The Faculty of Veterinary Science at Onderstepoort (FVS-OP), is the only Veterinary Faculty in South Africa, and is situated approximately 15km from Pretoria (CBD). FVS-OP is responsible for the training of veterinarians and veterinary nurses, as well as postgraduate students in a number of disciplines.
There are numerous different laboratories at OP, but for the purpose of this project, the focus will fall on the laboratories of the Department of Veterinary Tropical Diseases (DVTD). The remaining laboratories are excluded from this project due to time constraints. Figure 1 illustrates the structure of the laboratories at DVTD.
The laboratories at the faculty function in a support role for training and research. A number of these laboratories also engage in external service delivery, which may be divided into three key service categories. These categories consist of the commercial setting, the interdepartmental service delivery setting and the research setting. Figure 2 represents the three different service settings.
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Figure 1: Representation of the Laboratories of the Department of Veterinary Tropical Diseases
Categories of the Service delivery by the Laboratories at the Faculty of Veterinary Science at Onderstepoort
Fellow Veterinarians from the Veterinarian industry
Individuals/ Representatives of various additional industries
Postgraduate students from the University of Pretoria or any other tertiary institution
Individuals/representatives/businesses etc. from the public sector
Onderstepoort Veterinary Academic Hospital (OVAH)
Figure 2: Representation of Service Categories
THE SCOPE OF THE PROBLEM
The Faculty represents a typical service operations system. Service rendering has certain characteristics that differentiates it from manufacturing. Despite some vast differences among different services and definitional problems, there are indeed some characteristics that many services share. These characteristics are listed in Table 1 together with the typical concomitant operations difficulties.
TYPICAL OPERATIONS DIFFICULTIES
Services cannot easily be displayed, demonstrated or communicated
It is difficult to determine prices
Patent rights are difficult to obtain
Services cannot be kept in inventory
It is difficult to standardize services
Quality control is difficult
There is interaction between the client and the service system, and also with the service environment
The client may be part of the service or the service process
The division between marketing, human resource management and operations management is vague
Table 1: Characteristics of service operations systems and the concomitant challenges (Adendorff & De Wit, 2003:13)
According to Adendorff and De Wit (2003:4) "â€¦..the operations management systems found in all businesses will have certain characteristics in common, irrespective of the nature of the product or service supplied." A typical model for a service system consists of inputs, a transformation process and outputs. The diagram in Figure 3 illustrates how the service supplied by FVS-OP can be applied to this model.
Information feedback regarding service
Interaction with market influencing the system through acceptance or rejection of products/services
Environmental factors influencing input to the system
Figure 3: Illustration of how the service supplied by FVS-OP can be applied to this service operation model.
The services rendered by the laboratories comprise a vast number of different processes, procedures and diagnostic tests. The types of service delivered depend on the need expressed by the client who falls into one of the three specified service categories; the client's request is subsequently allocated to an adequate/applicable/specific laboratory.
The same services are available to clients from all three categories. However, the service fee charged differs between these service settings. The current problem becomes obvious when considering the fact that these differences are not based on any scientific techniques, particular financial model or formula, but rather on untested assumptions and estimations.
Whilst functioning by means of such unstructured methodologies, the financial status and hence viability of the laboratories is unclear compromising any decisions taken by the management team. The Faculty is also unsure of their business mandate regarding their financial aim, it is undecided whether their objective is to break even or to make a profit. This objective may also differ between the three service settings. These facts leads to the problem of the laboratories inter alia not being able to devise an appropriate pricing strategy for their services, doing appropriate capacity planning (both fixed and variable) or formulating a marketing strategy. "A laboratory is not a cost-center anymore it's a profit center." (Biomed Systems, 2009:??) Thus, an opportunity arises to assist the laboratory management with regards to the identified problems, by investigating their operations, processes and financial data with a view to developing an appropriate financial model for various scenarios, with the purpose of these laboratories functioning as a profitable and/or sustainable institution.
