scientific management of human resources

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SCIENTIFIC MANAGEMENT OF HUMAN RESOURCES TO ACHIEVE PERSISTENT BUSINESS PROCESS IMPROVEMENT Said Ali Hassan* and Ibrahim Abdulaziz Al-Darrab** * Professor, Industrial Engineering Dept., King Abdulaziz University, Jeddah, Saudi Arabia, and Decision Support Dept., Cairo University. ** Chairman of Industrial Engineering Dept., King Abdulaziz University, Jeddah, Saudi Arabia. ABSTRACT To meet the constant changes to the competitive marketplace all over the world, any organization needs to continually look for ways to optimize its business processes. To accomplish this, the organization should focus on three core areas: Process (Process-Focused Strategy), Tools/Technology (Business Process Automation), and People. The processfocused strategy makes a continuous review of the specific department’s processes, activities, documents, information flow, and make sure that process-focused strategies are in sync with the overall goals of the organization. Business Process Automation defines the process that can respond dynamically to changing market forces, and restructure processes and introduce, where necessary, updated technology to automate manual processes or update Tools/ Technology in order to maximize efficiencies. It is clear that processes and technology alone will not deliver competitive advantages or achieve persistent business process improvement. The key factors are the people involved, and the skills & talents they possess, and in that aspect the scientific management of the people or human resources will rise. Scientific Management of Human Resources or Human Resources Optimization is aiming at maximizing human performance at both organizational and individual levels. This is achieved by ensuring that the right capabilities are present within the organization, and ensuring that all resource processes are in line with the scientific methods and management optimization techniques. The impact of optimum decisions on business process improvement is a straightforward result. A list of some actual award-winning applications in diversity of organizations using Management Science Techniques to problems typically resulted in annual savings in the millions (or even tens of millions) of dollars. Furthermore, additional benefits (e.g., improved service to customers and better managerial control) sometimes were considered to be even more important than these financial benefits. Although most routine Management Science (or Operations Research) studies provide considerably more modest benefits than these awardwinning applications, the financial figures do accurately reflect the dramatic impact that large, well-designed studies occasionally can have as a result of seeking optimum or better decisions. To aid the decision maker in designing the optimum solutions to problems, a computer-based system to support decisions in unstructured and semi-structured decisions where interactive user-system is desired is to be designed. Human resources optimization is dedicated to productivity and profitability through flexible, creative and cost effective solutions designed to improve and streamline talent acquisition, optimization and retention. Scientific management methods for human resources optimization seek for how to make the best possible judgment about human resources optimization and having the best resource strategy (e.g. hiring, developing, deploying, firing, redeploying,

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analyzing resource allocation, focusing on future capabilities, increasing organizational and individual performance, and reaching the highest individual and group motivation). As such these methods provide practical and hands-on instructions on important economic or business variables related to human activities to enhance the process of successful transfer of management knowledge and skills from the management theory to the work environment. Illustration examples and real case studies are presented for all aspects of business process improvement, human capital acquisition, human resources scheduling and deploying, workforce planning, scheduling overtime, and computerized support systems to clarify the human resources optimization to key processes critical to managing the workforce and human resources. 1. BUSINESS PROCESS IMPROVEMENT Business Process Improvement is a continual necessity to insure efficient and effective performance. To meet the constant changes to the competitive marketplace, any organization needs to continually look for ways to optimize its Business Processes. It should provide a detailed analysis of its business processes, diagnose underlying problem areas, identify strengths & weaknesses, highlight opportunities and threats, and recommend process focused strategies to help maximize the organizations performance. To accomplish this, the organization should focus on three core areas: Process, Tools/Technology, and People. Process (Process-Focused Strategy) Every organization has a number of key processes that drive the performance effectiveness of their organization. The organization should conduct a thorough review of the specific department’s processes, activities, documents, and information flow. It should document the current processes, identify performance gaps, help determine activities that do not add value, identify potential areas that can be enhanced through automation, and look for areas to improve operational efficiencies. It should also make sure that process-focused strategies are in sync with the overall goals of the organization. Tools & Technology (Business Process Automation) Business process automation means defining the business rules and mapping them to the process that can respond dynamically to market forces. Looks at what tools/technology the organization uses to connect both internally and externally (clients & suppliers). Look to realign & restructure overall business processes and introduce, where necessary, updated technology to automate manual processes that prevent organizations from maximizing efficiencies. People (Human Resources Optimization) Processes and technology alone will not deliver competitive advantages. Just as important are the people involved, the skills & talents they possess, and insuring proper alignment to the roles needed to effectively carry out the key processes. Most important is the identification of the appropriate skills and talents to meet the current and future process needs, and also to determine if different areas in an organization have the appropriate people development and relevant performance metrics in place to insure optimal business performance.

