FORECASTING

1. What is Demand Management?

Demand Management is gauging the demand for a product or service in the future and planning the

manufacturing so there wouldn’t be supply and demand gaps.

The success of any business depends upon how they are creating the demand for a product in

the target market and then, how they are managing the supplies to fulfill that demand.

It is the process of prediction and the planning of demand, which comes regarding a particular

product.

2. What is Demand Management Process?

Demand management is the supply chain management process that balances the customers'

requirements with the capabilities of the supply chain. With the right process in place, management

can match supply with demand proactively and execute the plan with minimal disruptions. The

process is not limited to forecasting. It includes synchronizing supply and demand, increasing

flexibility, and reducing variability. In this paper, we describe the demand management process in

detail to show how it can be implemented within a company and managed across firms in the supply

chain. We examine the activities of each sub-process; evaluate the interfaces with corporate

functions, processes and firms; and provide examples of successful implementation.

3. What are the demand management techniques?

The three master production scheduling (MPS) environments of make-to-stock (MTS), assemble-to-

order (ATO), and make-to-order (MTO) utilize different demand management techniques. In MTS

production, highly detailed, item level forecasts are required, by location and time period. The

difficulty of forecasting individual items can be dealt with by using ratios or percentages of

aggregated forecasts as a surrogate for the individual units. This is appropriate in firms with steady

product mix ratios and allows management to devote time to forecasting overall sales. In the ATO

environment, the key issue is accurate order promise dates, which calls for a stable MPS. This is

achieved by using time fencing and related methods. In MTO environments, control of customer

orders in the production system is important. Usually, there is much uncertainty concerning future

orders, which may require extensive new design and engineering.

4. What are the components of Demand Management?

Forecasting - In this, a business will forecast the future demands of a particular product or

service that they offer. Forecasting is done as per the current trend, projected sales, and analytical

data of customer behaviour. It also helps businesses in preparing for any unexpected events in the

future.

Supply Planning - Businesses need to be prepared for the future requirements of the supplies.

Being well aware of the requirements of customers, competitors’ influence and market trends

help businesses in doing efficient supply planning.

Demand Analysis - It is important that your demand forecasting is based upon the current sales

data. Proper analysis of order history will help you do the right demand analysis for effective

Demand Management.

Sales and Operations Planning - For effective Demand Management, it is essential that you pay

attention to the roles, operations, and importance of all the stakeholders in the supply chain

system. Based on these reports, you can do effective Sales and Operations Planning to manage

the demand requirements.

5. What is Forecasting?

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FORECASTING

What is Demand Management? Demand Management is gauging the demand for a product or service in the future and planning the manufacturing so there wouldn’t be supply and demand gaps. The success of any business depends upon how they are creating the demand for a product in the target market and then, how they are managing the supplies to fulfill that demand. It is the process of prediction and the planning of demand, which comes regarding a particular product.
What is Demand Management Process? Demand management is the supply chain management process that balances the customers' requirements with the capabilities of the supply chain. With the right process in place, management can match supply with demand proactively and execute the plan with minimal disruptions. The process is not limited to forecasting. It includes synchronizing supply and demand, increasing flexibility, and reducing variability. In this paper, we describe the demand management process in detail to show how it can be implemented within a company and managed across firms in the supply chain. We examine the activities of each sub-process; evaluate the interfaces with corporate functions, processes and firms; and provide examples of successful implementation.
What are the demand management techniques? The three master production scheduling (MPS) environments of make-to-stock (MTS), assemble-to- order (ATO), and make-to-order (MTO) utilize different demand management techniques. In MTS production, highly detailed, item level forecasts are required, by location and time period. The difficulty of forecasting individual items can be dealt with by using ratios or percentages of aggregated forecasts as a surrogate for the individual units. This is appropriate in firms with steady product mix ratios and allows management to devote time to forecasting overall sales. In the ATO environment, the key issue is accurate order promise dates, which calls for a stable MPS. This is achieved by using time fencing and related methods. In MTO environments, control of customer orders in the production system is important. Usually, there is much uncertainty concerning future orders, which may require extensive new design and engineering.
What are the components of Demand Management?  Forecasting - In this, a business will forecast the future demands of a particular product or service that they offer. Forecasting is done as per the current trend, projected sales, and analytical data of customer behaviour. It also helps businesses in preparing for any unexpected events in the future.  Supply Planning - Businesses need to be prepared for the future requirements of the supplies. Being well aware of the requirements of customers, competitors’ influence and market trends help businesses in doing efficient supply planning.  Demand Analysis - It is important that your demand forecasting is based upon the current sales data. Proper analysis of order history will help you do the right demand analysis for effective Demand Management.  Sales and Operations Planning - For effective Demand Management, it is essential that you pay attention to the roles, operations, and importance of all the stakeholders in the supply chain system. Based on these reports, you can do effective Sales and Operations Planning to manage the demand requirements.
What is Forecasting?