Always on Time
Marked to Standard
The purpose of this project is to solve the business and management problems faced by the Faculty of Veterinary Science at Onderstepoort, with regard to their financial status and pricing strategy, as described in the project scope.
1.4 OBJECTIVES OF THE RESEARCH (PROJECT AIM)
The objectives of the research project are as follows. (moet ek hierdie in paragrawe uitskryf liewer? )
To solve the business and financial problems faced by FVS-OP.
To assist the management team to understand the sources of income applicable to the six laboratories.
To assist the management team to understand the real cost of running the laboratories by conducting a cost analysis and presenting the results.
To identify the problem areas in the running of the laboratories from a financial as well as an operational perspective.
To devise a demand forecasting model for the services provided by the laboratories.
To develop a linear programming model for the pricing strategy and to conduct a sensitivity analysis with the use of operations research methods, in order to optimize the cost allocation to different services provided by the laboratories and to conduct a sensitivity analysis on the pricing of the services provided. (het ek hierdie reg gestel?)
To assist the management team by means of a process for annual planning and budgeting of the operations at the laboratories, as an output form the mathematical program.
To use the tools and findings developed, to determine other areas for improvement, for example possible capacity constraints, preparation of the management budget etc.
LIMITATIONS OF THE STUDY
There are a few limitations to conducting this project of which most relate to the data which need to be analyzed. These limitations are as follows.
Amount of data available
Correctness of data
Applicability of data
Availability of people as information resources
Correctness of information provided by the Faculty staff
2.1 EXECUTIVE SUMMARY O F LITERATURE REV IEW
Ek gaan die summary skryf as Prof se die hele lit review is reg, en dit is heeltemal klaar
The aim of the entire literature study is to research and identify the appropriate methodologies to apply in order to develop a financial model for the problem at hand.
2.2 INDUSTRIAL ENGINEERING:
As shown in the diagram in Figure 4, Industrial Engineering integrates skills and knowledge from three different fields of science, which includes: Technical Sciences, Human Sciences and Economic Sciences
Figure 4: Industrial Engineering defined (www.kwaliteg.co.za/industrial, accessed 6 May 2010)
According to the South African Institute of Industrial Engineering (www.saiie.co.za) "Modern Industrial Engineering is concerned with the integration of resources and processes into cohesive strategies, structures and systems for the effective and efficient production of quality goods and services."
Additionally, the BNet Business Dictionary (http://dictionary.bnet.com/definition) defines Industrial Engineering as: "â€¦â€¦an applied science discipline concerned with the prediction, planning, evaluation, and improvement of company effectiveness. The purpose of industrial engineering is to maximize efficiency, quality, and production through the best use of personnel, materials, facilities, and equipment."
For the purpose of this project it will draw upon specialized knowledge and skills in the economic (with focus on financial) and management sciences and fuse it with engineering analysis methods and principles in order to find an optimal and practical solution to the problem at hand. Therefore the focus fill be on; planning, evaluating and predicting modeling techniques, in order to determine and maximize FVS-OP's efficiency, feasibility and profitability of their services rendered.
2.3 OPERATIONS MANAGEMENT:
"Operations management is defined as the management of the direct resources necessary to create the products and services supplied or provided by a business" (Adendorff and De Witt 2003:2)
According to Adendorff and De Witt (2003:6) management functions such as planning, organizing, control and leadership is taken as points of departure in order to explain the tasks of an operations manager in broad terms. All of these functions require decision making from the operations manager.
The described problem is a typical decision-making problem and there are various factors to consider when attempting to make the optimal decision. These factors include a sufficient problem statement, formulating a model, generating various solutions for the problem and choosing the most appropriate, then implementing the decision and monitoring the results.
