Business forecasting 101
What is Forecasting
There are two forecast types: judgment-based (e.g. “gut feel”) and quantitative (e.g. statistics). The most trustworthy forecasts combine both methods to support their strengths and mitigate their weaknesses.
Judgement forecasting uses only our intuition and experience. Our minds are able to make connections and understand the context in a way that no computer can. However, we’re also prone to certain biases that make analyzing large amounts of data difficult. Judgment forecasting is best where there is little to no historical data- such as new product launches, competitor actions, or new growth plans.
Quantitative forecasting uses analytics to analyze large amounts of historical data in order to discern trends and patterns. Quantitative forecasting is excellent at churning through large amounts of data and is less prone to bias. However, it is weakest when there is little to no historical data that can be analyzed. Quantitative forecasting relies, more or less, on identifying repeated patterns in your data so it may take a while to see the same pattern repeat more than once. Combining judgment and quantitative forecasting gets the best results.
Forecasts are usually a quantitative estimate of future trends and values. For the best forecasts, we recommend combining statistical analysis with staff insights. Statistics can uncover trends invisible to the human eye, but it lacks the ability to make important connections beyond the data itself. Staff will know important distinguishing features in product lines, understand customer response to promotions, and more.
Quality: Products are kept in good condition. Perishable items are not kept too long.
Speed: Optimal inventory location enables faster response to customer requests.
Dependability: Risk of stock-outs are minimal.
Flexibility: The company can continue responding to customer requests even as it switches to new products.
Adapted from Operations Management (4th Ed, Pearson).
Advanced-analytic forecasting optimizes inventory management in part by balancing the costs of holding too much inventory (facility, spoilage, loss, etc.) with the cost of holding too little inventory (lost sales & customers, missed production schedules, etc.). Inventory optimization finds and maintains optimal safety stock levels, reorder levels, order quantities, service levels, fill rates, and more.
|Time Series Forecasting||Model-Based Forecasting|
|Purpose||Tactical: short-term forecasting, determining seasonal patterns||Strategic: long-term forecasting, accounting for changes in the business environment and events with little data|
|Requires||Historical data||Management insight|
|Results||Short-term changes||Change in business practice|
|Applications||Demand planning, Production planning, Inventory management, Salesforce evaluation||Marketing strategy, Resource allocation, Business optimization, Comparing business plans|
Time Series Forecasting
Time series forecasting is the process of extracting trends and patterns from historical data and extrapolating them into the future. That is how historical sales can predict future sales. This technique is highly useful for short-range forecasts because the data are relevant and easy to spin forward. The technique also applies well in cases where business practices and conditions are relatively consistent.
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