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2. IMPACT OF OPTIMUM DECISIONS ON BUSINESS PROCESS IMPROVEMENT 2.1. The Impact of Operations Research Techniques Today, the impact of Operations Research techniques (or quantitative techniques) that seek to obtain the optimum solution to problems can be felt in many areas. This is indicated by the number of academic institutions offering courses in this subject at all degree levels. Many management consulting firms are currently engaged in operations research activities. These activities have gone beyond military and business applications to include hospitals, financial institutions, libraries, city planning, transportation systems, and even crime investigation studies. The impressive progress in the field of operations research is due in large part to the parallel development of the modern digital computer with its tremendous capabilities in computational speed and information storage and retrieval. In fact, had it not been for the digital computer, operations research with its large-scale computational problems would not have acquired the present promising status in all kinds of operational environments. Operations Research techniques have an impressive impact on improving the efficiency of numerous organizations around the world. In the process, quantitative techniques have a significant contribution to increasing the productivity of the economies of various countries. It appears that the impact of quantitative techniques will continue to grow. For example, according to the U.S. Bureau of Labor Statistics, quantitative techniques represent one of the fastest-growing career areas for U.S. college graduates. To give a better notion of the wide applicability of Operations Research, (or Management Science) in aiding to obtain optimum decisions, a list of some actual award-winning applications is presented in Table 1. Note the diversity of organizations and applications in the table. Some of these applications will be described in more details in the rest of this paper, while the curious reader can find a complete article describing each application in the January-February issue of Interfaces for the year cited in the third column of the table. The last column indicates that these applications typically resulted in annual savings in the millions (or even tens of millions) of dollars. Furthermore, additional benefits not recorded in the table (e.g., improved service to customers and better managerial control) sometimes were considered to be even more important than these financial benefits. Although most routine OR studies provide considerably more modest benefits than these award-winning applications, the figures in the rightmost column of the table do accurately reflect the dramatic impact that large, well-designed OR studies occasionally can have. From the table, it is clear that all types of factories, companies, and associations can benefit from the applications of optimization techniques. As a short list, we can mention the following list: water management, chemical plants, airports, refinery operations, police patrol officers, gasoline products, spare parts inventories, call centers, schedule massive projects, assigning airplane types, national trucking network, supply chain, weapons systems, production and distribution system, and customer service. The applications will cover all types of business activities: national management policy, mix of new facilities, pricing system, production operations, schedule shift work, optimize refinery operations and supply, distribution and marketing of products, optimally schedule and deploy officers, optimally blend available ingredients into gasoline products, spare parts inventories, trucking network, design of call centers, assigning airplane types to flights, restructure supply chain, plants, distribution centers, potential sites, and market areas, select and schedule massive projects, redesign the defense force and its weapons systems, redesign the production and distribution system schedule employees, and redesign the sizes of production lines.

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Table 1: Some award winning applications of operations research. Year of Publication

Organization

Nature of Application

The Netherlands Rijkswaterstaat

Develop national water management policy, including mix of new facilities, operating procedures, and pricing. Optimize production operations in chemical plants to meet production targets with minimum cost. Schedule shift work at reservation offices and airports to meet customer needs with minimum cost. Optimize refinery operations and supply, distribution, and marketing of products. Optimally schedule and deploy police patrol officers with a computerized system.

1985

Annual Saving $15 million

1985

$2 million

1986

$6 million

1987

$70 million

1989

$11 million

Optimally blend available ingredients into gasoline products to meet quality and sales requirements. Integrate a national network of spare parts inventories to improve service support.

1989

$30 million

1990

Optimize the design of a national trucking network and the routing of shipments. Design an effective needle exchange program to combat the spread of HIV/AIDS. Develop a PC-based system to guide business customers in designing their call centers. Maximize the profit from assigning airplane types to over 2500 domestic flights. Restructure the global supply chain of suppliers, plants, distribution centers, potential sites, and market areas. Optimally select and schedule massive projects for meeting the country's future energy needs. Optimally redesign the size and shape of the defense force and its weapons systems. Redesign the North American production and distribution system to reduce costs and improve speed to market. Optimally schedule employees to provide desired customer service at a minimum cost. Redesign the sizes and locations of buffers in a printer production line to meet production goals.