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Various tools are available when attempting to solve problems which include using models, quantitative solution methods and the systems approach. From an operations manager point of view, as well as from an industrial approach, mathematical models are the most applicable and important problem solving technique. When dealing with engineering problems the most appropriate method is usually to take a scientific approach, thus applying tools such as mathematical models. Schematic models such as a process map, flow charts, graphs and diagrams can be used in addition, to portray ideas, processes and methods.
Kosy (1984: ??) explains that a financial model is a representation of the activities of a business in terms of quantitative relationships among financial variables. Financial variables are variables that have some economic or accounting significance and the relationships among them can generally be expressed by formulas and conditional statements.
Kaye (1994:69) lists the seven stages of the modeling process as follows:
Specification of the model
Encoding the model
Testing the model
Resulting from inaccurate and inefficient planning, forecasting, financial management/management accounting and operations management, or the total lack thereof, the need for an operations management problem solution exists. A combination of the above mentioned problem solving techniques will be applied, including the use of models and the quantitative solution methods. The aim is to develop an all encompassing financial model aimed at the operations of the laboratories at FVS-OP. The development of a financial model for the laboratories will address the following areas:
Management accounting/Financial Management
Cost accounting/Cost analysis
Analysis of financial statements
Appropriate mathematical model to maximize profits / minimize losses by making use of linear programming methods.
The model should be practical and useful providing opportunities for continuous improvement and promote change in the institution's financial capacity. The model should be developed in such a way that it can be applied to each of the six laboratories.
2.4 MANAGEMENT ACCOUNTING/FINANCIAL MANAGEMENT
According to Whiteley (2004:.???) finance is one of the most important resources of a business, or indeed any organization. And the key to management of any resource is the ability to take control of it.
Management accounting can provide us with important information on the behavior of the firm, particularly the cost and production function.
The accountant prepares balance sheets, profit and loss accounts and funds flow statements, which represent both static and dynamic models of the firm. These pro-forma statements provide a consistent window through which to view the financial progress of the firm.
According to Kaye (1994:24) the accounting model is based on historical data collected procedurally and uses deterministic logic. It provides comparative and analytical information through trends and ratios. Improvements in management accounting practice have often resulted from techniques from operational research, for example probabilistic budgeting and decision trees.
With regards to this project, applicable financial management techniques are identified, which should be applied in order to get an overview of the institution's financial position.:
In order to analyze the firm's financial position, you have to identify the key areas where financial management has an input into management decision making.
These areas are pointed out (Whiteley 2004:vi) as follows:
Selling and Pricing
Cost accounting and Management Accounting
Insuring against risk
Tax compliance and planning
Internal and financial controls
Audit - external and internal
Analysis of financial statements
The following key areas are identified as applicable to this project and will be reviewed and researched accordingly.
Cost and management accounting
Analysis of financial statements
COST ACCOUNTING / COST ANALYSIS
The history of cost accounting is given by Morse (1978) 'Cost accounting paralleled the development of manufacturing technology and increases in business size. Its origins are in engineering and financial accounting'. In addition Baggot (1973:1) states that cost accounting is an important tool for business management to use as a decision-making criterion.
"With rising prices and increased competition, service companies are finding that knowing the costs of their products and services is vital to their health, if not to their existence" 1978 Sep-Oct;56(5):132-40., dearden j, "cost accounting comes to service industries"
22.214.171.124 Purpose and use of cost accounting / cost analysis
"Initially, it is possible to identify three separate elements of cost, namely, materials, labor and services for which the manufacturer must pay", (Baggot, 1973:5)
According to Whiteley (2004:90) cost accounting is concerned with the determination of costs for individual units of production of output. Whiteley (2004:90) further states that: "if the business knows how much each item has cost, it then knows how much, if any, profit, is made on its sale." Cost accounting directly addresses the problem of the question about FVS-OP's financial status.
