Manufacturing environment

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Manufacturing Resource Planning Models under Uncertainty and Commonality for Multi-products Multi-period Multistage Production Environment

Chapter 3: Literature Review

In this chapter, the following areas of research are investigated to lay the foundation for the intended mathematical models: manufacturing resources planning – background, benefits and limitations; manufacturing resources planning models under different uncertainties; and commonality in manufacturing resources planning models.

3.1 Evolution of manufacturing environment

The field of production planning and control has undergone tremendous change in the last 50 years. Prior to the 1960s, inventory was controlled by a manual system, utilizing various techniques: stock replenishment, reorder points, EOQ (economic order quantity) (McGarrie, 1998), and ABC classifications, to name a few (Ptak, 1991). Gilbert and Schonberger (1983) provide a history of production control, while Lee (1993) comments that by the mid-1970s, enough experience of material requirements planning (MRP) had been gained and the importance of the master production schedule (MPS) was realized.

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In the 1950’s, MRP were the first off-the-shelf business applications to support the creation and maintenance of material master data and bill-of-materials (demand-based 14 planning) across all products and parts in one or more plants. These early packages were able to process mass data but only with limited processing depth (Klaus et al., 2000). From the 1940s to the early 1960s, material control consisted of basic ‘order point’ formulae used to maintain a level average inventory balance. In 1965, Joseph Orlicky of the J. I. Case Company devised a new approach to material management, called material requirement planning (MRP) to serve as a platform to answer four questions, known as the ‘Universal Manufacturing Equation’ (Towers et al., 2005): What are we going to make, What does it take to make, What do we have and What do we have to get.

The respective answer of the first three questions lie in the blueprint of production plan: the master production schedule (MPS), the bill of material (BOM) and the physical inventory records themselves. While MRP was certainly a vast improvement over simple manual method, the potential to stretch its boundary even further was soon recognized. A company’s production is constrained by not only its inventory need but also by equipment and personnel capacity, facet of the plant not considered in the Universal Manufacturing Equation. MRP at its core is a time phased order release system that schedules and releases manufacturing work orders and purchase orders, so that sub-assemblies and components arrive at the assembly station just as they are required. As competitive pressures increased and users became more sophisticated, MRP evolved and expanded to include more business functions such as product costing and marketing.

In 1975 the next generation system, Closed-Loop MRP, integrated capacity factors into the MRP structure and used feedback on production status to maintain the validity of planning decisions as requirements changed. One crucial link in the manufacturing decision chain was still missing- the financial point of view. With advent of computer system in the early 1980s the development of effective shop-floor scheduling tools had at that time been dominated by the top down approach of manufacturing resource planning known as MRP II for controlling production operations (Towers et al., 2005).

The introduction of MRP II five years later served to bridge the gap. The operational Closed-Loop MRP plan, presented in material units such as pieces and pounds, was translated into financial dollar terms, enabling the entire organization to work off a single set of data. Simulation capability was also developed to answer ‘what – if’ planning questions with action oriented replies. A major purpose of MRP II is to integrate primary functions (i.e. production, marketing and finance) and other functions such as personnel, engineering and purchasing into the planning process to improve the efficiency of the manufacturing enterprise (Chen, 2001, Chung and Snyder, 2000, Mabert et al., 2001). MRP II has certain extensions like rough cut capacity planning and capacity requirements planning for production scheduling on the shop floor as well as feedback from manufacturing shops on the progress of fabrication.

Since the 1980s, the number of MRP II installations has continued to increase, as MRP II applications became available on mini and micro computers (Siriginidi, 2000). Like MRP, MRP II focused on the manufacturing process. Then MRP II was extended towards the more technical areas that cover the product development and production processes. Computer Integrated Manufacturing (CIM) supplied the entire conceptual framework for the integration of all business administrative and technical functions of a company, such as finance, sales and distribution, and human resources (Klaus et al., 2000). The next stage of MRP II evolution was just-in-time (JIT) methodology that combined with the plummeting price of computing to create the islands of automation in late 1980s.

Over the last 60 years, many PPC systems and philosophies have been developed. These include material requirements planning (MRP), manufacturing resource planning (MRP II), enterprise resource planning (ERP), just in time (JIT), optimized production technology (OPT), advanced production scheduling (APS), supply chain management (SCM) and customer relationship management (CRM), either used individually or jointly (Koh, 2004).

3.1.1 Material requirement planning (MRP)

Kulonda (2000) descried the evolution of MRP, dividing it in three different worlds. In the first world, MPS items typically are finished end items made to stock; MPS is stated in terms of forecast item demand converted to a series of production lots via time-phased order points or other rules. In the second world, the MPS could conceivably be stated as end items built entirely to order. If response time were not an issue, this approach would work quite well. Competitive force, however, often require shorter response times and inevitably some stocking of at least the longest lead time items occur. A relatively large number of different components are assembled to complete an end product that may have many specific variants. The third world of MRP has all the complexity of the second world with the additional complication that relatively numerous end items are built from relatively few raw materials. This can be visualized in part level count charts shown in 3.1.

Within the MRP system a number of rules need to be specified. They include: acceptable lot sizes, safety stocks and reject allowances. There are three principles of MRP. They are: dependent on demand for the final product; netting of inventory with expected deliveries and open orders to give a balance on-hand; and time phasing by using information on lead times and needs. Three basic MRP inputs to the system are: master production schedule (MPS); the structured BOM for the MPS; and information on inventories, open orders and lead times.

The aim of MRP systems is to minimize cost of inventories and maintain customer service levels. MRP benefits include the ability to rapidly re-plan and re-schedule in response to changes in a dynamic environment. It is flexible and responsive to the customer needs (Hines, 2004). The successes and disappointments of MRP as well as the key shortcomings of MRP (material requirement planning) are studied by Plenert (1999). He investigates consequences of the deficiencies means if they are not corrected. The difficulties encountered by firms in the implementation process of MRP may be traced back to a number of factors.

* The complexity of MRP systems, which, of course, is a relative concept varying according to the level of knowledge and experience available inside the firm prior to implementation (Wortmann, 1998, Wilson et al., 1994, Luscombe, 1994). There are usually several parameters to be initiated when implementing standard software.

* A considerable amount of intensive training is required. In fact, even though end-users are usually trained on a limited amount of functionality, key users need to acquire considerable technical competence.

* The organizations simply under-estimate the extent to which they have to change in order to accommodate their purchase. The effective management of technological change requires transformational leadership (Brown, 1994).

* One of the issues largely felt as critical concerns the resistance of managers and personnel to the organizational change that is induced by the adoption of new technologies. To this regard, several authors have underlined the importance of a sound involvement of shop-floor workers (Sommer, 1998, Weill et al., 1991).

* Valuable relevance has also been placed in the referring literature to technological problems, such as the unsuitability of MRP systems to optimize the internal workflow. In fact, frequent changes in schedules, a problem referred to as production nervousness, is an obstacle to successful implementation of MRP systems (Duchessi et al., 1998).

Material Requirements Planning (MRP) has fallen into disfavor in 1980s, as demonstrated by the extensive literature and conference material coming out of organizations like the American Production and Inventory Control Society (APICS) which discuss its shortcomings (Berger, 1987). MRP has received strong challenges of its effectiveness from Japan. It is believed that the only thing which is still keeping so many manufacturers with MRP is the difficulty in converting to other (Plenert, 1999). Looking at MRP’s basic philosophy, we should be able to focus our scheduling only on what materials are needed, and when they are needed (Plenert, 1990b, Ritzman et al., 1984, Chase and Aquilano, 1995, Lee and Schniederjans, 1994, Nahmias, 1997, Schroder et al., 1981). MRP allows greater flexibility in product customization.

The most obvious shortcoming in MRP usage is its focus on labor efficiency. Labor is not the resource that we need to be efficient at, especially since it causes inefficiencies in our most critical resource, materials. We need to minimize our routings, shortening lead times as much as possible. We need to do our buffering using safety capacity (labor and machine capacity buffers), not safety stock (materials capacity buffers) (Plenert, 1999). We should minimize the non-value-added steps to make them as efficient as possible. The other big builders of inventory are time and the large batch size.

3.1.2 Manufacturing Resource Planning (MRP II)

The theory of MRPII has been well discussed in the literature and focuses are normally put on concept, methodology, application and future development (Ip and Yam, 1998). MRP II (Manufacturing Resource Planning) is a hierarchically structured information system which is based on the idea of controlling all flows of materials and goods by integrating the plans of sales, finance and operations. The levels in an MRP II concept as outlined are applied to two plans in particular (Zapfel, 1996):

* Business Planning including Resource Requirements Planning (RRP) and

* Master Production Scheduling (MPS) including Rough-cut Capacity Planning (RCCP).

Business planning level of a company identifies its objectives. The business plan integrates the plans from sales, finance and operations. The planned aggregate sales income, the planned cost of sales and operations, and all other expenses per planning period provide a basis for calculating the planned net income of the firm. The planning horizon is often a year or longer and a planning period a month or longer. To be feasible, the production plan is examined by the so-called resource requirements planning (RRP); that is, the resources required by a given aggregate production plan can be calculated. MRP II offers simulation capabilities and marries the operating system with the financial system so that what-if questions can be answered using the software system. If the business plan leads to resource requirements which are not feasible or which are unsatisfactory, the user can change the plan and a new simulation run is started to calculate the modified resource requirements. These steps can be repeated until a feasible and satisfactory business plan is achieved. The aggregate production plan, accepted by the user, forms an important basis for master production scheduling.

