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BUS 306 Quantitative Business Analysis Assignment - Emirates College of Technology, UAE

Task - Write a project report.

Project Topic: Application Of Quantitative Techniques In Business And Economics

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Solution 1 -

EXECUTIVE SUMMARY:

The paper consists of the analysis done where in the fact that the quantitative techniques are applied for making business decisions and economics. The fact that there is much use of quantitative techniques by the management since it helps in having an approach which is more systematic in accumulation of data and provides aid to the decision making. Thus these techniques helps the business as well as the economics by providing domestication of the increased numbers through their accumulation and thereby helps in making economic business decision making. The decisions are thus made using and applying the models and view various phenomenon's in business and economics.

The decisions made under business and economics are uncertain and no one can ever claim that the act done are right and therefore the numerous techniques being quantitative in nature are used so that the business is able to analyse and assess the risks. Further, the quantitative techniques are used by the management of the business in all the aspects to have a better grasp of the problem by making effective decision based on the available information.

1. INTRODUCTION:

The business and the economics use the quantitative techniques for making decisions. With the increase in the number of data, effective organising and analysing the same was difficult earlier. But fortunately, there are numerous techniques that have developed in recent times which help the organization in the systematic way the assessment of the risk by the conducting quantitative analysis. For example, the most popular and the effective and systematic technique being used by a project manager for the management of the project is the PROJECT MANAGEMENT, quantitative technique wherein systematic approach is used for optimization of usage of the resources viz. manpower, machines, materials, time and money. The quantitative techniques have helped the business in making several major business decisions vide planning and scheduling production process, purchase of raw materials and stocking of the same, marketing campaigns evaluation, calculation of the amount of investments required for financing the business, projections made mathematically for the probability of success and use of the same in agriculture by analysing the decision tree and making assumptions of weather and accordingly focusing on the suitable crop that can be grown. Analysis of phenomenon in case of economics and business economics can be done using the quantitative techniques using various models.

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2. MODELS OF QUANTITATIVE ANALYSIS:

There are many models that are used while conducting quantitative analysis that accumulate data from various sources. It helps in the domestication of the large amount of accumulated data which further supports in making business and economic decisions. Some of the models that are largely used under Quantitative analysis are being discussed below:

  • Linear Programming Model: This model is a power technique of quantitative analysis which is designed for solving problems of allocation. This is a mathematical modelling technique wherein the linear function is either minimized or maximised which is subjected to various constraints. This model is used popularly for making quantitative decisions. The decisions made are majorly towards business planning, industrial engineering and also for sciences including social and physical.
  • Transportation and Assignment problems models: These models helps in selection of the routes in between the points of demand and supply so that the cost of transportation can be minimized taking into consideration the constraints if any that exists of any supply or demand point. The main purpose of the Transportation model is the minimization of the overall cost. The solution of the same is achieved through algorithm of assignment.

The assignment model on the other hand, is used for solving one to one problems of assignment viz. assignment of jobs to employees, machines to employees, jobs to machines and thereon. This model is one of a special case of the model of transportation. In case of the transportation model, the quantity of demand and supply is not always equal and the supply quantity can either be less equal or more. Similarly, in case of assignment models, the number of jobs can either be less, equal or more than the number of persons and machines available. These quantitative models are effective and simple with less time consuming.

  • Network Models: The network analysis is the technique which is being used by the managers, accountants and specialists. These contains two components which is activities and events. Under this model, a graphical representation of the relationship among the various activities of the project is made under network models. The major rule that applies while drawing a network is that an activity should be complete so that the subsequent activity can be started and until the tail event is reached no activity can start. The network model is one of the effective quantitative techniques which is being used in today's world by almost all the project managers for the management of the project and making effective decision.

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3. FIELDS OF BUSINESS COVERING THE APPLICATIONS:

Areas covering decision making through various models used to view the phenomenon.

The quantitative techniques are being used by managers for grasping problems in a better way for making effective decisions based on the available information. There are many aspects where managers uses quantitative techniques which are discussed as below:

