10 Qualitative Forecasting Methods
Sudhanshu Joshi
Learning Objectives:
The Learning objectives of the module are to address the following questions:
1. Understand the role of Qualitative forecasting and its significance in firm level decision making
2. To Identify the types of Qualitative forecast
3. To understand various applications of Qualitative forecast
1. Introduction
Accurate forecasts are very important for the supply chain. Inaccurate forecasts can lead to shortages and excesses throughout the supply chain. Shortages of materials, parts, and services can lead to missed deliveries, work disruption, and poor customer service. Conversely, overly optimistic forecasts can lead to excesses of materials and/or capacity, which increase costs. Both shortages and excesses in the supply chain have a negative impact not only on customer service but also on profits. Furthermore, inaccurate forecasts can result in temporary increases and decreases in orders to the supply chain, which can be misinterpreted by the supply chain.
Organizations can reduce the likelihood of such occurrences in a number of ways. One, obviously, is by striving to develop the best possible forecasts. Another is through collaborative planning and forecasting with major supply chain partners. Yet another way is through information sharing among partners and perhaps increasing supply chain visibility by allowing supply chain partners to have real-time access to sales and inventory information. Also important is rapid communication about poor forecasts as well as about unplanned events that disrupt operations (e.g., flooding, work stoppages), and changes in plans.
2. Essential elements of Good Forecasting:
The essential elements to be covered in to a Good forecasting, includes:
1. The forecast should be timely. Usually, a certain amount of time is needed to respond to the information contained in a forecast. For example, capacity cannot be expanded overnight, nor can inventory levels be changed immediately. Hence, the forecasting horizon must cover the time necessary to implement possible changes.
2. The forecast should be accurate, and the degree of accuracy should be stated. This will enable users to plan for possible errors and will provide a basis for comparing alternative forecasts.
3. The forecast should be reliable; it should work consistently. A technique that sometimes provides a good forecast and sometimes a poor one will leave users with the uneasy feeling that they may get burned every time a new forecast is issued.
4. The forecast should be expressed in meaningful units. Financial planners need to know how many rupees will be needed, production planners need to know how many units will be needed, and schedulers need to know what machines and skills will be required. The choice of units depends on user needs.
5. The forecast should be in writing. Although this will not guarantee that all concerned are using the same information, it will at least increase the likelihood of it. In addition, a written forecast will permit an objective basis for evaluating the forecast once actual results are in.
6. The forecasting technique should be simple to understand and use. Users often lack confidence in forecasts based on sophisticated techniques; they do not understand either the circumstances in which the techniques are appropriate or the limitations of the techniques. Misuse of techniques is an obvious consequence. Not surprisingly, fairly simple forecasting techniques enjoy widespread popularity because users are more comfortable working with them.
7. The forecast should be cost-effective: The benefits should outweigh the costs.
3. Steps in Forecasting Process
There are six basic steps in forecasting process:
1. Determine the purpose of the forecast. How will it be used and when will it be needed? This step will provide an indication of the level of detail required in the forecast, the amount of resources (personnel, computer time, and dollars) that can be justified, and the level of accuracy necessary.
2. Establish a time horizon. The forecast must indicate a time interval, keeping in mind that accuracy decreases as the time horizon increases.
3. Obtain, clean, and analyze appropriate data. Obtaining the data can involve significant effort. Once obtained, the data may need to be “cleaned” to get rid of outliers and obviously incorrect data before analysis.
4. Select a forecasting technique.
5. Make the forecast.
6. Monitor the forecast errors. The forecast errors should be monitored to determine if the forecast is performing in a satisfactory manner. If it is not, reexamine the method, assumptions, and validity of data, and so on; modify as needed; and prepare a revised forecast.
