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ScienceDirect Procedia CIRP 41 (2016) 99 – 104
48th CIRP Conference on MANUFACTURING SYSTEMS - CIRP CMS 2015
Manufacturing System Flexibility: Product Flexibility Assessment Meriem Lafoua,b,*, Luc Mathieua, Stéphane Poisb, Marc Alochetb a
Automated Production Research Laboratory (LURPA), ENS de Cachan, 61 Av. du Président Wilson, Cachan 94235 Cedex, France b Vehicle Production Engineering Direction, Technocentre RENAULT SA, 1 Av. du Golf, Guyancourt 78288 Cedex, France
* Corresponding author. Tel.:
+33-147-402-756; fax: +33-147-402-220. 0. E-mail address:
[email protected]
Abstract Current manufacturing industries are experiencing a paradigm shift towards more flexibility to respond quickly and efficiently to constant changing customers’ requirements, new technologies and increasing product variety. Product flexibility is the ability of the manufacturing system to cope with the growing product variety to ensure better system performance. The aim of this paper is to point out the importance of product - resources interfaces in product flexibility assessment. Based on industrial experience, three product flexibility inductors are identified, which are gripping, setting and tooling interfaces, in order to build indicators as close as possible to real industry conditions. This research work investigates new factors to quantify product flexibility and provide manufacturing system designers with efficient decision-making support tools. In order to show the relevance of our approach, experimental results from the automotive industry are presented. © 2015 The Authors. Published by Elsevier B.V. © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the Scientific Committee of 48th CIRP Conference on MANUFACTURING SYSTEMS - CIRP CMS (http://creativecommons.org/licenses/by-nc-nd/4.0/). 2015. Peer-review under responsibility of the scientific committee of 48th CIRP Conference on MANUFACTURING SYSTEMS - CIRP CMS 2015 Keywords: Manufacturing system; Product flexibility; Product variety
1. Introduction In the context of increasing customer requirements and changing needs, improved manufacturing methods, new technologies and government regulations, the lifecycle of the products is shortened. Consequently, individual manufacturing industries need to continuously upgrade their products, processes and technologies to remain competitive, which is a great challenge in an environment full of uncertainties. In [1], flexibility is defined as “the sensitivity of a manufacturing system to changes. The more flexible a system, the less sensitive to changes occurring to its environment it is”. Various types of flexibility are introduced in the literature. In [2], Chryssolouris summarized the flexibility in three main forms, which are Operation flexibility, Product flexibility and Capacity flexibility. This research work will focus on Product flexibility assessment. It has been shown in [3-6] that the product flexibility is an important aspect of manufacturing system performance. Nevertheless, in order for flexibility to be considered in the
design and operation phase, it should be defined in quantifiable terms [2]. Product flexibility, as defined in [1], is the ability of a manufacturing system to make a variety of part types with the same equipment. The aim of this research work is to identify inductors that enable high product flexibility, in order to rapidly respond to current market fluctuations. Those inductors are then used to build new product flexibility indicators in order to provide designers with the required decision-making support tools to deal with product variety. In the following sections, an overview of the existing methods for product variety management is primarily presented. Then, a summary of the main measures for product flexibility assessment are provided. After which, a detailed description of our approach is introduced as well as the results of its application to an automotive industry case.
2212-8271 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of 48th CIRP Conference on MANUFACTURING SYSTEMS - CIRP CMS 2015 doi:10.1016/j.procir.2015.12.046
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2. Framework and motivation Modern manufacturing systems are facing continuous changes in the environment they operate. These changes include the rapid introduction of new products, abrupt changes in product demand and more frequent modifications to existing products [7]. Many academic publications have pointed out that the quantification of flexibility is difficult to be handled and mostly limited to special cases [8]. These difficulties lay in some flexibility characteristics, such as its property of being a potential and its inherent multidimensionality [9]. However, under time and budget constraints, it’s very difficult to manage product variety while maintaining high system performance. The aim of this paper is to build indicators to quantify and evaluate product flexibility by integrating product-resources interfaces information. The originality of this approach lies in the consideration of the physical technological aspect, in an explicit way, for product flexibility modelling. We define resources as the entire physical elements which compose the manufacturing system, such as machines, tools, operators and material handling system. Hence, product-resources interfaces are the physical interfaces that describe the contact surfaces between the product and the resources, either with the material handling system (Setting and gripping interfaces), or the machines and tools (Tooling interfaces).