MRP II tends to link manufacturing, engineering, marketing, finance and management (Yusuf and Little, 1998); production operations-inventory production control, purchasing with production planning, Capacity Planning and Master Scheduling (Turbide, 1990); sales, logistics, production, engineering and supporting functions, the broad ingredients of almost all Manufacturing organization (Ip and Yam, 1998). It may also include costumer service- order entry, sales analysis, forecasting- with financial applications. The total is a single information control system that shares data among the various applications for the mutual benefit (Turbide, 1990). MRP II operates in a “pull” manner at the planning level. It is used for high-level planning of demand and inventory functions and preliminary capacity evaluations.

Ip and Yam (1998) afford a master plan which integrates the technology and management of the strategic elements, problem definition, MRP II solutions, technical and procedural design, and implementation management in order to minimize the frustration and conflicts universally found in MRP II implementation process as well as to reduce disconnection amongst different stages of the implementation process.

Ideally MRP II addresses operational planning in units; financial planning in money terms, and has simulation capability to answer “what-if” questions. It is made up of a variety of functions, each linked together: business planning, production planning, master production scheduling, material requirements planning, capacity requirements planning and the execution systems for capacity and priority. Outputs from these systems would be integrated with financial reports, such as the business plan, purchase commitment report, chipping budget, inventory production in money terms, etc. Manufacturing Resource Planning is a direct outgrowth and extension of a Material Resource Planning (MRP) (Higgins et al., 1998). MRP II definitions:

‘If I had to sum up MRP II in one word, the word I would choose is discipline. Allowed three words, they would be discipline/performance measurement’ – Sheldon (1991). He detailed the total implementation process, from inception to completion and divided the process into six steps, namely, education, common goal, fitness for use, accountability, performance measurement and systems/tools. In Table 3.1, the definition of MRP II is summarized.

Table 3.1: Definition of MRP II



MRP II is a well-defined process or set of calculations that is used to develop plans for the acquisition of the materials needed for production.

(Turbide, 1990)

MRP II is an information control philosophy that is often translated into software products containing, among other capabilities the MRP calculation function.

MRP II is a system designed for managing all the resources of a manufacturing company. It consists of a comprehensive set of planning tools and techniques which integrate all functional areas of an organization

(Tremblay, 1991)

MRP II is a method for the effective planning of all resources of a manufacturing company.

(Dougherty and Wallace, 1992)

Manufacturing resource planning (MRP II) is a long promising method that simplifies all the complex tasks of manufacturing management.

(Chambers, 1996)

MRP II is a hierarchically structured information system which is based on the idea of controlling all flows of materials and goods by integrating the plans of sales, finance and operations.

(Zapfel, 1996)

Manufacturing Resource Planning (MRP II) is a structured approach to optimize a company’s internal Supply Chain.

(Higgins et al., 1998)

MRP II is a method for the effective planning of all resources of the manufacturing company.

MRP II is an effective management system that has excellent planning and scheduling capability which can offer dramatic increases in customer service, significant gains in productivity, much higher inventory turns, and greater reduction in material costs.

(Ip and Yam, 1998)

MRP II system is a proactive materials strategy. It is a dynamic system and can adapt to change as it reflects upon the latest information in its planned order releases.

(Towers et al., 2005) MRP II benefits:

The potential benefits those may receive from the MRP II are summarized below:

* Empirical research suggests that companies able to implement MRP II successfully report enhanced competitive positions, improved customer service levels, a better financial position, increased plant efficiency, heightened morale in production, more effective co-ordination with marketing and finance, more efficient production scheduling and reduced inventory levels, fewer component shortages, reduced manufacturing costs and lead times and improvements in inventory turnover (Humphreys et al., 2001, Brown and Roberts, 1992, Roberts and Barrar, 1992).

* When customers and suppliers (internal or external) request information that have been fully integrated throughout the Supply Chain or when executives require integrated strategies and tactics in areas such as manufacturing, inventory, procurement and accounting, MRP II systems collate the data for analysis and transform the data into useful information that companies can use to support business decision-making (Broatch, 2001).

* MRP II systems, if implemented successfully, enhance and redesign business processes to eliminate non-value-added activities and allow companies to focus on core and truly value-added activities (Broatch, 2001).

* The focus of MRP II computer systems is on the efficiency and effectiveness of the internal processes. It offers a way to streamline and align business processes, increase operational and manufacturing efficiencies and bring order out of chaos (Nah, 2002).

* MRP II systems minimize the time and effort required to process business data and maximizes the application of that information. By facilitating data exchange throughout the organization, a MRP II system enables to coordinate such crucial activities as production planning, material planning, capacity planning and shop floor control (Plenert, 1999).

* MRP II is concerned mainly with scheduling of activities and the management of inventories. It is particularly useful where there is a need to produce components, items or sub-assemblies, which themselves are later used in the production of a final product. Organizations can improve their overall customer service through consistently meeting delivery promises, shortening delivery times and having products on hand when customer orders are received. MRP II can provide the necessary management information to ensure delivery promises can be kept. Where there is volatility in demand with unpredictable customer requirements and complex product structures, the information management capability of MRP II is particularly relevant (Towers et al., 2005).

* A well implemented MRP II system can: provide an organization with reliable lead times; meet its service delivery performance requirements; contribute to stable and consistent lead times and well informed decision-making; maintain lower level of safety stock; reduce the average inventory level and reduce inventory investments to a minimum (Towers et al., 2005).

* The uncertainty of demand can be minimized due to the fact that MRP II can provide an organization with a clear picture of the demand for a particular item and when organizations know their future needs they can negotiate their purchase agreements with suppliers and receive quantity discounts improving their financial position (Towers et al., 2005).

* Successful MRP II users have typically reported as much as 15 percent gain in manufacturing productivity, 50 percent reduction in overtime, 33 percent reduction in inventory investment and 80 percent reduction in inventory shortages (Towers et al., 2005). MRP II provides better control over the quantity and timing of deliveries of raw materials, parts, sub-assemblies and assemblies to production operations. Pitfalls of MRP II:

The main pitfalls of MRP II from various authenticated literature are listed below:

* Impressive though the benefits are, there is evidence suggesting that, as with so many similar technologies, few companies are able to maximize them. White et al. (1982) consider that 50 per cent of organizations do not achieve their objectives. Archer (1991) has said that 70 per cent of systems may be regarded as failures. Ho et al. (1992) has stated that ‘few firms have been able to realize the full potential offered by MRP II’. While relative percentages of successful and unsuccessful implementations differ from study to study, each demonstrates a surprisingly high failure rate.

* Implementation of MRP II system requires major managerial innovations and organizational changes in addition to the installation of computer hardware and software (Lau et al., 2002).

* The heart of an MRP II system is MRP. MRP II does consider resource capacity level when generating the POR schedule. If an overload is identified, it will flag and recommend the user to reschedule. The question is how frequent should the user reschedule? Both Ho et al. (1995) and Sridharan and LaForge (1989) showed that rescheduling induces system nervousness, which leads to further underperformance.

* MRP II has been criticized by a number of authors on the grounds that few benefits accrue for high implementation costs (Burns et al., 1991, Sum and Yang, 1993). Unsuccessful MRP II implementation not only deprives companies of potentially huge benefits but also results in financial losses and disruptions in operations (Towers et al., 2005).

* MRP II concept is only partially suited to production planning in the case of uncertain demand. There is little help with the necessary aggregation and disaggregation process, especially when demand uncertainty exists. It is difficult for the user of MRP II to find a robust aggregate plan for master production schedule (Zapfel, 1996).

Critics of MRP II points to the rigidity of the process: the logic that demands batches and multiple; the fixed lead time which takes no account of current capacity; the standard queue concept in front of a work center etc. Increasing competitive pressure, manifested by reduced lead times, smaller batch sizes, lower stocks and ever more demanding customers have pushed MRP II to its limits (Porter et al., 1996). Reasons for failure:

One of the principal reasons for the failure of MRP II and other large technologically sophisticated systems is that organizations simply underestimate the extent to which they have to change in order to assimilate what is in reality a new way of running the company (Humphreys et al., 2001).

MRP II failure have embraced technical problems; the difficulties involved in selecting and evaluating cost effective MRP II packages and a host of historical, cultural, structural and managerial issues (White, 1980, Kinnie et al., 1992, Wight, 1990, Wilson et al., 1994); expertise needed to implement and use effective MRP II systems; lead times management; design of the production environment, routing and quality information; Infinite capacity availability; batch and lot sizing (Higgins et al., 1998).

An accurate demand forecast is an essential foundation for the successful operation of an MRP II system. Poor sales forecasting had been identified by senior management as one of the main reasons for the MRP II implementation failure (Humphreys et al., 2001).

3.1.3 Enterprise Resource Planning (ERP)

The Gartner Group of Stamford, CT, USA, coined the term ERP in the early 1970s to describe the business software system. The name ERP was derived from the terms material requirements planning (MRP) and manufacturing resource planning (MRP II). The maturity stage of ERP occurred in the mid-1990s. ERP is the third generation of planning software. Material requirements planning (MRP) was the first generation, manufacturing resource planning (MRP II) the second and ERP the third. The primary purpose of ERP is to create a seamless integration of interrelated information throughout the business organization. A system of software programs is used to develop the necessary links between the various business functions so that needed information is readily available. There are 8 (eight) major functions and 33 (thirty three) sub-functions, as well as 22 (twenty two) primary modules and several sub-modules (Umble et al., 2001). A typical ERP implementation takes anywhere from one to five years (Mabert et al., 2003). ERP system is not just a pure software package to be tailored to an organization but an organizational infrastructure that affects how people work and that it “imposes its own logic on a company’s strategy, organization, and culture” (Shehab et al., 2004, Davenport, 1998, Lee and Lee, 2000). Definition of ERP

When customers and suppliers request information that have been fully integrated throughout the value chain or when executives require integrated strategies and tactics in areas such as manufacturing, inventory, procurement and accounting, ERP systems collect the data for analysis and transform the data into useful information that companies can use to support business decision-making. They allow companies to focus on core and truly value-added activities (Nah, 2002). These activities cover accounting and financial management, human resources management, manufacturing and logistics, sales and marketing, and customer relationship management. Table 3.2 shows definitions of ERP, cited in different literatures.

