Demand Forecasting and Sales Forecasting are different, and the results of each can have a dramatic impact on your profitability. Demand Forecasting and Sales Forecasting should be calculated with some similar and some different data points. While closely related, the two resulting forecast numbers will not be the same in most business situations. The forecast results will impact the inventory replenishment by impacting available inventory, expected inventory orders, and sales. An inventory replenishment system that is based on a demand forecast (demand driven) can reduce the risk of lost sales while improving service. This in turn delivers higher sales by connecting inventory levels with demand forecast.
What is Sales Forecasting
Sales Forecasting is the easier of the two choices: you load your sales history into the sales forecast engine and the system delivers a sales forecast. Sales Forecasting is critical for the retail business to create financial plans with the banks, plan sales growth, and plan resource strategies. Sales Forecasting systems have a ‘vanilla’ approach that is clean and simple, and it works without issues for the most basic of products. Legacy systems often will pair the sales forecasting with their demand planning tools to determine inventory replenishment for the business.
The problem to this approach? Sales Forecasting is a measure of the market response; it is not a measure of market demand.
Many types of events will create sales unit increases and decreases that raise or lower a sales forecast. However, a sales forecast engine may not react correctly.
For example, imagine a case in which sales are zero one week due to no available inventory. A sales forecast does not factor in the unavailable inventory all week, and the forecast will end up artificially low.
On the other hand, imagine a case where sales gain rapidly this week, but the price was marked down due to inventory overstock. Sales will obviously be high for that week, and the resulting sales forecast will be artificially high.
Sales Forecast Engines
A sales forecast engine is looking at total units or dollars to calculate a forecast. A recent trend has been to closely tie sales forecasting and demand planning. The reality is that the sales forecast alone often does not provide the right detail to run demand planning. Problems will occur when we confuse sales forecasting with demand forecasting.
Example events that potentially create bias (error) in the sales forecast and resulting plan include:
- Product Placement: Placement by the register or on a bottom shelf can dramatically affect sales.
- Product Price
- Promotions via website, smart phone, or print
- Changes to the competitive landscape: New Competition and Going out of business competition can impact a sales forecast significantly.
Demand Forecasting: additional data needed A sales forecast is what you believe a business or retailer can sell. The sales forecast doesn’t consider constrained supply, future events, pent-up demand, or bank money lending market policy. Today, some systems talk about collaboration tools that help improve sales forecast accuracy by adding a human element. However, the single greatest influence to a sales forecast is sales history; it’s the most reliable quantitative measure.
Demand Forecasting needs demand history inputs and Sales Forecasting uses sales history. Demand History is created by scrubbing the sales history and at times adding to the sales history. Demand Forecasting uses demand history with events to calculate a demand forecast. One example: Out of stock days with zero sales may need a demand history correction to show what would have occurred if inventory had been available. The demand history can be auto-calculated by systems like iKIS using BI analysis tools to filter sales types regular, lost, promo, event, and close-out demand. For many legacy systems, this is a manual process that requires a trained and informed user to filter the data and calculate demand history for input into the forecasting algorithms.
Differences between Demand Forecasting and Sales Forecasting for Inventory Replenishment
The differences between demand forecasting and sales forecasting are subtle in some places; for example, they both use sales history. The major difference is in what history is input into the algorithms. Demand forecasting must correct for a variety of external factors (like promotional events) to calculate base demand. Planning inventory replenishment requires scrubbing the sales data of events that will not repeat. Likewise, it also necessitates the ability to buy inventory for future, new events.
Demand Forecasting and Sales Forecasting are different and their respective uses should not be the same for the many reasons highlighted today. Technology hardware and the resulting software runs faster for less money and more accurately than even 5 years ago. That means today you have better choices to pick and choose for your business need. These choices provide significant opportunities to improve your inventory replenishment practices and achieve higher sales, lower operating costs, and better service for your customers.
Today we discussed some of the differences between demand forecasting and sales forecasting with a focus on sales forecasting. We have hardly stepped into the discussion opportunities of the two systems. Stay tuned or better yet SUBSCRIBE to our blog to read our follow up article about the relationship between Demand Forecasting and Inventory Replenishment.
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