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Integrative assessment and configuration of production systems
CIRP Annals - Manufacturing Technology 60 (2011) 457–460
Contents lists available at ScienceDirect
CIRP Annals - Manufacturing Technology jou rnal homep age : ht t p: // ees .e lse vi er. com/ci rp/ def a ult . asp
Integrative assessment and configuration of production systems G. Schuh (1)*, J. Arnoscht, A. Bohl, C. Nussbaum Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, Germany
A R T I C L E I N F O
A B S T R A C T
Keywords: Standardization Optimization Performance measures
Maximizing economies of scale and economies of scope simultaneously is a vital issue for producing companies in high wage countries. A decisive enabler for this is the management of product complexity. Due to the strong and far-reaching impact of product complexity on the value added chain, its management requires an integrative consideration of the entire product and production system. The following paper introduces a methodology facing this challenge. The core element of this methodology is an integrative and complexity-focused assessment model. Based on this model an approach for the comprehensive configuration of the product and production system is presented. ß 2011 CIRP.
1. Introduction A challenge to be mastered by manufacturing firms in high wage countries is to simultaneously provide a fit of the produced products to the customer’s individual needs while participating in the price competition of globalized markets. Therefore the parallel realization of economies of scale and economies of scope by finding the right level of standardization is critical. Beyond this overall level the structure-forming elements of the product and production system (production system in short) need to be aligned to eliminate complexity-related bottlenecks and over capabilities. For this reason a comprehensive methodology for the integrative analysis and design of production systems is required [1–3].
2. Existing approaches addressing the resolution of the dilemma between economies of scale and scope The concurrent design of products and processes as well as the resolution of the dilemma between scale and scope has been addressed by scholars originating from various disciplines. The methods developed and applied in this context can be differentiated into an assessment-focused [e.g. 4,5] and a design-focused group [e.g. 6]. Both assessing and designing elements are combined by a third group of approaches [e.g. 7,8]. Although every single of these three groups makes a valuable contribution to the resolution of the dilemma between scale and scope, they are continuously lacking an integrative consideration of all structure-forming elements of a production system. For this reason a comprehensive and systematic analysis of the complexity-related fit of these elements is not possible yet. Coming from the context of corporate policies, Bleicher [9] developed a model for the integrative assessment of the fit of a system’s elements by the segmentation into interrelated dichotomies. The development of the integrative assessment model,
* Corresponding author. 0007-8506/$ – see front matter ß 2011 CIRP. doi:10.1016/j.cirp.2011.03.038
discussed in the following section, is based rudimentarily on this principle. 3. Integrative assessment model 3.1. Definition of a constitutive framework As described before, an integrative assessment model needs to comprehend all major elements of a production system that bear a relation to the emergence and the consequences of complexity. Thus the assessment model necessitates a constitutive framework, which structures the field of observation in a mutually exclusive and collectively exhaustive manner. According to Malik [10] and Kaiser [11] complexity can be differentiated into external complexity, which emerges by the interaction with the customer or the environment respectively, and into internal complexity, caused endogenously by intracorporate structures and processes. While these types of complexity are non-overlapping, they exhibit a high degree of interdependency, since for example a high number of product variants generally entail an increased internal diversity of parts and processes to manufacture them. Another classification given by Wiendahl and Scholtissek [12] subdivides complexity into a product and a production-related type. Due to the interaction of product and process these types also are highly interdependent. Aggregating both classifications to one constitutive framework for the intended assessment model yields four domains to be considered by the assessment model. These domains are interpreted in the following section. 3.2. Fundamental domains of a production system Focusing on the product-related part of a production system from an external perspective leads to a consideration of the product program. This can be defined as the range of products offered to the customer. The challenge in this domain is to provide the customers with a product range that fits individual needs, but still is intelligible by each customer [13].