1992

$20 million +$250 million less inventory $17.3 million 33% less HIV/AIDS $750 million $100 million $800 million

Monsanto Corp.

United Airlines

Citgo Petroleum Corp. San Francisco Police Department Texaco, Inc.

IBM

Yellow Freight System, Inc. New Haven Health Depart. AT&T Delta Airlines Digital Equipment Corp. China

South African defense force Proctor and Gamble Taco Bell Hewlett-Packard

1993 19993 1994 1995

1995

$450 million

1997

$1.1 million

1997

$200 million

1998

$13 million

1998

$280 million more revenue 4

2.2. Decisions that Need Support To give a real application example for some decisions that are in need for support; the case of the Roadway Package System (RPS) will be presented. The first decision a new company like RPS that was known afterwards as FedEx Ground must make is where to open its first hubs and terminals? Using the wrong sites can cause large losses and even endanger the life of a newcomer. Other important decisions are: - Where to open its first hubs and terminals - How to attract customers? - Will the competitors reduce their fees? - What new services the competitors will offer? - What should you do in response? RPS developed a quantitative location model using SAS software package and it has grown from 3 hubs and 36 terminals to 21 hubs and over 300 terminals in 10 years. About 50 other decision support applications were prepared in three areas: - Market and research planning, - Strategic & operations planning, - Sales support. 2.3. Impact Indicators for Good Decisions 1- The results indicate the intervention was effective and accomplished its objectives: OR studies generally either test one or more interventions or they evaluate changes resulting from interventions already implemented. If all studies found the intervention under study to be effective, then research would be unnecessary. This indicator asks whether the intervention tested successfully improved front-line service delivery (e.g., increase in utilization of services, improved quality of services). Negative results can also be instructive, but they would not influence service delivery except to discontinue an ineffective strategy 2- The organization acted on the results: Acting on the results consists of implementing the actual services of the intervention or the activities to support those services (e.g., training courses, development of service delivery guidelines, changes in allocation of personnel, production and testing of materials, supervision, monitoring). 3- The organization scaled up the intervention in the same country. Most OR studies are conducted in a specific geographical area. Scaling up refers to implementing the intervention activities in additional geographical areas. It can but does not necessarily refer to expansion to the national level. 4- Another organization within the same country adopted the intervention. An organization that did not participate in the OR study adopts the intervention by implementing its primary components 5- Another country replicated the intervention: Some evidence should exist which links the original intervention to the activities carried out in the other country (e.g., program managers from other countries visited the project and subsequently adopted similar strategies). 5

6- A change in policy can be linked to the project: This indicator measures legislation or other official changes that potentially affect service or production delivery. 7- The organization conducted subsequent studies without external technical assistance: This indicator is included to reflect whether the organization has sufficient capacity to conduct these types of activities as a result of the previous OR experience and has the opportunity to do so. 8- The original donor funded new program activities based on the results of the study: New program activities are those activities tested in the intervention that the donor had not already funded. 9- Other donors provided new or expanded funding based on results of the studies: Other donors are those donor agencies that did not contribute financial support to the original OR project but subsequently funded the initiation or expansion of program activities. Specifically, service delivery or support activities, including training, production of materials, construction or renovations of facilities, and purchase of supplies and equipment. 3. DECISION SUPPORT SYSTEMS (DSS) 3.1. Definition of a DSS Because there are many approaches to decision-making and because of the wide range of domains in which decisions are made, the concept of decision support system (DSS) is very broad. A DSS can take many different forms. In general, we can say that a DSS is a computerized system for helping make decisions. A decision is a choice between alternatives based on estimates of the values of those alternatives. Supporting a decision means helping people working alone or in a group gather intelligence, generate alternatives and make choices. Supporting the choice making process involves supporting the estimation, the evaluation and/or the comparison of alternatives. In practice, references to DSS are usually references to computer applications that perform such a supporting role [1]. Finlay [3] and others define a DSS rather broadly as "a computer-based system that aids the process of decision making." In short, a Decision Support System (DSS) is a computer-based system to support decision makers in unstructured and semi-structured decisions where interactive user-system is desired. 3.2. Characteristics and Capabilities of DSS Because there is no exact definition of DSS, there is obviously no agreement on the standard characteristics and capabilities of DSS. Turban, E., Aronson, J. E., and Liang, T. P. [18] describe an ideal set of characteristics and capabilities of DSS. The key DSS characteristics and capabilities are as follows: 1. 2. 3. 4. 5.

