Careers: Business/Managerial Economics
Dear Sir request you to give the answer for the below qestions
Q-1) Explain the nature and significance of ‘Managerial Economics’. How is it
related to Macro Economics?
What is ‘Demand Forecasting’? What are its objective and types?
Q-2) Explain fully the concept of ‘Price Elasticity of Demand’.
Q-3) Explain the terms T.C., A.C. and M.C. with examples. Why does the long run
A.C. curve is saucer shaped?
Q-4) Give the classification of market on the basis of degree of competition.
Q-5) What is ‘Cost Benefit Analysis’? Describe the steps involved in it and its
Q-6) a) How does Government control monopoly?
b) What are the advantage and disadvantages of Economic Liberalization?
Q-7) Write notes on any Two:
a) Dynamic Theory of Profit.
b) Private Vs. Public Goods.
c) Exceptions to the ‘Law of Supply’.
d) Difficulties in the National Income Estimate.
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HERE IS SOME SOME USEFUL MATERIAL.
SOME ANSWERS HELD BACK DUE TO SPACE CONSTRAINT.
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Q-1) Explain the nature and significance of ‘Managerial Economics’. How is it
related to Macro Economics?
TRADITIONAL ECONOMICS IS
The social science that deals with the production, distribution, and consumption of goods and services and with the theory and management of economies or economic systems.
The foundations of TRADITIONAL economics and all the social sciences
has multiple fundamental flaws which includes an intrinsic inconsistency in utility theory.
In game theory undefined sums are used to define basic concepts, the characteristic function is ill-defined, and there are other fundamental errors.
In measurement theory the models of even the most elementary variables such as length and mass are incorrect, and in decision theory even what is being measured is not understood.
These errors must be removed explained and their correction outlined.
Correcting these theories will lead to better decisions by individuals, organizations, affecting everyday lives of people throughout the world.
IN BUSINESS, MANAGEMENT DECISION MAKING
NEEDS Steps of Structured Decision Making:
• Problem definition. What specific decision has to be made? What are the spatial and temporal scope of the decision? Will the decision be iterated over time?
• Objectives. What are the management objectives? Ideally, these are stated in
quantitative terms that relate to metrics that can be measured. Setting objectives falls in the realm of policy, an d should be informed by legal and regulatory mandates, as well as stakeholder viewpoints. A number of methods for stakeholder elicitation and conflict resolution are appropriate for clarifying objectives.
• Alternatives. What are the different management actions to choose from? This element requires explicit articulation of the alternatives available to the decision maker. The range of permissible options is often constrained by legal or political considerations, but structured assessment may lead to creative new alternatives.
• Consequences. What are the consequences of different management actions? How much of the objectives would each alternative achieve? In structured decision-making, we predict the consequences of the alternative actions with some type of model Depending on the information available or the quantification desired for a structured decision process consequences may be modeled with highly scientific computer applications or with personal judgment elicited carefully and transparently. Ideally, models are quantitative,
but they need not be; the important thing is that they link actions to consequences.
• Tradeoffs. If there are multiple objectives, how do they trade off with each other? In most complex decisions, the best we can do is choose intelligently between less-than- perfect alternatives. Numerous tools are available to help determine the relative importance or weights among conflicting objectives and to then compare alternatives across multiple attributes to find the ‘best’ compromise solutions.
• Uncertainty. Because we rarely know precisely how management actions will affect natural systems, decisions are frequently made in the face of uncertainty. Uncertainty makes choosing among alternative far more difficult. A good decision-making process will confront uncertainty explicitly, and evaluate the likelihood of different outcomes and their possible consequences.
• Risk Tolerance. Identifying the uncertainty that impedes decision-making, then
analyzing the risk that uncertainty presents to management is an important step in making a good decision. Understanding the level of risk a decision-maker is willing to accept, or the risk response determined by law or policy, will make the decision-making process more objectives-driven, transparent, and defensible.
• Linked decisions. Many important decisions are linked over time. The key to dealing effectively with linked decisions is to isolate and resolve the near-term issues while sequencing the collection of information needed for future decisions
TO MAKE SUCH STRUCTURED DECISIONS WITH
THE HELP OF ''TRADITIONAL ECONOMICS'' IS NOT
HENCE, HERE WE MAKE USE OF THE MANAGERIAL
Managerial economics (also called business economics), is a branch of economics that applies microeconomic analysis to specific business decisions. As such, it bridges economic theory and economics in practice. It draws heavily from quantitative techniques such as regression analysis and correlation, Lagrangian calculus (linear). If there is a unifying theme that runs through most of managerial economics it is the attempt to optimize business decisions given the firm's objectives and given constraints imposed by scarcity, for example through the use of operations research and programming.