4. Forecasting accuracy
Accuracy and control of forecasts is a vital aspect of forecasting, so forecasters want to minimize forecast errors. However, the complex nature of most real-world variables makes it almost impossible to correctly predict future values of those variables on a regular basis. Moreover, because random variation is always present, there will always be some residual error, even if all other factors have been accounted for. Consequently, it is important to include an indication of the extent to which the forecast might deviate from the value of the variable that actually occurs. This will provide the forecast user with a better perspective on how far off a forecast might be.
Decision makers will want to include accuracy as a factor when choosing among different techniques, along with cost. Accurate forecasts are necessary for the success of daily activities of every business organization. Forecasts are the basis for an organization’s schedules, and unless the forecasts are accurate, schedules will be generated that may provide for too few or too many resources, too little or too much output, the wrong output, or the wrong timing of output, all of which can lead to additional costs, dissatisfied customers, and headaches for managers.
Forecast accuracy is a significant factor when deciding among forecasting alternatives. Accuracy is based on the historical error performance of a forecast.
Three commonly used measures for summarizing historical errors are the mean absolute deviation (MAD) , the mean squared error (MSE) , and the mean absolute percent error (MAPE) . MAD is the average absolute error, MSE is the average of squared errors, and MAPE is the average absolute percent error. The formulas used to compute MAD, 1 MSE, and MAPE are as follows:
Example: Compute MAD, MSE, and MAPE for the following data, showing actual and forecasted numbers of accounts serviced.
| Period | Actual | Forecast | (A-F) Error | |Error| | Error2 | [|Error|/Actual] X 100 |
| 1 | 217 | 215 | 2 | 2 | 4 | .92% |
| 2 | 213 | 216 | -3 | 3 | 9 | 1.41 |
| 3 | 216 | 215 | 1 | 1 | 1 | .46 |
| 4 | 210 | 214 | -4 | 4 | 16 | 1.90 |
| 5 | 213 | 211 | 2 | 2 | 4 | .94 |
| 6 | 219 | 214 | 5 | 5 | 25 | 2.28 |
| 7 | 216 | 217 | -1 | 1 | 1 | .46 |
| 8 | 212 | 216 | -4 | 4 | 16 | 1.89 |
| -2 | 22 | 76 | 10.26% |
Using the figures available in the table
MAD= 22/8= 2.75
MSE= 76/ (8-1) = 10.86
MAPE= 10.26%/8= 1.28%
From a computational standpoint, the difference between these measures is that MAD weights all errors evenly, MSE weights error according to their squared value, and MAPE weights according to relative error.
5. Role of Forecasting in Operations Management
Forecasting is the basis of all planning decision in operation planning and management Used for both push and pull processes.
a) Production scheduling, inventory, aggregate planning
b) Sales force allocation, promotions, new production introduction
c) Plant/equipment investment, budgetary planning
d) Workforce planning, hiring, layoffs. All of these decisions are interrelated
6. Characteristic of Forecasting
a) Forecasts are always inaccurate and should thus include both the expected value of the forecast and a measure of forecast error
b) Long-term forecasts are usually less accurate than short-term forecasts
c) Aggregate forecasts are usually more accurate than disaggregate forecasts
In general, the farther up the operations of a company is, the greater is the distortion of information it receives
7. Approaches of Forecasting
There are two general approaches to forecasting: qualitative and quantitative. Qualitative methods consist mainly of subjective inputs, which often defy precise numerical description. Quantitative methods involve either the projection of historical data or the development of associative models that attempt to utilize causal (explanatory) variables to make a forecast. Qualitative techniques permit inclusion of soft information (e.g., human factors, personal opinions, hunches) in the forecasting process. Those factors are often omitted or downplayed when quantitative techniques are used because they are difficult or impossible to quantify. Quantitative techniques consist mainly of analyzing objective, or hard, data. They usually avoid personal biases that sometimes contaminate qualitative methods. In practice, either approach or a combination of both approaches might be used to develop a forecast.
