Statistical Techniques for Management Decision MakingPrepared By:Manisha K. ShahAssistant Professor,Shri D. N. Institute of Business AdministrationPriyal PatelStudent of MBACentre of ManagementDDIT, Nadiad

Abstract:Managerial activities have become complex and it is necessary to make right decisions to avoid heavy losses. Whether it is a manufacturing unit, or a service organization, the resources have to be utilized to its maximum in an efficient manner. The aim of this article is to consider the role which statistical methods can sensibly take in management research, and to look at some of the difficulties with typical uses of statistical methods and possible ways of reducing these difficulties.Keywords: statistics, Business Statistics, Management Decision Making, Quantitative techniques IntroductionThe future is clouded with uncertainty and fast changing, and decision-making a crucial activity cannot be made on a trial-and-error basis or by using a thumb rule approach. In such situations, there is a greater need for applying scientific methods to decision-making to increase the probability of coming up with good decisions. Quantitative Technique is a scientific approach to managerial decision-making. The successful use of Quantitative Technique for management would help the organization in solving complex problems on time, with greater accuracy and in the most economical way. Today, several scientific management techniques are available to solve managerial problems and use of these techniques helps managers become explicit about their objectives and provides additional information to select an optimal decision. This study material is presented with variety of these techniques with real life problem areas.Objectives:1. To identify the Major techniques used for Decision Making by Managers2. To identify the important of Statistical Techniques in Management decision making3. To identify the limitations of Statistical Techniques in Management decision makingMajor techniques used for Decision Making by ManagersQuantitative techniques help a manager improve the overall quality of decision making. These techniques are most commonly used in the rational/logical decision model, but they can apply in any of the other models as well. Among the most common techniques are decision trees, payback analysis, and simulations.Decision treesAdecision treeshows a complete picture of a potential decision and allows a manager to graph alternative decision paths. Decision trees are a useful way to analyze hiring, marketing, investments, equipment purchases, pricing, and similar decisions that involve a progression of smaller decisions. Generally, decision trees are used to evaluate decisions under conditions of risk.The term decision tree comes from the graphic appearance of the technique that starts with the initial decision shown as the base. The various alternatives, based upon possible future environmental conditions, and the payoffs associated with each of the decisions branch from the trunk.Decision trees force a manager to be explicit in analyzing conditions associated with future decisions and in determining the outcome of different alternatives. The decision tree is a flexible method. It can be used for many situations in which emphasis can be placed on sequential decisions, the probability of various conditions, or the highlighting of alternatives.Payback analysisPayback analysis comes in handy if a manager needs to decide whether to purchase a piece of equipment. Say, for example, that a manager is purchasing cars for a rental car company. Although a lessexpensive car may take less time to pay off, some clients may want more luxurious models. To decide which cars to purchase, a manager should consider some factors, such as the expected useful life of the car, its warranty and repair record, its cost of insurance, and, of course, the rental demand for the car. Based on the information gathered, a manager can then rank alternatives based on the cost of each car. A higherpriced car may be more appropriate because of its longer life and customer rental demand. The strategy, of course, is for the manager to choose the alternative that has the quickest payback of the initial cost.Many individuals use payback analysis when they decide whether they should continue their education. They determine how much courses will cost, how much salary they will earn as a result of each course completed and perhaps, degree earned, and how long it will take to recoup the investment. If the benefits outweigh the costs, the payback is worthwhile.SimulationsSimulation is a broad term indicating any type of activity that attempts to imitate an existing system or situation in a simplified manner. Simulation is basically model building, in which the simulator is trying to gain understanding by replicating something and then manipulating it by adjusting the variables used to build the model.Simulations have great potential in decision making. In the basic decisionmaking steps, Step 4 is the evaluation of alternatives. If a manager could simulate alternatives and predict their outcomes at this point in the decision process, he or she would eliminate much of the guesswork from decision making.Importance of Statistical Techniques in Management decision makingData and statistics can be used to concretely define and measure this uncertainty and predict when the next shipment is coming. Managerial decision-making with this statistical insight can avoid steering production, costs and customer service into bad avenues.1. Operational ValueMany businesses rely on their Information Technology (IT) systems to manage data, facilitate payments and run operations. Unforeseen bottlenecks can occur when IT runs a necessary system upgrade, if the implementation stalls and temporarily keeps your business from running smoothly. To combat this, some IT systems have statistical algorithms that find the likely cause for the blockage before your business hits a dead end. Other operational benefits of statistics are accurate demand forecasting and sufficient inventory planning.2. Strategic ValueStatistics can be used to guide long-term forecasts for strategic planning. Analytical methods like statistics support the understanding of the holistic impact that strategic initiatives can have on your business. For example, a statistical model can provide a baseline forecast of your revenues and expenses for years to come, which your team can adjust depending on new product introductions, new markets and competitor activities.