“Managerial economics is .. the application of economic principles and methodologies to the decision-making process within the firm or organization.”
“Managerial economics applies economic theory and methods to business and administrative decision-making.”
“Managerial economics refers to the application of economic theory and the tools of analysis of decision science to examine how an organisation can achieve its objectives most effectively.”
“It is the application of economic analysis to business problems; it has its origin in theoretical microeconomics.”
MANAGERIAL MODEL M BUILDING
• The steps:the hypothetical-deductive approach
• make assumptions about behaviour
• work out the consequences of those assumptions
• make predictions
• test the predictions against the evidence
• PREDICTIONS SUPPORTED? The model is accepted as a good explanation (for the moment)
• PREDICTIONS REFUTED? Go back and re-work the whole process
Should Assumptions be Realistic?
The assumption of profit-maximising may be unrealistic or inaccurate
However, what matters is the explanatory or predictive power of a theory (or model), not the descriptive realism of its assumptions.
A model built on unrealistic assumptions may give good predictions.
Assumptions are a necessary simplifying device
What Is A “Good” Model?
• It allows us to make predictions and set hypotheses
• The predictions can be tested against the empirical evidence
• The predictions are supported by the empirical evidence
• Economics in general takes a ‘positive’ and predictive approach not prescriptive or ‘normative’
• trying to explain “what is” not what “should be”
• the main objective is to understand how a market economy works
• Not very concerned about the descriptive realism of assumptions: “I assume X” does not mean “I believe X to be true”
• Some real tension if the models are used for prescription
• assume “perfect knowledge”: OK for model-building
• cannot say to a manager: “behave AS IF you had perfect knowledge”
• Comparative Statics
• begin with an initial equilibrium position - the starting point
• change something
• identify the new equilibrium, e.g:in neo-classical model of the firm
• When demand increases?
• When costs rise?
• When a fixed cost increases?
• This is the main purpose of the model -what it was designed to do
• Normative prescriptions
• it will cost me $30 per unit to supply something which will give me $20 per unit in revenue- should I do it?
• I must pay $20 billion to set up in my industry. Should I charge higher prices to get that money back?
• Positive and Normative are linked by “if?” IF the aim of the firm is to maximise profit what will it do/what should it do?
• What is the purpose of MANAGEERIAL economic analysis?
Why do we want to apply MANAGERIAL economic analysis to business problems?
For the businessperson: “to assist decision-making”, to provide decision-rules which can be applied The “normative” approach to theory: What should be?
These purposes are different, they can lead to misunderstanding, and economists are not always honest about the limitations of their approach for practical purposes.
What are these limitations?
If the aim is prediction, unrealistic assumptions are acceptable and may be needed;
for instance, the firm may be assumed to behave “as if” its managers had perfect knowledge of its environment
If the aim is to produce decision-rules which can be applied by practising managers, unrealistic assumptions will produce decision-rules which are not operational
for instance, set output and price by MC=MR
How Can Managerial Economics Assist Decision-Making?
1. Adopt a general perspective, not a sample of one
2. Simple models provide stepping stone to more complexity and realism
3. Thinking logically has value itself and can expose sloppy thinking
Why Managerial Economics?
• A powerful “analytical engine”.
• A broader perspective on the firm.
• what is a firm?
• what are the firm’s overall objectives?
• what pressures drive the firm towards profit and away from profit
• The basis for some of the more rigourous analysis of issues in Marketing and Strategic Management.
• The Structure-Conduct-Performance Paradigm:
Basic Conditions: factors which shape the market of the industry, e.g. demand, supply, political factors
Structure: attributes which give definition to the supply-side of the market, e.g. economies of scale, barriers to entry, industry concentration, product differentiation, vertical integration.
Conduct: the behavior of firms in the market, e.g. pricing behavior advertising, innovation.
Performance: a judgement about the results of market behaviour, e.g. efficiency, profitability, fairness/income distribution, economic growth.
. “A close relationship between management and managerial economics exists’’.
BUSINESS MANAGEMENT DECISIONS ARE MADE ON THE BASIS
OF THE STRATEGIC PLANNING.
The STRATEGIC PLANNING is THE SEED that
-opportunity to determine the environmental impact on the
organization / business.
-opportunity to assess the organization's strengths/ weaknesses.
-opportunity to determine the business opportunities/ threats
-opportunity to develop strategic plans for the company.
-opportunity to develop long term/short term plans.