Table 3.2: Definition of ERP



ERP systems are enterprise-wide on-line interactive systems that support cross-functional processes using a common database. ERP systems are designed to provide, at least in theory, seamless integration of processes across functional areas with improved workflow, standardization of various business practices, and access to real-time up-to-date data.

(Davenport, 1998)

ERP systems are complex and implementing one can be a challenging, time consuming and expensive project for any company.

ERP is not only an IT solution, but also a strategic business solution. As an IT solution, ERP system, if implemented fully across an entire enterprise, connects various components of the enterprise through a logical transmission and sharing of data.

(Norris et al., 2000)

ERP is a commodity, a product in the form of computer software.

(Klaus et al., 2000)

ERP is a development objective of mapping all processes and data of an enterprise into a comprehensive integrative structure.

ERP is a key element of an infrastructure that delivers a solution to business.

ERP a method for the effective planning and controlling of all the resources needed to take, make, ship and account for customer orders in a manufacturing, distribution or service company.

(Nah, 2002)

ERP system is a packaged business software system that allows a company to automate and integrate the majority of its business processes, and share common data and practices across the entire enterprise.

(Seddon et al., 2003)

ERP is a “do it all” system that performs everything from entry of sales orders to customer service. It attempts to integrate the suppliers and customers with the manufacturing environment of the organization.

(Shehab et al., 2004) Benefits of ERP

ERP systems have certain advantages such as low operating cost and improving customer service (Shehab et al., 2004). In implementing an ERP solution, an organization can quickly upgrade its business processes to industry standards, taking advantage of the many years of business systems reengineering and integration experience of the major ERP vendors (Myerson, 2002). The practical benefits of ERP are divided into five aspects by Seddon et al. (2003): operational, managerial, strategic, IT infrastructure, and organizational (Table 3.3).

Table 3.3: Benefits of ERP

Operational benefits:

By automating business processes and enabling process changes, they can offer benefits in terms of cost reduction, cycle term reduction, productivity improvement, quality improvement, and improved customer service.

Managerial benefits:

With centralized database and built-in data analysis capabilities, they can help an organization achieve better resource management, improved decision making and planning, and performance improvement.

Strategic benefits:

With large-scale business involvement and internal/external integration capabilities, they can assist in business growth, alliance, innovation, cost, differentiation, and external linkages.

IT infrastructure benefits:

With integrated and standard application architecture, they support business flexibility, reduced IT cost and marginal cost of business units’ IT, and increased capability for quick implementation of new applications.

Organizational benefits:

They affect the growth of organizational capabilities by supporting organization structure change, facilitating employee learning, empowering workers, and building common visions. Disadvantages of ERP:

ERP systems have some disadvantages due to the tight integration of application modules and data. Huge storage needs, networking requirements and training overheads are frequently mentioned ERP problems. However, the scale of business process re-engineering (BPR) and customizations tasks involved in the software implementation process are the major reasons for ERP dissatisfaction. ERP projects are large, costly and difficult and that they require large investment in capital and staff and management time (Adam and O’Doherty, 2000). Yen et al. (2002) identified the following disadvantages of ERP:

* Its high cost prevents small businesses from setting up an ERP system

* The privacy concern within an ERP system

* Lack of trained people may affect ERP’s efficiency

* Implementation of an ERP project is painful

* Customization is costly and time-consuming.

Some of these shortcomings have been discussed by O’Connor and Dodd (2000). Implementation of an ERP system is an extensive, lengthy and costly process, typically measured in millions of dollars. An ERP implementation can take many years to be completed and cost tens of millions of dollars for a moderate size firm and upwards of $100 million for large international organizations (Mabert et al., 2000). Even with significant investments in time and resources, there is no guarantee of a successful outcome (Mabert et al., 2003). According to Shehab et al. (2004), the ERP systems are complex and implementing one can be difficult, time-consuming and expensive project for a company. It costs tens of millions of dollar for a medium sized company and $300-500 million for large international corporations. There are also some possible hidden costs that may include losing some very intelligent employees after the initial implementation is done, continual implantation and training, waiting for return on investment and post-ERP depression. Moreover, even with significant investments in time and money, there is no guarantee of the outcome. 3.3 shows various shortcomings of the ERP systems. Critical Success factor for ERP:

Critical success factors are studied by many authors such as Pinto and Slevin (1987); Roberts and Barrar (1992); Thong et al. (1996); Arens and Loebbecke (1997); Bancroft et al. (1998); Bowen (1998); Falkowski et al. (1998); Bingi et al. (1999); Buckhout et al. (1999); Holland et al. (1999); Light et al. (2000); Sumner (1999); Krumbholz et al. (2000); Rosario (2000); Willcocks and Sykes (2000); Nah et al. (2001); Kumar et al. (2003); Razi and Tarn (2003). Some factors they argued are similar, but some not. After looking through the different factors, we based on our personal understanding the most-stated factors are classified and listed in Table 3.4.

Table 3.4: Classification of critical success factors of ERP




Top management support

ERP strategy

Business process reengineering

Project team & Change management

Retained the experienced employees

Consultant and vendor support

Monitoring and evaluation of performance

Problems anticipation (troubleshooting, bugs, etc.)

Organizational culture

Effective communication

Cultural diversity

3.1.4 Extended Enterprise Resource Planning (ERP II):

GartnerGroup originally envisioned ERP II in 2000 and earlier also tagged the ERP concept. ERP II is a business strategy and a set of industry-domain-specific applications that build customer and shareholder value by enabling and optimizing enterprise and inter-enterprise, collaborative-operational and financial processes (Bond et al., 2000). It is an important extension of ERP with regard to three key aspects (Møller, 2005):

* The combination of the corporate and the network perspectives on supply chain integration;

* The loosely coupled supply chain perspective; and

* The view on application architecture independent from specific vendors and systems.

3.1.5 Differences between MRP II & ERP:

A key difference between MRP II and ERP is that while MRP II has traditionally focused on the planning and scheduling of internal resources, ERP strives to plan and schedule supplier resources as well, based on the dynamic customer demands and schedules (Chen, 2001).

Implementing an ERP system is very expensive and time consuming. It can cost, according to Fortune 500 companies, US$30 million in license fees and US$200 million in consulting fees, not to mention additional millions in computers and networks and can take three (3) years or more before the system yields its maximum benefit (Adbinour-Heml et al., 2003). It was estimated that the spending on ERP systems in 1998 was about US$ 17 Billion (Nah, 2002).
3.2 Manufacturing resources planning models under different uncertainties

The main purpose of this section is to enhance the understanding of manufacturing resources planning models under uncertain conditions by documenting the current state of affairs. It is a comprehensive and up-to-date review of the existing literatures on manufacturing resource planning models under uncertainty.


Uncertainty refers measuring the degree of differences between the models and the respective real systems’ values or between the estimation of variables and their true values. The uncertainty can be caused by the errors associated with the model itself and the uncertainties of the model inputs. One of the challenges of multi-stage manufacturing system is the propagation and accumulation of uncertainty, which influences the conformity of the outputs. Modern manufacturing enterprises are facing increasing pressure to respond to production dynamics caused by disruption of uncertainty (Koh and Saad, 2003b). This section reviews the perspectives sources and factors for uncertainties in manufacturing systems. Perspectives, sources and factors of uncertainty:

Uncertainty means different things to different people. For example the error-estimation for a measurement is referred to as uncertainty (Figliola and Beasley, 1991). Yen and Tung (1993) attributed uncertainty mainly to a lack of perfect understanding with regard to phenomena or processes. Ayyub and Gupta (1994) characterized uncertainty as an inseparable companion of any measurement at the experimental level, and as the vagueness and incompleteness of understanding of complex real problems at the cognitive level. Zhao et al. (1995) defined uncertainty as the differences or errors between models and the reality. Oberkampf et al. (1999) described uncertainty as a potential deficiency in any phase or activity of a modeling process due to lack of knowledge. Delaurentis and Mavris (2000) provided the definition of uncertainty as incompleteness in knowledge (either in information or context) which causes model-based predictions to differ from the reality in a manner described by some distribution functions. Zimmermann (2001) defined stochastic uncertainty as the unknown of the future state of a system due to lack of information, and fuzziness uncertainty as the vagueness concerning the description of the semantic meaning of events, phenomena or statements themselves. Some researchers referred uncertainty as a form of disturbance (Lindau and Lumsden, 1995, Frizelle et al., 1998, Saad and Gindy, 1998). More definitions of uncertainty found in literatures are listed in Table 3.

Table 3: Definition of uncertainty



Uncertainty is defined as any unpredictable event that disturbs the production process in a manufacturing system that is planned by MRP, MRP II or ERP system.

(Koh and Saad, 2003a)

Uncertainty is defined as any unplanned events that occur during production, which disrupt orders execution.

(Koh and Saad, 2003b)

Uncertainty can be defined as any unpredictable event in manufacturing environments that disturbs operations and performance of an enterprise.

(Koh and Saad, 2002)

Uncertainty can be defined as any unpredictable event that disturbs the operation and production in a manufacturing system.

(Koh, 2004)

Uncertainty is the dissimilarity between the amount of information required to execute a task and the amount of information already infatuated.

(Mula et al., 2006a)

Situation where the current state of knowledge is such that (1) the order or nature of things is unknown, (2) the consequences, extent, or magnitude of circumstances, conditions, or events is unpredictable, and (3) credible probabilities to possible outcomes cannot be assigned.


Degree to which available choices or the outcomes of possible alternatives are free from constraints. Situation where neither the probability distribution of a variable nor its mode of occurrence is known.