8. Components and Methods of Forecasting
Companies must identify the factors that influence future demand and then ascertain the relationship between these factors and future demand
a) Past demand
b) Lead time of product replenishment
c) Planned advertising or marketing efforts
d) Planned price discounts
e) State of the economy
f) Actions that competitors have taken
9. Variety of Forecasting Techniques
a) Variety of forecasting techniques that is classified as judgmental, time-series, or associative.
b) Judgmental forecasts rely on analysis of subjective inputs obtained from various sources, such as consumer surveys, the sales staff, managers and executives, and panels of experts. Quite frequently, these sources provide insights that are not otherwise available.
c) Time-series forecasts simply attempt to project past experience into the future. These techniques use historical data with the assumption that the future will be like the past. Some models merely attempt to smooth out random variations in historical data; others attempt to identify specific patterns in the data and project or extrapolate those patterns into the future, without trying to identify causes of the patterns.
d) Associative models use equations that consist of one or more explanatory variables that can be used to predict demand. For example, demand for paint might be related to variables such as the price per gallon and the amount spent on advertising, as well as to specific characteristics of the paint (e.g., drying time, ease of cleanup).
10. Qualitative forecasts
In some situations, forecasters rely solely on judgment and opinion to make forecasts. If management must have a forecast quickly, there may not be enough time to gather and analyze quantitative data. At other times, especially when political and economic conditions are changing, available data may be obsolete and more up-to-date information might not yet be available. Similarly, the introduction of new products and the redesign of existing products or packaging suffer from the absence of historical data that would be useful in forecasting. In such instances, forecasts are based on executive opinions, consumer surveys, opinions of the sales staff, and opinions of experts.
Executive Opinions: A small group of upper-level managers (e.g., in marketing, operations, and finance) may meet and collectively develop a forecast. This approach is often used as a part of long-range planning and new product development. It has the advantage of bringing together the considerable knowledge and talents of various managers. However, there is the risk that the view of one person will prevail, and the possibility that diffusing responsibility for the forecast over the entire group may result in less pressure to produce a good forecast. Sales force Opinions: Members of the sales staff or the customer service staff are often good sources of information because of their direct contact with consumers. They are often aware of any plans the customers may be considering for the future. There are, however, several drawbacks to using sales force opinions. One is that staff members may be unable to distinguish between what customers would like to do and what they actually will do. Another is that these people are sometimes overly influenced by recent experiences. Thus, after several periods of low sales, their estimates may tend to become pessimistic. After several periods of good sales, they may tend to be too optimistic. In addition, if forecasts are used to establish sales quotas, there will be a conflict of interest because it is to the salesperson’s advantage to provide low sales estimates.
Consumer Surveys: Because it is the consumers who ultimately determine demand, it seems natural to solicit input from them. In some instances, every customer or potential customer can be contacted. However, usually there are too many customers or there is no way to identify all potential customers. Therefore, organizations seeking consumer input usually resort to consumer surveys, which enable them to sample consumer opinions. The obvious advantage of consumer surveys is that they can tap information that might not be available elsewhere. On the other hand, a considerable amount of knowledge and skill is required to construct a survey, administer it, and correctly interpret the results for valid information. Surveys can be expensive and time-consuming. In addition, even under the best conditions, surveys of the general public must contend with the possibility of irrational behavior patterns. For example, much of the consumer’s thoughtful information gathering before purchasing a new car is often undermined by the glitter of a new car showroom or a high-pressure sales pitch. Along the same lines, low response rates to a mail survey should— but often don’t—make the results suspect. If these and similar pitfalls can be avoided, surveys can produce useful information.
Other Approaches: A manager may solicit opinions from a number of other managers and staff people. Occasionally, outside experts are needed to help with a forecast. Advice may be needed on political or economic conditions in the United States or a foreign country, or some other aspect of importance with which an organization lacks familiarity. Another approach is the Delphi method , an iterative process intended to achieve a consensus forecast. This method involves circulating a series of questionnaires among individuals who possess the knowledge and ability to contribute meaningfully. Responses are kept anonymous, which tends to encourage honest responses and reduces the risk that one person’s opinion will prevail. Each new questionnaire is developed using the information extracted from the previous one, thus enlarging the scope of information on which participants can base their judgments.