-opportunity to develop a vision for the organization.
-opportunity to develop a mission statement for the organization.
-opportunity to develop business objectives for the organization.
-opportunity to develop business strategies for the organization.
-opportunity to develop the action/ implementation planning
guidelines, which provides the platform for
*helps to set up and develop organization and staffing.
*helps to set direction for the organization approach.
*helps to select the right leadership
*helps to select / set the most appropriate control.
STRATEGIC PLANNING IN BUSINESS IS THE PREMIER FUNCTION,
without this seed,
-you cannot organize your business
-without business organization , you cannot direct
-without direction, you cannot control.
-without control , you cannot get results.
For success/ results in business, you need STRATEGIC PLANNING.
HENCE STRATEGIC PLANNING IS THE PRIMARY seed
OF ANY BUSINESS ORGANIZATION [ SMALL OR LARGE ]
The process of strategic planning has become essential for the BUSINESS organization interested in obtaining significant results. It matters little whether the organization is large or small or whether it is in the private sector or government service. When an organization has the need to move into the future with a high degree of confidence in what that future holds, it needs strategic planning. The integrated approach of deciding on a set of long-range goals and then developing the objectives and plans to reach them is the most reliable tool that the organization can use to define its own future and ensure success. In other words, it is the surest way that the management team can become "system makers"––people who are willing to take the time to make things happen instead of responding only when their buttons are pushed.6
THE STRATEGIC PLANNING --the seed -- helps you tie those opportunities to plan and optimize at a high level
over the long term. You can set overall objectives for capital utilization for capital intensive
equipment, inventory and materials (direct and indirect), and labor.
With STRATEGIC PLANNING SYSTEM , you can:
• Drive tactical and operational plans based on STRATEGIC vision and direction
• Optimize asset utilization including capacity and materials
• Support growth by identifying and proactively removing constraints
• Reduce risk by evaluating alternatives and outcomes before deciding
• Simplify make/buy decisions.
THE STRATEGIC PLANNING SYSTEM
integrates the necessary competitive analyses, peer comparisons, and industry averages
that give STRATEGIC Planning the proper context. You see the entire set of
business opportunities and tie the financial analytics to the business issues, activities,
and processes that drive them. The result: a well aligned organization thats positioned
for long-term success.
¥ React faster to market changes
¥ Measure and compare your supply chain performance against competition
¥ Adjust your strategic plan frequently
¥ Analyze the ramifications of M&A opportunities
¥ Avoid excess warehouse capacity and unused equipment
¥ Perform long-range planning and analysis to determine the impact of simultaneous
business decision combinations
¥ Confidently optimize your supply chain network
THE STRATEGIC PLANNING SYSTEM delivers solutions that synchronize corporate planning with operations planning and
execution on a local and enterprise level, to ensure all assets are utilized to achieve strategic
objectives. This enables manufacturers to reduce the cost of goods sold, shorten lead-times for
orders and reduce inventory costs with improved supply chain collaboration and management.
Real Solutions...Measurable Results
. Key elements of the strategic planning process.
1. External Assessment OF THE ECONOMY
Areas for opportunities and threats IN THE ECONOMY
* Markets [ what is the market situation, which is forcing the change requirements
*Customers [ how can service the customer -internal / external -better .
* Industry [ is the industry trend ]
* Competition [ is it the competitive situation
*Factors of business [ causing the change]
* Technology [ is it technology change ]
PLANNING --ENVIRONMENTAL STRATEGY INTERFACE
AS THE BUSINESS EXPANDED, THE OPERATION WAS
AFFECTED BY VARIOUS ENVIRONMENTAL FACTORS
AND HENCE WERE INCORPORATED INTO THE PLANNING.