Fig. 2. Example of gripping interface from the automotive industry.
Two examples of interfaces, from the automotive industry, are shown in figures 1 and 2. For example, the encircled parts, in fig. 1, represent the interface between the body of the vehicle and the hanger. Whatever the body’s form, its variety is absorbed if it fits to the existing interface. As part of Design For Manufacturing (DFM) practices, these indicators will, firstly, be useful for the product designer who needs to integrate resources information to reduce the product technical variety and facilitate the introduction of new variants with minor modifications in the manufacturing system. Consequently, sharing technical information between the product and manufacturing system designers reduce the setup time and cost caused by the introduction of a new product by integrating adaptable interfaces, either in the product or in the resources, in an optimal way. Collaboration between these actors is a key to build real cost-effective and flexible production lines, which is the basis of the coevolution concept introduced in [10].
Fig. 1. Example of setting interface from the automotive industry.
Meriem Lafou et al. / Procedia CIRP 41 (2016) 99 – 104
3. Related work 3.1. Product variety management Effective management of product variety can provide important competitive advantages for a company. However, it is a challenge of manufacturing to produce variety of products with limited resources. There are various strategies that suggest several methods for variety management; these methods are well known in industries. In fact, to handle variety, manufacturers use several product design methods, including product family development [11], parts commonality [3], and product modularization [12]. In order to accommodate the increasing product variety, developing product families has been recognized as an effective means to achieve the economy of scale (the same basic components are used across different products of the family). To reduce the overall cost each manufacturer has to strike the balance between maximizing component commonality while maintaining the right level of differentiation between product variants [3]. However, as products and their variants evolve and change over time, the boundaries of product families change as well. Consequently, similarities between product family members are reduced, which makes product variety management more complex. Modular products refer to products, assemblies and components that fulfill various functions through the combination of distinct building blocks (modules) [12]. Through modularity, interfaces standardization is increased and then the introduction cost of a new variant is minimized. Modularity helps to achieve economy of scope through market segmentation by increasing product variants and allowing customers to choose the required modules combinations. Despite the popularity of these variety management methods, very little is known about how to design an effective strategy for using them or about the factors that influence the success of the strategy. 3.2. Product flexibility measures As reported by Chryssolouris [1], product flexibility enables a manufacturing system to make a variety of part types with the same equipment. In the last decade, many product flexibility measures were introduced. In [13], Lee introduced the Delayed Product Differentiation (DPD) which is a design concept aiming at the increase of product variety and manufacturing efficiency. It is based on delaying the differentiation point (i.e, the stage where a product assumes its unique identity) as much as possible. Many algorithms were developed in order to find the optimal differentiation point [14,15]. The limits and suitability of this strategy were discussed in [16]. In [17], product flexibility is evaluated by computing the expected cost of accommodating potential changes that may occur in the future. The smaller the expected change cost is, the less sensitive the system is to changes and thus, the system is considered as more flexible. Other flexibility measures were presented in [18], including the ζ analogy
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method and DESYMA (DEsign of SYstems for MAnufacture) which are suitable to assess the three main forms of flexibility, including product flexibility. ζ analogy method makes use of an analogy between a manufacturing and a mechanical system to compare different production systems when they have been exposed to a similar excitation from the external environment, while DESYMA is based on measuring flexibility with the help of demand probabilities. It combines economic measures, sensitivity analysis and manufacturing performance measures in an integrated manner [19]. Additionally, a methodology to design a product for flexibility were proposed in [5], the approach is based on two step procedures: (1) Decomposing the product in some rational manner, that it can be assessed for possible changes and (2) Finding the CPN (Change Potential Number) that gives an indication of how easily a change can be incorporated into a product. However, more well-defined metrics for product flexibility are needed, particularly those that integrate explicitly technological aspect. A primary research work, introduced by Lafou et al. [20], investigates the importance of setting and gripping interfaces in product flexibility assessment. Consequently, we propose in the following paragraph, to complete this approach by integrating tooling interfaces information. 4. The proposed approach 4.1. Presentation The proposed approach focuses on the importance of the interfaces between product and resources to build product flexibility indicators. What can make a product family less flexible than another one is initially the number of specific components that its variants require. These specific components impact much more its flexibility if they need specific tooling interfaces to be processed and specific gripping and setting interfaces to be handled. Indeed, specific interfaces may require more setup time and higher setup cost. All these characteristics provide information about the system sensibility while introducing a new product and consequently enable flexibility product assessment. The approach is defined according to the following steps:
Classification of the components on families.
