Volume 7 (21) Issue 22016 DOI 10.1515/vjes-2016-0011 Alternative Methods for Business Process Planning Veronica STEFAN Valentin RADU Valahia University of Targoviste, Romania veronica.stefan@ats.com.ro Abstract Identifying, analyzing and using the most appropriate and efficient methods for planning business processes is a key to success for every enterprise. Our paper are two objectives: to identify the most proper methods used for planning and applying for the same case study. We are looking to demonstrate the methodology of using some software tools and compare the obtained results. The application domain will be the planning of production processes but these methods can be extended to the service processes. The methodology is represented by the mathematical models and software applications in this fields, such as Simplex model theory, Solver Excel tools, WinQSE application, linear programming with PL.exe. The results of the research have a high applicability level and can be extended in other business fields too. Keywords: Business process, Simplex, Solver Excel, WinQSB, Linear programming JEL Classification: O21, O22, O32 Introduction Every organization, profit-oriented or not, large, small, manufacturing production or service have as core function producing some outputs from its processes. The production process can be approached as a cyber system, defined by three components: inputs, outputs, and achieving the production process. In this system, the production process transforms under worker surveillance the inputs, meaning productions factors (raw materials, work tools), in economic goods (goods, works, services), which constitute the output of the system [Filip F. G. (2007)]. For an organization to be effective and efficient in serving its customers, the managers must apply certain fundamental principles and methods of planning and control the process of production for the outputs [Frame, J. D. (2002)], [Westland J. (2006)]. Although planning and control approaches that will be presented are most commonly used in manufacturing companies, many of them have been adapted for use in services companies. 87
Volume 7 (21) Issue 22016 Planning systems and control in organizations are influenced by many factors such as the timing and synchronization, relationship with the customer and influence of the customer for the design of service, quality (a key dimension of quality being that is intangible, which makes it actual difficult to measure), business environment and its characteristics, analysis of the processes and information flows [Chapman S. N. (2001)]. The aim of the paper is the identification, analysis and compare of some alternative methods for the planning of production processes, methods that can be extended in the future for the service processes. 1. The research objectives and methodology The aim of the paper is identifying, analyzing and presenting the way of applying some several alternative methods of planning, seeking to compare results based on a jointly case study. Planning and control principles used in manufacturing companies can be adapted and used by the companies that provides services. Addressing the planning and control for organizations offering services must take into account additional factors that can influence the result, such as: the timing, scheduled the time and synchronization the events and the activities between customer demands related to delivery of the service from the company [Lewis J. P. (2001)]; relationship with the customer, in a service environment the customer is more involved in the design of the (service); the quality, a key dimension of service quality is that can be intangible, which makes it difficult to measure effectively; the storage, the companies services oriented, that actually do not involve physical goods in their production, often do not storing their output. It is impossible, for example, to store a haircut. The stock (mainly finished products) can be considered as the utilization of the enterprise production capacity ahead of actual demand. Some methods like Simplex model, application Solver Excel and WinQSB tool from PL.exe [Orr A. D. (2004)] will be used to illustrate their application for production companies, considering that they can be adapted for use in services companies too. For application of planning methods above mentioned is necessary to consider the whole ecosystem of planning activities flow, as they are illustrated in the following figure. The flowchart of planning and control activities Figure 1. 88 (Source: Chapman S. N. (2001))