Political (incl. Legal) [ [Poltical] EST[Environment][Legal] ]
-Environmental regulations and protection
[what are the government regualtions/ protection laws that must be observed ]
what tax hinder the business and what taxes incentives are available]
-International trade regulations and restrictions
[ does the government encourage exports / with high tariffs on imports]
-Contract enforcement law/Consumer protection
[does the government enforce on consumer protection ]
[ is the government encouraging skilled immigrants with temp. permits]
-Government organization / attitude
[ does the government have a very positive attitude towards this industry]
[ are there regulation for limiting competition]
[ politically , does the government have a very stable government ]
[ has the government adopted some of the modern safety regulations]
Economic [P[Economics][Social]TEL ]
[ what is the economic growth rate / what are the reasons ]
-Interest rates & monetary policies
[ are the interest rates under control / is there a sound monetary policies]
[is government spending is significant and is it under control ]
[what is the employment / unemployment policies of the government ]
[ has the taxation encouraged the industry ]
[ is there well managed exchange controls and is it helping the industry]
[ is the inflation well under control ]
-Stage of the business cycle
[ is your industry is on the growth pattern]
[ is the consumer confidence is high/ strong and if not, why ]
Social [ PE[Social]TEL ]
[is there balanced income distribution policy ]
-Demographics, Population growth rates, Age distribution
[ what is population growth and why ]
-Labor / social mobility
[ what are the labor policies and is there labor mobility]
[ are there significant lifestyle changes taking place--more modernization/ why ]
-Work/career and leisure attitudes
[ are the population career minded and are seeking better lifestyle]
[ what are the education policies / is it successful ]
[are the people becoming fashion conscious ]
-Health consciousness & welfare, feelings on safety
[ are the people becoming health consciousness]
[ is the living conditions improving fast and spreading rapidly]
Technological [ PES [Technology] EL]
Government research spending
[is the government spending on research and development]
Industry focus on technological effort
[are the industries focused on using improved technology]
New inventions and development
[ are new inventions being encouraged for developments]
Rate of technology transfer
[ is the rate of technology transfer is speeding up ]
(Changes in) Information Technology
[ is the information technology rapidly moving and is there government support]
(Changes in) Internet
[ is the internet usage rapidly increasing and why]
(Changes in) Mobile Technology
[is the Mobile technology rapidly developing and is there government support]
BASED ON THE EXTERNAL ECONOMIC ANALYSIS
WE CONDUCT THE FOLLOWING
2. Internal Assessment
Areas for strengths, weaknesses, and barriers to success
*Culture [ is the working culture change ]
* Organization [ is the organization demanding change ]
* Systems [ is it the systems change ]
* Management practices [ change in managemement process]
OTHER KEY DIMENSIONS
*Cost efficiency[ is it for cost efficiency ]
* Financial performance [ is it for financial performance improvement ]
* Quality [ is it for quality performance improvement
*Service [ is it for service performance improvement
*Technology[ is it for technology performance improvement
* Market segments [ is it for sales performance improvement
* Innovation[ is it for performance improvement
*new products[ is it for new product performance improvement
*Asset condition[ is it for financial performance improvement
*productivity[ is it for financial performance improvement
3. Source Strategic objectives and programs
The critical issues that must be addressed if the organization
Is to succeed
FROM THE ABOVE , DETERMINE THE CORE ISSUES
WHICH NEEDS TO SOLVED WITH YOUR INVESTMENT.
FROM THE ABOVE CORE ISSUES , DETERMINE YOUR
Your CORE PURPOSE
Your CORE OBJECTIVES
Your Core markets;
Your CORE strategic thrusts.
The arena of products, services, customers, technologies, distribution methods, and geography in which you'll compete to get results.
Desired attitudes and behavior toward internal and external stakeholders that
will yield the culture and business results you want and that you will execute and turn into
levels and tiers of strategies
OVERALL FINANCIAL POSTURE
Grow; hold; milk; get out
PRIORITIES AND POSTURES
(Grow; hold; milk)
Market; business unit; product/services
Cost /Value/ differentiation
Target customer Geography
Distribution Product design
People/skills / Facilities
Organizational / Product
structure / development
Management style /Incentives/rewards
technology / Systems
R&D / Service
FINANCING / Quality
Strategy Statement Content
v Priorities and Posture
Strategic thrust/competitive advantage
Internal strategic thrust
8. Strategic Program Content
KEY STEPS: who, what, when
FINANCIAL AND STRATEGIC
GAIN AND COST
PEOPLE: numbers and skills
People and organizational units outside your control who must contribute
LEVERAGE: the high leverage individuals and units who must contribute at lower levels
QUARTERLY: Programs and strategic numbers' progress
REWARDS AND CONSEQUENCES: Based on strategic performance of teams and individuals
SO NOW, WE CAN SEE HOW THE MICRO SPECIFIC BUSINESS MANAGEMENT DECISIONS ARE AFFECTED BY
THE GENERAL ECONOMIC FACTORS.
NOW WITH THIS ANALYSIS AND INFORMATION, THE MANAGER DEVELOPS
-targets for various functional responsibilities
Like manufacturing /marketing/sales etc
-then allocates resources as per the respective objectives.
What is ‘Demand Forecasting’? What are its objective and types?
