What are the basic types of forecasting?

Introduction to the Different Types of Forecasting

Statistical Forecasting
Non-Statistical Forecasting

Introduction

In the modern supply chain, forecasting is essential for companies that manufacture items for inventory and those that are not made to order. Manufacturers will use material forecasting to ensure that they produce material that satisfies their customers without creating an overcapacity situation where too much inventory is produced and sits on the shelf. Equally, forecasts should not be low and the manufacturer finds them without inventory to fulfill customer orders. The cost of failing to maintain an accurate forecast can be financially devastating.

Forecasts are developed for the company's finished goods, components and service parts. Forecasting is used by the production team to develop production or purchase order triggers, quantities and safety stock levels. Forecasts are not static and should be reviewed by management on a regular basis. This is to ensure that information on future trends, internal or external environment is included in the forecast to give more accurate calculations.

Statistical Forecasting

In supply chain management software, forecasting is a calculation that is fed data from real-time transactions and is based on a set of variables that are configured for several statistical forecast conditions. Planning professionals need to use software to provide the best forecasting situation and often it is left unchecked for long periods of time without any review. To make the best use of forecasting techniques in supply chain software, planners must review their decisions in relation to the internal and external environment. They should adjust calculations to provide more accurate forecasts based on current information.

Statistical forecasting is the best estimate of what will happen in the future based on past demand. Historical demand data can be used to generate forecasts using simple linear regression. It gives equal importance to historical period demand and projects future demand. However, today's forecasts place more emphasis on recent demand data than older data. This is called smoothing and is produced by giving more weight to recent data. Exponential smoothing refers to greater weight given to recent historical periods. Hence two months back period carries more weight than six months back period. The weighting is called the alpha factor and the higher the weighting, or alpha factor the shorter the historical periods used to make the forecast. For example, a high alpha factor gives more recent periods a higher weight and demands from periods a year or two ago are weighted so lightly that they have no effect on the overall forecast. A lower alpha factor means that historical data is more relevant for forecasting.

Historical periods usually contain demand data from a specific month, ie June or July. However, this introduces error into the calculation as some months have more days than others and the number of working days may vary. Some companies use daily demand to reduce this error, although if the forecaster understands the error, monthly historical periods can be used with tracking indicators to determine if the forecast is significantly higher than actual demand. Disturbed form. The level at which a tracking signal indicates deviation is determined by the forecaster or software and varies between industries, companies and products. A small deviation may require intervention when the forecast ed product is of high value, while a low-valued product may not require such a high level of forecast scrutiny.

Non-Statistical Forecasting

Non-statistical forecasting is found in supply chain management software where demand is forecast ed based on quantities determined by production planners. This is when the planner enters a subjective quantity of what they believe demand will be without reference to historical demand. Other non-statistical forecasting occurs when demand for an item is based on the results of material requirements planning (MRP). It takes the demand for finished goods and explodes the bill of materials to calculate the demand for component parts. Component demand can then be modified by the planner based on their assessment and knowledge of the current environment. The resulting forecast is based on current demand and will not include any demand from previous periods. Many companies will use a combination of non-statistical and statistical forecasting in their product line.

Statistical forecasting is based on complex calculations and future demand can be determined based on historical period demand. Forecasting gives the planner a guide to future demand, but no forecast is completely accurate

What are the basic types of forecasting?

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