According to Morse (1978:???) cost accounting also aids in the planning and control of ongoing operations, special decisions such as the development of pricing policy or the evaluation of alternative actions, and external reporting'. By calculating the costs of each of the tests performed, the operations can be analyzed and recommendations can be made with regards to the price setting strategy, while including operations research which will be discusses later. Based on the results, a decision must be made, whether or not to continue performing these tests or to increase the price charged to clients. Another option is to identify areas where costs can be minimized /reduced.
126.96.36.199 Methods, tools and techniques
According to Hernandez (2003:440) relatively few studies address the cost-effectiveness of laboratory testing, especially the cost-effectiveness beyond dealing with direct and indirect costs in the laboratory. However, methods/strategies have been identified on cost accounting and effectiveness in the business and service sector.
One of the key concepts identified regarding cost accounting (Wood 1985:4) is that of the cost centre, which is the smallest unit of production for which costs can be allocated. It can be applied to a process, a single item or a batch of items.
According to Wood (1985:9) and various other studies it is important to identify and define different types of cost:
Direct Costs: Costs that can be traced to a single cost centre (Salaries, materials, resources etc.)
Indirect Costs: costs that are not directly traceable to a cost centre.
Fixed Costs: Costs that don't vary with the volume or level of activity.
Variable Costs: Costs that change proportionally with changes in volume, that is, the level of activity.
Overhead Costs: Includes all indirect production costs, that is, all production costs other than direct material and direct labor.
Note: The sum of direct and indirect costs equals to the total cost, furthermore the sum of fixed costs and variable costs also equals to the total cost, but they are not the same. Direct costs can both be a fixed or a variable cost.
Whiteley (2004:91) describes 4 methods of costing:
Standard Costing: Standard costing is a method which calculates a cost for each item of production under standard conditions. This consists of variable and fixed costs.
Absorption Costing: method in which the fixed costs are allocated in proportion to the number of direct labor hours.
Activity based Costing: tries to establish in greater detail the true cost structures of cost centers - that is, processes, batches, items etc. This method allocates fixed costs on a more appropriate basis than direct labor hours.
Throughput Costing: This type of costing applies to manufacturing plants and attempts to provide relevant cost information, by identifying bottlenecks in the production plant.
In an article published online, Brezmes et al. (2002, p. ) undertook a study to estimate the cost of each of the tests performed in the clinical microbiology laboratory of a general hospital by making use of the workload register method of the college of American pathologists (CAP). This method falls into the absorption costing category and places a lot of emphasis on the workload accompanying each test. Table 2 represents an extract from the study. (nie seker of hierdie detail nodig is nie)
Consumable material cost
Time units (hours)
Accessioning of specimen and identification
Sheep blood agar
Inoculation of 2 pieces of media
Reading of 2 usually sterile sample plates
Recording and reporting of results
Table 2 Example of Workload and consumable material costs of a microbiological product
For each product the following costs were calculated:
Direct personnel cost
Indirect personnel cost
Direct material cost
Indirect material cost
Technical and Administrative Staff cost
Remaining laboratory cost (capital and structural cost)
In conclusion to the study, it is noted that the real cost of a product is the sum of allocated direct costs and indirect costs.
Thus the following calculations and conclusions could be made from this study:
Calculation of the average workload per product
Direct material Costs
Calculation of global expenses
Actual costs of tests performed
The results of this costing analysis was used to determine the profitability of this laboratory and also used in the price setting strategy for the tests performed by this lab.
2.4.2 ANALYSIS OF FINANCIAL STATEMENTS
"The financial statements measure performance and tell where a business stands in financial terms" (Harrison and Horngren 2005:5).
188.8.131.52 Purpose and use of financial statement analysis
The main purpose of financial statements is to indicate the profitability of an institution/business/company. The applicable financial statements for internal analysis are known as management accounts.
Whiteley (2004) describes different types of management accounts, which are used for internal analysis:
A profit and loss account includes the following:
Income: Shows the gross income (turnover) from sales of products or services rendered.