  • Project Management: The quantitative techniques is widely applied in project management for optimisation of the allocation of manpower, materials, machines, money and time. The delivery time of the project is estimated using the techniques of quantitative analysis with effective allocation of the work and jobs to machine/manpower.
  • Planning and Scheduling Production: One of the complex issue that exists in the production process is the determination of the size and the location of the facility of production. There is analysis of the product mix so that the customer demands can be met. The major goal of the production process in minimising the production cost and maximisation of profit which is achieved by them using the quantitative techniques.
  • Purchasing Raw Materials and Stocking them up: The quantitative techniques helps in analysing the quantity of product that is required to be produced and thereby helps in determination of the amount of raw materials that is required to be procured. Furthermore, it also helps in analysis of the inventory that should be maintained to keep the demand and supply points uninterrupted.
  • Marketing:There are large amount of data gathered while conducting marketing campaigns. Therefore, the managers uses the quantitative techniques for the analysis of the same by setting budgets, allocating purchases of media, adjusting the product mix and adapting preferences of the customers.
  • Finance: The managers of finance depends majorly on the quantitative techniques for evaluating the amount of investment required to be maintained or required for smoothening the running of business. All this is achieved using discounted cash flow models and return on capital. There is analysis conducted for contribution of profit, cost of production, productivity standards, hiring and firing positions.
  • Research and Development: The funds have to be spent on the research and development for developing and enhancing business position. However, the outcome of that can never be ascertained. Therefore, the managers uses the mathematical techniques for determining the probability of success so that the investment decisions can be made.
  • Agriculture:The farmers also adopts the quantitative techniques for making decisions related to agriculture. Decision Trees are being used by them along with assumptions over the weather so that they can decide upon the crop that can be grown.

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4. APPLICATION OF TECHNIQUES IN DECISION MAKING:

For the survival of the business, making decisions effectively is of utmost importance. This is because of the fact that the business decisions are made taking into consideration the amount of information being limited in nature. Problems of decision making are into two main types viz. deterministic and probabilistic. These are discussed as below:

  • Deterministic: This model of solving of problem is dependent on the inter-relation in between the factors that are uncontrollable and the process of optimization of performance of the system. This model have certain assumptions which have direct relation to the current business condition.
  • Mathematical:This model of solving of problem is dependent on the mathematical equation which determines the decision of the business. This model uses numerical forms for making business decisions.

The management uses many other techniques for making effective decision making. One of the major analysis that is conducted is Sensitivity Analysis. This requires analysing of the information and data that are very sensitive and will affect the business in a major way. This analysis helps in making decisions effectively as because the survival of the business is solely dependent upon it.

5. CONCLUSION:

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The fact that can be ascertained is that the managers uses the Quantitative Techniques in almost all the aspects of the business. The fact that by using these techniques improved business and economic decisions irrespective of their extent of criticalness can be made so that there is better and sustainable results. The various models viz. linear programming, transportation and assignment model and network models focusses on making better expectations of performances so that in case of deviations in the actual performance with the standard performance is observed it can be analysed and the necessary steps can be taken. The quantitative methods have their emphasis on the various data collected through polling, questionnaire and surveys. These data are further analysed using the various techniques to have actions set for attaining the desired objectives. The quantitative techniques further helps in making systematic decision since these are the scientifically proven methods which are adapted for solving the problems and making decisions. This technique helps the management contributing towards the overall improvement in the quality of making decisions. These techniques further helps in making decisions that are rationale and logical. The quantitative techniques is found to be effective because of its inbuilt characteristics that includes gathering data through instruments that are structured, large number of sample size, results being repeated and replicated because of the high amount of reliability of the same.

The quantitative technique as observed is majorly the study of the methods and tools which have their focus majorly on the measurement of the objectives of the business and economics and also includes the study of analysis of numbers so that a conclusion can be drawn on the problems. This is a method which is scientifically tried and tested and is used by the business and economics for solving problems that arises in due course of business and is also a major tool used for making decisions in the various field of business like agriculture, production process, finance, marketing, etc. The quantitative techniques are used for processing the information required for effective management of all the part of the business process viz. planning, leading, organising and controlling. The analysis starts with simple techniques and consists of many techniques that are designed keeping in thoughts the extended business functioning and the environment in which the business operates. This is because there is high amount of uncertainty and risks involved within the business environment.

Solution 2 -

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Executive summary

Quantitative methods usually employ the use of numerical and statistical data for formulating a decision that would be in the best interests of the organisation of any particular industry. There are many models that are being popularly used for cost optimisation and process enhancement, such as the linear programming model, and the network model. This report intends to explain the applications of these models in terms of the logistics industry. The manner in which the different quantitative business and economic models can aid in operations as well as decision-making shall be discussed.

