C:\Users\raoh\Desktop\Paper_1.xps The Journal of Engineering Research Vol. 8 No. 2 (2011) 1-9 1. Introduction Nowadays, industrial organizations complexity exists as an immense international interest and knowl- edge for the scientific basis. Studying the importance of reducing complexity into industrial organizations was recommended as one of several solutions to __________________________________________ *Corresponding author’s e-mail:garbie@squ.edu.om recovery the existing financial recession (Garbie 2009 and 2010; Garbie and Shikdar 2009, 2010 and 2011). It was defined as systemic characteristics which inte- grate several key dimensions of the industrial environ- ment including size, variety, information, uncertainty, control, cost, and value (Kamrani and Adat 2008). Flexibility and agility are considered as the most desir- Complexity Analysis of Industrial Organizations Based on a Perspective of Systems Engineering Analysts I. H. Garbie* and A. A. Shikdar Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, P.O. Box 33, Postal Code 123, Al Khoud, Muscat, Oman Received 4 April 2011; accepted 18 September 2011 Abstract: Complexity in industrial organizations became more difficult and complex to be solved and it needs more attention from academicians and technicians. For these reasons, complexity in industrial organizations represents a new challenge in the next decades. Until now, analysis of industrial organiza- tions complexity is still remaining a research topic of immense international interest and they require reduction in their complexity. In this paper, analysis of complexity in industrial organizations is shown based on the perspective of systems engineering analyst. In this perspective, analysis of complexity was divided into different levels and these levels were defined as complexity levels. A framework of analyz- ing these levels was proposed and suggested based on the complexity in industrial organizations. This analysis was divided into four main issues: industrial system vision, industrial system structure, industri- al system operating, and industrial system evaluating. This analysis shows that the complexity of indus- trial organizations is still an ill-structured and a multi-dimensional problem. Keywords: Complexity, Industrial organization, Systems engineering 2 I.H. Garbie and A.A. Shikdar ability of certain system properties for the industrial organizations. These properties will give industrial enterprises more ability to cope with increased envi- ronmental uncertainty and adapting to the faster pace of change of today's markets (Giabchetti et al. 2003). The industrial organizations are sometimes viewed as an intrinsic structural property of the system. The structural property is defined as how individual system components relate to each other and how the relation- ship determines overall system behavior (Arteta and Giabchetti, 2004). Sometimes, manufacturing systems complexity considers the product variety. This means increasing in the product variety increases in the com- plexity in the manufacturing systems (Kuzgunkaya and ElMaraghy 2006). Also there is another classifica- tion of industrial organizations complexity: time-inde- pendent complexity and time-dependent complexity. Time -independent complexity is used to add the com- plexity arising from the designer's perception while time-dependent complexity is either combinational or periodic (Kuzgunkaya and ElMaraghy 2006). There are several concepts such as: product, process, and operation complexity can be used as a component in industrial enterprises. The product com- plexity focuses on product features and specifications while the process complexity analysis focuses on the tools, equipment and operations used to manufacture it (Hu et al. 2008). Supply chain management complex- ity such as: upstream complexity, internal manufactur- ing complexity, and downstream complexity are con- sidered as complexity issues regarding manufacturing systems (Bazarth et al. 2009). Manufacturing strategy also plays an important role in complexity in industri- al enterprise such as Just-in-time manufacturing (lean manufacturing), flexible manufacturing, cellular man- ufacturing, agile manufacturing, concurrent engineer- ing, etc. The complexity measures are defined as not only intrinsic to the system being studied but also depend on extrinsic properties of the observer. Although most measurements were concentrated on operational measures, both structural and operational characteristics are important to the performance of the system as a whole. Operational complexity measures the uncertainty associated with the material and infor- mation flows of the system (Arteta and Giabchetti 2004). The complexity levels in industrial firms are estimated through several case studies based on gener- al framework which includes a questionnaire focusing on each issue in a firm (Garbie and Shikdar 2010). This paper is organized into several sections. Section 1 presents the importance of complexity con- cept. Section 2 reviews previous research work about the complexity. Analysis issues are explained in sec- tion 3. Section 4 presents a hypothetical example. Conclusions and recommendations for further work will be introduced in Section 5. 