Identification of the families that have setting interfaces.
Identification of the families that have gripping interfaces.
Identification of the families that have tooling interfaces.
Calculation of the synergy for each interface type before and after the introduction of the new variant.
The synergy between the existing components’ families and interfaces, S, is defined as follows:
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ܵൌ
்௧௨௧௦௪௧௧௦ ்௧௨௧௦
(1)
The synergy measure reaches its maximum value when only one interface is required for the entire component family members. While its minimal value corresponds to 1, which represents the case where each component has its specific interface. This is applicable whether for setting, gripping or tooling interfaces. Consequently, when a new variant is introduced, at least one different component is introduced. The aim of this modelling is to compute the synergy of the product- resource interfaces before and after the introduction of the new variant, in order to assess the ability of the resources to cope with product variety. The value of product flexibility is then calculated as follows:
்ܵூ௧
: Product family-tooling interfaces synergy after new variant introduction : Product flexibility value for Plant X
ܨǡ௧
The mathematical indicator is built so as to give priority to interfaces commonality while keeping functional product variety. For instance, an increasing number of common tooling, gripping and setting interfaces facilitates the introduction of new variants even if they have specific components. It should be noted that the weighting factor,݇୧ , depends on the company strategy and priorities.
ܵௌூ ൌ
σୀଵ ܥி
ೖ
σୀଵ ܫ௦ி
ሺ͵ሻ
ೖ
ܨ ൌ
ୗೌೝ
ୗ್ೝ
െͳ
(2)
ܵௌூ௧ ൌ
σୀଵ ܥᇱ ி
ೖ
ሺͶሻ
σୀଵ ܫᇱ ௦ி
ೖ
4.2. Mathematical formulation ܨ ܥி
th
: The i component family : Number of components in ୧ before new variant introduction ܥԢி : Number of components in ୧ after new variant introduction : Number of component families with setting ݈ interfaces ݉ : Number of component families with gripping interfaces ݊ : Number of component families with tooling interfaces ܫி : Number of gripping interfaces for ୧ before new variant introduction ܫԢி : Number of gripping interfaces for ୧ after new variant introduction ܫ௦ி : Number of setting interfaces for ୧ before new variant introduction ܫԢ௦ி : Number of setting interfaces for ୧ after new variant introduction ܫ௧ி : Number of tooling interfaces for ୧ before new variant introduction ܫԢ௧ி : Number of tooling interfaces for ୧ after new variant introduction ݇ : Weighting factor ܵௌூ : Product family-Setting interfaces synergy before new variant introduction ܵௌூୟ୲ୣ୰