The Importance of Demand Forecasting
Forecasting product demand is crucial to any supplier, manufacturer, or retailer. Forecasts of future demand will determine the quantities that should be purchased, produced, and shipped. 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. Thus, once a customer order materializes, it can be fulfilled immediately – since most customers are not willing to wait the time it would take to actually process their order throughout the supply chain and make the product based on their order. An order cycle could take weeks or months to go back through part suppliers and sub-assemblers, through manufacture of the product, and through to the eventual shipment of the order to the customer.
Firms that offer rapid delivery to their customers will tend to force all competitors in the market to keep finished good inventories in order to provide fast order cycle times. As a result, virtually every organization involved needs to manufacture or at least order parts based on a forecast of future demand. The ability to accurately forecast demand also affords the firm opportunities to control costs through leveling its production quantities, rationalizing its transportation, and generally planning for efficient logistics operations.
In general practice, accurate demand forecasts lead to efficient operations and high levels of customer service, while inaccurate forecasts will inevitably lead to inefficient, high cost operations and/or poor levels of customer service. In many supply chains, the most important action we can take to improve the efficiency and effectiveness of the logistics process is to improve the quality of the demand forecasts.
Forecasting Demand in a Logistics System
Logistics professionals are typically interested in where and when customer demand will materialize. Consider a retailer selling through five superstores in Boston, New York, Detroit, Miami, and Chicago. It is not sufficient to know that the total demand will be 5,000 units per month, or, say, 1,000 units per month per store, on the average. Rather it is important to know, for example, how much the Boston store will sell in a specific month, since specific stores must be supplied with goods at specific times. The requirement might be to forecast the monthly demand for an item at the Boston superstore for the first three months of the next year. Using available historical data, without any further analysis, the best guess of monthly demand in the coming months would probably
be the average monthly sales over the last few years. The analytic challenge is to come up with a better forecast than this simple average.
Since the logistics system must satisfy specific demand, in other words what is needed, where and when, accurate forecasts must be generated at the Stock Keeping Unit (SKU) level, by stocking location, and by time period. Thus, the logistics information system must often generate thousands of individual forecasts each week. This suggests that useful forecasting procedures must be fairly "automatic"; that is, the forecasting method should operate without constant manual intervention or analyst input.
Forecasting is a problem that arises in many economic and managerial contexts, and hundreds of forecasting procedures have been developed over the years, for many different purposes, both in and outside of business enterprises. The procedures that we will discuss have proven to be very applicable to the task of forecasting product demand in a logistics system. Other techniques, which can be quite useful for other forecasting problems, have shown themselves to be inappropriate or inadequate to the task of demand forecasting in logistics systems. In many large firms, several organizations are involved in generating forecasts. The marketing department, for example, will generate high-level long-term forecasts of market demand and market share of product families for planning purposes. Marketing will also often develop short-term forecasts to help set sales targets or quotas. There is frequently strong organizational pressure on the logistics group to simply use these forecasts, rather than generating additional demand forecasts within the logistics system. After all, the logic seems to go, these marketing forecasts cost money to develop, and who is in a better position than marketing to assess future demand, and "shouldn’t we all be working with the same game plan anyway…?"
In practice, however, most firms have found that the planning and operation of an effective logistics system requires the use of accurate, disaggregated demand forecasts. The manufacturing organization may need a forecast of total product demand by week, and the marketing organization may need to know what the demand may be by region of the country and by quarter. The logistics organization needs to store specific SKUs in specific warehouses and to ship them on particular days to specific stores. Thus the logistics system, in contrast, must often generate weekly, or even daily, forecasts at the SKU level of detail for each of hundreds of individual stocking locations, and in most firms, these are generated nowhere else.
An important issue for all forecasts is the "horizon;" that is, how far into the future must the forecast project? As a general rule, the farther into the future we look, the more clouded our vision becomes -- long range forecasts will be less accurate that short range forecasts. The answer depends on what the forecast is used for. For planning new manufacturing facilities, for example, we may need to forecast demand many years into the future since the facility will serve the firm for many years. On the other hand, these forecasts can be fairly aggregate since they need not be SKU-specific or broken out by stockage location. For purposes of operating the logistics system, the forecasting horizon need be no longer than the cycle time for the product. For example, a given logistics system might be able to routinely purchase raw materials, ship them to manufacturing
locations, generate finished goods, and then ship the product to its field locations in, say, ninety days. In this case, forecasts of SKU - level customer demand which can reach ninety days into the future can tell us everything we need to know to direct and control the on-going logistics operation.
It is also important to note that the demand forecasts developed within the logistics system must be generally consistent with planning numbers generated by the production and marketing organizations. If the production department is planning to manufacture two million units, while the marketing department expects to sell four million units, and the logistics forecasts project a total demand of one million units, senior management must reconcile these very different visions of the future.