2. Literature Review Several research works have been published in this area but none of work mentioned the concept of design for complexity. A methodology based on a simulation model to analyze the complexity in mixed-model assembly production systems was suggested by (Kamrani and Adat 2008). A measurement framework to analyze the structural properties of the enterprise system was presented (Giabchetti et al. 2003). A com- plexity measure was developed for the business process level inside the organization on one product (e.g. the prepaid phone card) (Arteta and Giachetti, 2004). A new metrics for assessing the structural com- plexity of system configurations was suggested (Kuzgunkaya and ElMaraghy 2006). They based on machine complexity, buffer type complexity, and material handling system complexity as a structural complexity although they can be considered as an operational or dynamical complexity. Product, process, and operational complexity in modeling and assessment of manufacturing complexity were intro- duced (ElMaraghy and Urbanic 2003, 2004; ElMaraghy et al. 2005). Each assessment was evalu- ated independently. Three different types of complexity were suggest- ed (Bazarth et al. 2009) to represent and model supply chain complexity such as: upstream complexity, inter- nal manufacturing complexity, and downstream com- plexity. They used these complexities to study the impact effect on a manufacturing plant performance. A coding system of machines, buffers, and material handling equipment to measure complexity based on time-independent complexity (static or structural com- plexity) of those major components was classified and designed by (ElMaraghy et al. 2005). The structural (static) complexity measure to evaluate the complexi- ty in mixed assembly lines was used (Hu et al. 2008). Several case studies involving 14 Italian companies were conducted (Perona and Miragliotta 2004) to investigate how complexity can affect a manufacturing company's performance. An analytical model for measuring system complexity was presented based on information entropy and probability distribution of resource allocations (Cho et al. 2009). Operational complexity was measured as a function of cost through supply chain management systems (Wu et al. 2007). They indicated that inventory costs are associated with operational complexity. An entropic related to com- plexity measures to quantify the complexity associat- ed with information content of schedules and varia- tions between schedules was used (Huatuco et al. 2009). Yang (2010) presented a computational approach to investigate effecting of scheduling with processing 3 Complexity Analysis of Industrial Organizations Based on a Perspective of Systems Engineering Analysts time on two stage hybrid flow shop systems. A mathe- matical model was suggested to identify functional requirements and the associated design parameters regarding complexity level (Tomiyama et al. 2007). Kashyap and Sinha (2011) studied the complexity related to the mental fatigue required of a person for doing a specific job. While they used how to manage stress, they developed a general model to estimate overall complexity of a profession although they tried to evaluate the job complexity of an engineer. Mazur and Chen (2011) presented organizing work, commu- nication and managing conflict as the most important issues among team members to complete the project. They considered multifunctional knowledge, team- work capabilities and working relationships with organizing work as a complex problem. 3. Analysis Issues To identify the main issues of industrial organiza- tions complexity (IOC), there are five important ques- tions to be asked such as the following to describe how the complexity of industrial enterprises can be studied. These questions were presented by (Garbie and Shikdar 2011) in analyzing and estimating the com- plexity levels in industrial organizations. 1. How the complexity issues of an industrial organi- zation is identified and analyzed? 2. How the complexity level of an industrial organi- zation is estimated? 