Forecasting is the process of making predictions of the future based on past and present data and most commonly by analysis of trends. A commonplace example might be estimation of some variable of interest at some specified future date. Prediction is a similar, but more general term. Both might refer to formal statistical methods employing time series, cross-sectional or longitudinal data, or alternatively to less formal judgmental methods. Usage can differ between areas of application: for example, in hydrology the terms "forecast" and "forecasting" are sometimes reserved for estimates of values at certain specific future times, while the term "prediction" is used for more general estimates, such as the number of times floods will occur over a long period. In preparing plans for the future, the management authority has to make some predictions about what is likely to happen in the future. It shows that the managers know something of future happenings even before things actually happen. Forecasting provides them this knowledge. Forecasting is the process of estimating the relevant events of future, based on the analysis of their past and present behavior. The future cannot be probed unless one knows how the events have occurred in the past and how they are occurring presently. The past and present analysis of events provides the base helpful for collecting information about their future occurrence. Thus, forecasting may be defined as the process of assessing the future normally using calculations and projections that take account of the past performance, current trends, and anticipated changes in the foreseeable period ahead. Whenever the managers plan business operations and organizational set-up for the years ahead, they have to take into account the past, the present and the prevailing economic, political and social conditions. Forecasting provides a logical basis for determining in advance the nature of future business operations and the basis for managerial decisions about the material, personnel and other requirements. It is, thus, the basis of planning, when a business enterprise makes an attempt to look into the future in a systematic and concentrated way, it may discover certain aspects of its operations requiring special attention. However, it must be recognized that the process of forecasting involves an element of guesswork and the managers cannot stay satisfied and relaxed after having prepared a forecast. The forecast will have to be constantly monitored and revised—particularly when it relates to a long- term period. The managers should try to reduce the element of guesswork in preparing forecasts by collecting the relevant data using the scientific techniques of analysis and inference. Forecasting is a useful tool for planning. For instance, in sales planning, it helps to estimate and forecast market share of a firm. Firms may find it difficult to project sales of their product. Identifying future sales problems is not easy for companies, small or big. In some cases, it is very difficult to get information about future market sales. Sales forecasting, in such a case, is not just an estimation of sales; it is also matching sales opportunities – actual and potential – with sales planning and procedures.

A forecast requires assessment of two sets of factors: (a) The outside forces which influence business operations, such as the weather, government activity and competitive behaviour. These forces are uncontrollable; (b) The internal marketing methods or practices of the firm that are likely to affect its operations, such as product quality, price, advertising, distribution and service.

increases organizational and managerial efficiency in terms of framing and implementing organizational plans and policies. C. Reduces Risk Though forecasting cannot eliminate risk, it reduces it substantially by estimating the direction in which environmental factors are moving. It helps the organization survive in the uncertain environment by providing clues about what is going to happen in future. If managers know in advance about changes in consumer preferences, they will bring required modifications in their product design in order to meet the changed expectations of the consumers. Thus, forecasting cannot stop the future changes from happening but it can prepare the organizations to face them when they occur or avoid them, if they can. D. Coordination Forecasting involves participation of organizational members of all departments at all levels. It helps in coordinating departmental plans of the organization at all levels. People in all departments at all levels are actively involved in coordinating business operations with likely future changes predicted as a result of forecasting. Thus, forecasting helps in movement of all the plans in the same direction. E. Effective Management By identifying the critical areas of functioning, managers can formulate sound objectives and policies for their organizations. This increases organizational efficiency, effectiveness in achieving the plans, better management and effective goal attainment. F. Development of Executives Forecasting develops the mental, conceptual and analytical abilities of executives to do things in planned, systematic and scientific manner. This helps to develop management executives.

