Demand Forecasting

There are some important differences in the way inventory requirements are determined that are related to the type of demand for the products in question.

Demand Types

• Independent demand occurs where the demand for one particular product is not related to the demand for any other product.
• Dependent demand occurs where the demand for a particular product is directly related to another product.

System of Demand Requirement

• A push system is the more traditional approach where inventory replenishment is used to anticipate future demand requirements. A push approach to inventory planning is usually based on a set plan that is predetermined according to certain rules of inventory reordering. This approach is a proactive one in the sense that it is planned on the basis of estimated, or forecast, demand for products from customers.
• A pull system is where the actual demand for a product is used to ‘pull’ the product through the system. The pull approach is a reactive one where the emphasis is on responding directly to actual customer demand, which pulls the required product through the system. The idea of a pull system is that it can react very quickly to sudden changes in demand.

Lead-Time Gap

The total time it takes to complete the manufacture and supply of a product is often known as the logistics lead time. Customers are generally prepared to wait for a limited period of time before an order is delivered. This is the customer’s order cycle time. The difference between the logistics lead time and the customer’s order cycle time is oft en known as the lead-time gap.

It is the existence of this lead-time gap that necessitates inventory being held. The extent of the lead-time gap, measured in length of time, determines how much inventory must be held. The greater the lead-time gap, the greater the amount of inventory that must be held to satisfy customer requirements. Thus, the more this gap can be reduced, the less inventory will be required.

Demand Forecasting Methods

Different methods of demand forecasting are used to try to estimate what the future requirements for a product or SKU might be so that it is possible to meet customer demand as closely as possible. Forecasting, thus, helps the inventory holding decision process to find answers to questions about what to stock, how much to stock and what facilities are required.

There are several different approaches that can be used for forecasting. These are

• Judgemental methods– subjective assessments based on the opinions of experts, such as suppliers, purchasing, sales and marketing personnel, and customers. These methods are used when historic demand data are very limited or for new products. They include brainstorming, scenario planning and Delphi studies.
• Causal methods– used where the demand for a product is dependent on a number of other factors. These factors may be under the control of the company (promotions, price), under other control (competitors’ plans, legislation) or external (seasonality, weather, the state of the economy). The main method used is regression analysis, where a line of ‘best fi t’ is statistically derived to identify any correlation of the product demand with other key factors.
• Projective methods– these forecasting techniques use historic demand data to identify any trends in demand and project these into the future. They take no direct account of future events that may affect the level of demand. There are several different projective forecasting methods available, and it is important to select the most appropriate alter- native for whatever demand is to be measured. Two of the most common methods of forecasting are described. One of the most simple is the moving average, which takes an average of demand for a certain number of previous periods and uses this average as the forecast of demand for the next period. Another, more complicated, alternative is known as exponential smoothing. This gives recent weeks far more weighting in the forecast. Forecasting methods such as exponential smoothing give a much faster response to any change in demand trends than do methods such as the moving average.

Guidelines for effective demand forecasting are

• Ensure from the outset that there is a clear plan for identifying and using the most appropriate factors and methods of forecasting. Understand the key characteristics of the products in question and the data that are available. Consider the different quantitative and qualitative methods that can be used and select those that are relevant. If necessary and feasible, use a combination of different methods. Identify ways of double-checking that the eventual results are meaningful – it is unsafe merely to accept the results of a mechanical analytical process. Forecasting at individual SKU level is a typical ‘bottom-up’ approach, so check results with suitable ‘top-down’ information.
• Take care to review the base data for accuracy and anomalies. Poor data that are analysed will produce poor and worthless results. Where necessary, ‘clean’ the data and take out any abnormalities.
• A typical range of company products can and do display very different characteristics. Thus, it is usually necessary to identify key differences at the outset and group products with similar characteristics together. It is likely to be valid to use different forecasting methods for these product groups. Use techniques such as Pareto analysis to help identify some of the major differences: high versus low demand, high versus low value, established products versus new products, etc.
• Use statistical techniques to aid the understanding of output and results (standard deviation, mean absolute deviation, etc). There may be a number of relevant issues that can impact on the interpretation of results: the size of the sample, the extent of the time periods available.
• Any forecasting system that is adopted needs to be carefully controlled and monitored because changes occur regularly: popular products go out of fashion and technical products become obsolete. Control should be by exception, with tracking systems incorporated to identify rogue products that do not fit the expected pattern of demand and to highlight any other major discrepancies and changes.