Direct Costs: Cost of Sales
Overhead Expenses: Categorizes different types of expenses (for example: Administration, Transport, Finances etc.).
The profit and loss account shows the result of the business activities for the given period, and most importantly whether the account result is a deficit or a surplus
Table 3 is an example of a simple profit and loss account.
Profit and Loss Account for the year ended 31st December 2004
Cost of Services
Table 3: Demonstration of a Profit and Loss Account
Indicates the financial position of the business, at the final date for which the profit and loss account is prepared.
Includes the following:
Capital: Nett assets of the business
Fixed Assets: Long term assets of the business
Depreciation: All fixed assets are subject to depreciation
Current Assets: Short term assets of the business
Current Liabilities: Short term liabilities
Long-term liabilities: Liabilities due for payment more than one year after the balance sheet date.
Table 4 is an example of a simple Balance Sheet.
Balance sheet as at 31st December 2004
Long term liabilities
Profit and loss account
Table 4 Demonstration of a Balance Sheet
'Figures in accounts are not just numbers on a piece of paper. They represent something substantive - something that has happened in the real world. Discovering what they mean is a vital tool in exercising financial control', (Whiteley, 2004, p. )
184.108.40.206 Methods, tools and techniques
Whiteley (2004) describes the usefulness of computing key ratios and conducting a statistical analysis, 'By comparing different figures within the set of accounts, certain key ratios and relationships can be measured. This may provide useful information in determining the profitability of the institution'.
The statistical analysis of the financial statement is necessary in order to obtain necessary information and get the intended results, this includes the calculation of certain key ratios.
where a = sales turnover
e = cost of sales
f = gross profit
p = net profit
'The figures do not exist on their own. They are at their most useful when shown in comparison to something else. The most common comparisons are between the current and corresponding previous accounting periods, and of actual with budgeted figures' (Whiteley, 2004, p9).
By analyzing the financial statements of FVS-OP it will be possible to determine their financial state.
2.5 DEMAND MANAGEMENT:
2.5.1 FORECASTING: (moet ek hier verwys na die rekenaar program wat ek gaan gebruik om die forecasting mee te doen??)
"Every day, managers make decisions without knowing what will happen in the future. Making good estimates is the main purpose of forecasting." (Murdick et al. 1990:47)
220.127.116.11 Purpose and use of forecasting
Demand forecasting in a service operations management system such as FVS-OP, involves estimating future demand for the services rendered and should be used as an engineering tool to reduce the risk in decision making. Various forecasting techniques exist and in order to apply the most applicable technique to the Faculty, it is important to initially review some of the common characteristics of the different forecasting techniques as discussed by Adendorff and De Wit (2003:97).
Forecasting techniques generally accept that the causative effects that prevailed in the past will continue in the future.
Forecasts are seldom perfect, so that the actual demand will almost always vary form the projected demand.
Forecasts for item or product groups are often more accurate than forecasts for individual items. However, the choice of such groups is critical to the success of the forecast.
The accuracy of a forecast declines s the forecast horizon lengthens. This implies that forecast for the near future are more accurate than forecasts over the long run.
Adendorff and De Witt (2003:98) further provide the steps to follow in the demand forecasting process:
Determine the objective of the forecast and the moment when it is needed.
Determine the time horizon involved in the forecast.
Choose a suitable forecasting technique.
Gather and analyze relevant data.
Monitor the forecast to determine whether it is performing accurately (If not, adapt the process to arrive at a revised forecast).
Murdick et al. (1990:50) points out that the choice of forecasting method, like most operating decisions, is an economic one. Thus the following factors need to be taken into account prior to choosing the appropriate forecasting technique.