G. Schuh et al. / CIRP Annals - Manufacturing Technology 60 (2011) 457–460
458
The internal and product-related view relates to a consideration of the inner structure of the product, i.e. its functions, technologies and components as well as their interaction. This inner structure is represented by the product architecture. The dilemma of the product architecture is to realize the intended variety of the product program while decreasing the intra-corporate variety of components, equipment, processes, etc. [14]. The external and production-related domain corresponds to the supply chain, which is understood as the outward logistic interface of the production system. The problem of the supply chain is to maintain delivery performance while reducing inventories [15]. The internally production-related domain refers to the intracorporate structures performing the production processes. This domain is defined as the production structure. From complexity perspective the challenge of this domain is to achieve and sustain economies of scale by a high level of resource utilization as well as learning effects. This challenge also implies a dilemma, since with highly diversified product range resource utilization is generally augmented by a flexible reallocation of operations, which in return is compromising learning effects [3]. The domains and their corresponding partial dilemmas derived before were verified for their relevance and sufficiency within a delphi-workshop with a panel of complexity management experts from eight German manufacturing firms. Fig. 1 illustrates the four domains of a production system derived before. 3.3. Interrelated partial dilemmas As described in the previous section, the main dilemma between scale and scope can be broken down into challenges for each domain or partial dilemmas, respectively. In contrast to the main dilemma the partial dilemmas are operationalizable, as it is shown in the following. Furthermore, the resolution of the partial dilemmas enables a production system to resolve the main dilemma between scale and scope. However, as the deduced domains are highly interdependent, the resolution of one partial dilemma is linked to the resolution of the other: For example a production system does not benefit from solving the product architecture dilemma by a sophisticated modular product platform, if the derived product program variety can not be made understood by the customer. Thus a fit between the four sections is to be established. 3.4. Main dimensions describing the domain-related dichotomies In order to operationalize the operating point of a production system referring to the four dilemmas, the concept of paradoxical series of tension by Bleicher [9], based on the definition of dichotomous extrema, is adapted and extended. For the intended assessment model these extrema are maximum values of characteristic measures of a production system. Therefore a key task in the development of this model is to identify characteristic and dichotomous measures – called main dimensions – that represent the driving forces behind each dilemma.
[()TD$FIG]
Product
External
Internal
Product Program
Product Architecture
Fit to individual customer needs vs. intelligibility of variety
External variety vs. internal commonality Domains for the integrated assessment of production systems
Production
Supply Chain
Production Structure
Delivery performance vs. reduction of inventories
Resource utilization vs. standardized process sequences
Fig. 1. Domains for the integrated assessment of production systems.
To solve this task, describing variables for the operating point of a production system corresponding to each dilemma were collected via empirically inductive research and via literature review. The collected variables were filtered for their significance and their measurability in an industrial environment. The residual variables were clustered into dichotomous main dimensions according to their correlation. Hereby two antagonistic main dimensions per partial dilemma could be isolated, which correspond to the dichotomous extrema by Bleicher. These main dimensions can be described by corresponding questions. Main dimensions of the product program Fit of variety: ‘‘Does the product program offer the features and options requested by the customers?’’ Explainability at the point of sale: ‘‘Can the variety of the product program be made understood by the customers?’’ Main dimensions of the product architecture Product architecture flexibility: ‘‘Does the product architecture feature the right degree of freedom at the right positions to facilitate the intended product program without compromising architecture standards?’’ Product architecture commonality: ‘‘Does the product architecture augment economies of scale by product standardization?’’ Main dimensions of the production structure Process commonality: ‘‘Does the production structure allow for stable and standardized processes?’’ Resource utilization: ‘‘Can a high level of utilization be achieved with all resources of the production structure?’’ Main dimensions of the supply chain Supply chain capital efficiency: ‘‘Can the logistic operations be performed with a low level of capital employed?’’ Supply chain effectiveness: ‘‘Are the customers provided with the right products at the right time? A more detailed deduction of the four domains and especially the related main dimensions can be found in [16]. 3.5. Mathematical description of the main dimensions Given that these main dimensions are to be consolidated into a quantitative assessment model, a mathematical description for each main dimension is needed. These descriptions were derived from the filtered, describing and influencing variables referred to before. For this the variables were put into a relationship, and were normalized to a range between zero (worst case) and one (ideal case) by the use of reference parameters. For the reason of maintaining the required scope of this paper, the mathematical descriptions of the main dimensions are not explained in detail, but summarized in Table 1. 3.6. Consolidation of the main dimensions With the objective of creating a self-contained assessment model all main dimensions are to be consolidated into one system. According to the four partial dilemmas a layout composed of four interdependent coordinate systems was chosen. To represent the partial dilemmas each coordinate system consists of the two dilemma-related main dimensions. This yields a non-favorable (0,0)-state and an ideal (1,1)-state in each coordinate system. The latter of these states corresponds to a perfect resolution of a partial dilemma. Between both states a definite direction of optimization can be plotted. This direction of optimization is intersected orthogonally by hyperbolic shaped isoquants. These isoquants