The definitions in the literature indicate that context and intent are important factors in determining the viewpoint taken. This is not surprising since uncertainty is present in all engineering models, regardless of the type of phenomena under study. Control system design, structural design, and financial forecasting are examples (both within and outside the bounds of engineering) of the wide range of activities where uncertainty modeling and management plays a central role.

An important part of managing uncertainty is identifying as many sources/factors of uncertainty as possible. Koh and Saad (2003b) identified eight uncertainties that are most likely to affect customer delivery performance. The factors pertinent to uncertainty reported in different issues of publications are summarized in Table 4.

Ho (1989) categorizes uncertainties into two groups: (i) environmental uncertainty, and (ii) system uncertainty. Environmental uncertainty includes uncertainties afar the production process, such as demand uncertainty and supply uncertainty. System uncertainty is allied to uncertainties within the production process, such as operation yield uncertainty, production lead time uncertainty, quality uncertainty, failure of production system and changes to product structure, to mention a few. Uncertainty can also be classified differently from the view point of its sources as below:

* Natural uncertainty, also referred to as inherent uncertainty and physical randomness, which is due to the physical variability of a system (Yen and Tung, 1993, Ayyub and Chao, 1997, Hazelrigg, 1996)

* Model uncertainty due to simplifying assumptions in analytical and prediction models, simplified methods and idealizing representations of real performances (Yen and Tung, 1993, Ayyub and Chao, 1997, Delaurentis and Mavris, 2000, Du and Chen, 2000, Gu et al., 1998)

* Measurement uncertainty resulting from the limitation of measurement methodologies and the capability of measurement systems (Delaurentis and Mavris, 2000, Yen and Tung, 1993, Hazelrigg, 1996)

* Operational and environment uncertainty (Delaurentis and Mavris, 2000, Yen and Tung, 1993)

* Statistical uncertainty due to incompleteness of statistical data and the use of sampled information to estimate the characteristics of these parameters (Ayyub and Chao, 1997)

* Subjective uncertainty related to expert-based parameter selection, human factors in calculation, fabrication and judgment (Ayyub and Chao, 1997)

Table 4: Factors of uncertainty

Factor (s) of uncertainty


System uncertainty

(Sommer, 1981, Miller et al., 1997, Hsu and Wang, 2001, Reynoso et al., 2002)

Lead time uncertainty

(Ould-Louly and Dolgui, 2004, Mohebbi and Choobineh, 2005, Koh and Gunasekaran, 2006, Brennan and Gupta, 1993, Dolgui and Ould-Louly, 2002, Huang et al., 1982, Mayer and Nusswald, 2001)

Environmental uncertainty, Supply uncertainty

(Ho et al., 1995, Billington et al., 1983, Güllü et al., 1999)

Operation yield uncertainty

(Huang et al., 1985, Dalal and Alghalith, 2009, Kim and Gershwin, 2005)

Interrelationship between levels

(Kim and Hosni, 1998)

Demand uncertainty

(Bourland and Yano, 1994, Ho et al., 1995, Ho and Carter, 1996, Brennan and Gupta, 1993, Escudero and Kamesam, 1993, Vargas and Metters, 1996, Miller et al., 1997, Kira et al., 1997, Mohebbi and Choobineh, 2005, Grabot et al., 2005, Mula et al., 2006b, Koh and Gunasekaran, 2006, Balakrishnan and Cheng, 2007, Mula et al., 2007, Anosike and Zhang, 2007, Arruda and do Val, 2008, Ben-Daya and Noman, 2008, Grubbstrom, 1999, Agatz et al., 2008, Ahmed et al., 2003, Mukhopadhyay and Ma, 2009, Tang and Grubbström, 2002)

Probabilistic market demand and product sales price

(Lan and Lan, 2005, Mula et al., 2007, Leung et al., 2007, Dalal and Alghalith, 2009)


(Mula et al., 2006b, Mula et al., 2007, Kim and Hosni, 1998, Shabbir et al., 2003)

Resource breakdown/ uncertainty

(Koh and Gunasekaran, 2006, Balakrishnan and Cheng, 2007, Arruda and do Val, 2008, Xu and Li, 2007, Sanmartí et al., 1995)

Changing product mix situation

(Anosike and Zhang, 2009)

Labor hiring, labor lay-offs

(Leung et al., 2007)

Quantity uncertainty

(Koh et al., 2002, Guide and Srivastava, 2000)

Cost parameters

(Mayer and Nusswald, 2001, Shabbir et al., 2003)


(Heese and Swaminathan, 2006, Mukhopadhyay and Ma, 2009, Kim and Gershwin, 2005, Mayer and Nusswald, 2001)

The model uncertainty is further classified as i) input uncertainty as referred to as input parameter uncertainty, external uncertainty and precision uncertainty (Gu et al., 1998, Du and Chen, 2000); ii) bias uncertainty which is induced in transforming the physical principles of scientific theory into analytical or raw models for engineering use and transforming the analytical or raw models into numerical simulation models (Gu et al., 1998); iii) model parameter uncertainty arising from the limited information in estimating the characteristics of model parameters (Ayyub and Chao, 1997, Manners, 1990); iv) model structure uncertainty (Laskey, 1996) which is due to the assumption and simplification of the model structure.


Uncertainty can be measured by the frequency of its occurrence, and analyzing the relative contribution and resulting effect on delivery performance. It can quantify whether the impact is minor or major. Uncertainties in manufacturing have heterogeneous effects due to the interrelationships between resources and operations. The lead-time and demand uncertainties are individually and interactively significant determinants of system performance (Brennan and Gupta, 1993).

A high level of lead time and demand variability has a strong effect both on the level of optimal safety lead times and optimal safety stocks. In case of a high demand variability and low lead time variability, the lowest cost are obtained by using safety stocks. In case with simultaneous high variability in demand and lead time, the lowest cost was obtained by using safety lead times. When uncertainty in processing time increases, the algorithmic scheduling policies become complex (1997). Again increasing manufacturing flexibility leads to increased performance and to knob the uncertainty (Swamidass and Newell, 1987).

Koh and Saad (2006) have shown that the poor supplier delivery performance, schedule/work-to-list not controlled, machine capacity shortages, finished product completed—not delivered, unacceptable product quality and engineering design changes during/after production have significant effect on late delivery. The causes of uncertainty produce knock-on and compound effects on late delivery. Compound effects are more difficult to control as compared to knock-on effects. Occurrence of uncertainty at a different time window does not change the characteristic and nature but may change the effect.

Many conceptual and mathematical models are proposed and used to manage competitive production/manufacturing under uncertainty. This section reviews the factors, their effects and models found in literatures. Conceptual models under uncertainty:

Various techniques are used to tackle the effect of uncertainty, e.g. overtime production, subcontracting, outsourcing, holding safety stock, and keeping safety lead-time. These techniques are adopted to minimize the effect of uncertainty on delivery to customer. The well known techniques are: buffering and dampening (Lindau and Lumsden, 1995, Frizelle et al., 1998, Saad and Gindy, 1998, Koh and Gunasekaran, 2006, Koh and Saad, 2006). Buffering technique is referred as a more physical arrangement, e.g. inventory buffer; whilst dampening technique is referred as a relatively intangible arrangement, e.g. safety lead-time (Koh and Gunasekaran, 2006, Koh et al., 2000).

The safety stock and safety lead-time are the key robust techniques used by many researchers (Guide and Srivastava, 2000). This justifies the research effort in applying safety stock or safety lead-time to manage uncertainty. But more system nervousness might be produced when using safety stock (Sridharan and LaForge, 1989). This finding aligns with the conclusion from Ho et al. (1995). Buzacott and Shanthikumar (1994) found that the use of safety lead-time is preferred than safety stock when it is possible to make accurate forecasts of future required shipments over the lead-time. These findings limit the robustness of safety stock and safety lead-time with the constraint of the lead-time variation information (Koh and Gunasekaran, 2006). Within MRP controlled batch-manufacturing environment (using simulation modeling), Guide and Srivasta (2000) and Koh et al. (2000) suggested the use of safety stock when faced with quantity uncertainty or safety lead time when faced with timing uncertainty. Overtime and multi-skilling labor techniques are as well found to be used by practitioners, though they have conflicting role on delivery performances (Koh et al., 2000). SMEs usually apply fire-fighting techniques to deal with uncertainty (Koh et al., 2000). This implies that they do not manage uncertainty systematically and hence do not prepare themselves for the future if the same uncertainty recurs (Koh and Saad, 2006).

Vargas and Metters (1996) proposed a “dual-buffer” heuristic, the first for triggering production and the second for replenishing stock internally, which outperforms a single buffer heuristic in tackling demand uncertainty. Ho et al. (1995) developed an uncertainty-dampening framework to reduce system nervousness caused by external supply uncertainty, external demand uncertainty and internal supply uncertainty. It was found that holding safety stock, safety capacity, safety lead-time and rescheduling are useful to buffer and dampen these uncertainties. Ho and Carter (1996) simulate static dampening, automatic rescheduling and cost-based dampening techniques to tackle external demand uncertainty. They conclude that the system improvement is dependent on the appropriate use of the dampening techniques and the lot-sizing rules. Holding safety capacity and rescheduling were also found to be the common buffering and dampening techniques used by many practitioners (Koh and Gunasekaran, 2006).

Pagell and Krause (1999) suggested that there is no relationship between the measures of environmental uncertainty and operational flexibility and there is no relationship between an enterprise’s performance and its effort to align the level of operational flexibility with its external environment. It means that fitness of flexibility in managing uncertainty depends on specific types of uncertainty and an enterprise’s environment.

