-driven world.
The role of any good Supply Chain Manager is to ensure regular reporting of the variances between top-down executive targets, and the bottom-up demand of the market. Effectively, this is the budgeting process, and achieving a balance between demand and supply is the best way to stay on target with financial projections.
This process has traditionally been managed by looking back at sales, and using historical information to forecast forward. But let’s be honest, adopting this approach usually means getting it wrong, because responding to historical data that was already built on a flawed model makes very little sense.
True demand vs projected demand
The term “true demand” refers to the amount of product an organisation could feasibly sell in an unconstrained market. If a business has a well-oiled supply chain that runs like clockwork, it should be able to flex and adapt according to demand. So all that’s left for that business to do is put its ear to the ground, find out what the market wants and provide it. The value of sales information in this situation is high. But historical sales information for companies with poor supply chain models is largely useless as all they tell an organisation is what they were able to sell with clunky processes and no visibility of wider consumer trends and macroeconomic shifts.
Minimising lost sales opportunity
The gap between what a business was able to sell and what the market would have bought is referred to as “lost sales opportunity”. Once businesses start thinking in terms of true demand, they are able to minimise this gap and start working towards satisfying the actual demands of the market, rather than hitting sales forecasts.
Digital tools that pull in information from thoughtfully compiled data sets all over the internet can help organisations to understand current demand for their product at any given time. And with technology like Artificial Intelligence and Machine Learning on board, these organisations can set seemingly unrelated data against sales performance, to observe where the impact can be attributed to external circumstances, and predict likely anomalies in demand forecasting.
This technology is the key to businesses unlocking their true sales potential and has the power to move them from sales forecasting, to demand planning. When organisations can more accurately measure the likely demand for their product, they are able to truly optimise all of their operations, across all departments.
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