The Delphi method has been applied to a variety of situations, not all of which involve forecasting. The discussion here is limited to its use as a forecasting tool. As a forecasting tool, the Delphi method is useful for technological forecasting, that is, for assessing changes in technology and their impact on an organization. Often the goal is to predict when a certain event will occur. For instance, the goal of a Delphi forecast might be to predict when video telephones might be installed in at least 50 percent of residential homes or when a vaccine for a disease might be developed and ready for mass distribution. For the most part, these are long-term, single-time forecasts, which usually have very little hard information to go by or data that are costly to obtain, so the problem does not lend itself to analytical techniques. Rather, judgments of experts or others who possess sufficient knowledge to make predictions are used.
11. Forecast based on Time series
A time series is a time-ordered sequence of observations taken at regular intervals (e.g., hourly, daily, weekly, monthly, quarterly, annually). The data may be measurements of demand, earnings, profits, shipments, accidents, output, precipitation, productivity, or the consumer price index. Forecasting techniques based on time-series data are made on the assumption that future values of the series can be estimated from past values. Although no attempt is made to identify variables that influence the series, these methods are widely used, often with quite satisfactory results.
Analysis of time-series data requires the analyst to identify the underlying behaviour of the series. This can often be accomplished by merely plotting the data and visually examining the plot. One or more patterns might appear: trends, seasonal variations, cycles, or variations around an average. In addition, there will be random and perhaps irregular variations. These behaviours can be described as follows:
a. Trend refers to a long-term upward or downward movement in the data. Population shifts, changing incomes, and cultural changes often account for such movements.
b. Seasonality refers to short-term, fairly regular variations generally related to factors such as the calendar or time of day. Restaurants, supermarkets, and theatres experience weekly and even daily “seasonal” variations.
c. Cycles are wave like variations of more than one year’s duration. These are often related to a variety of economic, political, and even agricultural conditions.
d. Irregular variations are due to unusual circumstances such as severe weather conditions, strikes, or a major change in a product or service. They do not reflect typical behavior, and their inclusion in the series can distort the overall picture. Whenever possible, these should be identified and removed from the data.
e. Random variations are residual variations that remain after all other behaviors have been accounted for.
12. Other Forecasting Methods
a) Focus Forecasting. Some companies use forecasts based on a “best recent performance” basis. This approach, called focus forecasting, was developed by Bernard T. Smith, and is described in several of his books. 2 It involves the use of several forecasting methods (e.g., moving average, weighted average, and exponential smoothing) all being applied to the last few months of historical data after any irregular variations have been removed. The method that has the highest accuracy is then used to make the forecast for the next month. This process is used for each product or service, and is repeated monthly.
b) Diffusion Models. When new products or services are introduced, historical data are not generally available on which to base forecasts. Instead, predictions are based on rates of product adoption and usage spread from other established products, using mathematical diffusion models. These models take into account such factors as market potential, attention from mass media, and word of mouth. Although the details are beyond the scope of this text, it is important to point out that diffusion models are widely used in marketing and to assess the merits of investing in new technologies
13. Trend-Adjusted Exponential Smoothing
A variation of simple exponential smoothing can be used when a time series exhibits a linear trend. It is called trend-adjusted exponential smoothing or, sometimes, double smoothing, to differentiate it from simple exponential smoothing, which is appropriate only when data vary around an average or have step or gradual changes. If a series exhibits trend, and simple smoothing is used on it, the forecasts will all lag the trend: If the data are increasing, each forecast will be too low; if decreasing, each forecast will be too high. The trend-adjusted forecast (TAF) is composed of two elements: a smoothed error and a trend factor.