G. Schuh et al. / CIRP Annals - Manufacturing Technology 60 (2011) 457–460
459
Table 1 Mathematical description of the main dimensions. Dimension and equation
Legend
Fit of variety n X Nas-is;i FVPP ¼ 1n N
FVPP: fit of variety of the product program n: number of product attributes Nas-is,i: as-is sales volume for attribute i Nplan,i: planned sales volume for attribute i
plan;i
i¼1
Explainability at point of sale n X V i;expl EFPP ¼ nV1oa
EFPP: explainability at the point of sale n: number of sales persons Vi,expl: number of product variants which can be explained by sales person i Voa: overall number of product variants
i¼1
Product architecture flexibility 0 11 m m X X F PA ¼ U stand;i @ U j A j¼1
FPA: product architecture flexibility n: number of standard variants Ustand,i: realized sales volume by stand variant i m: overall number of product variants Uj: sales volume realized with variant j
j¼1
Product architecture commonality m X Abs 1 wi Abs i K PA ¼ m
KPA: product architecture commonality m: overall number of part numbers wi : value portion of part number i related to all part numbers of the product program Absi: quantity of sales of part number i, AbsOA: quantity of sales of all part numbers
OA
i¼1
Process commonality r PKPS ¼ 1 Rmax ¼ 1 pr2
PKPS: process commonality r: number of used process sequences Rmax: theoretical maximum of sequences p: overall number of resources
Resource utilization 0 X 1 APS ¼ To ti
APS: resource utilization o: number of available resources T: duration of underlying period ti: process time of resource i within time span
i¼1
Supply chain capital efficiency V KESC ¼ max 1 UT ; 0
KESC: supply chain capital efficiency VT: Avg. value of inventories related to considered product program within period UT: revenues generated by considered product program in underlying period
Supply chain effectiveness !1 q q X X M FQ ;i M OA;i LGSC ¼
LGSC: supply chain effectiveness q: number of sub-periods within period MFQ,i: quantity delivered on time within period i MOA,i: overall quantity ordered within period i
T
i¼1
i¼1
represent operating points, which are of indifferent value. Without the implementation of new solution principles a production system is restricted to its specific isoquants, while taking adjustment measures, e.g. product architecture flexibility can be substituted for commonality by defining less constraining standards. This conforms to a movement along an isoquant, since it does not impact the resolution of the domain-related dilemma. Therefore the isoquants can be interpreted as a multidimensional maturity degree model. Fig. 2 assembles the integrative assessment model form the derived domains and main dimensions. By the arrangement of the
Explainability at the Point of Sale
11
Product Architecture Flexibility
0
Product Program
Product Architecture
II
II II
IV
III
Prod duct Archite ecture Commo onality
Supply Chain 0
Production Structure
11 Supply Chain Capital Efficiency
Resource Utilization
Fig. 2. Integrative assessment model.
0
Econ nomie es of Scale e
Proc cess Comm monality 0
Supply y Chain Effectiv veness
1 1
1 1
Fit of V Variety
0
0
0
0
conom mies of Sc cope Ec
[()TD$FIG]
four coordinate systems an upper left triangle is generated, which encompasses the scope-driven main dimensions, i.e. those dimensions facilitating the satisfaction of individual customer needs. In return the lower right triangle represents the scaledriven main dimensions, focusing on cost advantages. The four directions of optimization converge in the center of the assessment model. This ideal state equals the total resolution of the superior dilemma between scale and scope. 3.7. Experimental validation For preliminary validation purposes the integrative assessment model was applied at a medium-sized, German company designing, manufacturing and distributing metal hardware. The assessment was focused on one particular product line as well as the related production resources and processes, but was conducted for a time-frame of four subsequent fiscal years. Thus dynamic effects could be uncovered. The experimental validation demonstrated a general operability of the assessment model, as the main dimensions could be quantified using the mathematical descriptions presented before. Moreover specific conclusions on the operating point of this production system could be drawn: The analyzed product line is offered in a variety, which is tailored to the individual requirements of a small group of main customers. For this reason fit of variety and explainability at the point of sale are attained at high levels (FVPP = 0.91, EFPP 1). This variety affects the product architecture reducing commonality (FPA = 0.64, KPA = 0.32) and the production structure inducing a low degree of resource utilization (PKPS = 0.88, APS = 0.50). The supply chain is geared to delivery performance (KESC = 0.65, LGSC = 0.96). Dynamic effects could be identified as an increasingly globalized sales activity led to a decline of the fit of variety of the product program tailored to specific customers. Furthermore, a softening of product architecture
[()TD$FIG]
G. Schuh et al. / CIRP Annals - Manufacturing Technology 60 (2011) 457–460
460
Integrative assessment of the production system I
II
IV
III
1
Focusingon on Focusing misalignedelements elementsof of misaligned theproduction productionsystem system the
Analysis of complexity-related interdependencies
Priorizationof ofcritical critical Priorization productand andprocess process product features features Recursion by iterated analysis and assessment
2
Developmentof of Development candidatesfor for candidates constitutive features constitutive features
Implementation of integrative principal solutions
3
Evaluation Evaluation andselection selectionof of and constitutivefeatures features constitutive
4 Definition of constitutive features of the production system
Fig. 3. Integrative configuration approach.