Enns (2002) investigated the effects of forecast bias and demand uncertainty in a batch production environment using integrated MRP planning and execution test bed. The effects of uncertainty on delivery performance in an MRP-controlled batch manufacturing environment with multiproduct and multilevel depended demand is modeled using simulation (Koh, 2004). Also an MRP order release timing logic is developed and modeled with unique method called the tagging configuration, which is conceptualized from the parent and child in MRP systems (Koh and Saad, 2003b). Knowledge-management approach is used by Koh and Gunasekaran (2006) for managing uncertainty in manufacturing enterprises that use MRP, MRP II or ERP for production planning. Manufacturing enterprises should use both tacit knowledge of uncertainties and buffering and dampening techniques, simultaneously with the explicit knowledge that is generated by the intelligent agent, for managing uncertainty (Koh and Gunasekaran, 2006). The effectiveness of the buffering and dampening techniques for specific types/sources of uncertainties, and the effects on delivery performance are also investigated.

Newman et al. (1993) proposed a dynamic equilibrium model to demonstrate the trade-offs and interrelationships between manufacturing flexibility innate in an enterprise’s processes and infrastructure, the uncertainties faced by the enterprise and the way in which the enterprise’s processes and infrastructures are buffered with inventory, lead-time and capacity. Trade-off between flexibility and uncertainty is required to achieve system agility (Prater et al., 2000).

Molinder (1997) proposes simulated annealing to find good safety stock and safety lead time under the stochastic demand and lead time. He has analyzed the amount of lead time and demand variability and the influence of the stock-out cost/inventory holding cost ratio. Mayer and Nusswald (2001) have proposed simulation model with integrated quality factors with manufacturing cost and lead times. The models considered single stage production system.

With the existing manufacturing system’s structures and constraints as well as considering the system reconfiguration and restructure, an agent-based approach is presented by Anosike and Zhang (Anosike and Zhang, 2009) to achieve optimized utilization of resources in changing demand distribution and product mix situation. A business model to manage the uncertainty in manufacturing, which is planning and scheduling of production using MRP, MRP II or ERP is proposed by Koh and Saad (2006). How and to what extent uncertainty disturbs is examined, and diagnosis the underlying causes for uncertainty through a questionnaire survey. The conceptual techniques are summarized in Table 5.

Table 5: Conceptual techniques for uncertainty




(Lindau and Lumsden, 1995, Ho et al., 1995, Buzacott and Shanthikumar, 1994, Frizelle et al., 1998, Saad and Gindy, 1998, Guide and Srivastava, 2000, Koh et al., 2000, Koh, 2004, Koh and Saad, 2006, Koh and Gunasekaran, 2006)


(Lindau and Lumsden, 1995, Ho et al., 1995, Ho and Carter, 1996, Frizelle et al., 1998, Saad and Gindy, 1998, Buzacott and Shanthikumar, 1994, Guide and Srivastava, 2000, Koh, 2004, Koh et al., 2000, Koh and Gunasekaran, 2006, Koh and Saad, 2006)

Overtime labor, Multi-skilling labor and Fire-fighting techniques (SMEs usually apply)

(Koh et al., 2000, Koh and Saad, 2006)

Overtime production

(Koh et al., 2000, Koh et al., 2002, Koh and Saad, 2006, Koh and Gunasekaran, 2006)

Subcontracting and outsourcing

(Koh and Gunasekaran, 2006, Koh et al., 2002)


(Koh and Gunasekaran, 2006, Vargas and Metters, 1996)

Safety capacity and rescheduling

(Koh and Gunasekaran, 2006, Ho et al., 1995)

Knowledge management approach

(Koh and Gunasekaran, 2006)

Questionnaire survey

(Koh and Saad, 2006)

Execution test bed

(Enns, 2002)

Simulation model

(Koh, 2004, , 1997, , 2001)

Agent based approach

(Anosike and Zhang, 2009)

Dynamic equilibrium model

(1993, Prater et al., 2000)

Conceptual models are widely used in design and manufacturing. However, no models can completely capture all the characteristics of the simulated physical system. It is asserted that the values of the physical variables which describe the behavior of the physical system in Heisenberg uncertainty principle are impossible to specify accurately and simultaneously. Heisenberg’s Uncertainty Principle states that it is impossible to know both the exact position and the exact velocity of an object at the same time.

Increased integration and simplification both of technologies and infrastructures can decrease internal uncertainty. Better integrated buyer/vendor relationships have been shown to reduce external uncertainty. The use of computers and numerical control manufacturing technology has also provided a potentially cost-effective means of accommodating manufacturing uncertainty through enhanced flexible automation. Mathematical Models under uncertainty:

In order to address uncertainties, several mathematical models have been proposed. Examples of these models include interval model, convex model, fuzzy sets and random models. Interval model, introduced in the early 1900s, can give rigorous bounds for a solution and applied to different fields (Lew et al., 1994, Moore, 1966, Simoff, 1996, Chen et al., 2004, Chen and Ward, 1997, Kubota and Hori, 1999, Penmetsa and Grandhi, 2002). Convex model extended the interval model from one dimension to multi-dimension, and have been used in construction engineering, mechanical engineering, structural engineering, mechanics and other fields (Lindberg, 1992, Ben-Haim, 1994, Ben-Haim, 1996, Ben-Haim, 1997, Attoh-Okine, 2002). Fuzzy sets, introduced by Zadeh (1965), were initially used in fields such as economics, social sciences to address the uncertainties induced by the imprecise and vague information. Afterwards they are extended to engineering areas (Wood and Antonsson, 1989, Wood et al., 1989, Wood et al., 1992). Random model represents the uncertainty through probability mass function or probability density function, also has considerable applications in engineering (Deng, 1989, Pawlak, 1985). This section reviewed the mathematical models within the periphery of this heading.

The mathematical programming (MP) approaches to cope with capacity constraints are commenced by Billington et al. (1983) and Chung and Krajewski (1984). They considered the lead time as an implicit outcome of the altercation of demand and finite capacity. The models mainly deal with the scheduling problem in a multistage production system with some constraints but without any uncertainty. This moved many authors to assess substitute planning models in a rolling schedule context. Spitter et al. (2005) have talked about the timing of production during the planned lead times of items and investigated the effects of production timing on the safety stocks and the inventory costs. Similarly, Belvaux and Wolsey (2001) conferred assorted models for lot sizing under capacity constraints, where the lead times are implicit outputs of the optimization procedure. Bourland and Yano (1994) developed multi-objective optimization model that considers capacity slack, safety stock and overtime, which aims to minimize the expected cost per unit time of inventory, overtime and set-up costs (where applicable). These models incorporated the fluctuation in demand only. Ould-Louly and Dolgui (2004) have investigated a multi-period and multi-component supply planning problem for assembly systems with random lead time and fixed demand. The lead times of different types of components follow the same distribution in the model.

A manufacturing resource planning algorithm, matrix based formulation, which can handle limited production capacity is presented by Harris et al. (2002), but no imperfection in information. Shabbir et al. (2003) have addressed a multi-stage capacity expansion problem with uncertainties in demand and cost parameters, and economies of scale in expansion costs. Choi and Enns (2004) have developed the relationship to establish the lot-sizes that minimize costs for the single and multiple product cases under the particular production rate as well as the liaison to determine both lot sizes and throughput rates that maximize profits. The model deals with the variation in the arrival times of component and all other parameters are certainly known. Combinatorial manufacturing resource planning (CMRP) model with the concept of balancing the machine productivity and the human capability as well as step by step algorithm to reach the maximum profit solution under the deterministic market demand is constructed by Lan and Lan (2005). They extended the applicability of the CMRP model to achieve the optimum manufacturing resource planning under the forecasts of probabilistic market demand and product sales price. Kim and Hosni (1998) formulated multi-level capacitated optimization model and a relatively efficient heuristic working under MRP II environment which considers work center capacities and interrelationship between levels in lot-sizing computation. The model provides optimal lot size plan for small problems in deterministice situation and does not allow shortage which is very unrealistic. Escudero and Kamesam (1993) originate a stochastic programming model for MRP with uncertainty in demand which is given as a random parameter. Though the models considered multiple levels, the holistic view of the uncertainty is absent.

Under the demand uncertainty i) Ben-Daya and Noman (2008) developed integrated inventory inspection models with and without replacement of nonconforming items discovered during inspection, ii) Arruda and do Val (2008) have represented a discrete event model of a multi-stage, multi-product P&S (production and storage) in which a single facility used for production of various products and iii) Lusa et al. (2008) presents a multistage scenario stochastic optimization model when planned working time is considered as annualized hours (AH). But the articles have failed to look after more frequent uncertain factors (like lead time, price etc.) and their combined disruptions and interactions.

Grabot et al. (2005) suggested F-MRP (Fuzzy-MRP) model, to handle the uncertainty and imprecision of demand allowing to pass through all the MRP II steps (Material requirement planning, Load balancing, Scheduling). Mula et al. (2006b) presents a new linear programming model for medium term production planning in a capacity constrained MRP, multi-product, multi-level and multi-period manufacturing environment. Mula et al. (2007) developed a fuzzy production planning model to generate production plans under conditions of uncertainty in parameters as important as market demand, capacity and costs data. The interaction and the combined impacts are not included in the conclusions.

Xu and Li (2007) have created a modeling schema to address the manufacturing resource for process planning, especially for process reasoning. Robust optimization model for a medium-term planning horizon is developed by Leung et al. (2007) to solve multi-site production planning problem with uncertain data. Robust optimization includes two distinct constraints: a structural constraint and a control constraint. Structural constraints are formulated following the concept of linear programming and its input data are free of any noise, while control constraints are taken as an auxiliary constraint influenced by noisy data. Tthe proposed model is more practical for dealing with uncertain economic scenarios instead of production parameters.