2. Defining working hypotheses Volume 7 (21) Issue 22016 We considered the situation of planning the number of assortment by each product type, so as to obtain the maximum benefit, considering the productivity conditions [Rusu, A., (2007)]. Our case study will establish the following hypotheses: A machine is working 25 hours per week and produces three articles. The benefit after the sale of these three items is 40 Ron / each piece for the first item, 120 Ron / each piece for the second article and 30 Ron / each piece for the third article. Within an hour the machine can achieve 50 pieces for the first article or 25 pieces of the second article or 75 pieces for the third article. The weekly demand does not exceed 1,000 pieces for the first article, 500 pieces for the second article and 1500 pieces for the third article. We need to plan the production for each of the three articles, for the enterprise to ensure maximum benefit. For the mathematical model of the problem we consider the variables x1, x2, x3 for the quantities that must be produced for each of the three articles: max f 40* x1120* x2 30* x3 x1 1000 x2 500 x3 1500 1 1 1 x1 x2 x3 25 50 25 75 x1, x2, x3 0 1 As x 1 represents the number of hours in a week when the machine produces a 50 quantity x 1 from the first product, the constraint referring the number of hours has the next form: 3*X1 + 6*X2 + 2*X3 <= 3750 3. Results analysis 3.2. Using SIMPLEX method The mathematical model formulated above, where x1, x2, x3 represent each quantities of the three articles that must be produced, is brought to the next standard form: 89
Volume 7 (21) Issue 22016 max f 40 * x1120* x2 30 * x3 x1 x4 1000 x2 x5 500 x3 x6 1500 x1 *3 6 * x2 2 * x3 x7 3750 x1, x2, x3, x4, x5, x6, x7 0 The matrix of restrictions coefficients and vectors Pi is the following: It is noted that the base vectors are P 4 P5 P6 P7 so basic unknowns are x 4 x5 x6 x7. The first admissible basic solution, which was obtained by canceling the secondary unknowns, is X 0 0 0 1000 500 1500 3750 T 90 0 The first SIMPLEX table is: Base C B P 0 40 120 30 0 0 0 0 P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 4 0 1000 1 0 0 1 0 0 0 P 5 0 500 0 1 0 0 1 0 0 P 6 0 1500 0 0 1 0 0 1 0 P 7 0 3750 3 6 2 0 0 0 1 z k -c k 0-40 -120-30 0 0 0 0 P 4 0 1000 1 0 0 1 0 0 0 P 2 120 500 0 1 0 0 1 0 0 P 6 0 1500 0 0 1 0 0 1 0 P 7 0 750 3 0 2 0 0-6 1 z k -c k 60.000 0 0-30 0 120 0 0 P 4 0 1000 1 0 0 1 0 0 0 P 2 120 500 0 1 0 0 1 0 0 P 6 0 1125-3/2 0 0 0 3 1-1/2 P 3 30 375 3/2 0 1 0-3 0 1/2 z k -c k 71.250 5 0 0 0 30 0 15
The differences z k - c k is calculated as follows: Volume 7 (21) Issue 22016 Z 1 c 1 = C B * P 1 c 1 = 0*1 + 0*0 + 0*0 + 0*3 40 = -40 Z 2 c 2 = C B * P 2 c 2 = 0*0 + 0*1 + 0*0 + 0*6 120 = -120 etc. The solution is not optimal because there are negative value on the line z k - c k. Get in min(-40, -120, -30) = -120, that means the vector P 2 Get out min(500/1, 3750/6) = 500, that means the vector P 5 Pivot line is divided by 1, so remain unchanged. To make 0 on the pivot column on multiply: pivot line with 0 and add to P4 line pivot line with 0 and add to P6 line pivot line with -6 and add to P7 line It repeat the four above steps and we note that P 3 get in and P 7 get out Optimality criterion is satisfied: all z k - c k differences are greater or equal to 0. The optimal solution can be find on the column P 0 : x 1 =0, x 2 =500, x 3 =375, x 4 =1000, x 5 =0, x 6 =1125, x 7 = 375, so it will produce 0 items from article 1, 500 items from the second article and 375 items from the third article. The benefits are 40*0 + 120* 500 + 30 *375 = 71.250 3.3 Using SOLVER from Excel SOLVER is an add-in program in MS Excel [Pyron T. (2004)] used to find an optimal value, maximum or minimum, for one formula in one cell, named Objective cell. This value is subject to constraints on the values of other formula cells on a worksheet. Solver works with a group of cells called decision variables cells for computing the formulas in the objective and constraint cells. Solver adjusts the values in the decision variable cells to satisfy the limits on constraint cells and produce the result for the objective cell. With Solver we can determine the maximum or minimum value of one cell by changing other cells. Using Solver we reserve cell B2 for the number of products from the first article, cell B3 for the number of products from the second article and B4 for the third article. The cell B5 will contain the left side of the constraint referring to hours, respectively: B5= 3*B2+6*B3+2*B4 The objective function to be maximized is inserted in cell B6: B6 = 40*B2+120*B3+30*B4 91