The Nature of Customer Demand
Most of the procedures in this chapter are intended to deal with the situation where the demand to be forecasted arises from the actions of the firm’s customer base. Customers are assumed to be able to order what, where, and when they desire. The firm may be able to influence the amount and timing of customer demand by altering the traditional "marketing mix" variables of product design, pricing, promotion, and distribution. On the other hand, customers remain free agents who react to a complex, competitive marketplace by ordering in ways that are often difficult to understand or predict. The firm’s lack of prior knowledge about how the customers will order is the heart of the forecasting problem – it makes the actual demand random.
However, in many other situations where inbound flows of raw materials and component parts must be predicted and controlled, these flows are not rooted in the individual decisions of many customers, but rather are based on a production schedule. Thus, if TDY Inc. decides to manufacture 1,000 units of a certain model of personal computer during the second week of October, the parts requirements for each unit are known. Given each part supplier’s lead-time requirements, the total parts requirement can be determined through a structured analysis of the product's design and manufacturing process. Forecasts of customer demand for the product are not relevant to this analysis. TDY, Inc., may or may not actually sell the 1,000 computers, but that is a different issue altogether. Once they have committed to produce 1,000 units, the inbound logistics system must work towards this production target. The Material Requirements Planning (MRP) technique is often used to handle this kind of demand. This demand for component parts is described as dependent demand (because it is dependent on the production requirement), as contrasted with independent demand, which would arise directly from customer orders or purchases of the finished goods. The MRP technique creates a deterministic demand schedule for component parts, which the material manager or the inbound logistics manager must meet. Typically a detailed MRP process is conducted only for the major components (in this case, motherboards, drives, keyboards, monitors, and so forth). The demand for other parts, such as connectors and memory chips, which are used in many different product lines, is often simply estimated and ordered by using statistical forecasting methods such as those described in this chapter.
General Approaches to Forecasting
All firms forecast demand, but it would be difficult to find any two firms that forecast demand in exactly the same way. Over the last few decades, many different forecasting techniques have been developed in a number of different application areas, including engineering and economics. Many such procedures have been applied to the practical problem of forecasting demand in a logistics system, with varying degrees of success. Most commercial software packages that support demand forecasting in a logistics system include dozens of different forecasting algorithms that the analyst can use to generate alternative demand forecasts. While scores of different forecasting techniques exist, almost any forecasting procedure can be broadly classified into one of the following four basic categories based on the fundamental approach towards the forecasting problem that is employed by the technique.
1. Judgmental 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. That is, in a judgment-based technique we gather the knowledge and opinions of people who are in a position to know what demand will be. For example, we might conduct a survey of the customer base to estimate what our sales will be next month.
2. Experimental Approaches. Another approach to demand forecasting, which is appealing 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 and to extrapolate the results to a larger population. For example, firms will often test a new consumer product in a geographically isolated "test market" to establish its probable market share. This experience is then extrapolated to the national market to plan the new product launch. Experimental approaches are very useful and necessary for new products, but for existing products that have an accumulated historical demand record it seems intuitive that demand forecasts should somehow be based on this demand experience. For most firms (with some very notable exceptions) the large majority of SKUs in the product line have long demand histories.
3. Relational/Causal Approaches. The assumption behind a causal or relational forecast is that, simply put, 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. For example, if we sell umbrellas at a sidewalk stand, we would probably notice that daily demand is strongly correlated to the weather – we sell more umbrellas when it rains. Once we have established this relationship, a good weather forecast will help us order enough umbrellas to meet the expected demand.
4. "Time Series" Approaches. A time series procedure is fundamentally different than the first three approaches we have discussed. In a pure time series technique, no judgment or expertise or opinion is sought. We do not look for "causes" or relationships or factors which somehow "drive" demand. We do not test items or experiment with
customers. By their nature, time series procedures are applied to demand data that are longitudinal rather than cross-sectional. That is, the demand data represent experience that is repeated over time rather than across items or locations. The essence of the approach is to recognize (or assume) that demand occurs over time in patterns that repeat themselves, at least approximately. If we can describe these general patterns or tendencies, without regard to their "causes", we can use this description to form the basis of a forecast.
In one sense, all forecasting procedures involve the analysis of historical experience into patterns and the projection of those patterns into the future in the belief that the future will somehow resemble the past. The differences in the four approaches are in the way this "search for pattern" is conducted. Judgmental approaches rely on the subjective, ad-hoc analyses of external individuals. Experimental tools extrapolate results from small numbers of customers to large populations. Causal methods search for reasons for demand. Time series techniques simply analyze the demand data themselves to identify temporal patterns that emerge and persist.