: Product family-Setting interfaces synergy after new variant introduction
ܵீூ
: Product family-gripping interfaces synergy before new variant introduction : Product family-gripping interfaces synergy after new variant introduction
ܵீூ௧ ்ܵூ
: Product family-tooling interfaces synergy before new variant introduction
ܵீூ ൌ
σ ୀଵ ܥி ೕ
σ ୀଵ ܫி
ሺͷሻ
ೕ
ܵீூ௧ ൌ
ᇱ σ ୀଵ ܥி
ೖ
ᇱ σ ୀଵ ܫி
ሺሻ
ೕ
்ܵூ ൌ
σୀଵ ܥி
σୀଵ ܫ௧ி
ሺሻ
்ܵூ௧ ൌ
σୀଵ ܥԢி
σୀଵ ܫԢ௧ி
ሺͺሻ
ܨǡ௧ ൌ
ሺ݇ଵ ܵ ்ூ ݇ כଶ ܵீூ ݇ כଷ ܵௌூ ሻ௧
ሺ݇ଵ ܵ ்ூ ݇ כଶ ܵீூ ݇ כଷ ܵௌூ ሻ
െ ͳሺͻሻ
5. Application In this section, we experiment the product flexibility model, presented above, in two automotive industry plants where are commonly assembled several variants. We consider that a new variant, X, is candidate to be assembled in both of the mentioned plants, as shown in fig.3. All other constraints and logistic considerations being equals, product flexibility analysis are performed in order to identify the plant which is able to accommodate the new variant with minor modifications in its resources. To simplify the computation, equal weighting factors are considered: ݇ଵ ൌ ݇ଶ ൌ ݇ଷ
(10)
Meriem Lafou et al. / Procedia CIRP 41 (2016) 99 – 104
103
The required information is summarized below in tables 1 and 2: Table 1. Product flexibility results for Plant 1. ࡲ
ࡲ
ࡵ࢚ࡲ
ࡵ࢙ࡲ
ࡵࢍࡲ
ࡲ
Ԣࡲ
ࡵԢ࢚ࡲ
ࡵԢ࢙ࡲ
ࡵԢࢍࡲ
ܨଵ
10
4
4
2
ܨଵ
11
4
4
3
ܨଶ
14
3
2
3
ܨଶ
15
4
2
4
ܨଷ
8
3
-
-
ܨଷ
8
3
-
-
ܨସ
8
3
-
-
ܨସ
9
4
-
-
ܨହ
9
3
-
-
ܨହ
9
3
-
-
ܨ
4
3
-
-
ܨ
4
3
-
-
ܨ
6
3
-
-
ܨᇱ
6
3
-
-
଼ܨ
8
2
-
-
଼ܨ
8
2
-
-
ܨଽ
6
2
-
-
ܨଽ
6
2
-
-
ܨଵ
6
4
-
-
ܨଵ
7
4
-
-
ܨଵଵ
6
2
-
-
ܨଵଵ
6
2
-
-
ܨଵଶ
10
2
-
-
ܨଵଶ
11
3
-
-
ܨଵଷ
4
2
-
-
ܨଵଷ
4
2
-
-
-
-
ܨଵସ
6
3 ࡿࡿࡵࢇࢌ࢚ࢋ࢘
-
4
ࡿࡳࡵ࢈ࢋࢌ࢘ࢋ
4,8
ࡿࡳࡵࢇࢌ࢚ࢋ࢘
3,71
ࡿࢀࡵ࢈ࢋࢌ࢘ࢋ
2,69
ࡿࢀࡵࢇࢌ࢚ࢋ࢘
2,62
ܨଵସ
6 ࡿࡿࡵǡ࢈ࢋࢌ࢘ࢋ
3
ࡲࡼǡࡼࢇ࢚
4,33
-18%
Table 2. Product flexibility results for Plant 2. ࡲ
ࡲ
ࡵ࢚ࡲ
ࡵ࢙ࡲ
ࡵࢍࡲ
ࡲ
Ԣࡲ
ࡵԢ࢚ࡲ
ࡵԢ࢙ࡲ
ࡵԢࢍࡲ
ܨଵ
13
4
4
1
ܨଵ
14
4
4
1
ܨଶ
8
3
2
1
ܨଶ
8
3
2
1
ܨଷ
6
2
-
-
ܨଷ
6
2
-
-
ܨସ
5
3
-
-
ܨସ
5
3
-
-
ܨହ
8
2
-
-
ܨହ
9
3
-
-
ܨ
7
3
-
-
ܨ
7
3
-
-
ܨ
7
3
-
-
ܨᇱ
7
3
-
-
଼ܨ
6
2
-
-
଼ܨ
7
2
-
-
ܨଽ
4
2
-
-
ܨଽ
4
2
-
-
ܨଵ
6
2
-
-
ܨଵ
6
2
-
-
ܨଵଵ
4
2
-
-
ܨଵଵ
4
2
-
-
ܨଵଶ
10
2
-
-
ܨଵଶ
10
2
-
-
1
-
-
ܨଵଷ
1
-
-
2
-
-
ܨଵସ
3
-
-
ܨଵଷ
4
ܨଵସ
5 ࡿࡿࡵǡ࢈ࢋࢌ࢘ࢋ ࡿࡳࡵ࢈ࢋࢌ࢘ࢋ ࡿࢀࡵ࢈ࢋࢌ࢘ࢋ
4 6
3,5
ࡿࡿࡵࢇࢌ࢚ࢋ࢘
3,67
10,5
ࡿࡳࡵࢇࢌ࢚ࢋ࢘
11,00
2,82
ࡿࢀࡵࢇࢌ࢚ࢋ࢘
2,77
ࡲࡼǡࡼࢇ࢚
+8%
Fig. 3. Study case description.
