[completed] 8과 Forecating and Demand Management

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Introduction

why forecast? forecasting is inevitable in developing plans to satisfy future demand. many factors influences the demand for a firm's products and services. althoght it is not possible to identify all of them, or their effec on demand, it is helpful to condiser some major factors.

• general business and economic conditions
• competitive factors
• market tends such as changing demand
• the firm's own plans for advertising, promotion,pricing and product changes

Demand Management

marketing focuses on meeting customer needs, but operations , through materials management, must provide the resources. the coordination of plans by these two parties is demand management.

demand management is the function of recognizing and managing all demands for products and/or service.it occurs in the short, medium and long term.

in the long term, demand projections are needed for sttategic business planning of such things as facilities.

in the medium term, the purpose of demand management is to project aggregate demand for production planning.

in the short term, demand management is needed for items and is associated with master production scheduling. 

demand management includes several major activities, all of which are primarily market driven.

• identifying all products and service demand in the defined markets. this includes forecasting but also involves possible segementating of markets, classifying customers, and identifying demand that does not add value and therefore should be ignored.

this includes identifying customer desires for existing or possible new product, or service design and features.

• identifying and understanding all aspects fo the market that will potentially impact customer demand. this includes economics conditions and indicators, govermental laws and regulations. and sources of existing or potential competitions, including possible new competitors.
• synchronizing identified market demand with company capabilities
• setting priorities for demand when supply will not cover all demand
• making delivery promises. 
• interfacing between manufacturing planing and control and the marketplace.
• order processing

in each of these cases, production(supply) is being planned to react to anticipated demand as shown by the forcast.

there are several other activities or components to demand management. they include the following.

• setting and maintaining appropriate customer service levels
• planning for new product introductions and pahse-out of obsolete inventory.
• planning and managing interplant shipments and distribution requirements planning
• establishing inventory target levels and maintaining them.
• establishing performance metrics for demand and using them to evaluate performance

collaborative planning, forecasting and replenishment

as the concept of supply chain continue to develop and mature, another approach to identify demand has been developed. called collarborative, planning, forecasting, and replenshiment(CPFR), the approach establishes a relationship between trading partners in a suply chain. they then treate a joint business plan from which sales forecasts can bedeveloped and commucinated between the supply shainpartners. this approach tends to be a closed-loop approach, where results are analyzed after plans are executed.

order processing

order processing occurs when a customer's order is received. the product may be delivered from finished inventory or it may be made or assembled to order.

Demand Forecasting

forecasts differ depending on what is be to done. they must be made for the strategic plan, the strategic business plan, the sales and oepration plan and the master production schedule.

the strategic plan and the business plan are concerned with overall markets and the direction of the economy over the next 2 to 10 years and more. theire purpose is to provide time to plan for those things that take long to change.

for manufacturing, it means forecasting those items needed for production planning, such as budgets, labor palnning, long lead time procurement items and overall inventory levels. forecasts are made for group or families of product rather than specific end items. but Master Production Scheduling is concerned with production activity from the present to a few months ahead. also forecase is made for individual item.

Characteristics of Demand

there is differency between sales and demand. sales implies what is actually sold, whereas demand shows market or customer requests.

Demand patterns

if historical data for demand are pletted against a time scale, they will shoe any shapes or consistent patterns that exist.  a pattern is the general shape of a time series. althogh some individual data points will not fall exactlry on the pattern, they tend to cluster around it.

the pattern show that actual demand vaires from period to period. there are four reasons for this : trend, seasonality, random variation, and cycle.

• trend
• seasonality
• random variation
  • occurs where many factors affect demand during specific periods and occur on a random basis. the variation may be small, with actual demand falling close to the pattern or it may be large, with the point widely scattered. the pattern of variation can usually be measured.
• cycle

stable versus dynamic

those that retain the same general shape are called stable and thoese that do not are called dynamic.

notice the average demand is the same for both stable and dynamic patters. it is usually the average demand that is forecast.