Judgmental Approaches to Forecasting
By their nature, judgment-based forecasts use subjective and qualitative data to forecast future outcomes. They inherently rely on expert opinion, experience, judgment, intuition, conjecture, and other "soft" data. Such techniques are often used when historical data are not available, as is the case with the introduction of a new product or service, and in forecasting the impact of fundamental changes such as new technologies, environmental changes, cultural changes, legal changes, and so forth. Some of the more common procedures include the following:
Surveys. This is a "bottom up" approach where each individual contributes a piece of what will become the final forecast. For example, we might poll or sample our customer base to estimate demand for a coming period. Alternatively, we might gather estimates from our sales force as to how much each salesperson expects to sell in the next time period. The approach is at least plausible in the sense that we are asking people who are in a position to know something about future demand. On the other hand, in practice there have proven to be serious problems of bias associated with these tools. It can be difficult and expensive to gather data from customers. History also shows that surveys of "intention to purchase" will generally over-estimate actual demand – liking a product is one thing, but actually buying it is often quite another. Sales people may also intentionally (or even unintentionally) exaggerate or underestimate their sales forecasts based on what they believe their supervisors want them to say. If the sales force (or the customer base) believes that their forecasts will determine the level of finished goods inventory that will be available in the next period, they may be sorely tempted to inflate their demand estimates so as to insure good inventory availability. Even if these biases could be eliminated or controlled, another serious problem would probably remain. Sales people might be able to estimate their weekly dollar volume or total unit sales, but they are not likely to be able to develop credible estimates at the SKU level that the logistics system will require. For
these reasons it will seldom be the case that these tools will form the basis of a successful demand forecasting procedure in a logistics system.
Consensus methods. As an alternative to the "bottom-up" survey approaches, consensus methods use a small group of individuals to develop general forecasts. In a “Jury of Executive Opinion”, for example, a group of executives in the firm would meet and develop through debate and discussion a general forecast of demand. Each individual would presumably contribute insight and understanding based on their view of the market, the product, the competition, and so forth. Once again, while these executives are undoubtedly experienced, they are hardly disinterested observers, and the opportunity for biased inputs is obvious. A more formal consensus procedure, called “The Delphi Method”, has been developed to help control these problems. In this technique, a panel of disinterested technical experts is presented with a questionnaire regarding a forecast. The answers are collected, processed, and re-distributed to the panel, making sure that all information contributed by any panel member is available to all members, but on an anonymous basis. Each expert reflects on the gathering opinion. A second questionnaire is then distributed to the panel, and the process is repeated until a consensus forecast is reached. Consensus methods are usually appropriate only for highly aggregate and usually quite long-range forecasts. Once again, their ability to generate useful SKU level forecasts is questionable, and it is unlikely that this approach will be the basis for a successful demand forecasting procedure in a logistics system.
Judgment-based methods are important in that they are often used to determine an enterprise's strategy. They are also used in more mundane decisions, such as determining the quality of a potential vendor by asking for references, and there are many other reasonable applications. It is true that judgment based techniques are an inadequate basis for a demand forecasting system, but this should not be construed to mean that judgment has no role to play in logistics forecasting or that salespeople have no knowledge to bring to the problem. In fact, it is often the case that sales and marketing people have valuable information about sales promotions, new products, competitor activity, and so forth, which should be incorporated into the forecast somehow. Many organizations treat such data as additional information that is used to modify the existing forecast rather than as the baseline data used to create the forecast in the first place.
Experimental Approaches to Forecasting
In the early stages of new product development it is important to get some estimate of the level of potential demand for the product. A variety of market research techniques are used to this end.
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”.
Consumer Panels are also used in the early phases of product development. Here a small group of potential customers are brought together in a room where they can use the product and discuss it among themselves. Panel members are often paid a nominal amount for their participation. Like surveys, these procedures are more useful for analyzing product attributes than for estimating demand, and they do not constitute true “demand experiments” because no purchases take place.
Test Marketing is often employed after new product development but prior to a full-scale national launch of a new brand or product. The idea is to choose a relatively small, reasonably isolated, yet somehow demographically "typical" market area. In the United States, this is often a medium sized city such as Cincinnati or Buffalo. The total marketing plan for the item, including advertising, promotions, and distribution tactics, is "rolled out" and implemented in the test market, and measurements of product awareness, market penetration, and market share are made. While these data are used to estimate potential sales to a larger national market, the emphasis here is usually on "fine-tuning" the total marketing plan and insuring that no problems or potential embarrassments have been overlooked. For example, Proctor and Gamble extensively test-marketed its Pringles potato chip product made with the fat substitute Olestra to assure that the product would be broadly acceptable to the market.