6. Discussion The obtained results can be interpreted as follows: The introduction of variant X requires at least one additional component in the existing components’ families. For instance, in plant 1, the variant has added five components against four in plant 2. Some of this variety is successfully absorbed (ܨଵ ) whereas some other specific components require specific interfaces (ܨଵ ǡ ܨଶ ǡ ܨସ ǡ ܨଵଶ ). For plant 1, small variations are noticed for setting and tooling interfaces synergies. Meanwhile, gripping interfaces synergy has significantly been reduced (-22%) because both of the additional components require specific gripping interfaces.
ࡿࡳࡵࢇࢌ࢚ࢋ࢘
ࡿࡳࡵ࢈ࢋࢌ࢘ࢋ
െ ͳ ൌ െʹʹΨ
(11)
For plant 2, the variety introduced is successfully managed. In fact, only small variations are noticed which conducted to a positive product flexibility value (+8%). The product flexibility measure,ܨǡ௧ , reflects the synergy variation and the ability of the manufacturing system to absorb the product variety. Its value changes depending on the number of specific interfaces required relatively to the specific components introduced. Consequently, this measure enables to identify the plant that can quickly launch the new variant production. In our case study: ܨǡ௧ଶ ܨǡ௧ଵ
(12)
The interaction between the components’ families and product-resources interfaces is an important factor for product flexibility assessment. In fact, the introduction of a new variant constitutes a disruptive element for the existing manufacturing system. Its ability to accommodate this variety traduces its flexibility degree.
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For instance, when this information is combined it has a total effect which may be greater than if it is considered individually. This effect may be negative, which is the case for plant 1 (-18%) or positive as it is shown for plant 2 (+8%). The higher are the system product-resources interfaces synergies, the higher its product flexibility is regarding the introduced variety.
[2] [3]
[4]
7. Conclusions and perspectives
[5]
The main factors for product flexibility assessment were investigated with the help of industrial insights and empirical observations in order to be as close as possible to real industry conditions. Product flexibility measure is particularly useful to quantify the product family synergy, to compare workshops flexibility and to argue the decision concerning in which plant a specific variant can be produced. The first experimental results showed the relevance of this indicator to help product and manufacturing systems designers to take the right decisions to ensure an optimal flexibility for their manufacturing system. Future work should incorporate a further and more detailed analysis of the financial aspect for the final decision. Factors, such as the setup times and costs, regional considerations (government regulations, labor cost...) should be taken into account. Some of future research work opportunities in this area are highlighted below: The standardization of interfaces is a very powerful key concept to challenge product engineering on the introduction of new variants. However, sensitivity analysis should be performed for the proposed mathematical models in order to test the robustness of the obtained results and increase the understanding of the relationships between the identified factors. Integrating process information in the product flexibility assessment, such as the sequence of tasks and the cycle time, enables better modelling of product flexibility issue. Management of product variety is not simple because of numerous factors that induce additional complexity. Reducing manufacturing system’s design complexity should be deeply investigated to increase product flexibility. Developing new manufacturing processes that require very small fixed cost and short setup time, such as additive manufacturing, represent real opportunity to respond to the increasing demand for highly personalized products.