Support for decision makers in semi-structured and unstructured problems. Support managers at all levels. Support individuals and groups. Support for interdependent or sequential decisions. Support intelligence, design, choice, and implementation. 6

6. Support a variety of decision processes and styles. 7. DSS should be adaptable and flexible. 8. DSS should be interactive and provide ease of use. 9. Effectiveness balanced with efficiency (benefit must exceed cost). 10. Complete control by decision-makers. 11. Ease of development (modification to suit needs and changing environment) by end users. 12. Support modeling and analysis. 13. Data access. 14. Standalone, integration and Web-based. 3.3. Benefits of a DSS The basic DSS benefits are: 1- Increased number of alternatives 2- Better understanding of the business 3- Fast response to unexpected situations 4- Ability to carry out ad hoc analysis 5- New insights and learning 6- Improved communication 7- Cost savings 8- Time saving 9- Better use of data resources 10- More effective teamwork 3.4. A Schematic View of a DSS Turban [18] defines it more specifically as "an interactive, flexible, and adaptable computerbased information system, especially developed for supporting the solution of a nonstructured management problem for improved decision making. It utilizes data, provides an easy-to-use interface, and allows for the decision maker's own insights." So a DSS is composed of the subsystems shown in Figure 1:

Data: external and internal

Other computer-based systems

Data management

Model management User interface

Manager (User)

Figure 1: A schematic view of DSS

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Figure 1 provides only a basic view of a DSS. The data management subsystem includes the database which contains relevant data from external and internal sources and is managed by software called the data base management system (DBMS). Some advanced DSS contains a knowledge management subsystem; this subsystem can support any of the other subsystems or act as an independent component. It provides intelligence to augment the decision maker's own. The model management subsystem comprises a software including financial, statistical, management science, and other quantitative models that provide the system's analytical capabilities, and appropriate software management. Modeling languages for building custom models are also included. This software is often called a model base management system (MBMS). The user interface enables the communication with the user to command and communicate with the DSS. The user in this case is considered to be part of the system. 3.5. Common Reasons for Computerized Support Systems There are many reason to use computerized support systems, the most common reasons are: 1. Speedy computations: • Large numbers of computations very quickly and at low cost. • Timely decisions. 2. Cognitive limits: • Diverse information. • Access and process vast amounts of stored information. • Coordination and communication of group-work. 3. Cost reduction: • Experts may be costly. • Communicate from different locations. • Online access. 4. Technical support: • Complex computations. • Data may be stored in different databases. • Data may include graphics. 5. Quality support: • More alternatives can be evaluated. • Risk analysis can be performed quickly. • Views of experts (some of whom are in remote locations). • Perform complex simulations. • Checking many possible scenarios. 3.6. Indicators for Evaluating DSS Projects The extent that a DSS could help an organization differs from one organization to another and from one application to another. There are some indicators by which we can judge to what level the intended DSS helped an organization in a specific application, and by such indicators we can evaluate a DSS project. These indicators are: 1- The implementing organization actively participated in the design of the project: The design of the OR project is the formulation of the study, which includes identifying the problem, establishing the objectives, designing the intervention, and selecting a research