3. How can an industrial organization reduce its com- plexity? 4. Which issues are more important than others? 5. How can industrial organizations identify the adverse factors for reducing complexity? Based on these questions, full analysis of complex- ity regarding these issues will be analyzed and explained through the recommended issues from per- spective of systems engineering analysts. These can be represented into four main phases as follows (Fig. 1). Figure 1 shows a briefly industrial organizations com- plexity procedure consisting of four phases. Each phase will be discussed with its associated issues. It can be noticed from Fig. 1 that the procedure of ana- lyzing industrial organizations complexity follows the four phases parallel. Based on these concepts and issues mentioned in the previous sections, it can be noticed that the indus- trial organization complexity (IOC) consists of major issues. These issues are: industrial organization vision complexity (IOVC), industrial organization design complexity (IODC), industrial organization operating complexity (IOOC), and industrial organization evalu- ating complexity (IOEC) (as shown in Fig. 1). The mathematical model of industrial organization compo- nents and the corresponding complexity relationships between them in order to emphasize on particular vision, design, operating, and evaluating is presented in the following equations (1 and 2). As each compo- nent or element in these systems is a potential source of uncertainty (due to its state), the measuring of com- plexity for each one is highly valuable. Then, IOCL is clearly modeled as the following Eq. (1) as a function of previous sub-complexities. Eq. (1) can be rewritten as Eq. (2). Each term represents sub-complexity measure of complexity measure of industrial organiza- tion (IOCL). Adding these terms with relative weights is considered. These weights can be used as a reason existing to differentiate between major issues of com- plexity. IOCL = f (IOVC, IODC, IOOC, IOEC) (1) IOCL = wIOVC [IOVC] + wIODC + wIOOC [IOOC] + wIOEC [IOEC] (2) Figure 1. Four phases for design for industrial enterprises complexity 4 I.H. Garbie and A.A. Shikdar Where: IOCL = industrial organization complexity level, IOVC = industrial organization vision complexity, IODC = industrial organization design complexity, IOOC = industrial organization operating complexity, IOEC = industrial organization evaluation complexity. wIOVC, wIODC, wIOOC, and wIOEC are relative weights of organization vision, organization structure, organization operating, and organization evaluation, respectively. Because the trade-offs frequently exist between these objectives, a comprehensive analysis for each individual measure is needed. The value of these relative weights may reflect the system analyst's subjective preferences based on his/her experience or can be estimated using tools such as Analytical Hierarchy Process (AHP). In this paper, the relative weights using the AHP are estimated and changed fre- quently according to the new circumstances by deci- sion maker or a group of decision makers (Abdi 2006). These groups are represented in senior management level, manufacturing and/or production engineers, plant managers, operators, and suppliers. These rela- tive weights can be estimated using AHP according to the next matrix. For example, suppose , then this means that industrial organization design complexity (IODC) is four times more important than operating complexity (IOOC). 3.1 Phase 1: Industrial Organizations Vision Complexity The industrial organizations vision complexity is the first step in the design for industrial enterprises complexity. The major issue of this phase is how to collect the main components (elements) of industrial organizations vision. The organization vision usually specifies what supply chain management (SCM) repre- senting in number of suppliers (NOS), demand vari- ability (DV) representing in number of customers (NOC), introducing s new product (NP), product life cycle (PLC) representing also in product development (PD), and time to market (TTM) requires and how they are affected and effecting on the complexity of indus- trial organizations. The industrial organizations vision complexity (IEVC) will be represented mathematical- ly as a function of these issues as shown in the follow- ing Eq. (3). IEVC = f (SCM (NOS), DV (NOC), NP, PLC (PD), TTM) (3) 3.2 Phase 2: Industrial Organizations Design Complexity The second phase, industrial organizations design complexity (IODC) procedure is the designing for sys- tem complexity itself. It is mainly concerned with dif- ferent elements to represent the