standards resulted in a simultaneous increase of product architecture flexibility and decrease of commonality. This first trial is to be followed by a more voluminous validation of the assessment model in order to proof its informational value under varying constraints and to discover how the model can be adapted specifically. Furthermore, this will enable a benchmarkbased scaling as well as an industry-depending weighting of the main dimensions and isoquants. 4. Integrative configuration of production systems The assessment model referred to before allows for an identification of misaligned elements of the production system considering the resolution of the scale-scope-dilemma. In order to eliminate these misalignments, the question of the right level of standardization for the according elements is to be solved by integrative configuration of the production system. Fig. 3 illustrates the necessary steps for this integrative configuration. Based on the identification of misaligned elements (1), an analysis of complexity-related interdependencies (2) is to be conducted. This analysis provides an understanding of how the varieties of domain-related features interact, e.g. how different options for a product feature necessitate diverse manufacturing technologies, and thereby induce a reduction of process commonality in the production structure. By this analysis the drivers of misalignment can be identified. To counteract these misalignment drivers, integrative solution principles are to be implemented (3). By definition, these principles contribute to the resolution of partial dilemmas across the four domains. Examples for integrative solution principles are the clustering of product features according to customer needs [13], the design of modular product architectures [17,18], the design for variation of manufacturing systems [19] or the implementation of a high resolution supply chain [20]. The application of the principle solutions results in constitutive features of the production system (4). These constitutive features define the underlying regularities of standardization for the product and production. Product-related constitutive features might be a standardized interface or a unified product technology, while production-related constitutive features could be an invariant assembly sequence or process technology. The impact of the definition of constitutive features is to be tracked by an iterated application of the assessment model to control the configuration process and to identify potential for further improvement. The data employed for this analysis can either be attained by long-term monitoring or by simulation. 5. Conclusion In order to resolve the dilemma between scale and scope, the fit of all structure-forming elements of a production system needs to be considered. To provide methodological support at
this point, a comprehensive configuration logic based on an integrative assessment model for production systems was presented. As derived systematically from a constitutive framework, this assessment model focuses on four domains: the product program, the product architecture, the production structure and the supply chain. To enable a quantitative evaluation in each of these domains performance measures were introduced. The operability and informational value of the assessment model has been verified by experimental application at a manufacturing company. Building on the assessment model, an approach for the integrative configuration logic was outlined. This logic facilitates to establish a complexity-related fit of the production system by determining the right level of standardization for each structureforming element. In the manner of a closed-loop control system the assessment model is applied in the configuration logic as a controller piloting the configuration process. Acknowledgements The presented results have been developed within the Cluster Domain ‘‘Individualized Production’’ of the Cluster of Excellence ‘‘Integrative Production Technology for High-Wage Countries’’ funded by the Deutsche Forschungsgemeinschaft (DFG). The author’s sincere thanks also go to the companies who supported in the verification of the assessment model.