Models for the optimum batch quantity in a multi-stage system with rework process have been developed for two different operational policies by Sarker et al. (2008). The mathematical expression of this model is corrected by Cárdenas-Barrón (2009). The models deal with the optimal batch sizing with rework consideration in a stable system. A model of the EOQ type is developed and analyzed by Dobos and Richter (2004), in which a producer serves a stationary product demand. This demand is served by producing or procuring new items as well as by recycling some part of the used products coming back to the producer at a constant rate. They have examined a production/recycling system with predetermined production-inventory policy and assume that there is no difference between newly produced and recycled items which is not realistic. Dobos and Richter (2006) further extend the model with quality parameter. The models emphasised mainly of the inventory issues rather the production system.

Kogan and Lou (2003) have considered a multi-stage, continuous-time dynamic model for multistage production and one-product-type system which is the extension to the classical single-period newsboy problem. Products flow from one stage to the next. It is assumed that the demand during the planning horizon is unknown, but the cumulative demand at the end of the planing horizon is known. The objective is to adjust the production rates during the planning horizon in order to minimize total costs. No uncertainty is considered except imperfection in demand.

Kim and Gershwin (2005) have proposed Markov process model that integrated quality and productivity. They considered only one line of production which can not simply extend/apply to a multiple production lines. Dalal and Alghalith (2009) have modeled for production decision making under price and production uncertainty.

Tang and Grubbström (2002) have investigated the possibility of establishing a method for Master Production Schedule (MPS) under stochastic demand, evaluated the the replanning action and provided a model for estimating appropriate MPS parameter (like length of replanning interval, the length of interval to freez the plan etc.). The uncertaintyin parameters other than demand is ignored. Leung (2009) has generalized a number of integrated models with/without lot streaming and with/without complete backorders under the integer-multiplier coordination mechanism, and then individually derive the optimal solution to the three- and four-stage model. The models use confirms parameters without uncertainty.

Chen and Chang (2008) have introduced a Fuzzy Economic Production Quantity (FEPQ) model with defective productions that cannot be repaired. In this model, a fuzzy opportunity cost and trapezoidal fuzzy costs under crisp production quantity or fuzzy production quantity are considered.

Balakrishnan and Cheng (2007) have reviewed cellular manufacturing, an important application of Group Technology (GT), under conditions of multi-period planning horizons, with demand and resource uncertainties. They addressed the change in demand over time caused by product redesign and uncertainties due to volume variation, part mix variation, and resource unreliability.

From the authors’ observation, the broad classifications of the uncertainty models are: Conceptual models (like yield factor, safety stock, safety lead time etc.), Artificial intelligent based models (like fuzzy set theory, fuzzy logic, multi-agent system etc.), Simulation models (like heuristic method, network modeling, queuing theory etc.) and Analytical models (like mathematical programming, stochastic programming etc.).

3.3 Commonality in manufacturing resources planning models

The main purpose of this section is to enhance the understanding of commonality models in manufacturing resource planning by documenting the current state of affairs. This is based on a comprehensive review of the articles from authentic publications on resources commonality for the various product mixes and the pertinent models.

3.3.1 Commonality Perspective

In practice, commonality can be categorized from two perspectives, namely, engineering and management. From an engineering perspective, commonality refers to cases where several different components are replaced by a newly designed component that can perform the function of each one of them, or a cluster of equivalent components, one of which substitutes all the others. The common component must at least provide all the functionality of component it replaces. From a managerial perspective, commonality is present when some stock keeping units (SKUs) of a manufacturing system are used in more than one finished product. The term ‘commonality’ refers in literature are shown in Table 1.

Table 1: Definition of commonality



Eynan (1996)

Commonality is an approach which simplifies the management and control of inventory and also reduce inventory.

Meyer and Lehnerd (1997)

Commonality is a group of related products that share common characteristics, which can be features, components, and/or subsystems. It is a set of subsystems and interfaces that form a common structure from which a stream of derivative products can be efficiently developed and produced.

Ma et al. (2002)

Component commonality generally refers to an approach in manufacturing in which two or more different components for different end products (of perhaps the same product family) are replaced by a common component that can perform the function of those it replaces.

Mirchandani and Mishra (2002)

Component commonality refers to a manufacturing environment where two or more products use the same components in their assembly. Commonality is an integral element of the increasingly popular assemble-to-order strategy that inventories certain critical components- typically, with long lead time and expensive- in a generic form.

Labro (2004)

Commonality is the use of the same version of component across multiple products. It is a cost decreasing strategy in a stochastic-demand environment because by pooling risks the total volume of the common component can be forecasted more accurately.

Ashayeri and Selen (2005)

Commonality is defined as the number of parts/components that are used by more than one end product, and is determined for all product families.

Humair and Willems (2006)

For manufacturing echelon, commonality refers to the parts or subassemblies that are shared among different items. For distribution echelons, it refers to the end items that are knitted together or bundled as assortments to customers.

3.3.2 Parts commonality measurement

The parts commonality measurement includes the process for evaluation of product commonality and methods to achieve commonality in product family. These measures and methods vary considerably in purpose and process: the nature of the data gathered (some are extensively quantitative while some are more qualitative), the ease of use, and the focus of the analysis. However, they all share the goal of helping designers to resolve the tradeoff between too much commonality (i.e. lack of distinctiveness of the products) and not enough commonality (i.e. higher production costs). In literatures there are many commonality indices to measure the commonality within a family of products/processes. Commonality indices

The commonality index is a measure of how well the product design utilizes standardized components. A component item is any inventory item other than an end item, which goes into higher-level items (Dong and Chen, 2005). Several commonality indices are found in reported literatures to measure the commonality within a family of products. Commonality is defined as the number of parts/components that are used by more than one end product and is determined for all product family (Ashayeri and Selen, 2005). Within a product family, commonality index is a metric to assess the degree of commonality. It is based on different parameters like the number of common components, component costs, manufacturing processes, etc. In designing a new family of products or analyzing an existing family, these indices are used very often as a starting point. They are intended to provide valuable information about the degree of commonality achieved within a family and how to improve a system’s design to increase commonality in the family and reduce costs. However, there have been only limited comparisons between many of these commonality indices and their usefulness for product family (Wazed et al., 2009; Thevenot and Simpson, 2006, 2004). Several component-based indices are summarized in Table 2.

Table 2: Commonality indices


Commonality measure for

No commonality

Complete commonality

DCI – Degree of commonality index (Collier, 1981)

The whole family


TCCI – Total const commonality index (Wacker and Treleven, 1986)

The whole family



PCI – Product line commonality index (Kota et al., 2000)

The whole family



%C – Percent commonality index (Siddique et al., 1998)

Individual product with a family



CI – Commonality index (Martin and Ishii, 1996, 1997)

The whole family



– Component part commonality (Jiao and Tseng, 2000)

The whole family


CMC – Comprehensive metric for commonality (Thevenot and Simpson, 2007)

The whole family


1 Commonality Models

The use of common components in design, production and assembly operations has become more prevalent in the last few years. Research in this area has also blossomed and researchers have addressed a variety of operations related issues. The authors reviewed the papers that are directly relevant and also discuss component commonality issues considered in other research streams in inventory management. The number of common components, number of products, number of levels in bill-of-material or number of echelons in assembly, number of components used per unit of product, planning horizon, demand distribution of products, costs measure, service level metrics, objective and common component allocation policy can be used to classify the commonality models. Two product-single common component models

Baker et al. (1986) have studied the effect of commonality in a two-product, two-level model with independent and uniformly distributed demand and service level requirements. Their model minimizes the total components safety stock subject to aggregate and bottleneck required service level constraints. Implicitly they assumed that all components have equal costs and equal usage. Gerchak et al. (1988) further investigated and extended Baker et at. (1986)’s model to consider general demand distribution for any number of products and minimized the total inventory cost. They found that some important properties do not hold when the components costs are arbitrary. They showed that a decrease in total inventory cost resulted from the use of commonality. Bagchi and Gutirrez (1992) have maximized the aggregate service level subject to a constraint on the total component availability when the product demands have the exponential or geometric distributions. They found that, for two-product case, replacing product-specific components with common components leads to increasing marginal returns on aggregate service level. They also considered minimizing inventory holding cost subject to service constraints and derive properties of the optimal total cost. Eynan and Rosenblatt (1996) have studied the economic implications of component commonality in a single period problem. They compared the total component acquisition cost for two products in three different situations, distinguished by the number of common components (none, one and two) subject to an aggregate service level constraint. The cost of common components are allowed to be more instead of equal to product specific components as in early studies like Backer et al. (1986), Gerchak et al. (1988) and Bagchi and Gutierrez (1992). Eynan (1996) shows analytically that commonality results in larger savings for negatively correlated demand case and small savings for positively correlated demand case when compared with independent demand case. They minimized a cost measure subject to service level constraints. In this article the author used correlated demands instead of independent demand as considered in the earlier studies. These studies have restricted focus on qualitative and quantitative investigation inventory effects of making use of commonality in assemble-to-order system under various demand patterns and focuses on the service level and safety stocks only. But the scenario of the parts price, quality and uncertainty of their arrival, sensitivity of the finished products etc. are not incorporated in the model. Dependent demand situation and uncertainty are overlooked.

Desai et al. (2001) have analyzed design configurations by formulating a model that incorporated the marketing and manufacturing trade-offs. They developed model for three possible design configurations: unique, premium-common and basic common. One of the two components can potentially be common between the two products or go as a distinct component in each of the two products. The analysis covered only the quality issues of the common component and costs, uncertainty is not incorporated.

Mirchandani and Mishra (2002) have developed an optimization model considering a two-stage assemble-to-order system with two products having uniformly distributed demand, one common component and product specific components. Each product has a desired product-specific service level which is also referred to as order fill rate and each component incurs an acquisition cost that equals the product of its unit cost and its order quantity. The authors considered same demand distribution for each product and constant lead time in the assemble-to-order environment.