Dependent Versus Independent Demand

dependent demand for a product or service occurs where the demand for the item is derived from that of a second item. requirements form dependent demand items need not be forecast but are calculated from that of the independent demand item. only independent demand items need to be forecated.

Principles of Forecasting

forecasts have four major characteristics or principles.

1. forecasts are usually wrong
2. every forecast should include an etimate of error
3. forecasts are mor accurate for families or groups
4. forecasts are more accurate for nearer time periods.

Collectoin and Preparation of Data.

forecasts are usually based on historical data manipulated in some way using either judbement or a statical technique.  thus the forecast is only as good as the data on which it is based. 

• record data in the same terms are needed for the forecast. there is often a problem in determining the purpose of the forecast and what is to be forecast. there are three dimensions to this:
  • if the purpose is to forecast demand on production, data based on demand, not shipments are needed. shipments show when goods were shipped and not necessarily when the customer wanted them.
  • the forecast period, in weeks, months or quaters, should be the same as the shcedule period. 
  • the items forecasted should be the same as those controlled by manufacturing.for example, if there are a variety of options that can be supplied with a particular product, the demand for the product and for each option should be forecast.
• record the circumstances relating to the data
  • demand is influenced by particular events and these should be recorded along with the demand data. it is vital that these factors be related to the demand history so they may be included or removed for future conditions.
• record the demnad separatley for diffrent customer groups
  • many firms distribute their goods through different channels of distribution, each having its own demand characteristics. for example, a firm may seel to a number of wholesalers that order relatively small quantities regularly and also sell to a major retailer that buy a large lot twice a year. forecasts of average demand would be meaningless, and each set of demands should be forecast seperately. 

Forecasting Techniques

there are many forecasting methods, but they can usually be classified into categories.

forecasting techniques generally may be qualitative or quantitative and they can be based on external or internal factors.

• Qualitative Techniques
  • are projections based on judgement, intuition, and informed opinions. by the nature, they are subjective. such techniques are used to forecast general business trends. production and inventory forecasting is usually concerned with the demand for particular end items and qualitative techniques are seldome appropriate.
  • when attempting to forecast the demand for a new product, there is no history on which to base a forecast. in these cases, the techniques of market research and historical analogy might be used.  another method is to test-market a product.
    • market research is a systematic, formal and conscious procedure for testing to determine customer opinion or intentions.
    • historical analogy is based on a comparative analysis of the introduction adn grouth of similar products in the hope that the new product behave in a similar fashion.
  • there are several other methods of qualitative forecasting. an example of one such method is the delphi method.
• Quantitative Techniques
  • are projections based on historical or numerical data, whether it be from inside or outside the organization.
• Extrics(external) Techniques
  • are projections based on external indicators that relates to the demand for the company's products. the theory is that the demand for a product group is directly proportional or correlates, to activity in another field. examples of correlation follow;
    • sales of bricks are proportional to housing starts.
    • sales of automobile tires are proportional to gasoline consumption.
  • housing starts and gasoline consumption are called economic indicators. they describe economic conditions prevailing during a given time period. 
  • leading indicator
    • as an example, housing starts will lead to the need for housing materials, including roofing materials, electronical materials. housing starts are , therefore, a good leading indicator for demand for many related housing materials.
  • the problem is to find an indicator that correlates with demand and one that preferably leads demand, that is, one that occurs before the demand does.
  • external forecasting is mose useful in forecasting the total demand fro a firm's products or the demand for families of products. as such, it is used most often in business and production planning rather that the forecating of individual end items.
• Intrisic(internal) Techniques
  • use historical data to forecast. this data is usually recorded in the company and is readily available. internal forecasting techniques are based on the assumption that what happend in the past will happen in the future. the best guide to the future is what has happened in the past.