Span of the forecast
Urgency with which the forecast is needed
Frequency that updates must be made
Mathematical sophistication available to the company
Antecedent data availability
Variability of fluctuation range and frequency
Output characteristics required:
Detail or degree of disaggregation
According to Adendorff and De Wit (2003:95) the demand for a product/service is related to the pattern exhibited over a certain course of time. These components consist of:
18.104.22.168 Methods, tools and techniques
Classification of forecasting methods:
There are various types of forecasting methods, of which the following are applicable to Demand Forecasting as noted by Fitzsimmons et al. (2006:324):
Regression models: Estimates produced from a predictive equation derived by minimizing the residual variance of one or more predictor (independent) variables.
Time series models
Moving average models: Recent values of the forecast variables averaged to predict future outcomes.
Exponential smoothing models: An estimate for the coming period based on a constantly weighted combination of the forecast estimate for the previous period and the most recent outcome.
Savage (2003:155) describes causal forecasting as a method which determines the extent to which changes in one quantity cause changes in another. Whereas time series analysis is described as a method where future values of some quantity is predicted based on past values of the same quantity. Considering the objectives of this project time series method appear most applicable and will be discussed in more detail.
The moving average models which is classified as a time series model, is used when the assumption can be made that the demand for services will stay fairly steady over time. Equation 1 serves as a mathematical presentation of this type of model.
Where n is the number of periods in the moving average
An adaptation to the moving average model is the weighted moving average model, which is applied when a trend or pattern in the demand has been identified. By using weights more emphasis can be placed on recent values. Equation 2 serves as a mathematical presentation of this type of model.
Problems concerning moving average models include a lag effect when measured against actual data. Another problem becomes apparent, when increasing the size of n to smooth out fluctuations results in a model which is less sensitive to real changes in the data.
Adendorff and De Wit (2003:101) describe exponential smoothing as a variation of the moving average technique where the forecast for the next period is arrived at by calculating a moving average for a number of previous periods. This model makes use of a smoothing constant which is presented by the variable Î± which is used to weigh data. The value for Î± is found by means of experimentation, and should represent a value between 0 and 1. According to Wild (2002:172) a low Î± value results in a consistent forecast, whilst a high Î± value results in a forecast which is reactive to change. Equation 3 serves as mathematical representation for this type of model.
where = the new forecast
= the previous forecast
Î± = smoothing constant ( 0 â‰¤ Î± â‰¤ 1 )
= previous period's actual demand
Simple exponential smoothing fails to respond to trends, thus Exponential Smoothing with Trend Adjustment should be applied in conjunction with simple exponential smoothing, when the data used exhibits a trend. An addition to the simple exponential smoothing model is the smoothing constant Î² which is used to smooth out the trend. Equation 4 serves as a mathematical representation for this type of model.
where = smoothed trend for period t
= smoothed trend for previous period
= trend smoothing constant
= simple exponential smoothed forecast for period t
= forecast for previous period
Forecast including trend ( (5)
Fitzsimmons and Fitzsimmons explain that simple exponential smoothing can be extended to account for seasonal effects on a set of data. The technique entails, initially removing the seasonality from the data to smooth those data, then putting the seasonality back to determine the forecast. Equation 9 and 10 serves as mathematical representations for this type of model.
Determining the accuracy of demand forecasts
Montgomery and Johnson (1976:155) establish that no forecasting system will produce a perfect forecast of future observations. There will always be some difference between the forecast for a future time period and the actual realization for that period. Thus it is necessary to calculate the accuracy of the demand forecast in order to improve the technique and hence the results.
Adendorf and De Wit discuss three criteria by which to measure the accuracy of the forecasting model, being the calculation of, the mean absolute deviation, the standard deviation or of the mean absolute percentage error. Each criterion is described and accompanied by the relevant equation.
The mean absolute deviation gives an indication of the extent of the difference between forecast and actual demand. Equation 6 serves as a mathematical representation for this criterion.
where = the difference between the actual demand and the forecast
n = the number of error terms
When the value of the standard deviation is small relative to the demand quantities, it indicates that the forecast is fairly accurate. Equation 7 serves as a mathematical representation for this criterion.