3. Research ValueInstead of repeatedly reacting to lost sales from insufficient inventory, you can use statistics to learn about your customers behaviour like how they react to promotions and when and what they buy. These research studies allow businesses to be proactive through predicting customer behaviour and creating better marketing plans. Moreover, statistics can be used in the development and pricing of new products via survey analysis and regression models.4. Ensuring QualityAnyone who has looked into continuous improvement or quality assurance programs, such as Six Sigma or Lean Manufacturing, understands the necessity for statistics. Statistics provide the means to measure and control production processes to minimize variations, which lead to error or waste, and ensure consistency throughout the process. This saves money by reducing the materials used to make or remake products, as well as materials lost to overage and scrap, plus the cost of honoring warranties due to shipping defective products.5. ConsiderationsKnow what to measure, and manage the numbers; dont let the numbers do the managing for you, or of you. Before using statistics, know exactly what to ask of the data. Understand what each statistical tool can and cant measure; use several tools that complement one another. For example, dont rely exclusively on an "average," such as a mean rating. Customers using a five-point scale to rate satisfaction wont give you a 3.84; that may indicate how the audience as a group clustered, but its also important to understand the width of the spread using standard deviation or which score was used by the greatest number of people, by noting the mode. Finally, double-check the statistics by perusing the data, particularly its source, to get a sense of why the audiences surveyed answered the way they did.Limitations of StatisticsStatistics has a number of limitations, pertinent among them are as follows:(i) There are certain phenomena or concepts where statistics cannot be used. This is because these phenomena or concepts are not amenable to measurement. For example, beauty, intelligence, courage cannot be quantified. Statistics has no place in all such cases where quantification is not possible.(ii) Statistics reveal the average behaviour, the normal or the general trend. An application of the 'average' concept if applied to an individual or a particular situation may lead to a wrong conclusion and sometimes may be disastrous. For example, one may be misguided when told that the average depth of a river from one bank to the other is four feet, when there may be some points in between where its depth is far more than four feet. On this understanding, one may enter those points having greater depth, which may be hazardous.(iii) Since statistics are collected for a particular purpose, such data may not be relevant or useful in other situations or cases. For example, secondary data (i.e., data originally collected by someone else) may not be useful for the other person.(iv) Statistics are not 100 per cent precise as is Mathematics or Accountancy. Those who use statistics should be aware of this limitation. (v) In statistical surveys, sampling is generally used as it is not physically possible to cover all the units or elements comprising the universe. The results may not be appropriate as far as the universe is concerned. Moreover, different surveys based on the same size of sample but different sample units may yield different results.(vi) At times, association or relationship between two or more variables is studied in statistics, but such a relationship does not indicate cause and effect' relationship. It simply shows the similarity or dissimilarity in the movement of the two variables. In such cases, it is the user who has to interpret the results carefully, pointing out the type of relationship obtained.(vii) A major limitation of statistics is that it does not reveal all pertaining to a certain phenomenon. There is some background information that statistics does not cover. Similarly, there are some other aspects related to the problem on hand, which are also not covered. The user of Statistics has to be well informed and should interpret Statistics keeping in mind all other aspects having relevance on the given problem.4