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DEMAND FORECASTING • Demand forecasting means estimation of the demand for the good in the forecast period. • It is a process of estimating a future event by casting forward past data. • The past data are systematically combined in a predetermined way to obtain the estimate of future demand.
Demand forecasting is the activity of estimating the quantity of a product or service that consumers will purchase. Demand forecasting involves techniques including both informal methods, such as educated guesses, and quantitative methods, such as the use of historical sales data or current data from test markets. Demand forecasting may be used in making pricing decisions, in assessing future capacity requirements, or in making decisions on whether to enter a new market.
Necessity for forecasting demand • Often forecasting demand is confused with forecasting sales. But, failing to forecast demand ignores two important phenomena. There is a lot of debate in demand-planning literature about how to measure and represent historical demand, since the historical demand forms the basis of forecasting. The main question is whether we should use the history of outbound shipments or customer orders or a combination of the two as proxy for the demand.
Forecasting Horizons • Long Term • 5+ years into the future • R&D, plant location, product planning • Principally judgment-based • Medium Term • 1 season to 2 years • Aggregate planning, capacity planning, sales forecasts • Mixture of quantitative methods and judgment • Short Term • 1 day to 1 year, less than 1 season • Demand forecasting, staffing levels, purchasing, inventory levels • Quantitative methods
PURPOSE OF FORECASTING Purpose of short-term forecasting • Appropriate production scheduling so as to avoid the problem of over-production & the problem of short-supply. • Helping the firm to reducing costs of purchasing raw materials. • Determining appropriate price policy. • Setting sales targets & establishing controls & incentives. • Evolving a suitable advertising & promotion programme. • Forecasting short-term financial requirements.
PURPOSES OF LONG-TERM FORECASTING • Planning of a new unit or expansion of an existing unit. A multi-product firm must ascertain not only the total demand situation, but also the demand for different items separately. • Planning long-term financial requirements. As planning for raising funds requires considerable advance notice, long –term sales forecasting are quite essential to assess long-term financial requirements. • Planning man-power requirements. Training & personnel development are long-term propositions, taking considerable time to complete.
IMPORTANCE OF DEMAND FORECASTING • Demand forecasts are necessary since the basic operations process, moving from the suppliers' raw materials to finished goods in the customers' hands, takes time. Most firms cannot simply wait for demand to emerge and then react to it. Instead, they must anticipate and plan for future demand so that they can react immediately to customer orders as they occur. In other words, most manufacturers "make to stock" rather than "make to order" – they plan ahead and then deploy inventories of finished goods into field locations
General Approaches to Forecasting • JUDGEMENTAL APPROACHES: The essence of the judgmental approach is to address the forecasting issue by assuming that someone else knows and can tell you the right answer. • EXPERIMENTAL APPROACHES: When an item is "new" and when there is no other information upon which to base a forecast, is to conduct a demand experiment on a small group of customers • RELATIONAL/CAUSAL APPROCHES: There is a reason why people buy our product. If we can understand what that reason (or set of reasons) is, we can use that understanding to develop a demand forecast. • TIME SERIES APPROACHES: A time series is a collection of observations of well-defined data items obtained through repeated measurements over time.
Types of Forecasting Models • Types of Forecasts • Qualitative --- based on experience, judgment, knowledge • Quantitative --- based on data, statistics • Methods of Forecasting • Naive Methods --- eye-balling the numbers • Formal Methods --- systematically reduce forecasting errors • time series models (e.g. exponential smoothing) • causal models (e.g. regression) • Focus here on Time Series Models • Assumptions of Time Series Models • There is information about the past • This information can be quantified in the form of data • The pattern of the past will continue into the future
Forecasting Examples • Examples from student projects • Demand for tellers in a bank • Traffic on major communication switch • Demand for liquor in bar • Demand for frozen foods in local grocery warehouse • Example from Industry: American Hospital Supply Corp. • 70,000 items • 25 stocking locations • Store 3 years of data (63 million data points) • Update forecasts monthly • 21 million forecast updates per year
Least square method Formulas used ∑y= ha+b∑x ∑xy=a∑x + b∑x^2
Experimental Approaches • Customer Surveys are sometimes conducted over the telephone or on street corners, at shopping malls, and so forth. The new product is displayed or described, and potential customers are asked whether they would be interested in purchasing the item. While this approach can help to isolate attractive or unattractive product features, experience has shown that "intent to purchase" as measured in this way is difficult to translate into a meaningful demand forecast. This falls short of being a true “demand experiment”.
TIME SERIES APPROACHES SIMPLE MOVING AVERAGE • In a moving average, the forecast would be calculated as the average of the last “few” observations. If we let M equal the number of observations to be included in the moving average, then: Z’t+1 =1/M ∑i=t+M-1Zi • For example, if we let M=3, we have a "three period moving average", and so, for example, at t = 7: Z’8= (Z7+Z6+Z5) /3
Simple Exponential Smoothing • A popular way to capture the benefit of the weighted moving average approach while keeping the forecasting procedure simple and easy to use is called exponential smoothing, or occasionally, the “exponentially weighted moving average”. In its simple computational form, we make a forecast for the next period by forming a weighted combination of the last observation and the last forecast: Z’ t+1 =aZt +(1-a)Zt
Where α is a parameter called the “smoothing coefficient”, “smoothing factor”, or “smoothing constant”. Values of αare restricted such that 0 < α < 1. The choice of α is up to the analyst. In this form, α can be interpreted as the relative weight given to the most recent data in the series.
Gold price forecast for the next 10 years • An analysis by the Standard Chartered bank suggests that the gold price will triple due to shortages in gold production. The bank's research team looked at the production levels of 345 gold mines and came to the conclusion that the gold production will be only 3.6% annualy over the next five years. The demand for gold, however, has been growing at a much faster pace, driven by purchases of gold by Asian central banks. This forecast is unique for two reasons: first, most gold price predictions are based on inflationary and crisis scenarios, while this one looks at the supply-demand equation. Second, banks usually tend to be rather conservative in their gold price predictions. An interesting read, indeed.
Superfund's Aaron Smith expects gold to increase 50% to 100% by 2014, as measured in major currencies. He also thinks that an ounce of silver will trade for around $100 within the same timeframe. • Reuter's analyst Wang Tao predicts silver to be worth $55 within the next few years. • Recent developments, such as the legalization of gold and silver as official currency in Utah or the purchase of $1bn worth of gold by the University of Texas and similar big institutions may accelerate this development.