complexity of it. These elements are: product structure and design (PSD), sys- tem design (SD), and manufacturing strategies (MS). For each main element, there are several sub-main ele- ments which play an important role in the value of complexity. For example, the PSD has four different types to represent the complexity in the product design such as number of parts per product (NNP), number of operations per part (NOP), processing or manufactur- ing time per operation (PT), product size and weight (PSW). All of them have a significant effect on the complexity of manufacturing/production process. System design (SD) is playing a major role in com- plexity in industrial organizations. It can be observed how complexity is the analysis of the system design (SD). The SD divides the complexity analysis into three major issues: production system size (PSS), material handling system (MHS), and plant layout sys- tem (PLS). For the PSS, there are three different clas- sifications of production system: small-sized produc- tion system (SSPS), medium-sized production system (MSPS), and large-sized production system (LSPS). Each classification type represents or introduces a sig- nificant effect on complexity. Also, the material handling system (MHS) and plant layout system (PLS) play an important role in identify- ing the complexity in industrial organizations. The MHS consists of material handling equipments (MHE) with different types of equipments (e.g. conveyor, trucks, forklifts, crane, etc.), material handling storage system (MHSS) (e.g. manual or automated storage and retrieval), and identification systems (IS). How it can be thought about all previous components and degree of complexity related to each one. Facility planning or plant layout system (PLS) has a significant effect on complexity by different types of configuration. It can be seem that functional layout (FL) or process layout is more complex than product layout (PL) and/or cel- lular layout (CL). Complexity with respect to manu- facturing strategies (MS) is totally different than (SD) and (PSD) because it was looking for which strategy can be applied. Complexity in lean manufacturing (LMS) for example is affected by increased product variety if it is comparing with product layout (PL) in mass production system. But in general, complexity in lean manufacturing system (LMS) is low comparing it with agile manufacturing system (AMS), flexible man- 5 Complexity Analysis of Industrial Organizations Based on a Perspective of Systems Engineering Analysts ufacturing system (FMS), and reconfigurable manu- facturing system (RMS). Regarding complexity in AMS, FMS, and RMS, they can deal easily with any changes in product design (modifications), unpre- dictable demand, etc. but infrastructure of these sys- tems itself is more sophisticated and complicated to lead these systems to be more and more complex. The mathematical expression of industrial organi- zations design complexity (IODC) can be modeled as the following Eqs. (4 and 5) in different facets. IODC = f (PSD, SD, MS) (4-1) IODC = wPSD (PSD) + wSD (SD) + wMS (MS) (4-2) IODC = f (NPP, NOP, PT, PSW, MHS, PSS, PLS, LMS, FMS, AMS, RMS) (5) The wPSD wSD and wMS are relative weights of prod- uct design, system design and manufacturing strate- gies, respectively. Eq. (5) can be rewritten as a very board general representing the lowest level of informa- tion in industrial organization design complexity as the following Eq. (6). (6) 3.3 Phase 3: Industrial Organizations Operat- ing Complexity Once the industrial organization system vision and design complexities become available to the perspec- tive of industrial systems analysts and designers, the design for complexity related to the system operating becomes urgent to be analyzed and evaluated. This phase may involve further activities in data collection and processing. Design for industrial organizations operating or dynamic complexity (IOOC) is different than previous ones (system vision, and system design). In this analysis, it can be noticed that there are three major items of complexity: resource status of operat- ing complexity (RSOC), work in progress complexity (WIPC), and business operations complexity (BOC). Resources mean equipment (e.g. machining equip- ment, forming equipment, material handling equip- ment, etc.) and human. In this analysis, it will be con- centrated on the resource reliability (RR), resource capability or flexibility (RC), resource utilization (RU), resource scheduling/ rescheduling (RS/R), and human scheduling/rescheduling (HS/R). For example, maintenance level plays a vital role in resource relia- bility. This means that the lower the