References [1] ElMaraghy WH, Urbanic RJ (2003) Modelling of Manufacturing Systems Complexity. Annals of the CIRP 52(1):363–366. [2] Feldmann K, Slama S (2001) Highly Flexible Assembly, Scope and Justification. Annals of the CIRP 50(2):489–498. [3] Fisher ML, Ittner CD (1999) The Impact of Product Variety on Automobile Assembly Operations, Empirical Evidence and Simulation. Management Science 45(6):771–786. [4] Hu SJ, Zhu X, Wang H, Koren Y (2008) Product Variety and Manufacturing Complexity in Assembly Systems and Supply Chains. Annals of the CRIP 57(1):45–48. [5] Martin MV, Ishii K (1997) Design for Variety, Development of Complexity Indices and Design Charts. Proceedings of the ASME Design Engineering Technical Conferences 1997, Sacramento, . [6] Erixon G (1998) Modular Function Deployment, A Method for Product Modularisation, Dissertation, KTH Stockholm. [7] Martin MV (1999) Design for Variety, A Methodology for Developing Product Platform Architectures, Dissertation Stanford University. [8] Wagner C, Nyhuis P (2009) The Global Variant Production System, Globalization in a Systematic Way. Proceedings of the 14th Annual International Conference on Industrial Engineering, Anaheim, . [9] Bleicher K (2004) Das Konzept integriertes Management. 7th ed. Campus, Frankfurt am Main. [10] Malik F (2000) Systemisches Management. 2nd ed. Haupt, Bern. [11] Kaiser A (1995) Integriertes Variantenmanagement mit Hilfe der Prozesskostenrechnung, Dissertation, Universita¨t St. Gallen. [12] Wiendahl H-P, Scholtissek P (1994) Management and Control of Complexity in Manufacturing. Annals of the CIRP 43(2):533–540. [13] Gottfredson M, Aspinall K (2005) Innovation Versus Complexity, What is too Much of a Good Thing? Harvard Business Review 83(11):62–71. [14] Ulrich KT (1995) The Role of Product Architecture in the Manufacturing Firm. Research Policy 24(3):419–440. [15] Reichhart A, Holweg M (2007) Creating the Customer-responsive Supply Chain, A Reconciliation of Concepts. International Journal of Operations and Production Management 27(11):1144–1172. [16] Schuh G, Arnoscht J, Bohl A, Kupke D, Nußbaum C, Quick J, Vorspel-Ru¨ter M (2011) Assessment of the Scale-Scope Dilemma in Production Systems. Production Engineering 5(Special Issue):1–10. [17] Schuh G, Arnoscht J, Rudolf S (2010) Integrated Development of Modular Product Platforms. PICMET 2010 Proceedings, 1928–1940. [18] Schuh G, Lenders M, Arnoscht J (2009) Focussing Product Innovation and Fostering Economies of Scale based on Adaptive Product Platforms. Annals of the CIRP 58(1):131–134. [19] ElMaraghy H, Azab A, Schuh G, Pulz C (2009) Managing Variations in Products, Processes and Manufacturing Systems. Annals of the CIRP 58(1):441–446. [20] Brosze T, Bauhoff F, Stich V, Fuchs S (2010) High Resolution Supply Chain Management. IFIP Advances in Information and Communication Technology 338:325–332.
Contents lists available at ScienceDirect
CIRP Annals - Manufacturing Technology jou rnal homep age : ht t p: // ees .e lse vi er. com/ci rp/ def a ult . asp
Integrative assessment and configuration of production systems G. Schuh (1)*, J. Arnoscht, A. Bohl, C. Nussbaum Laboratory for Machine Tools and Production Engineering (WZL), RWTH Aachen University, Aachen, Germany
A R T I C L E I N F O
A B S T R A C T
Keywords: Standardization Optimization Performance measures
Maximizing economies of scale and economies of scope simultaneously is a vital issue for producing companies in high wage countries. A decisive enabler for this is the management of product complexity. Due to the strong and far-reaching impact of product complexity on the value added chain, its management requires an integrative consideration of the entire product and production system. The following paper introduces a methodology facing this challenge. The core element of this methodology is an integrative and complexity-focused assessment model. Based on this model an approach for the comprehensive configuration of the product and production system is presented. ß 2011 CIRP.
1. Introduction A challenge to be mastered by manufacturing firms in high wage countries is to simultaneously provide a fit of the produced products to the customer’s individual needs while participating in the price competition of globalized markets. Therefore the parallel realization of economies of scale and economies of scope by finding the right level of standardization is critical. Beyond this overall level the structure-forming elements of the product and production system (production system in short) need to be aligned to eliminate complexity-related bottlenecks and over capabilities. For this reason a comprehensive methodology for the integrative analysis and design of production systems is required [1–3].