Van Mieghem (2004) has analyzed a single unified model with five input and two products under the no-commonality and with commonality. This introduces the revenue-maximization option of commonality as a second benefit that is independent of the traditional risk-pooling benefit. The pure commonality (where each product requires one dedicated and one common component) strategies are never optimal unless complexity costs are introduced. He considered the probabilistic forecast of demand, financial data (price minus any marginal assembly and transportation costs; inventory incurs unit purchasing and holding costs and unmet demand incurs shortage costs) and net-work data. Uncertainties in other parameters like lead time, quality etc. not considered.

Lin et al. (2006) have setup a multi-period model of component commonality with lead time. They analyzed the quantitative relationship between lead time of common component and the inventory level and find some efficient ways to: customization level, optimize inventory management and lower costs. Multiple product-multiple component models

Baker (1985) has used a two level bill of materials to illustrate that in assemble-to-order situation for multi-products with uncertain demand, commonality reduces the total safety stock. However, component commonality complicates the determination of the product specific service levels. His studies indicated that the optimal safety stock strongly depends on the correlation of external demands for different items and commonality. Gerchak and Henig (1986) have modeled a multiple period, multiple product and multiple component problem as a stochastic dynamic program. Their profit maximization objective function considered component acquisition cost and revenue from product sales. They showed that commonality always results in an increase in the safety stock of product-specific components, as compared to the no commonality case. Gerchak and Henig (1989) have further studied the impact of commonality in a more general setting. They showed that the multiple period problems allowing for partial backlog, shortage costs, component dependent holding cost and partial spoilage also has a myopic solution. They identified their model as a stochastic program but do not solve it. Srinivasan et al. (1992) have considered a multi-period problem in which the inventory holding cost in each period is minimized subject to product-specific service level constraints. First they formulated the problem as a stochastic program with chance constraints. They then reformulated using ‘cumulative up to period t’ variables that allows a heuristic decomposition of the problem by time period. They showed that in large problems, ignoring commonality can increase the inventory related costs enormously. These studies mainly concentrated on the effects of commonality on stocks under demand variations.

Jonsson and Silver (1989b) have minimized the number of products short, subject to a budget constraint on the number of components in stock. Assuming normally distributed demand, the authors used numerical integration to determine the optimal solution. The model maximizes the profit subject to a budget constraint on the value of the components. Jonsson et al. (1993) also considered this problem but used a scenario aggregation approach to formulate the problem and an augmented Lagrangian relaxation to provide good solution to it. The authors incarnate budget limitation for material used for specific demand distribution in assemble-to-order environment.

Zhang (1997) has studied a general multi-period, multiple product, multiple component model with deterministic lead times. The objective is to minimize acquisition costs subject to product-specific order fill rates. Unsatisfied demand is back-ordered. He used a multivariate normal distribution to characterize the demand in each period.

Hillier (1999a) has developed a simple multiple-period model with service level constraints to compare the effects of commonality in single-period and multiple-period cases. The results are drastically different for these two cases. When the common component is more expensive than the components it replaces, commonality is often still beneficial in the single-period model, but almost never in the multiple-period model. Hillier (2002a) has developed a model that considers purchasing, ordering, inventory and shortage costs where components are replenished independently according to lot-size, reorder point policy. He showed that order pooling is a significant benefit under commonality; in many cases it is much more important than the risk pooling benefit.

Ma et al. (2002) have formulated a multi-period and multistage assembly network model with multiple products and stochastic demands, and proposed a scheme to express the desired base-stock level at each stocking point as a function of the corresponding achieved fill rate. They have demonstrated analytically whether introducing commonality at a particular stage or delaying the point of differentiation by one more stage can be justified. They concluded that a key factor for commonality and postponement decisions is the interactions between processing and procurement lead times.

Mohebbi and Choobineh (2005) have studied the impact of introducing component commonality into an assemble-to-order environment when demand is subject to random variations, and component procurement orders experience random delays. By using simulated data, it shows that component commonality significantly interacts with existence of demand and supply chain uncertainties, and benefits of component commonality are most pronounced when both uncertainties exist. They consider a two-level ATO environment that produces three finished products only.

Chew et al. (2006) have studied the trade-off between the gain through risk pooling and the loss due to component mismatched in a two-echelon assembled-to-stock (ATS) system when component sharing is allowed. They studied these conflicting effects by comparing a particular component sharing policy, namely the equal-fractile allocation policy, with a make-to-stock system which does not allow the allocation of common components.

Nonas (2007) has considered the problem of finding the optimal inventory level for components in an assembly system where multiple products share common components in the presence of random demand. The inventory problem considered is modeled as a two stage stochastic recourse problem where the first stage is to set the inventory levels to maximize expected profit while the second stage is to allocate components to products after observing demand. Other component commonality models

Researchers have included common components in several other studies with substantially different research objectives. These studies describe the implications of commonality, measure the extent of commonality or study inventory problem with common components.

Dogramaci (1979) has investigated detailed mathematical programming formulations and captured more reality, including setup costs and design complexity costs. He showed that commonality is beneficial because it decreased the standard deviation of demand forecast for components and hence reduced inventory costs.

Collier (1981) has studied the effect of degree of part standardization on MRP system performance. He defined a measure called degree of commonality index (DCI), as the average number of immediate parents for each component divided by the number of products. He introduced first the degree of commonality index (DCI) and used statistical methods to show the relationship between DCI and setup and holding costs. In a subsequent study Collier (1982) uses DCI to evaluate the impact of commonality on safety stock. He showed that when a common component replaces product-specific components, the aggregate safety stock reduces by. The same service level can be maintained with reduced safety stock when commonality is increased. But the results are based on very restrictive assumptions according to what have pointed out in McClain et al. (1984) and Collier (1984).

Cohen et al. (1989) have studied stocking policies for spare parts. They used heuristic approaches to determine base stock inventory for each component to minimize expected ordering, holding, shortage and transportation costs. Cohen et al. (1992) proposed extension of their model to develop (s, S) policies for a convergent spare parts logistics system, with item fill rate constraints, to multiple product system with component commonality. Tang (1992) has developed a production rule for a multistage assembly system containing common components to determine the inventory of the components and their allocation to the products when there is yield loss and uncertain end product demand.

Grotzinger et al. (1993) have considered the commonality problem with a single common component and multiple products, in an assemble-to-forecast environment. The components are allocated to products when the demand is uncertain, but the common component can be re-allocated to different products when demands change. Balakrishnan et al. (1996) have developed bisection algorithms which included commonality to determine integer assembly release quantities in an assemble-to-forecast environment under known inventory and demand distribution during procurement and assembly lead times. Vakharia et al. (1996) have used simulation to investigate the impact of component commonality on the work-load of a firm using an MRP system. They found that it decreases the average shop load, particularly when the number of setups is high, but increases the variability in loadings and system disruption.

Lee and Tang (1997) have proposed standardization, i.e. use of common components or processes, besides modular design and process restructuring, as means to postpone the point of product differentiation. They illustrated the costs and benefits of these approaches using a simple model. Ha (1997) has studied allocation of common components in a make-to-stock production system with two priority demand classes and backordering. Ha (1999) also studied a similar problem for several demand classes with lost sales.

Hillier (1999b) has considered the possibility of replacing a number of different parts by a single common part. In the single period case, it is shown that even when the common part is somewhat more expensive, it might still be cost-effective to utilize. However, in the multi-period case, it is shown that the break-even cost of the common part is often just a few percent more expensive than the unique parts. The added purchasing costs over multi-periods quickly dominate any holding cost savings achieved through risk pooling. Hillier (2000) has developed a multi-period single-stage model for multi-product scenario with single common item under general demand distribution for an uncapacitated, periodic review, assemble-to-order (ATO) inventory system.

Thonemann and Brandeau (2000) have modeled the component design problem as a mathematical program that considers production, inventory holding, setup and complexity costs (the cost in indirect functions caused by component variety). This study showed that an optimum design achieves high cost saving by using significantly fewer variants than a no-commonality design but significantly more variants than full commonality design.

Hillier (2002b) has analyzed the effect of commonality on costs when the common part is more expensive than the parts it would replace in a multi-period case. He investigated the possibility of using both cheaper unique parts and a more expensive common part. Initial demand is met with unique part. The common part is used as only backup, when one or more of the unique parts stocks out. This paper concluded that the strategy of using commonality as backup dominates the strategy of no commonality or pure commonality and it is worthwhile even if the common part is significantly more expensive than the unique parts.

Labro (2004) has reviewed the component commonality literature through a management-accounting lens, focusing on the cost effects of an increase in the use of same version of a component across multiple products. He presented a review of the OM literature and reconciled it with management-accounting literature on cost drivers and cost of complexity. ABC is introduced as a framework to classify the effects on an increase in component commonality on costs indentified in the existing literature. Zhou and Grubbstrom (2004) have focused on the effect of commonality in multi-level production-inventory systems, especially assembly systems under deterministic demand, capacity constraints and no backlog conditions. This study is confined in two cases of different complexity, the first when commonality only involves purchased items with lead times that can be disregarded. The second is when commonality affects items which are subject to some kind of processing, the simplest sub-case being when purchased items are not available until after some delay.

Heese and Swaminathan (2006) have analyzed a stylized model of a manufacturer who designs a product line consisting of two products for sale to two market segments with different valuations of quality. They investigated what circumstances support component sharing as a profitable strategy and, more specifically, which components are the best candidates for commonality. The manufacturer determines the component quality levels, the amount of effort to reduce production costs and whether to use common or different components for the two products.

Kranenburg and Houtum (2007) have developed a multi-item, single-site/stage spare parts inventory model with multiple groups to study the effect of commonality on spare parts provisioning costs for capital goods under the service level constraints. The study shows that the savings obtained by shared stocks are significantly affected by the commonality percentage and the degree to which the commonality occurs in the expensive SKUs.

