4 Causes of Forecast Error and How Distributors Can Avoid Them
Reconsider relying on sales history alone, pre-determined inventory agreements, keeping rare items in stock, and depending on sales reps to ...Read Post
To forecast demand and plan inventory needs in their own distribution centers and at customer locations, distribution companies tend to depend on historical sales data, as well as a usually arbitrary sales goal for the year.
For example, if they want to grow 5% in 2020, they increase stock of the items sold over the past 12 months by the same amount based on their aggregated sales history. They may add in seasonality, but in general, most distributors’ forecasts follow this formula. They may also add in salespeople’s forecasts for their own territories based on expected purchases. Distributors typically assume that the same demand for individual items will occur at the same time in the same quantity each year.
Most know there are flaws in this method. But everyone is working to get as close as possible to the stock levels required to best serve their customers. Not surprisingly, distributors are always looking for more ways to increase accuracy and reduce forecast error.
At its most basic, forecast error is the difference between the forecast demand and the actual demand. A lot of calculations go into forecast error, but the bottom line is that the greater the difference between actual demand and forecast demand, the greater the impact on a distributor’s bottom line.
As forecast error goes up, the risks go up:
Forecast accuracy matters. It drives decisions on what to buy and when, as well as what to stock and where. It could also drive decisions around hiring personnel and where you allocate your most expensive resource. It can mean the difference between meeting your customers’ needs, or service breaking down when customers need it most.
The simplest way to reduce forecast error is to base demand planning on actual usage data vs. historical sales. The difference: Usage reflects actual consumption of an item. In other words, just because a product was sold to a customer doesn’t mean that product was used. Customers are notoriously overstocked. Distributors can reduce inventory significantly when they base purchasing off usage, and not just past sales. When you parse the data, some distributors have found they have up to 80% more inventory than they need.
With the eTurns TrackStock Automated Replenishment Apps, distributors track and replenish inventory for customers at the point-of-use. Distributors can leverage this usage data to optimize their distribution centers and to consign inventory at customer sites, reduce carrying cost and improve customer service.
It’s simple, but powerful. When you can be certain of your near-term demand planning, you reduce forecast error. If usage is constant or fairly constant for an item, such as a customer’s bread and butter (like switches and wall plates for an electrical contractor), you can be confident that future purchasing patterns will follow past usage.
The key is a shorter time frame. Using a full year of generic aggregated data presents too many opportunities for error. eTurns TrackStock looks at 30-45 days of usage data – continually updated, in real time – that can then be extrapolated for longer periods. Our Precise Demand Planning solution excludes data from items that aren’t used regularly, and it incorporates item data that comes into the system that is new – such as a new product line that a customer suddenly wants but could not be forecast based on historical sales.
Distributors have significant opportunities to tighten their relationships with their customers this year, but they need tools that can support these value-added services without straining cashflow or cutting into their bottom lines. That means they need to reduce forecast error to ensure they are stocking what they need, when they need it. Automated replenishment through the eTurns TrackStock Replenish and TrackStock Manage Apps gives them a powerful Min/Max Tuning Dashboard that uses a customer’s actual demand data to optimize inventory and save cash, not only in the first year, but on an ongoing basis.
That saves the distributor – and the customer – money.