2. Existing approaches addressing the resolution of the dilemma between economies of scale and scope The concurrent design of products and processes as well as the resolution of the dilemma between scale and scope has been addressed by scholars originating from various disciplines. The methods developed and applied in this context can be differentiated into an assessment-focused [e.g. 4,5] and a design-focused group [e.g. 6]. Both assessing and designing elements are combined by a third group of approaches [e.g. 7,8]. Although every single of these three groups makes a valuable contribution to the resolution of the dilemma between scale and scope, they are continuously lacking an integrative consideration of all structure-forming elements of a production system. For this reason a comprehensive and systematic analysis of the complexity-related fit of these elements is not possible yet. Coming from the context of corporate policies, Bleicher [9] developed a model for the integrative assessment of the fit of a system’s elements by the segmentation into interrelated dichotomies. The development of the integrative assessment model,
* Corresponding author. 0007-8506/$ – see front matter ß 2011 CIRP. doi:10.1016/j.cirp.2011.03.038
discussed in the following section, is based rudimentarily on this principle. 3. Integrative assessment model 3.1. Definition of a constitutive framework As described before, an integrative assessment model needs to comprehend all major elements of a production system that bear a relation to the emergence and the consequences of complexity. Thus the assessment model necessitates a constitutive framework, which structures the field of observation in a mutually exclusive and collectively exhaustive manner. According to Malik [10] and Kaiser [11] complexity can be differentiated into external complexity, which emerges by the interaction with the customer or the environment respectively, and into internal complexity, caused endogenously by intracorporate structures and processes. While these types of complexity are non-overlapping, they exhibit a high degree of interdependency, since for example a high number of product variants generally entail an increased internal diversity of parts and processes to manufacture them. Another classification given by Wiendahl and Scholtissek [12] subdivides complexity into a product and a production-related type. Due to the interaction of product and process these types also are highly interdependent. Aggregating both classifications to one constitutive framework for the intended assessment model yields four domains to be considered by the assessment model. These domains are interpreted in the following section. 3.2. Fundamental domains of a production system Focusing on the product-related part of a production system from an external perspective leads to a consideration of the product program. This can be defined as the range of products offered to the customer. The challenge in this domain is to provide the customers with a product range that fits individual needs, but still is intelligible by each customer [13].
G. Schuh et al. / CIRP Annals - Manufacturing Technology 60 (2011) 457–460
458
The internal and product-related view relates to a consideration of the inner structure of the product, i.e. its functions, technologies and components as well as their interaction. This inner structure is represented by the product architecture. The dilemma of the product architecture is to realize the intended variety of the product program while decreasing the intra-corporate variety of components, equipment, processes, etc. [14]. The external and production-related domain corresponds to the supply chain, which is understood as the outward logistic interface of the production system. The problem of the supply chain is to maintain delivery performance while reducing inventories [15]. The internally production-related domain refers to the intracorporate structures performing the production processes. This domain is defined as the production structure. From complexity perspective the challenge of this domain is to achieve and sustain economies of scale by a high level of resource utilization as well as learning effects. This challenge also implies a dilemma, since with highly diversified product range resource utilization is generally augmented by a flexible reallocation of operations, which in return is compromising learning effects [3]. The domains and their corresponding partial dilemmas derived before were verified for their relevance and sufficiency within a delphi-workshop with a panel of complexity management experts from eight German manufacturing firms. Fig. 1 illustrates the four domains of a production system derived before. 3.3. Interrelated partial dilemmas As described in the previous section, the main dilemma between scale and scope can be broken down into challenges for each domain or partial dilemmas, respectively. In contrast to the main dilemma the partial dilemmas are operationalizable, as it is shown in the following. Furthermore, the resolution of the partial dilemmas enables a production system to resolve the main dilemma between scale and scope. However, as the deduced domains are highly interdependent, the resolution of one partial dilemma is linked to the resolution of the other: For example a production system does not benefit from solving the product architecture dilemma by a sophisticated modular product platform, if the derived product program variety can not be made understood by the customer. Thus a fit between the four sections is to be established. 3.4. Main dimensions describing the domain-related dichotomies In order to operationalize the operating point of a production system referring to the four dilemmas, the concept of paradoxical series of tension by Bleicher [9], based on the definition of dichotomous extrema, is adapted and extended. For the intended assessment model these extrema are maximum values of characteristic measures of a production system. Therefore a key task in the development of this model is to identify characteristic and dichotomous measures – called main dimensions – that represent the driving forces behind each dilemma.
[()TD$FIG]
Product
External
Internal
Product Program
Product Architecture
Fit to individual customer needs vs. intelligibility of variety
External variety vs. internal commonality Domains for the integrated assessment of production systems
Production
Supply Chain
Production Structure
Delivery performance vs. reduction of inventories
Resource utilization vs. standardized process sequences
Fig. 1. Domains for the integrated assessment of production systems.