Introduction to quantitative methods in management

Quantitative methods in management refers to the use of data analysis through mathematical methods for analysing and undertaking a course of action for complex problems (Gal, Stewart and Hanne, 2013). The primary growth behind the reliance on quantitative methods is the increase in the utilisation of big data (Barnes, 2013). Furthermore, quantitative methods often provide a systematic approach for evaluating the various phenomena when it comes to economics and business. The emergence of big data and the increase in the utilisation of numerical data has made it mandatory for businesses in virtually every industry to use quantitative methods for the processing and evaluation of the large volumes of statistical data. Decision-making is an important economic benefit that can be obtained through accumulating data and utilising a quantitative method (Zavadskas and Turskis, 2011). Several models are often used for this purpose, such as linear programming, network model, and transportation and assignment problems. The following project will discuss in detail, the application of these quantitative models in the context of the logistics industry.

Application of linear programming in logistics

A business, no matter how enormous it may be, is faced with a limited number of resources in terms of labour, production equipment, facility space, and other supplies. Thus, managers face the massive challenge of having to allocate these numerous resources in an optimal manner (Bazaraa, Jarvis and Sherali, 2011). Linear programming is that mathematical tool that is popularly used in operations research as well as in the analysis of management techniques, with the intention of achieving an optimal outcome, such as low operating costs, high profits and so on. These decisions are subject to those certain constraints, such as limitations in supplies, labour or other resources, and linear programming has been used continually by researchers to analyse the issues and provide solutions for the same.

A linear programming model typically seeks to either minimise or maximise a function that is subject to a set of given constraints, both of which are linear in nature (Demirel, Demirel and Gökçen, 2016). The linear programming model typically consists of the following components or sets: decision variables, objective function, and constraints.

Linear programming is essentially an important quantitative method, since it can be easily applied to real-world problems. This model has been successfully implemented in numerous spheres, including logistics. The answers generated usually allows for addressing the problem of "what if" for any business issue.

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In logistics, linear programming helps formulate the practical problems as mathematical ones, which helps minimise or maximise the objective function. The external and internal constraints applicable for the function are usually accompanies by effective algorithms, which help evaluate the available solutions for deciding on an approach that will be bestsuited for the business.

Linear programming helps optimise the logistics network by utilising the field of operations. This optimisation is largely dependent on the mathematical assumptions and inputs that are then analysed based on a given implemented formula (Falsini, Fondi and Schiraldi, 2012). The optimisation of the logistics network has its own benefits, such as -

1. Improved cost to service

2. Improved inventory management

3. Reduction in cross-functional waste

4. Resiliency of the supply chain

5. Improvements in the resiliency of the supply chain.

Logistics and warehousing have numerous constraints, which has made it all the more important to call for the application of linear programming models. It helps with many strategic decisions, such as -

1. Selecting and deciding on the optimal location, size, and number of the logistics centres

2. Determination of the appropriate sourcing strategy, pertaining to which vendors are being dealt with

3. Determination of the best channels of distribution, such as the selection of the retailers, etc.

Linear programming aims at collecting and analysing the available data in such a manner as to reduce costs and increase efficiency. The manufacturing unit of the business usually sends the goods right after they have been produced, to the warehouses for packaging and storage. Transportation systems are largely reliant on linear programming for determination of the costs as well as time efficiency (Bazaraa, Jarvis and Sherali, 2011). It can be used for analysing the best pathways and for selecting the course of action, which will further decrease expenses while optimising the processes.

Application of transportation and assignment problems model in logistics

Just like the linear programming model, the transportation and assignment problem model harbours the objective to minimise the distribution costs of a product, especially when it comes to the transportation process from its source of origin to its stated destination (Vielma, Ahmed and Nemhauser, 2010). This model is quite important in terms of being applied in logistics, as the industry deal with the movement of goods on a frequent basis.

The purpose of the transportation and assignment problems model is not just a simple mathematical formula, but instead has a specific purpose, which is the representation of the systems of the real world from a definite perspective (Burt and Puchinger, 2012). Some of the factors that constitute the transportation problems, especially in case of logistics, are delays, accidents, congestions, and other external factors of the environment.

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The reasons why this quantitative approach needs to be studied and applied are numerous -

1. For building new roads

2. For maintenance of the infrastructure

3. For modification of the public transport

4. For upgrading the city circulation of goods

5. For modifying policies.

Pollution, economy, traffic jams, and accessibility are some of the reasons as to why this model is implemented in case of logistics.

This theory is essentially the provider of quantitative information, which facilitates the smarter movement of goods as far as the surrounding environment is concerned. In other words, it is an important tool for decision making in the logistics industry. It is important to mathematically formulate the decisions, because every warehouse as well as production plants have their own limited capacities, and the specific combinations of these plants and warehouses can have largely varying costs (Speranza, 2018).

There are three primary methods that can be implemented within this model, which have been elaborated below -

North West Corner Model: This is among the simpler methods that are used for the generation of a feasible solution. The name is so because the calculation starts from the upper left corner of the transportation matrix.