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methodology. Active participation involves contributing original ideas to the work, not simply attending meetings. 2- The implementing organization actively participated in the implementation of the study: Active participation indicates that the organization was involved in decision-making and played a technical role in the implementation of the study, for example hiring new staff, conducting training, or analyzing and interpreting results. 3-The implementing organization participated in developing programmatic recommendations: This indicator asks whether these organizations participated, as well as how, for example, collaboration in report preparation, through formal meetings, and in working groups at dissemination conferences. 4- The study accomplished its research objectives: Each study is designed with one or more objectives. This indicator determines whether the study achieved each of its objectives. 5- The intervention was implemented as planned (or with some modifications): Changes between the proposal and implementation of the intervention frequently occur and often are for the better. This indicator seeks to determine whether the organization carried out all of the activities specified in the intervention, allowing for some change in response to local realities. If not, the reviewer should identify any changes between the design and actual realization of these activities. This indicator is not intended to penalize an organization for making modifications. Rather, it ascertains that the organization made some meaningful change in service delivery (that there was something to evaluate). An intervention study fails to show any change in the desired outcome for two plausible reasons: (1) the organization never implemented the intervention or implemented it so weakly that the study hardly constituted a fair test of its potential effectiveness, or (2) the organization fully implemented the intervention but it failed to show the expected results. This indicator attempts to eliminate the first possibility by determining that the intervention was in fact implemented. 6- The researchers completed the study without delays (or other adjustments to the timeline) that would compromise the validity of the research design: Study activities are often delayed. This indicator seeks to identify delays that affected the timing of the intervention or that could have reduced the effectiveness of certain activities. 7- Key personnel remained constant over the life of the project: Key personnel are any personnel with a decision-making role in the design or implementation of the subproject. Such personnel include the Principal Investigator, the study coordinator, and counterparts in the collaborating agencies, including key service personnel or government officials actively participating in implementation. 8- The study design was methodologically sound and free of flaws affecting the final results: Evaluators should assess this item based on the methodology section of the report and (if appropriate) on discussions with the researchers. Generally, the external evaluator (not a staff member of any of the participating organizations) makes an informed decision on this point; key informants may have less knowledge or experience to make this judgment.

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9. The research design was feasible in the local context: Feasible means reasonable or manageable, a design that could be repeated without unduly draining financial or human resources. Local context includes not only program-related factors but also socio-cultural or political factors, among others. 10. The implementing organization judged the technical assistance to be useful: To qualify for a full score, both elements must be positive. If, for example, the advice was technically sound, but counterparts reacted negatively to the manner in which the OR team provided assistance (e.g., in an offensive or condescending way, imposed upon them), then the study should receive a lower score on this indicator. 11. Stakeholders judge results of the study to be credible/valid in the local context: This indicator refers to the judgment of stakeholders (policymakers, researchers, donors, program managers). Utilization of results would be likely limited if stakeholders seriously questioned the validity of the results. 12. Research was programmatically relevant: The perceptions of the same stakeholders listed above determine relevance. Relevant research addresses a priority problem of the program. 13- Results were disseminated to key audiences, including policymakers, program managers, service providers, and donors: This indicator seeks to determine whether the dissemination strategies used effectively reached the target audience. Key audiences are those in a position to act on the results (e.g., policymakers, key decision-makers or service providers in implementing/collaborating agencies, and donor agency staff). 14- Results are readily available in written form: This indicator verifies the existence of a document on the key findings of the study that is well presented (of professional quality) and is locally available in sufficient quantity. This document may appear in a variety of media (e.g. website, CD-ROM) in addition to print. Ideally, results should be available in various formats appropriate to the intended audience: final reports and journal articles for donors, and summaries or research briefs for decision makers and program managers. 4. HUMAN RESOURCES OPTIMIZATION (HRO) 4.1. Definition of (HRO) Human Resources Optimization (HRO), or Human Capital Optimization (HCO) is dedicated to productivity and profitability through flexible, creative and cost effective solutions designed to improve and streamline talent acquisition, optimization and retention. Human Resources Optimization is the maximization of the performance at both organizational and individual level by ensuring the right capabilities are present within the organization, the resource management processes are in line with the overall business strategy and competency management is integrated with all business processes. Human Resources Optimization is a process that on the one hand delivers input for making strategic choices, and on the other hand translates the business strategy into practical aspects. Regardless of size or industry, Human Capital Optimization has these top priorities: 1- Identifying, attracting and acquiring outstanding talent, 2- Maximizing the productivity, 3- Minimizing the operating costs, 4- Planning the retention of the human capital. 10