To solve this task, describing variables for the operating point of a production system corresponding to each dilemma were collected via empirically inductive research and via literature review. The collected variables were filtered for their significance and their measurability in an industrial environment. The residual variables were clustered into dichotomous main dimensions according to their correlation. Hereby two antagonistic main dimensions per partial dilemma could be isolated, which correspond to the dichotomous extrema by Bleicher. These main dimensions can be described by corresponding questions. Main dimensions of the product program Fit of variety: ‘‘Does the product program offer the features and options requested by the customers?’’ Explainability at the point of sale: ‘‘Can the variety of the product program be made understood by the customers?’’ Main dimensions of the product architecture Product architecture flexibility: ‘‘Does the product architecture feature the right degree of freedom at the right positions to facilitate the intended product program without compromising architecture standards?’’ Product architecture commonality: ‘‘Does the product architecture augment economies of scale by product standardization?’’ Main dimensions of the production structure Process commonality: ‘‘Does the production structure allow for stable and standardized processes?’’ Resource utilization: ‘‘Can a high level of utilization be achieved with all resources of the production structure?’’ Main dimensions of the supply chain Supply chain capital efficiency: ‘‘Can the logistic operations be performed with a low level of capital employed?’’ Supply chain effectiveness: ‘‘Are the customers provided with the right products at the right time? A more detailed deduction of the four domains and especially the related main dimensions can be found in [16]. 3.5. Mathematical description of the main dimensions Given that these main dimensions are to be consolidated into a quantitative assessment model, a mathematical description for each main dimension is needed. These descriptions were derived from the filtered, describing and influencing variables referred to before. For this the variables were put into a relationship, and were normalized to a range between zero (worst case) and one (ideal case) by the use of reference parameters. For the reason of maintaining the required scope of this paper, the mathematical descriptions of the main dimensions are not explained in detail, but summarized in Table 1. 3.6. Consolidation of the main dimensions With the objective of creating a self-contained assessment model all main dimensions are to be consolidated into one system. According to the four partial dilemmas a layout composed of four interdependent coordinate systems was chosen. To represent the partial dilemmas each coordinate system consists of the two dilemma-related main dimensions. This yields a non-favorable (0,0)-state and an ideal (1,1)-state in each coordinate system. The latter of these states corresponds to a perfect resolution of a partial dilemma. Between both states a definite direction of optimization can be plotted. This direction of optimization is intersected orthogonally by hyperbolic shaped isoquants. These isoquants
G. Schuh et al. / CIRP Annals - Manufacturing Technology 60 (2011) 457–460
459
Table 1 Mathematical description of the main dimensions. Dimension and equation
Legend
Fit of variety n X Nas-is;i FVPP ¼ 1n N
FVPP: fit of variety of the product program n: number of product attributes Nas-is,i: as-is sales volume for attribute i Nplan,i: planned sales volume for attribute i
plan;i
i¼1
Explainability at point of sale n X V i;expl EFPP ¼ nV1oa
EFPP: explainability at the point of sale n: number of sales persons Vi,expl: number of product variants which can be explained by sales person i Voa: overall number of product variants
i¼1
Product architecture flexibility 0 11 m m X X F PA ¼ U stand;i @ U j A j¼1
FPA: product architecture flexibility n: number of standard variants Ustand,i: realized sales volume by stand variant i m: overall number of product variants Uj: sales volume realized with variant j
j¼1
Product architecture commonality m X Abs 1 wi Abs i K PA ¼ m
KPA: product architecture commonality m: overall number of part numbers wi : value portion of part number i related to all part numbers of the product program Absi: quantity of sales of part number i, AbsOA: quantity of sales of all part numbers
OA
i¼1
Process commonality r PKPS ¼ 1 Rmax ¼ 1 pr2
PKPS: process commonality r: number of used process sequences Rmax: theoretical maximum of sequences p: overall number of resources
Resource utilization 0 X 1 APS ¼ To ti
APS: resource utilization o: number of available resources T: duration of underlying period ti: process time of resource i within time span
i¼1
Supply chain capital efficiency V KESC ¼ max 1 UT ; 0
KESC: supply chain capital efficiency VT: Avg. value of inventories related to considered product program within period UT: revenues generated by considered product program in underlying period
Supply chain effectiveness !1 q q X X M FQ ;i M OA;i LGSC ¼
LGSC: supply chain effectiveness q: number of sub-periods within period MFQ,i: quantity delivered on time within period i MOA,i: overall quantity ordered within period i
T
i¼1
i¼1
represent operating points, which are of indifferent value. Without the implementation of new solution principles a production system is restricted to its specific isoquants, while taking adjustment measures, e.g. product architecture flexibility can be substituted for commonality by defining less constraining standards. This conforms to a movement along an isoquant, since it does not impact the resolution of the domain-related dilemma. Therefore the isoquants can be interpreted as a multidimensional maturity degree model. Fig. 2 assembles the integrative assessment model form the derived domains and main dimensions. By the arrangement of the
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Fig. 2. Integrative assessment model.