maintenance level is the lower in machine capacity (reliability). Also, work in progress complexity (WIPC) representing in buffer between workstations or departments is consid- ered one of measuring degree of complexity inside the production plant (factory). There are several important issues that can be used to evaluate the business operations complexity (BOC). These issues are: organization plans (OP), organizing work (OW), structure of management levels (SML), staffing developing and motivation (SDM), decision making (DM), communication between and within management levels (CML), managing conflict, change, culture and stress (MCS), and finally leader- ship roles in management (LR). Then, industrial organ- izations operating complexity (IOOC) can be modeled to measure or evaluate the complexity level as the fol- lowing Eqs. (7 and 8). IOOC = f (RSOC, WIPC, BOC) (7-1) IOOC = wRSO (RSO)+wWIP (WIP)+wBOC (BOC) (7-2) IOOC = f (RR, RC, RU, RS/R, HS/R, WIP (BS), OP, OW, SML, SDM, DM, CML, MCS, LR) (8) 3.4 Phase 4: Industrial Organizations Evalua- tion Complexity The fourth phase in the design for industrial organ- izations complexity (IOEC) procedure is the complex- ity regarding the system evaluation. As industrial organizations have a great impact on the performance measurements, they still have a problem in measuring these complexities especially regarding selection of the objectives. In this paper, there are five different objectives that can be used to evaluate the complexity. They are: product cost (PC), response (R) representing in manufacturing lead time, system productivity (SP) representing in system utilization, product quality (PQ) representing in number of scrap (defect rate), and appraising and rewarding performance (ARP). They also can be modeled mathematically as the following Eq. (9). IOEC = f (PC, R, SP, PQ, ARP) (9) 4. A Hypothetical Example This numerical example is used to estimate the level of complexity of industrial organizations. This can be implemented through three sequential steps. The first step is used to estimate the relative weights of indus- trial organizations based on the four major issues, IOVC, IODC, IOOC, and IOEC. The relative weights between these issues are estimated by using the AHP as the following matrix based on the pair wise compar- isons of the four major issues. It can be noticed that the relative weights of IOVC is estimated to be equiv- alent to the IODC, twice as important as the IOOC and four times more important than the IOEC. The IODC is estimated to be three times more important than the IEOC and four times more important than IOEC. The 6 I.H. Garbie and A.A. Shikdar IOOC is also estimated to be twice as important as the IEEC. As a result, the values of relative weights are estimated at 0.35, 0.40, 0.16, and 0.09 for IOVC, IODC, IOOC, and IOEC, respectively. Eq. (2) is used to calculate the industrial enterprises complexity level (IOCL) incorporating of the four major issues as the following Eq. (10). IOCL= 0.35 [IOVC] + 0.40 [IODC] + 0.16 [IOOC] +0.09 [IOEC] (10) In the second step, the multiple regression models are used to estimate and formulate the complexity lev- els for the major and sub-major issues. The values were assumed to follow a uniformly distributed ran- dom variable with known parameters as shown as in Table 1 for 50 generating values for each issue. The multiple regression models for industrial organizations vision complexity, industrial organizations design complexity (product structure and design, system design and manufacturing strategies), industrial organ- izations operating complexity (resource status and business), and industrial organizations evaluation complexity are presented as the following Eqs. (11- 17), respectively. This was done by using MINITAB statistical software package to generate a random vari- able and formulating a multiple regression model. IEVC = -0.0356- 0.0374 (NOS) - 0.000013 (NOC) + 0.0065 (PD) + 0.0842 (TTM) (11) PSDC = 0.359-0.00436 (NPP) + 0.0353 (NOP) + 0.0123 (PT) + 0.0759 (ind1) - 0.0420 (ind 2) + 0.0241 (ind 3) - 0.0039 (ind 4) ind 1, ind 2, ind 3, and ind 4 are used for the product size and weight. (12) SDC = 0.2440 + 0.00479 (SSPS) -0.00795 (MSPS) + 0.00018 (LSPS) + 0.0848 (FL) - 0.0681 (CL) + 0.0609 (PL) + 0.092 (MHSS) + 0.0290 (MHE) + 0.189 (IS) (13) MSC = 0.441 -0.059 (LMS) + 0.245 (FMS) - 0.028 (AMS) - 0.024 (RMS) (14) ROSC = 0.598- 0.379 (RR) + 0.509 (RC) + 0.113 (RU) - 0.50 (RS/R) + 0.233 (HS/R) (15) BOC = 0.980 - 0.0029 (OP) - 0.0076 (OW) + 0.0015 (SML) + 0.171 (S) +0.234 (D) + 0.0050 (M) - 0.0266 (DM) - 0.0642 (CML) - 0.134 (MCS) - 0.289 (LR) (16) IOEC = 0.457 + 0.00210 (PC) + 0.0088 (R) - 