4.2. Benefits of (HRO) The basic HRO benefits are: 1- Maximizes Return on Investment (ROI). 2- Acquiring the best talent that is right for the company. 3- Reduced cost of acquisition. 4- Reduced high cost of turnover. 5- A great culture and environment that fosters happier, healthier, more productive people. 6- Optimized deployment of resources. 7- Correct balance between external and internal resources. 8- Optimized organizational and individual performance. 9- Increased employee motivation. 5. HUMAN RESOURCES OPTIMIZATION: CASE STUDIES 5.1. Human Resources Acquisition The very important questions facing any organization in human capital acquisition are the following: • Whom to hire? • What to work? • Which shift? • Number to be hired? • Budget? To give an actual case study, we will take the case of Pfizer Company, the company wishes to invest in the following five projects: 1. Antidepressant that does not cause serious mood swings. 2. A drug that addresses manic-depression. 3. A less intrusive birth control method for women. 4. A vaccine to prevent HIV infection. 5. A more effective drug to lower blood pressure. A number of senior scientists (n) to lead the five projects are evaluated according to the number of publications in the five fields of researches; the obtained data can be arranged in the following table, where aij is the number of publications in field i by scientist j, for i = 1,2, …,5, and j = 1, 2, …, n: j



n

… … …

a1j

… … …

a1n a2n

... …

… …

aij …

… …

ain …

a52



a5j

Scientist Project 1 2 …

1

2

a11 a21 …

a12 a22 ..

i …

… …

5

a51



a5n

The obvious and very important question which will face the decision maker will be: Whom to hire? And to which project to hire?

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A systematic solution to such problems can be obtained by formulating a linear programming model, and solving in order to obtain the optimum solution that will maximize the total efficiency of performing the five projects. 5.2. Hiring Additional Human Resources South African (Union Airways) is adding more flights, and it needs to hire additional customer service agents. An important question is raised: How many more should be hired? An OR team is studying how to schedule the agents to provide satisfactory service with the smallest personnel cost, (the minimum number of customer service agents on duty at different times of the day). The wages specified in the contract differ by shift because some shifts are less desirable than others. 5.3. Scheduling and Deploying Police Patrol Officers An OR study, conducted for the San Francisco Police Department resulted in the development of a computerized system for optimally scheduling and deploying police patrol officers. The objectives of this study were identified as: 1. Maintain a high level of citizen safety. 2. Maintain a high level of officer morale. 3. Minimize the cost of operations. The used solution was a computerized system for optimally scheduling and deploying police patrol officers. The main results of that system were: • It provided annual savings of $11 million, • An annual $3 million increase in traffic citation revenues, • A 20 percent improvement in response times. 5.4. Workforce Planning Consider a Hospital where every doctor works five consecutive days, and then takes two days off. Suppose that the number of doctors needed on each day of the week differ according to the different number of patients on each day, and suppose that the these numbers are as follows: Doctors needed: Day: Number:

Mon. n1

Tue. n2

Wed. n3

Thu. n4

Fri. n5

Sat. n6

Sun. n7

It is needed to minimize the number of Doctors, while maintaining the performance of keeping the minimum required number of doctors on each day of the week, the question will be: How many doctors should work on each day? The mathematical model is formulated by considering the number of doctors that start working on Monday as x1, and start on Tuesday as x2,…, and start on Sunday as x7. The objective function is clearly: Min. x1 + x2 + x3 + x4 + x5 + x6 + x7 The problem Constraints: For the number of doctors working on Monday: x1 + x4 + x5 + x6 + x7 ≥ n1

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Similar constraints can be constructed for the other six days. The obtained optimum solution will state the optimum number of doctors planned for each day of the week. The optimum solution for such a problem with real data is given in the following Figure: Required Assigned

Mon 14

Tue 13

Wed 15

Thu 16

Fri 19

14

14

15

19

19

Sat 18

Sun 11

4 7 1 7 0 3 0 Total =25

18

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5.5. Scheduling Overtime The SilComputer Co. (a factory for producing computers) currently has 5,000 notebook computers. The expected demand over the next four months is as follows: 7,000; 15,000; 10,000; and 8,000. Production capacity: The production capacity in normal time is 10,000 computers/month at a cost of $2000 per notebook, and 2,500 computers at a cost of $2200 per notebook in overtime. Computers produced in a month either to meet the demand, or in inventory for use later. Each computer in inventory is charged $100 each month. The question raised is: How should SilComputer Schedule the production in normal time and in overtime at minimum cost? Modeling: Let the time periods t = 1, 2, 3, 4. xt is the number of notebooks in period t produced at regular time, yt is the number of notebooks in period t produced at overtime. it is the inventory level at the end of period t. The objective is to minimize the total cost of production in normal and over time and the cost of inventory of excess computers in the inventory.