"The mean absolute per cent error (MAPE) determines the direct relationship between the difference and the actual demand during a chosen period, expresses it as a percentage and then determines the mean for all the data points" (Adendorff et al. 2003:106). This criterion can be used to compare different forecasting techniques. Equation 8 serves as a mathematical representation for this criterion.
Choosing the best forecasting model
It is important to choose a forecasting model which is accurate but also simple and easy to understand and use. The accuracy can be estimated by means of equation 6, 7 and 8 and according to Murdick et al. (1990:74) the selection of the forecasting model depends on four basic factors including, time requirements, resource requirements, input characteristics available or required and output characteristics required. The factors should be decided upon and the most applicable method chosen.
2.5.2 BREAK-EVEN ANALYSIS
According to Whiteley (2004:33) break-even analysis is a technique for discovering what volume of sales must be achieved for the business to meet al its costs before making a profit.
22.214.171.124 Purpose and use of break-even analysis
This technique can be applied to the business as a whole, or to divisions or departments. At OP it will be applied to divisions, aka the various laboratories. It will also not be applied in the manner of calculating what volume of sales must be achieved but rather seeing what they must charge for their services in just to break even before making a profit. This information can be integrated with the chosen method for price setting strategies.
126.96.36.199 Methods, tools and techniques
When doing a break even analysis costs need to be classified as either fixed or variable costs.
Fixed costs- Costs which the business will incur regardless of the volume of sales (or activity level). Examples include administrative salaries, buildings, insurance expenses, depreciation and overheads.
(In OP's case, the costs that the laboratory will incur, regardless if the tests are performed or not)
Variable costs- Costs which are directly related to the items sold (or activity level). Examples include direct labor costs, direct materials, fuel costs and marketing costs.
(In OP's case, the costs incurred when tests are performed)
The total costs are defined as the sum total of the total fixed costs and total variable costs (Adendorff et al. 2003:112)
According to Adendorff and De Wit (2003:111) the break-even point can be determined by two methods, namely the numerical or the graphical method.
Graphical depiction of break-even point is provided in Figure â€¦..
Figure â€¦. Graphical depiction of break-even point (copyright Â© Blank and Tarquin 2008).
This chart represents only one item being sold over a period of time. (In OP's case this would show the performing of a specific test over a period of time).
An overall break-even chart should be constructed, all the individual charts should be aggregated. This type of chart will show which products are profitable, and which are not. (In OP's case you'll be able to see which test is making the labs money, and which tests are just costing money)
Mathematically, the formula for break-even point can be shown as:
TR = TC
Profit = 0
Profit = 0
TR represents the total revenues and TC represents total costs or expenses for an operation.
TR = TC
Expected unit sales (Q) x Unit price (P) = Fixed cost (FC) + Total variable cost (VC)
Q x P = FC +Variable unit cost (V) x Expected unit sales(Q)
QxP = FC + (VxQ)
(QxP) - (VxQ) = FC
Q(P-V) = FC
Q = FC / (P-V)
Here, Q (expected unit sales) is break-even point in sales. As seen from the above formulation, break-even analysis depends on fixed costs, variable costs, unit price of a
"Operational Research is concerned with determination of the optimum course of action in a decision problem, under the restriction of limited resources." Soek die Source tussen die boeke??
2.6.1 LINEAR PROGRAMMING
"Linear programming is a mathematical procedure for optimizing constrained problems with the objective of maximizing profit of minimizing cost." (Adendorff and De Witt 2003:82)
188.8.131.52 Purpose and use of linear programming
Murdick et al. (1990:492) explains that linear programming models are widely used mathematical techniques designed to help operations managers in planning and decision making. This technique is used in this project to devise an optimal pricing model, to aid FVS-OP in its price setting strategy.