Some Important Intrinsic(internal) Techniques

• demand this month will be the same as last month
• demand this month will be the same as demand the same month last year
• ru;es such as these, based on a single month or past  period, are of limited used when there is much random fluctuation in demand. usually methods that average out history are better because they dampen out some effects of random variation. but such simple average would not be responsive to trends or changes in level of demand. a better method would be to used a moving average.
• average demand
  • this raises the question of what to forecast. demand can fluctuate because of random variation. it is best to forecast the average demand rather than second-quess what the effect of random fluctuation will be. assuming that a forecast should include an estimate of error, a forecat of average demand shuld be made, and the estimate of error applied to it.
• moving average
  • to take the average demand for the last three or six periods and use that figure as the forecast for the next period. at the end of the next period, the first period demand is dropped and the latest period demand added to determine a new average to be used as a forecast. this forecast would always be based on the average of the actual demand over the specified period. the point is that a moving average always lag a trend, and the more periods included in the averages, the greater the lag will be. on the other hand, if there is no trend but actual demand fluctuates considerably due to random variation, a moving average based on a few periods reacts to the fluctuation rather then forecasts the agerage.
  • moving averages are best used for forecasting products with stable demand where there is little trend or seasonality. moving averages are also useful to filter out random fluctuations. this has some common sense since periods of high demand are often followed by periods of low demand since the toal market demand is usually constant and consumers often buy goods ahead of tiem due to sales or other outside influences, including the weather and holiday events. buying early will lower future sales.
  • one drawback to using moving average is the need to retain several periods of history for each item to be forecast. this will require a great deal ot computer storage or clerical effort.
• exponential smoothing
  • gives the same results as a moving average but without the need to retain as much data and with easier calculations because the previously calculated forecast has already allowed for this history. therefore, the forecast can be based on the old calculated forecast and the new data.
  • less weight could be put on the latest actual demand and more weight on the old forecasts. notice that this forecast did not rise as much as the previous calcultaion in which the old forecast and the latest actual demand were given the same weight. one advantage to exponential smoothing is that the new data can be given any weight wanted. the weight given to latest actual demand is called a smoothing constant.
  • exponential smoothing provides a routine method for reqularly updating item forecasts. it works well when dealing with stable items and fro short-range forecasting.
  • if a trend exists, it is possible to use a slightly more complex formula, called double exponential smoothins.
  • a problem exists in selecting the best alpha factor. if a low factor such as 0.1 is used, the old forecast will be heavily weighted. and changing trends will not be picked up as quickly as migh be desired. a good way to get the best alpha factor is to use computer simulation. using past actual demand, forecast are made with different alpha factos to see which one best suits the historical demand pattern for particular products.

Seasonality

• manay products have seasional  or periodic demand pattern.
• seasonal index
  • a useful indicators of the degree of seasonal variation for a product is the seasonal index. this index is an estimate of how much the demand during the season will be above or below the average demand for the product.
  • the formular for the seasonal index is as follows;
    • seasonal index = period average demand/ average demand fro all periods.
  • the deseasonalized demand ( the average demand for all periods is a valus that averages out seasonality.
    • deseasonalized demand = period average demand/ seasonal index
• seasonal forecasts
  • the equation for developing seasonal indicies is also used to forecast seasonal demand. if a company forecasts average demand for all periods, the seasonal indices can be used to calculate the seasonal forecast.
• deseasonal demand
  • forecast do not consider random variation. they are made for average demand, and seasonal demand is calculated from the average using seasonal indicies. the forecast average demand is also the deasonalized demand. historical data is of actual seasonal deamnd, and it must be deseanalized before it can be used to develp a forecast of average demand.
  • also if comparisons are made between sales in different periods, they are meaningless unless deseasonalized data is used.
  • deseasonalized data must be used for forecasting. forecasts are made for average demand. and the forecast for seasonal demand is calculated from the average demand using the appropriate seaon index.
    • use only deseasonalized data to forecast
    • forecast deseasonalized demand, not seasonal demand.
    • calculate the seasonal forecast by applying the seasonal index to the base forecast.

Tracking the Forecast

ther is no point in continuing with a plan based on poor forecast data, so the forecast must be tracked. tracking the forecast is the process of comparing actual demand with the forecast.