Jans et al. (2008) have proposed a mixed integer nonlinear optimization model to find the optimal commonality decision in an industrial production-marketing coordination problem. They focused on production and development cost savings instead inventory cost savings and integrate information from different functional areas such as production, marketing and accounting.

The authors have reviewed the articles chronologically. It is observed that all of the studies have restricted views on costs and/or safety stocks under the service level constraint with known distribution of demands. However, few studies have considered the lead time (Zhang, 1997, Ma et al., 2002, Lin et al., 2006) and quality (Heese and Swaminathan, 2006) of common components. In an assemble-to-order environment, all the studies except (Mohebbi and Choobineh, 2005) considered uncertainty in demand only. Process commonality and modeling

Two sources of commonality are identified: the component part commonality and the process commonality. The formulation of the component part commonality is based on the mindset of counting the average applications per component part and takes into account product volume, quantity per operation, and the price/cost of the component part. The process commonality index incorporates such concerns as process flexibility, lot sizing, and scheduling sequencing into one analytical measurement (Jiao and Tseng, 2000). Measure of process commonality is a similar measure to that of the component commonality. The fewer processes involved in the production of a product and in the production of the entire plant, the more flexible the plant can be to customer needs. The number and diversity of component parts and the corresponding processes reflect the complexity of product design and that of production planning and control.

Tsubone et al. (1994) discerned the process commonality from the component part commonality, and integrated these two types of commonality in assessing manufacturing performances. The authors pointed out the necessity to clarify two different sources of commonality, namely the component part commonality and the process commonality, for systematic studies of the product family. However, their measurements of commonality are only suitable for a two-level product model, which is of little use for practical purposes. Treleven and Wacker (1987) also advocated distinguishing process commonality from component part commonality. Unlike with component part commonality, to have high process commonality, a great similarity of components is not necessary, since one process can produce diverse components. In addition, it may be argued that high commonality of internally produced component parts would necessitate high commonality of the processes involved. However, the reverse is not true, such as for those externally purchased parts. Furthermore, unique (distinct) items may share similar (or in the extreme case, identical) processes.

The managerial implications of the process commonality index can be manifested at both strategic and operational levels. At the strategic level, the process commonality index could be used to evaluate the impact of product family designs on existing process capabilities, and vice versa.

Process models are often multi-stage procedures to conduct all or portions of the design process when designing products with commonality, plat-forms, or product families in mind. For example, Jiao and Tseng (1999) present a detail process to establish product families and Germani and Mandorli (2004) propose a procedure leading to self-configuring components in product architecture design. Another five-step model for product family design is presented by Farrell and Simpson (2003). Yet a different approach to commonalize product subsystems has been suggested by Qin et al. (2005). They use actual data on critical parameters of existing products to construct similarity matrices with in turn enable cluster formation, i.e. common platform definition. In general, the engineering literature, and in particular text books, tend to provide detailed step-by-step advice on how to proceed when designing modular products and products with common components (Kamrani and Salhieh, 2002, Ulrich and Eppinger, 2000).

Connecting both product and process, Jiao et al. (2000) proposed a data structure that integrates the bill-of-materials with the bill-of-operations. Jiao and Tseng (2000) developed a process commonality index that incorporates concerns as process flexibility, lot sizing and scheduling sequencing into their measurement instruments. Balakrishnan and Brown (1996) viewed ‘commonality across products as shared set of processing steps from ingot casting to some intermediate hot or cold forming step’ in their work on aluminum tube manufacturing. Product platform for part/process commonality

A component is defined as a manufactured object that is the smallest (indivisible) element of an assembly and is represented by a set of design variables. A product is an artifact that is made up of components. The product architecture is the configuration (or topology) of components within the product. A module is a component or subassembly that can be interchanged within the product architecture to produce a variety of similar products. A product platform is the set of all components, manufacturing processes, and/or assembly steps that are common in a set of products.

Product family is a group of related products that share common characteristics, which can be features, components, and/or subsystems. The key to designing a successful product family is the product platform. In general, a platform is “the lowest level of relevant common technology within a set of products or a product line” (McGrath, 1995), but a slightly broader definition is “a set of subsystems and interfaces that form a common structure from which a stream of derivative products can be efficiently developed and produced” (Meyer and Lehnerd, 1997).

Platforms increase business flexibility through platform scaling up and down. Indeed the platform approach allows aggressive market strategies, reduced costs and times in developing new derivative products. The case of Compaq (Meyer and Lehnerd, 1997) represents an outstanding case in which the company succeeded in leveraging one basic platform through different market segments. In particular the company extended its “beachhead” platform in both segments of customers and tiers of performance level.

The product platform is the common core of the family – including form, components, interfaces, and technology base – from which derivative products can be generated through modification, augmentation and renewal. But focusing on custom products can result in ‘a failure to embrace commonality, compatibility and standardization’ (Meyer and Lehnerd, 1997). As Robertson and Ulrich (1998) point out, ‘‘by sharing components and production processes across a platform of products, companies can develop differentiated products efficiently, increase the flexibility and responsiveness of their manufacturing processes, and take market share away from competitors that develop only one product at a time.” Companies such as Sony (Sanderson. and Uzumeri, 1995), Hewlett Packard (Feitzinger and Lee, 1997), Kodak (Wheelwright and Clark, 1995), Black and Decker (Meyer and Lehnerd, 1997) and Volkswagen (Bremmer, 1999) have successfully employed product platform strategies to increase product variety while reducing development costs, manufacturing costs, and time-to-market. A key feature in a product family is platform commonality.

The concept of a product platform has been receiving increased attention in product development and operations management. Several authors have been concerned with it (Meyer, 1997; Wheelwright and Clark, 1992; Nobeoka, 1993; Meyer and Utterback, 1993; Sundgren 1995, 1998; Robertson and Ulrich, 1998). Reviewing the definitions of the platform concept which are provided in the literature, a substantial difference is immediately evident in the approach which researchers adopt. A possible way for clustering them is considering the degree of generality of the platform definition researches provide.

Considering the similarity of a set of products the product platform can include two cases: i) product platform for a set of products with similar function and close parameters; ii) product platform for a set of products with same function and different parameters. The product platform can be seen as the common items shared by a set of products. Therefore, the essential of product platform building can be seen as a process that the common items are identified by some means. From aspect of product design, the common items can be parameters, structure, form, parts and components or modules of a set of similar products. From the aspect of product manufacture, the product platform includes those fixed tools and process to produce the common parts and components (Huangao et al., 2006).

Platform-based product development offers a multitude of benefits including reduced development time and system complexity, reduced development and production costs, and improved ability to upgrade products (Wei et al., 2009). According to different platform leveraging strategies, there are two basic types of platforms; they are the module-based and the scale-based.

3.3.3 Simulation, Experiments and Empirical studies

Three types of simulation can be identified in the selected set of references. The first type is found in paper using mathematical modeling approaches that supplement and test their models with numerical simulations. For example, considering downward substitution in their multi-period model Rao et al. (2004) demonstrate the size of the inventory saving that their model predicts with simulation. Similarly, Dong and Chen (2005) illustrate the impact of component commonality on order fill rate, delivery time and total cost via simulation. A second type of simulation that has experienced an increase in popularity recently is agent-based modeling. A number of recent studies use agent-based modeling in the framework of complex adaptive system (Kuauffman, 1995). For example, Ethiraj and Levinthal (2004) explore the performance effect of what they called under and over commonality. Finally, a third type of simulation study uses real data to simulate effects of commonality. For example, Lin et al. (2000) study the inventory reduction effects on different complexity reduction approaches, such as feature elimination, feature substitution and feature postponement with data of an IBM midrange computer family with over 200 members and hundreds of feature codes.

The use of experiments in the study of commonality is atypical. The impact of parts commonality on customers’ product valuation with the help of an experiment, so far an available example, is studied by Kim and Chhajed (2001). Studying the effects of commonality in vertical line extensions from both low-end and high-end products, they find that the use of commonality can increase the valuation of the low-end product but decrease the value of the high-end product.

Commonality and its effects have been studied empirically in rare cases. Safizadeh et al. (1996) have studied the product-process matrix. In their empirical study, they find that part commonality allows sustaining high plant performance despite violating the alignment between product and process. The view that increasing component commonality in real organizations can actually be quite difficult due to the lack of downstream information and often mis-fitting incentive structures is supported through a couple of case studies by Nobelius and Sundgren (2002).

3.4 Conclusions

From the above writings we are very much confident to conclude that-

1. Two stream of literature are identified in the state-of-art of manufacturing resource planning (MRP II) models within the scope of this writing. The uncertainty models/studies have ignored the commonality issue and the commonality models did not care of the uncertainty events, in contrary. Hence, this thesis attempts to make a bridge between these two wings.

2. It is pellucid that very little research has been conducted in the turf of multi-stage production systems under uncertainty and commonality. So far no research has conducted in developing any model to study the uncertainty issues in multi-period, multiple products and multi-stage environment for manufacturing resources planning. The effects of incorporation of component commonality in the aforesaid models and on the system parameters remain in the fissure and hence needed research attention.

3. Analytical study of effects of multiple common items in multi-period and multi-stage production system with multiple end products. Effects of commonality in the higher stages of the production system need to be analyzed. Present available researches have considered the entry stage only.

4. Previously most of the mathematical models hardly considered two-stage cases with single common component and single/multi period in deterministic conditions. The effect of uncertainty factors (viz. lead time, quality, break-down etc.) in commonality models completely ignored in the earlier researches. Therefore, development of mathematical models for multi-period and multistage production system with multiple end products and multiple common components under uncertain condition is in the virgin area of research and in the field of knowledge.


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