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2000 x1 + 2000 x2 + 2000 x3 + 2000 x4 + 2200 y1 +2200 y2 + 2200 y3 + 2200 y4 + 100 i1 + 100 i2 + 100 i3 The constraints are: xt ≤ 10000, and yt ≤ 2500, for t = 1, 2, 3, 4 5000 + x1 + y1 = 7000 + i1 i1 + x2 + y2 = 15000 + i2 i2 + x3 + y3 = 10000 + i3 i3 + x4 + y4 = 8000 . Such a linear programming model could be easily solved to obtain the optimum solution constitutes the production in the normal and in the overtime that minimizes the total cost> 6. CONCLUSIONS 1. Processes and technology alone will not deliver competitive advantages or insure persistent business process improvement. Just as and more important are the people involved. 2. The impact of Operations Research techniques (or quantitative techniques) that seek to obtain the optimum solution to problems can be felt in many areas. Almost all types of factories, companies, and associations can benefit from the applications of optimization techniques in various applications. 3. Many of the unstructured and semi-structured decisions need Decision Support Systems to support decision makers where interactive user-system is desired. 4. There are many reasons to use computerized support systems. The most common reasons are: speedy computation, cognitive limits, cost reduction, technical and quality support. 5. Human Resources Optimization (HRO) will ensure best decisions leading to persistent business process improvement. 6. HRO has these top priorities: Identifying, attracting and acquiring outstanding talent, maximizing the productivity, minimizing the operating costs, and planning the retention of the human capital. 7. There are many real worldwide success stories and case studies that can be repeated with needed modifications to suit other similar applications in different organizations. References 1. Druzdzel, M. J. and R. R. Flynn (1999), Decision Support Systems. Encyclopedia of Library and Information Science, A. Kent, Marcel Dekker, Inc. 2. Ender, Gabriela, (2005-2007), E-Book about the OpenSpace-Online® Real-Time Methodology: Knowledge-sharing, problem solving and results-oriented group dialogs in real-time about topics that matter, Download http://www.openspaceonline.com/OpenSpace-Online_eBook_en.pdf . 3. Finlay, P. N. (1994), Introducing decision support systems. Oxford, UK Cambridge, Mass., NCC Blackwell, Blackwell Publishers.

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4. Gachet, A. (2004). Building Model-Driven Decision Support Systems with Dicodess. Zurich, VDF. 5. Gaddomski, A.M. at al., (2001), "An Approach to the Intelligent Decision Advisor (IDA) for Emergency Managers., Int. J. Risk Assessment and Management, Vol. 2, Nos. 3/4. 6. Haag, Cummings, McCubbrey, Pinsonneault, Donovan (2000), Management Information Systems: For The Information Age. McGraw-Hill Ryerson Limited: 136-140. ISBN 0072-81947-2. 7. Holsapple, C.W., and A. B. Whinston, (1996). Decision Support Systems: A KnowledgeBased Approach. St. Paul: West Publishing, ISBN 0-324-03578-0. 8. Marakas, G. M. (1999), Decision support systems in the twenty-first century, Upper Saddle River, N.J., Prentice Hall. 9. Power, D. J. (1997), What is a DSS? The On-Line Executive Journal for Data-Intensive Decision Support 1(3). 10. Power, D. J., (2000), Web-based and model-driven decision support systems: concepts and issues, in proceedings of the Americas Conference on Information Systems, Long Beach, California. 11. Power, D. J. (2002), Decision support systems: concepts and resources for managers, Westport, Conn., Quorum Books. 12. Power, D.J. (2003), A Brief History of Decision Support Systems DSS Resources, COM, World Wide Web, version 2.8, May 31. 13. Reich, Yoram; Kapeliuk, Adi, Decision Support Systems., Nov. 2005, Vol. 41 Issue 1, pp.1-19. 14. Sauter, V. L., (1997), Decision support systems: an applied managerial approach, New York, John Wiley. 15. Silver, M. (1991), Systems that support decision makers: description and analysis, Chichester, New York, Wiley. 16. Sprague, R. H. and H. J. Watson, (1993), Decision support systems: putting theory into practice, Englewood Clifts, N.J., Prentice Hall. 17. Turban, E. (1995), Decision support and expert systems: management support systems, Englewood Cliffs, N.J., Prentice Hall. ISBN 0-024-21702-6.00. 18. Turban, E., Aronson, J. E., and Liang, T. P.(2005), Decision Support Systems and Intelligent Systems, New Jersey, Pearson Education, Inc.

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