0.172 (SP) - 0.40 (PQ) + 0.117 (ARP) (17) The third step is used to determine the complexity level of IOVC, PSDC, SDC, ROSC, and IOEC, respec- tively by optimizing Eqs. (11-17). The constraints are identified based on the range values of each sub-issue which are listed in Table 1. To determine the relative weights between PSDC, SDC, and MSC and between RSOC, WIPC, and BOC, the following matrices are used to estimate these val- ues and the pair wise comparison between product structure and design, system design and manufacturing strategies is illustrated in these matrices. With respect to IODC, it can be noticed that a PSDC is estimated to be equivalent to the SDC and twice as important as a MSC. The same estimation is done relate to the SDC with PSDC and MSC. Regarding the IOOC, it can be noticed that RSOC is estimated to be four times more important than the WIPC and two times as important as the BOC. The BOC is also esti- mated to be four times more important than WIPC. As a result, the relative weights for IODC between PSDC, SDC, and MSC are estimated at 0.40, 0.40, and 0.20, respectively. Also, the relative weights for IOOC between RSOC, WIPC, and BOC are estimated at 0.544, 0.110, and 0.345, respectively. The results of complexity level in each sub-issue and major issue are illustrated in Table 2. Then, Eq. (10) is used again to calculate the global complexity level based on the information taken from Table 2 and the estimated rel- ative weights as the following. IOCL = 0.35 [0.4582] + 0.40 [0.3099] + 0.16 [0.3023] + 0.09 [0.4570] = 0.3740 It can be noticed from the results given from the previous equation and Table 3 that the level of com- plexity in this enterprise equals to 37.40% and this value seems ranked in a medium range. It seems that vision complexity represents more important (0.16/0.3740 = 42.78%) than design complexity (33.15%). The percentage values of operating and evaluating complexity are 13.36% and 10.70%, 7 Complexity Analysis of Industrial Organizations Based on a Perspective of Systems Engineering Analysts Table 1. Data for complexity issues based on uniform distribution, U [a, b] U represents the uniform distribution with [a] the lower limit and [b] is the upper limit, *represents the product size (small, small-medium, medium, medium-large, large), *'represents the product weight (light, light-medium, medium, medium-heavy, heavy), **represents the degree of variety in functional layout, **'represents the number of manufacturing cells, **''represents the variety of models (single, batch, and mixed), ***represents the appraising and rewarding performance as a percentage. 8 I.H. Garbie and A.A. Shikdar respectively. These values can be totally different from an industrial organization to another one based on the sub-major issues and the relative weights between sub- major and major issues. Regarding the complexity reduction, it can be noticed that if the values of sub- major issues reduced, the complexity level will be reduced too. It also seems that the vision complexity and design complexity represent the adverse factors for reducing complexity. With respect to IOVC, it is not easily to reduce the number of suppliers, for exam- ple, and it should increase this number. Also, for IODC, the number of parts per product, number of operations per part, processing time per operation, product size and weight, production system size are representing adverse factors for reducing complexity because they cannot be changed. 5. Conclusions It can be noticed from this analysis that complexi- ty issues are not simple. It required emphasize on each of the main issues and the sub-main. Hence, industrial organizations complexity (IOC) will involve four major issues: vision complexity, design complexity, operating complexity, and evaluation complexity. Analyzing complexity in industrial enterprises is based on the four main issues and it can be mathemat- ically expressed as a total global function as the fol- lowing Eq. (18). IOC = f (SCM (NOS), DV (NOC), NP, PLC (PD), TTM, NNP, NOP, PT, PSW, MHE, MHSS, IS, SSPS, MDPS, SLPS, FL, CL, PL, LMS, FMS, AMS, RMS, RR (ML), RC < RU, RS / R, HS, / R, WIP (BS), OP, OW, SML, DM, CML, LR, SDM, MCS, PC, R, SR, PQ, ARP) (18) The IOC issues should be dynamic and they should evolve with and adapt to the changing internal and external environment. Until now, the IOC remains a research topic of immense international interest. This will represent the degree of freedom of industrial organizations designers to identify which issue is more significant than others. The main contribution in this paper is how to identify and model the components of industrial enterprises complexity