Least cost method: The allocation as indicated through this method is extremely useful, as it considers the methods that would require the lowest costs, and thus, would result in the reduction of the time that would be necessary for reaching a solution that is optimal.

Vogel's approximation method: This is usually the most preferred methods when it comes to this particular model in case of the logistics function. This is because the solution that is initially obtained is generally very close to the solution that would be finally calculated as being the optimally feasible one (Samuel. and Venkatachalapathy, 2011).

Using either of three outlined methods, a feasible initial solution is formulated, after which it is tested for optimality. The stepping-stone method and the modified distribution methods are commonly used for evaluating and testing the solutions. The stepping-stone method calculates the net change in the cost, that can be calculated by following the closed path of the value within the solution. The modified distribution model is a lot more efficient, as it evaluates the values and checks each one of them for the opportunity costs for extracting the optimal solution.

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Application of network model in logistics

In the logistics industry, when any company is pursuing the design of its network model, all the possible elements that would be influencing it are the labour pool, consumer markets, government incentives, and the quality of requirements. Analysis of all these factors make it possible to give rise to a model that would give the logistics function an enhanced insight into the choices they wish to undertake (Pishvaee, Farahaniand Dullaert, 2010). Using modelling techniques is necessary for companies that are forming a new logistics network, as it helps to gain a quantitative outlook regarding the cost efficiency, functioning and customer service efficiency. Explained below are some of the network modelling techniques that can be applied for the industry -

Optimisation modelling: This is derived from the mathematic procedures that would offer the best solutions based on the formula that is being used. The input for the model is not subjective at all, and consideration is given only to data and assumptions, such as the extent of customer satisfaction that is to be obtained, the number of distribution centres, the number f manufacturing plants, and so on (Lee, Dong and Bian, 2010). This technique is useful when it comes to drawing a relationship between the limitations for supply and demand, especially in terms of distribution centres, manufacturing plants, and market areas. The goal is to ultimately minimise the costs by optimising the distribution patterns, and this is done wholly by the use of mathematical formula and not any subjective input.

Simulation models: This is defined as the creation of a model that would be based on the events of the real world, and then experiments can be pursued to analyse how the changes can affect the cost of the overall logistics network. For instance, changing the constraints might enable to get an idea about the cost-effectiveness of the network.

A significant amount of data, especially in terms of warehousing, transportation, material handling, inventory levels and labour costs are necessary for making the changes to the constraints, and for analysing them. A drawback is that when the simulation model is changed, it would not necessarily give rise to a logistics network model that is optimum in nature (Kayikci, 2010). In fact, only the changes would be evaluated, and thus, this model is necessary and quite useful when logistics companies make massive decisions pertaining to the network and wish to observe the effects of the changes.

Heuristic model: This is quite similar to the simulation model, but do not usually lead to the generation of a solution that is optimum in nature. This model is useful for reducing a large problem down to a size that is much more manageable. There is no true guarantee for a solution and there could be heuristic models that produce different answers to the same question, all of which might even contradict each other. However, the results would still be useful when creating a logistics network model.

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For this purpose, the heuristic model in logistics is often referred to being as the basic or the "rule of thumb" as it has its own many utilities (Kaya and Urek, 2016). For instance, this model can help consider, which site would be the best for warehousing in case of a distribution centre that is quite far away from the market areas. This model will then consider all the regions that fall under the set parameter, and shell out answers that would be bestsuited to the given situation.

Conclusion

Quantitative methods are, therefore, useful in optimising the various business processes as well as the economy, as can be deduced from the above analysis of its applications. It considers numerical data and does not consider subjective inputs, which essentially adds to its accuracy. In the logistics industry, linear programming is all the more important, since the logistics function is such that it would require the optimisation of costs and the efficient management of the processes. Large volumes of statistical data can be processed and quantified only through this method, and thus, decision pertaining to lowering the costs, and achieving the optimal outcomes can thus be obtained. Algorithms are usually utilised for the process, and it has numerous benefits such as improved inventory management and supply chain resiliency. Since logistics deals largely with the movement of commodities, it makes the application of the transportation and assignment problems model all the more important for consideration. Many factors contribute to problems in this arena, but necessary steps can be taken to avoid the external issues. All decisions are typically formulated through mathematical calculations, as the differences in costs and other warehousing attributes can be quite varied and thus need to be analysed efficiently. Finally, the network model in logistics is one that provides a quantitative outlook through its own set of techniques, and it is an important model that helps improve cost efficiency, customer service quality, and the overall functioning of the processes.

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