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four coordinate systems an upper left triangle is generated, which encompasses the scope-driven main dimensions, i.e. those dimensions facilitating the satisfaction of individual customer needs. In return the lower right triangle represents the scaledriven main dimensions, focusing on cost advantages. The four directions of optimization converge in the center of the assessment model. This ideal state equals the total resolution of the superior dilemma between scale and scope. 3.7. Experimental validation For preliminary validation purposes the integrative assessment model was applied at a medium-sized, German company designing, manufacturing and distributing metal hardware. The assessment was focused on one particular product line as well as the related production resources and processes, but was conducted for a time-frame of four subsequent fiscal years. Thus dynamic effects could be uncovered. The experimental validation demonstrated a general operability of the assessment model, as the main dimensions could be quantified using the mathematical descriptions presented before. Moreover specific conclusions on the operating point of this production system could be drawn: The analyzed product line is offered in a variety, which is tailored to the individual requirements of a small group of main customers. For this reason fit of variety and explainability at the point of sale are attained at high levels (FVPP = 0.91, EFPP 1). This variety affects the product architecture reducing commonality (FPA = 0.64, KPA = 0.32) and the production structure inducing a low degree of resource utilization (PKPS = 0.88, APS = 0.50). The supply chain is geared to delivery performance (KESC = 0.65, LGSC = 0.96). Dynamic effects could be identified as an increasingly globalized sales activity led to a decline of the fit of variety of the product program tailored to specific customers. Furthermore, a softening of product architecture
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G. Schuh et al. / CIRP Annals - Manufacturing Technology 60 (2011) 457–460
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Integrative assessment of the production system I
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Fig. 3. Integrative configuration approach.
standards resulted in a simultaneous increase of product architecture flexibility and decrease of commonality. This first trial is to be followed by a more voluminous validation of the assessment model in order to proof its informational value under varying constraints and to discover how the model can be adapted specifically. Furthermore, this will enable a benchmarkbased scaling as well as an industry-depending weighting of the main dimensions and isoquants. 4. Integrative configuration of production systems The assessment model referred to before allows for an identification of misaligned elements of the production system considering the resolution of the scale-scope-dilemma. In order to eliminate these misalignments, the question of the right level of standardization for the according elements is to be solved by integrative configuration of the production system. Fig. 3 illustrates the necessary steps for this integrative configuration. Based on the identification of misaligned elements (1), an analysis of complexity-related interdependencies (2) is to be conducted. This analysis provides an understanding of how the varieties of domain-related features interact, e.g. how different options for a product feature necessitate diverse manufacturing technologies, and thereby induce a reduction of process commonality in the production structure. By this analysis the drivers of misalignment can be identified. To counteract these misalignment drivers, integrative solution principles are to be implemented (3). By definition, these principles contribute to the resolution of partial dilemmas across the four domains. Examples for integrative solution principles are the clustering of product features according to customer needs [13], the design of modular product architectures [17,18], the design for variation of manufacturing systems [19] or the implementation of a high resolution supply chain [20]. The application of the principle solutions results in constitutive features of the production system (4). These constitutive features define the underlying regularities of standardization for the product and production. Product-related constitutive features might be a standardized interface or a unified product technology, while production-related constitutive features could be an invariant assembly sequence or process technology. The impact of the definition of constitutive features is to be tracked by an iterated application of the assessment model to control the configuration process and to identify potential for further improvement. The data employed for this analysis can either be attained by long-term monitoring or by simulation. 5. Conclusion In order to resolve the dilemma between scale and scope, the fit of all structure-forming elements of a production system needs to be considered. To provide methodological support at
this point, a comprehensive configuration logic based on an integrative assessment model for production systems was presented. As derived systematically from a constitutive framework, this assessment model focuses on four domains: the product program, the product architecture, the production structure and the supply chain. To enable a quantitative evaluation in each of these domains performance measures were introduced. The operability and informational value of the assessment model has been verified by experimental application at a manufacturing company. Building on the assessment model, an approach for the integrative configuration logic was outlined. This logic facilitates to establish a complexity-related fit of the production system by determining the right level of standardization for each structureforming element. In the manner of a closed-loop control system the assessment model is applied in the configuration logic as a controller piloting the configuration process. Acknowledgements The presented results have been developed within the Cluster Domain ‘‘Individualized Production’’ of the Cluster of Excellence ‘‘Integrative Production Technology for High-Wage Countries’’ funded by the Deutsche Forschungsgemeinschaft (DFG). The author’s sincere thanks also go to the companies who supported in the verification of the assessment model.
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