in any industrial firms (organizations) at any time considering these components. The authors intend to extend this research to apply this analysis and formulation the suggested model to estimate and optimize the degree of complexity in any industrial enterprises towards full validation of the complexity theory which will be dis- cussed and presented in the future research. Acknowledgments The authors would like to acknowledge the finan- cial support provided by the Sultan Qaboos University (Grant No. IG/ENG/MIED/10/01) to carry out this research work. Table 2. Complexity levels in the four phases of industrial enterprises Table 3. Values of major issues regarding the complexity level 9 Complexity Analysis of Industrial Organizations Based on a Perspective of Systems Engineering Analysts References Abdi MR (2006), Performance evaluation of reconfig- urable manufacturing. The Proceedings of the 36th CIE Conference on Computer and Industrial Engineering, Taipei, Taiwan 2479-2491. Arteta BM, Giachetti RE (2004), A measure of agility as the complexity of the enterprise system. Robotics and Computer-Integrated Manufacturing 20:495-503. Bazarth CC, Warsing DP, Flynm BB, Flynn EJ (2009), The impact of supply chain complexity on manu- facturing plant performance J. of Operations Management 27:78-93. Cho S, Alamoudi R, Asfour S (2009), Interaction- based complexity measures of manufacturing sys- tems using information entropy. Int. J. of Computer Integrated Manufacturing 22(10): 909- 922. ElMaraghy WH, Urbanic RJ (2003), Modeling of manufacturing systems complexity. Annals of the CIRP 53(1):363-366 ElMaraghy WH, Urbanic RJ (2004), Assessment of manufacturing complexity. Annals of the CIRP, 53(1):401-406. ElMaraghy HA, Kuzgunkaga O, Urbanic RJ (2005), Manufacturing systems configuration complexity. Annals of the CIRP 54:445-448. Garbie IH, Shikdar A (2009), Determination of com- plexity levels in industrial firms. Proceedings of the Int. Conference of Industrial Engineering Research (IERC), Miami, Florida, USA, May 30- June 3:1423-1428. Garbie IH (2009), A vision for reconfiguring industri- al organization due to the global recession. Proceedings of the 39th of Int. Conference on Computers and Industrial Engineering, France, 658-663, July 6-8, 2009. Garbie IH, Shikdar A (2010), Complexity level in industrial firms: Case studies and implementation. Proceedings of the 2010 International Conference on Industrial Engineering and Operations Management (IEOM 2010), Dhaka, Bangladesh, January 9-10, 2010. Garbie IH (2010), A roadmap for reconfiguring indus- trial enterprises as a consequence of global eco- nomic crisis (GEC). J. of Service Science and Management 3(4):419-428. Garbie IH, Shikdar A (2011), Analysis and estimation of complexity level in industrial firms. Int. J. of Industrial and Systems Engineering 8(3):1-23. Giabchetti RE, Martinez LD, Saenz OA, Chen CS (2003), Analysis of the structural measures of flexibility and agility using a measurement theo- retical ramework. Int. J. of Production Economics 86: 47-62. Hu SJ, Zhu X, Koren WY (2008), Product variety and manufacturing complexity in assembly systems and supply chains. Annals of the CIRP 57: 45-48. Huatuco LH, Efstathiou J, Calinescu A, Sivadasan S, Kariuki S (2009), Comparing the impact of differ- ent rescheduling strategies on the entropic-related complexity of manufacturing systems. Int. J. of Production Research 47(1):4305-4325. Kamrani AK, Adat A (2008), Manufacturing complex- ity analysis: A simulation-based methodology-col- laboration engineering. Theory and Practice edit- ed by Karmrani AK Springer, 227-248. Kashyap N, Sinha S (2011), Estimation of job com- plexity in an industrial organization. Int. J. of Industrial and Systems Engineering Vol. 7(1):26- 44. Kuzgunkaya O, ElMaraghy HA (2006), Assessing the structural complexity of manufacturing systems configurations. Int. J. of Flexible Manufacturing Systems 18:145-171. Mazur LM, Chen SJ (2011), A task-member assign- ment model for complex engineering. Int. J. of Industrial and Systems Engineering 7(1):1-25. Perona M, Miragliotta G (2004), Complexity manage- ment and supply chain performance assessment. Int. J. of Production Economics 90:03-115. Tomiyama T, Amelio VD, Urbanic J, ElMaraghy HW (2007), Complexity of multi-disciplinary design. Annals of the CIRP 56(1):85-188. Wu Y, Frizelle G, Efstathiou J (2007), A study on the cost of operational complexity in customer-suppli- er systems. Int. J. of Production Economics 106:217-229. Yang J (2010), A new complexity proof for the two- stage hybrid flow shop scheduling problem with dedicated machines. Int. J. of Production Research 48(5):531-1538.