[6]
Acknowledgements The authors are pleased to acknowledge the valuable contribution of Automated Production Research Laboratory (LURPA) ENS CACHAN/PARIS-SUD and the financial support of the Vehicle Production Engineering Direction of RENAULT SA. References [1]
Chryssolouris G, Efthymiou K, Papakostas N, Mourtzis D, Pagoropoulos A Flexibility and complexity: is it a trade-off?
[7]
[8]
[9]
[10]
[11]
[12]
[13] [14]
[15]
[16]
[17] [18]
[19]
[20]
International Journal of Production Research, 2013, p. 1-15. Chryssolouris G Manufacturing Systems - Theory and Practice, 2nd Edition, 2006, Springer-Verlag, New York. Koren Y The global manufacturing revolution: product-processbusiness integration and reconfigurable systems (Hoboken, N.J.: Wiley), 2010, ISBN 978-0-470-58377-7. Nazarian E, Ko J, Wang H Design of multi-product manufacturing lines with the consideration of product change dependent inter-task times, reduced changeover and machine flexibility. Journal of Manufacturing Systems, 2010, vol. 29(1), p. 35-46. Palani Rajan PK, Van Wie M, Campbell MI, Wood KL, Otto K N An empirical foundation for product flexibility. Design Studies, 2005, vol. 26(4), p. 405–438. Francalanza E, Borg J C, Constantinescu C L A case for assisting ‘product family’manufacturing system designers. Procedia CIRP, 2012, vol.3, p. 376-381. Spicer P, Koren Y, Shpitalni M, Yip-Hoi, D Design Principles for Machining Systems Configurations, CIRP Annals, 2002, vol. 51(1), p. 275-280. Abele E, Liebeck T, Worn A Measuring Flexibility in Investment Decisions for Manufacturing Systems, CIRP Annals, 2006, vol. 55(1), p. 433-436. Sethi A-K, Sethi S-P Flexibility in Manufacturing: A Survey, The International Journal of Flexible Manufacturing Systems, 1990, vol. 2, p. 289-328. Tolio T, Ceglarek D, ElMaraghy H A, Fischer A, Hu S J, Laperrière L, Váncza, J SPECIES—Co-evolution of products, processes and production systems. CIRP Annals-Manufacturing Technology, 2010, vol. 59 (2), p. 672-693. Du X, Jiao J, Tseng M M Architecture of product family: fundamentals and methodology. Concurrent Engineering, 2001, vol. 9(4), p. 309325. Huang C C, Kusiak A Modularity in design of products and systems. Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on, 1998, vol. 28(1), p. 66-77. Lee HL, Tang CS Modelling the costs and benefits of delayed product differentiation. Management Science, 1997, vol. 43(1), p. 40–53. He D, Kusiak A, Tseng TLB Delayed product differentiation: a design and manufacturing perspective. Computer-Aided Design, 1998, vol. 30(2), p. 105–113. Blecker T, Abdelkafi N Modularity and delayed product differentiation in assemble-to-order systems: analysis and extensions from a complexity perspective. In Proc., Int. Mass Customization Meeting, 2005, p. 29–46. Gupta D, Benjaafar S Make-to-order, make-to-stock, or delay product differentiation? A common framework for modeling and analysis. IIE Transactions, 2004, vol. 36(6), p. 529–546. Chryssolouris G Flexibility and its measurement. CIRP AnnalsManufacturing Technology, 1996, vol. 45(2), p. 581-587. Georgoulias K, Papakostas N, Makris S, Chryssolouris G A toolbox approach for flexibility measurements in diverse environments. CIRP Annals-Manufacturing Technology, 2007, vol. 56(1), p. 423-426. Alexopoulos K, Burkner S, Milionis I, Chryssolouris G DESYMA-An integrated method to aid the design and the evaluation of reconfigurable manufacturing systems. In Proceedings of the 1st International Conference on Changeable, Agile, Reconfigurable and Virtual Production, 2005, p. 467-475. Lafou M, Mathieu L, Pois S, Alochet M Convertibility Indicator for Manual Mixed-model Assembly Lines. Procedia CIRP, 2014, vol. 17, p. 314-319.