Measures to Increase the effectiveness of Forecasing: Forecasting provides information to facilitate decision-making and planning. In the complex and turbulent environment, forecasts may go wrong and so would the plans based on these forecasts. This may prove hazardous for the company but making plans not based on forecasts is more hazardous. Forecasting is therefore, necessary. Since future may not behave as predicted and deviations may occur, forecasting skills should improve to reduce the range of errors. This amounts to making forecasting effective. The following measures can help in increasing the effectiveness of forecasting: A. Forecasting methods should be simple. Complex methods can confuse data rather than provide meaningful information. B. Compare forecasts with the situation of “no change”. Changes may not always occur and “no change” situation may prove to be accurate many times. C. Long range forecasts should not depend upon a single forecasting method. Several forecasting methods should be adopted and average of their results should be used to make predictions. D. Forecasts should not be made for very long periods. Length of forecasts should be shortened to improve their accuracy. Accuracy of forecasts decreases as the time period of prediction increases.

E. Managerial skill should be improved to make reliable forecasts for planning decisions. Whatever forecasts are made, they should have complete support of the top management to make their implementation effective. F. Forecasts should be based on facts and figures and not personal biases of the forecaster.

Process of Forecasting The following steps usually result in effective forecasting: 1. Determine the objective for which forecast is required: Managers should know the reasons why forecasts are required. If there are rapid changes in the environment, it is necessary to forecast the environmental factors. Past records of the companies provide useful framework to know how effective forecasts have been in the past in making business operations successful. Unless managers are clear of the reasons why forecasts are required to be made, the right choice of technique and also the right forecasts will not be made. Wrong forecasts lead to wrong business decisions, faulty planning and losses for business organizations. 2. Select the appropriate forecast method: Depending upon the objective for which forecast is required, managers select the appropriate forecasting technique. These techniques may be quantitative or qualitative in nature. Based on past and present response of companies to environmental variables, these techniques represent future trend or behavior of business activities. This future behavior is supposed to be the likely outcome of forecasting method adopted. 3. Compare the actual results: Though managers put in the best of efforts to forecast the future operations, the forecasts may still go wrong or the environmental changes may take place other than those predicted. In either case, the results or outcomes of forecasts will be different from those projected. This may require in making new forecasts or changes in plans because of changes in environmental factors. The actual results are, thus, compared with the forecasted results and deviations are detected as soon as possible so that necessary changes can be made in the forecasts or the plans. 4. Review and revise the forecasts: If the actual results happen to be as projected, these forecasts become the basis for future forecasting. If, however, actual results are different from those projected, the forecasts are reviewed and revised to ensure better outcomes in the next forecasting period.
Steps in Forecasting 1. Developing the Basis: The future estimates of various business operations will have to be based on the results obtainable through systematic investigation of the economy, products and industry. 2. Estimation of Future Operations: On the basis of the data collected through systematic investigation into the economy and industry situation, the manager has to prepare quantitative estimates of the future scale of business operations. Here the managers will have to take into account the planning premises.

weights to the individuals and their suggestions based on, for example, their experience, their past success in forecasting and other people’s views of their abilities. The obvious problems associated with this method include constructing an appropriate questionnaire, selecting an appropriate panel of experts and trying to deal with their inherent biases.