14. Techniques of seasonality
Seasonal variations in time-series data are regularly repeating upward or downward movements in series values that can be tied to recurring events. Seasonality may refer to regular annual variations. Familiar examples of seasonality are weather variations (e.g., sales of winter and summer sports equipment) and vacations or holidays (e.g., airline travel, greeting card sales, visitors at tourist and resort centers). The term seasonal variation is also applied to daily, weekly, monthly, and other regularly recurring patterns in data. For example, rush hour traffic occurs twice a day—incoming in the morning and outgoing in the late afternoon. Theaters and restaurants often experience weekly demand patterns, with demand higher later in the week. Banks may experience daily seasonal variations (heavier traffic during the noon hour and just before closing), weekly variations (heavier toward the end of the week), and monthly variations (heaviest around the beginning of the month because of Social Security, payroll, and welfare checks being cashed or deposited). Mail volume; sales of toys, beer, automobiles, and turkeys; highway usage; hotel registrations; and gardening also exhibit seasonal variations.
Seasonality in a time series is expressed in terms of the amount that actual values deviate from the average value of a series. If the series tends to vary around an average value, then seasonality is expressed in terms of that average (or a moving average); if trend is present, seasonality is expressed in terms of the trend value.
15. Choosing a Forecasting Technique
The two most important factors are cost and accuracy. How much money is budgeted for generating the forecast? What are the possible costs of errors, and what are the benefits that might accrue from an accurate forecast? Table 1-1 exhibit various parameters to be considered while selecting a forecasting technique
Table 1-1: Choosing a forecasting technique
| Forecasting
Method |
Amount of Historical
Data |
Data Pattern | Forecast Horizon | Preparation Time | Personnel
Background |
| Moving Average | 2 to 30 observations | Variation around an average | Short | Short | Little sophistication |
| Simple exponential smoothing | 5 to 10 observations | Variation around an average | Short | Short | Little sophistication |
| Trend-Adjustment exponential smoothing | 10 to 15 observations | Trend | Short to medium | Short | Moderate sophistication |
| Trend Models | 10 to 20 seasonality at least 5 per season | Trend | Short to medium | Short | Moderate
sophistication |
| Seasonal | Enough to see 2 peaks and troughs | Handles cyclical and seasonal patterns | Short to medium | Short to medium | Little sophistication |
| Causal
regression Models |
10 observations per independent variable | Can handle complex pattern | Short, medium or long | Long development
time, short |
Considerable
sophistication |
There is a need to also understand forecasting factors, by range of forecast
| Factor | Short Range | Intermediate range | Long Range |
| 1. Frequency | Often | Occasional | Infrequent |
| 2. Level of aggregation | Item | Product family | Total output
Types of Product/Service |
| 3. Type of Model | Smoothing
Projection Regression |
Projection
Seasonal Regression |
Managerial Judgment |
| 4. Degree of Management
involvement |
Low | Moderate | High |
| 5. Cost per forecast | Low | Moderate | High |
Discussion
A manager can take a reactive or a proactive approach to a forecast. A reactive approach views forecasts as probable future demand, and a manager reacts to meet that demand (e.g., adjusts production rates, inventories, the workforce). Conversely, a proactive approach seeks to actively influence demand (e.g., by means of advertising, pricing, or product/service changes).
Generally speaking, a proactive approach requires either an explanatory model (e.g., regression) or a subjective assessment of the influence on demand. A manager might make two forecasts: one to predict what will happen under the status quo and a second one based on a “what if” approach, if the results of the status quo forecast are unacceptable.
Suggested Readings:
a) Moon(2013): Demand and Supply Integration: The Key to World-Class Demand Forecasting. FT Press Operations Management.
b) Anderson (2013): Operations Management For Dummies. For Dummies
c) Makridakis(1998): Forecasting: Methods and Applications. John Wiley & Sons.
d) Munson (2011): The Supply Chain Management Casebook: Comprehensive Coverage and Best Practices in SCM. FT Press Operations Management
e) Drake (2015): Advances in Business, Operations, and Product Analytics: Cutting Edge Cases from Finance to Manufacturing to Healthcare. Pearson FT Press.