Scanner Panel Data procedures have recently been developed that permit demand experimentation on existing brands and products. In these procedures, a large set of household customers agrees to participate in an ongoing study of their grocery buying habits. Panel members agree to submit information about the number of individuals in the household, their ages, household income, and so forth. Whenever they buy groceries at a supermarket participating in the research, their household identity is captured along with the identity and price of every item they purchased. This is straightforward due to the use of UPC codes and optical scanners at checkout. This procedure results in a rich database of observed customer buying behavior. The analyst is in a position to see each purchase in light of the full set of alternatives to the chosen brand that were available in the store at the time of purchase, including all other brands, prices, sizes, discounts, deals, coupon offers, and so on. Statistical models such as discrete choice models can be used to analyze the relationships in the data. The manufacturer and merchandiser are now in a position to test a price promotion and estimate its probable effect on brand loyalty and brand switching behavior among customers in general. This approach can develop valuable insight into demand behavior at the customer level, but once again it can be difficult to extend this insight directly into demand forecasts in the logistics system.
Relational/Causal Approaches to Forecasting
Suppose our firm operates retail stores in a dozen major cities, and we now decide to open a new store in a city where we have not operated before. We will need to forecast
what the sales at the new store are likely to be. To do this, we could collect historical sales data from all of our existing stores. For each of these stores we could also collect relevant data related to the city's population, average income, the number of competing stores in the area, and other presumably relevant data. These additional data are all referred to as explanatory variables or independent variables in the analysis. The sales data for the stores are considered to be the dependent variable that we are trying to explain or predict.
The basic premise is that if we can find relationships between the explanatory variables (population, income, and so forth) and sales for the existing stores, then these relationships will hold in the new city as well. Thus, by collecting data on the explanatory variables in the target city and applying these relationships, sales in the new store can be estimated. In some sense the posture here is that the explanatory variables "cause" the sales. Mathematical and statistical procedures are used to develop and test these explanatory relationships and to generate forecasts from them. Causal methods include the following:
Econometric models, such as discrete choice models and multiple regression. More elaborate systems involving sets of simultaneous regression equations can also be attempted. These advanced models are beyond the scope of this book and are not generally applicable to the task of forecasting demand in a logistics system.
Input-output models estimate the flow of goods between markets and industries. These models ensure the integrity of the flows into and out of the modeled markets and industries; they are used mainly in large-scale macro-economic analysis and were not found useful in logistics applications.
Life cycle models look at the various stages in a product's "life" as it is launched, matures, and phases out. These techniques examine the nature of the consumers who buy the product at various stages ("early adopters," "mainstream buyers," "laggards," etc.) to help determine product life cycle trends in the demand pattern. Such models are used extensively in industries such as high technology, fashion, and some consumer goods facing short product life cycles. This class of model is not distinct from the others mentioned here as the characteristics of the product life cycle can be estimated using, for example, econometric models. They are mentioned here as a distinct class because the overriding "cause" of demand with these models is assumed to be the life cycle stage the product is in.
Simulation models are used to model the flows of components into manufacturing plants based on MRP schedules and the flow of finished goods throughout distribution networks to meet customer demand. There is little theory to building such simulation models. Their strength lies in their ability to account for many time lag effects and complicated dependent demand schedules. They are, however, typically cumbersome and complicated.
Time Series Approaches to Forecasting
Although all four approaches are sometimes used to forecast demand, generally the time-series approach is the most appropriate and the most accurate approach to generate the large number of short-term, SKU level, locally dis-aggregated forecasts required to operate a physical distribution system over a reasonably short time horizon. On the other hand, these time series techniques may not prove to be very accurate. If the firm has knowledge or insight about future events, such as sales promotions, which can be expected to dramatically alter the otherwise expected demand, some incorporation of this knowledge into the forecast through judgmental or relational means is also appropriate.
Many different time series forecasting procedures have been developed. These techniques include very simple procedures such as the Moving Average and various procedures based on the related concept of Exponential Smoothing. These procedures are extensively used in logistics systems, and they will be thoroughly discussed in this chapter. Other more complex procedures, such as the Box-Jenkins (ARIMA) Models, are also available and are sometimes used in logistics systems. However, in most cases these more sophisticated tools have not proven to be superior to the simpler tools, and so they are not widely used in logistics systems. Our treatment of them here will therefore be brief.