Scenario Planning
- One method for dealing with situations of even greater uncertainty is scenario planning. This is usually applied to long-range forecasting, again using a panel. The panel members are usually asked to devise a range of future scenarios. Each scenario can then be discussed, and the inherent risks considered. Unlike the Delphi method, scenario planning is not necessarily concerned with arriving at a consensus but looking at the possible range of options and putting plans in place to try to avoid the ones that are least desired and taking action to follow the most desired.
Sales Force Composite
- Salespeople are a good source of information with regard to customers’ future intentions to buy a product.
Customer Surveys
- By using a customer survey, a firm can base its demand forecast on the customers’ purchasing plans.
What are some methods of Quantitative Forecasting? 1. Time Series Model Simple time series plot a variable over time then, by removing underlying variations with assignable causes, use extrapolation techniques to predict future behavior. The key weakness with this approach is that it simply looks at past behavior to predict the future, ignoring causal variables which are taken into account in other methods such as causal modelling or qualitative techniques. For example, suppose a company is attempting to predict the future sales of a product. The past three years’ sales, quarter by quarter, are shown in Figure S6.3(a). This series of past sales may be analyzed to indicate future sales. For instance, underlying the series might be a linear upward trend in sales. If this is taken out of the data, as in Figure S6.3(b), we are left with a cyclical seasonal variation. The mean deviation of each quarter from the trend line can now be taken out, to give the average seasonality deviation. What remains is the random variation about the trends and seasonality lines, Figure S6.3(c). Future sales may now be predicted as lying within a band about a projection of the trend, plus the seasonality. The width of the band will be a function of the degree of random variation.

A. Forecasting unassignable variations The random variations which remain after taking out trend and seasonal effects are without any known or assignable cause. This does not mean that they do not have a cause, however, just that we do not know what it is. Nevertheless, some attempt can be made to forecast it, if only on the basis that future events will, in some way, be based on past events. We will examine two of the more common approaches to forecasting which are based on projecting forward from past behavior. These are: o Moving- Average The moving-average approach to forecasting takes the previous n periods’ actual demand figures, calculates the average demand over the n periods, and uses this average as a forecast for the next period’s demand. Any data older than the n periods plays no part in the next period’s forecast. The following table shows the weekly demand for Eurospeed, a European-wide parcel delivery company. It measures demand, on a weekly basis, in terms of the number of parcels which it is given to deliver (irrespective of the size of each parcel). Each week, the next week’s demand is forecast by taking the moving average of the previous four weeks’ actual demand. Thus, if the forecast demand for week t is Ft and the actual demand for week t is At, then: For example, the forecast for week 35: o Exponential Smoothing There are two significant drawbacks to the moving-average approach to forecasting. First, in its basic form, it gives equal weight to all the previous n periods which are used in the calculations (although this can be overcome by assigning different weights to each of the n periods). Second, and more important, it does not use data from beyond the n periods over which the moving average is calculated. Both these problems are overcome by exponential smoothing, which is also somewhat easier to calculate. The exponential- smoothing approach forecasts demand in the next period by taking into account the actual demand in the current period and the forecast which was previously made for the current period. It does so according to the formula:

These more complex networks comprise many variables and relationships, each with their own set of assumptions and limitations. While developing such models and assessing the importance of each of the factors and understanding the network of interrelationships is beyond the scope of this text, many techniques are available to help managers undertake this more complex modelling and also feedback data into the model to further refine and develop it, in particular structural equation modelling.

Other Forecasting Methods
1. Straight line method The straight-line method is one of the simplest and easy-to-follow forecasting methods. A financial analyst uses historical figures and trends to predict future revenue growth. In the example provided below, we will look at how straight-line forecasting is done by a retail business that assumes a constant sales growth rate of 4% for the next five years. A. The first step in straight-line forecasting is to find out the sales growth rate that will be used to calculate future revenues. B. To forecast future revenues, take the previous year’s figure and multiply it by the growth rate.
2. Simple Linear Regression Regression analysis is a widely used tool for analyzing the relationship between variables for prediction purposes. In another example of revenue forecasting methods here, we will look at the relationship between radio ads and revenue by running a regression analysis on the two variables. Please refer to the following illustration. Continuing the illustration, we can now plot the given set of data to the model.

Forecast Accuracy Forecast bias – persistent tendency for forecast to be greater or less than the actual values of a time series. Forecast error – difference between the actual value and the value that was predicted for a given period. Forecast error measures: A. Bias – indicates on an average basis, whether the forecast is too high (negative bias indicates over forecast) or too low (positive bias indicates under forecast). The Running Sum of Forecast Errors (RSFE) provides a measure of forecast bias. B. Mean Absolute Deviation (MAD) – indicates on an average basis, how many units the forecast is off from the actual data. Please refer to the given example:

Forecasting Techniques in Business Management: A Comprehensive Guide, Assignments of Management Theory

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FORECASTING