Volume 7 (21) Issue 22016 Figure 2. Inserting the input values in Solver Excel In the menu Subject to the constraints we inserted one by one each constraint, like in the next figure. The number of products for the first article is less than or equal to 1000, the number of products for the second article is less than or equal to 500, the number of products for the third article must be less than or equal to 1500, and the last constraint, the required number of hours worked by machine B5 <= 3750. The image in the left contains the first constraint, the number of products for the first article is less than or equal to 1000, and the last constraint is referring, to hours: Introducing the constraints Figure 3. With Options button we assure that X1, X2, X3 variables have integers values greater than or equal to 0, marking the option Assume non-negative. In the main menu of Solver we complete the target cell that must be maximum with $B$6 and next we modify the cells with option By changing Cells, $B$2: $B$4. Using the Solve button we obtain the window with final results: Solver Results Figure 4 92
Volume 7 (21) Issue 22016 Selecting Keep Solver Solution and choosing a feedback with Reports Answer, in the worksheet Excel we find the final results and the next values: X1 = 0, X2 =500, X3=375, and the benefits 71.250. Figure 5. Results Report and sensitivity analysis 3.4 Using WinQSB for linear programming Linear programming can be used in the production management to solve problems of distribution of production on different machines in terms of maximizing profits for determining the quantities of various goods to be produced [Harts D. (2007)]. We will solve with linear programming the same mathematical model, with objectives and restrictions. 93
Volume 7 (21) Issue 22016 WinQSB interface with model restrictions and variables Figure 6. Selecting Solve the Problem from the menu Result we obtain the solution: The results with WinQSB Figure 7. The final Simplex table with WinQSB solution Figure 8 94
Volume 7 (21) Issue 22016 The graphical presentation of the WinQSB solution Figure 9 The result is the same with the two previous methods: X1= 0, X2=500, X3 = 375 and the benefits 71 250. Thus, with linear programing is much easier to work and to find the final result. In addition, there is the possibility of cooperating with business intelligence applications for integrate the result with other application and to aggregate for complex analysis. Conclusions Key performance for an organization are depending of a related number of factors, its overall efficiency becoming ensured with a continuous and consistent planning process across the organization. One of the most important factor of efficiency is the rigorous and correct planning, based on the most scientific theories, innovative methods and software tools. These software solutions for planning provides more accurate and documented decision support, with significantly improved performance. Providing real-time performance information enables to all levels of decision in a company to take corrective actions, which diminishes the chain propagation of errors and lead to an increased efficiency. The planning process is multi-dimensional and ensures a high degree of objectivity, unlimited from the capacity of a single decision factor. Considering more variables we have a better way to decide, the result is better refined and accurate. Although the costs associated with traditional ways of planning can be considerably reduced, the benefits of implementing a tool to assist the planning process are 95
Volume 7 (21) Issue 22016 highly increased and the costs of implementing such solutions is quickly pays off through the performance achieved. Using the potential of information technology has proved always a success and the methods utilized contribute to a high level of significantly increased performance. References Chapman, S. N., (2001) The fundamentals of production planning and control, Ed. Prentice Hall, Upper Saddle River, NJ 07458, ISBN 0-13-017615-X Filip, F. G., (2007) Sisteme suport pentru decizii, Editura tehnică Frame, J. Davidso, (2002) The new project management: tools for an age of rapid change, complexity, and other business realities, San Francisco, John Wiley & Sons Lewis, James P. (2001) Project planning, scheduling and control: a hands-on guide to bringing projects in on time on budget, 3rd ed. Toronto, McGraw-Hill Orr, Alan D., (2004) Advanced project management: a complete guide to the processes, models and techniques, London, Kogan Page Pyron, Tim, (2004) Special edition using Microsoft Office Project 2003, Indianapolis, Que Westland, Jason, (2006) The project management life cycle: a complete step-by-step methodology for iniating, planning, executing & closing a project successfully, London, Kogan Page Harts, D., (2007) Microsoft Office 2007 Business Intelligence, ISBN-13: 978-0071494243, McGraw-Hill Osborne Media Rusu, A., (2007) Cercetari operationale, Iasi http://www.microsoft.com/bi/ http://www.wiley.com/college/tech/winqsb.htm 96