Why Sales Forecasting Systems are Wrong for Inventory Replenishment

Sales Forecasting is the wrong tool for inventory replenishment and inventory planning. Sales Forecasting, by its very name itself, is a measure of total sales. In our last article, we discussed that the key difference between sales forecasting and demand forecasting is whether (or not) sales data is broken out into the type of sale, analyzed, and the results input into the forecasting algorithms. Sales type might include any or all of the following: regular, lost, promo, event, and closeout sales. Without the knowledge that sales went up or down due to market factors like out of stock and promotions, a sales forecasting system will forecast based only on the total sales. This may not be the intended goal for inventory replenishment or inventory management.

Key Limitations to Sales Forecasting

Sales Forecasting, by its very nature, doesn’t know why sales rise or fall and cannot connect events to sales behavior. For example, when sales were down 20% four weeks ago, you probably knew this was due to constrained supply which created out of stock issues. The sales forecasting system will react to the 20% drop by lowering the forecast. The resulting inventory replenishment orders from the new forecast will be low, creating a repeat scenario of lost sales again next month.

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Sales forecasting systems by design do not understand the product was out of stock and cannot correctly calculate a forecast for inventory replenishment. If used for inventory management in the wholesale and retail environments, sales forecasting will often overstate and understate the inventory at the same time within a department.

Demand Forecasting with Sales Type

Demand Forecasting will break out the sales by type with some methodology. While both sales forecasting and demand forecasting could be using the same algorithms, the sales input data can be different. The sales types used in demand forecasting might include:

Regular
Lost Sales: Lost Sales occur when a product is active, has a positive forecast and inventory is unavailable
Promotion – Sales due to promotions: news ad, smart-phone, price, BOGO
Event – Sales due to events: competition, manufacturer coupon, and store placement, etc.
Close-out – sales due to closeout pricing, mark-downs, etc.

Any of these sales types should impact a demand forecast and your inventory replenishment system should be able to respond correctly to the different sales types.

Modern Rules for Demand Forecasting

Understanding the type of sale and the resulting sales impact is critical to determining the inventory levels needed in the future. In a previous blog series, we discussed that lost sales are the sum of daily sales forecast for the period in which there was no inventory available to service the customer. An inventory replenishment system will have some methodology to allow you to backfill manually the lost sales. Some systems like our iKIS have automated analysis tools that calculate the number of lost sales for you and save the number as demand history. That’s important because now the lost sales can be included with the regular sales in updating the DEMAND forecast at the end of the period. If this did not occur, then the system would lower the forecast.
As another example: imagine you place a product on sale at a great price, and total sales increase by 25%. When the product goes off sale next month, a sales forecasting system will raise its forecast, but a demand forecasting system will know what sales were regular and what sales resulted from the promotion. The sales forecast system would see and react to the increase by generating orders for too much inventory next month, and you will have overstock. Demand Forecasting systems have technology that analyzes the sales by type and event so that as things repeat, the system can calculate the correct demand forecast in the future for both regular and promotional sales.

Gain your Competitive Edge in Strategy, Cost Control & Performance Tracking

Demand Forecasting provides additional benefits of strategy, controlling costs, and performance flags. With an accurate demand forecast, you can maintain lower inventories, follow just in time (JIT) methodology, and channel promotions where you have the largest ROI. Your ability to predict demand accurately is a critical difference between you and the competition. Demand Forecast accuracy can be tracked and recorded each month to help validate the suggested inventory. With accurate demand forecasting, inventory replenishment costs – inventory costs, acquisition costs, and carry costs – can be rapidly reduced at the same time increasing service levels which will raise sales. The performance data can be used as a confidence guide to further your success and focus attention on the products to improve.

Sales Forecasting for Financials not Inventory Replenishment

Sales Forecasting Systems are great for financials and some planning processes. You need demand forecasting to manage your inventory to the most profitable point in your business. The impacts of constrained supply, out of stock, promotions, events, markdowns, and closeouts are not seen in a sales forecasting system. You need a Demand Forecasting system that can separate and measure the different demand types and provide analysis that correctly impacts the demand forecast and recommends the right amount of inventory to purchase. Most legacy systems use a sales forecasting methodology. Unless you can find where a system is breaking sales out by type and using the data to impact inventory orders, then it probably utilizes a sales forecasting methodology. Don’t let yourself be fooled by the words ‘demand forecasting’; now you can test the claim! New software technologies can move you ahead of the competition in sales and profit.

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We are here and ready to help. Contact us for a free consultation about your forecast accuracy and inventory management opportunities. You can also request a demo and see how things can start to improve in your business in 90 days.

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Stuart D

Stuart Dunkin is a strategic visionary who leads by calling on his broad range of experience as a former retail executive as well as a consultant for top retailers and for the E3/JDA software company. He founded Data Profits to solve a real-world problem plaguing retailers today – the disjointed relationship between supply chain data, people and business goals. He has experienced first-hand the challenges and frustrations that occur when decisions are made based on wrong data, which often leads to the wrong actions being taken with crippling results like overstock, out-of-stock, and increased working capital.
Stuart graduated from Auburn University, with bachelor of science in business administration.