184.108.40.206 Methods, tools and techniques
All linear programming models are developed based on four main characteristics which are described by Adendorff and De Wit (2003:82) as:
The objective function (Z): The objective function is a linear relationship with a single objective. It is a mathematical expression in terms of the decision variables relevant to the particular problem.
The decision variables(): These are the components about which a decision must be made to optimize the objective function.
Coefficients (): These are the parameters that determine the quantities in which the decision variables in the constraints and the objective function must be combined.
Constraints: The constraints are equations or inequalities that place restrictions on the combination in which decision variables can occur in the final solution of the problem.
Linear programs are mostly applied to minimization and maximization problems. With regards to this project, it will be used as a maximization problem in order to identify the optimal pricing strategy for FVS-OP. The linear programming model can be solved by means of a graphical solution or by a computer solution. Considering the amount of data applicable to this particular project and the complexity of the problem, the computer solution seems to be the best option. Various computer solution tools are available of which the Lingo programming tool and Microsoft Excel are most commonly used and readily available to apply to this project.
2.6.2 PRICE SETTING STRATEGIES: (PRICING)
Pricing of goods or services for sale is a key management decision. As mentioned before FVS-OP's objective is not clear in terms of wanting to make a profit or wanting to break even. "The objective of pricing is to determine the price that maximizes a company's profit" (Blom 2007:14). It the case of this project the objective can also be to break even.
220.127.116.11 Purpose and use of pricing strategy
The main goal of this pricing strategy is to answer the question of how to determine the optimal price for the three different service setting, in order to make a profit or to cover all costs and break even.
18.104.22.168 Methods, tools and techniques
Blom (2007:14) identifies two functions that influence a company's price, the cost function and the price response function. Multiplying price with sales results in the company's profit and that revenue minus the cost gives profit.
A traditional pricing strategy is Cost-plus-pricing. This method consists of setting the price at your product cost, including the product cost plus fixed costs plus a profit margin. This method requires accurate cost accounting and a specified markup value to use as the profit margin. Although this is a fairly accurate technique there proves to be some room for improvement.
The aim is to develop a price setting strategy for FVS-OP which results in the best policy in order for the Faculty to either break even or to be profitable. This objective exhibits characteristics of an optimization problem, which can be presented by a linear programming model, accompanied by a sensitivity analysis approach, to examine the result when different pricing strategies are applied.
In a study done by Bitran and Caldentey (2002:11), a pricing model have been generated for revenue management, which can be adjusted to model as a deterministic or stochastic dynamic program to solve problems such as the one at the FVS-OP.
The idea is to adjust this proposed model in order to do a sensitivity analysis to come up with the optimal strategy for the three different service settings at FVS-OP.
Given the functions identified by Blom, and the pricing problem identified, a linear programming model surfaces as the most applicable technique to solve FVS-OP's problem regarding their price setting strategy.
3. SELECTION OF APPROPRIATE ENGINEERING METHODS, TOOLS AND TECHNIQUES
4. DEVELOPMENT OF SUPPLEMENTARY METHODS, TOOL AND TECHNIQUES
5. DATA & INFORMATION GATHERING AND ANALYSIS
5.1 INCOME GENERATED BY THE LABORATORIES:
It is important to develop an overview of the current situation regarding the major income generated by the various laboratories at FVS-OP, to be able to start with a cost analysis. After analyzing the financial state of the Faculty it will be possible to make recommendations on possible additional incomes such as subsidiary from the University of Pretoria, the government etc. Figure nkjgbkjh represents a summary of this overview.
Grants from the National Research Fund (NRF)
Individuals doing research
Onderstepoort Veterinary Academic Hospital (OVAH)
Laboratories of the department of Veterinary Tropical Diseases
Private Veterinary practices from the Veterinary industry
Students doing research
Individuals / representatives of various industries
Donations from private companies and individuals
Courses presented by FVS-OP to external clients
Figureâ€¦.. Representation of the income generated by the laboratories of DVTD