• forecast error is the difference between actual demand and forecast deamdn. error can occur in two ways:bias and random variation.
  • bias
    • cumulative actual demand may not be the same as forecast.
    • bias exist when the cumulative actual deamnd varies from the cumulative forecast. this means the foecast average deamdn has been wronf.
    • bias is a systematic error in which the actual demand is consistently above or below the forecast demand. when bias exists, the forecast should be evaluated and possibly changed to imptove its accuracy.
    • the purpose of tracking the forecast is to be able to react to forecast error by planning around it or by reducing it. when an unacceptably large error or bias is observed, it should be investigated to determine its cause.
    • tracking cumulatibe demand will confirm timing error or exceptional one-time events.
  • random variation
    • in a given period, actual demand will vary about the average demand.the variability will depend upon the demand pattern of the product.
    • notice there is much random variatio, but the average error is zero.
• mean abdolute deviation - there are several ways to measure error. one method commonly used due to its ease of calculation is mean absoulte deviation(MAD). 
  • althouth the total error is zero, there is still condiserable variation each month. total error would be useless to measure the variation. one way to measure the variabiltiy is to calculate the total erro ignoring the plus and minust signs and take the average. this is mean absolute deviation.
    • mean implies an average
    • absoulte menas without reference to plus and minus
    • deviation refers to error
      • MAD = sum of absoulte deviation/ number of observation.
    • this type of knowlede, when taken with the MAD fro the data, can provide significant imput into planning for safety inventory.
• normal distribution
  • the mean absoulte deviation measures the difference(error) bet actual demand and forecast. a graph of the number of times(frequency) actual demand is of a particular value produces a bell-shpaed curve. this distribution is called a normal distribution .
  • there are two important characteristics to normal curves
    • the central tendency or average and the dispersion or spread of the distribution.
• uses of mean absolute deviation
  • mean absoulte deviation has several uses. some of the most important follow
• tracking signal
  • bias exists when cumulative actual demand varies from forecast. the probelm is in in guessing whether the variance is due to random variation or bias. if the variation is due to random variation, the error will correct itself. and nothing should be done to adjust the forecast.
  • however, if the error is due to bias, the forecast should be corrected.
  • a tracking signal can be used to monitor the quality of the forecast. there are several procedures used, but one of the simpler ones is vased on a comparison of the cumulative sum of the forecast errors to the mean absolute deviation.
• contingency planning
  • suppose a forecast is made that demand for dorre slammer will be 100 units and that capacity for making them is 110 units. mean absoulte deviation of actual demand about the forecast historically has been calculated at 10 units. this emans there is a 60% chance that actual demand will be between 90 and 110 units and 40% chance that they will not. with this information, manufacturing management might be able to devise a contigency plan to cope with the possible extra demand.
• safety stock
• P/D ratio
  • a more reliable way of producing what is really needed is the use of the P/D ratio.
  • P, or production lead time is the stacked time for a product
  • D, or demand lead time is the customer's lead time. it's the time from when a customer place an order wntil the goods are delivered.
  • the traditional way to guard against inherent error in forecasting is to include safety stock in inventory. there is an added expense to the extra inventory  carried 'just in case'. one other way is to make more accurate predictions. there are five ways to move in this direction.
    • reduce P time
    • force a match between P and D. 
      • Moving in this direction can be done in two ways;
        • make the customer's d time equal to your p time.
        • sell what you forecasted
        • simplify the product line
        • standardize products and process
        • forecast more accurately,

KEY TERMS

Average Demand

Bias

Collaborative Planning,Forecasitng, and replenishment(CPFR)

Demand Lead time

demand management

deseasonalized demand

dynamic

economic indicators

exponential smoothing

extrincis forecasting techniques

forecast error

intrinsic forecasting techniques

leading indicators

mean absolute deviastion(MAD)

moving averages

normal distribution

order processing

preduction leadtime

qualitatie techniques

quantitatie techniques

random variation

seasonal index

seasonlity

smoothing constant 

stable

traking signal

trend