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ScienceDirect Procedia CIRP 12 (2013) 438 – 443
8th CIRP Conference on Intelligent Computation in Manufacturing Engineering
Design of a network of scalable modular manufacturing systems to support geographically distributed production of mass customized goods D.T. Matt*, E. Rauch Faculty of Science and Technology, Free University of Bolzano, Piazza Università 1, 39100 Bolzano, Italy * Corresponding author. Tel.: +39 0471 017 000; fax: +39 0471 009. EE mail address:
[email protected].
Abstract In our globalized economic system, industrial production has to step outside the boundaries of individual enterprises and single production units [1]. The increasing globalisation, new models for growth and Internationalization like Franchising, a high level of responsiveness as well as rising fuel costs and therefore higher logistics costs are significant for the development versus geographically distributed production sites. The purpose of this paper is to examine a best practice company case to identify the critical success factors and guidelines for the design of networks of scalable modular manufacturing systems to support geographically distributed production of mass customized goods. Information was collected through multiple site visits and semiect, as well as examination of relevant company documentations. With the help of the Axiomatic Design (AD) approach, the information was structured into a multi-level tree of relations between functional requirements (FR) and suitable design parameter (DP). The structured AD based analysis of the design pathway helped to identify a theoretical framework of guidelines for production system designers. An industrial case is taken to illustrate the single design steps and the relative outcomes. © 2013 The Authors. Published by Elsevier B.V. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Professor Roberto Teti Selection and peer review under responsibility of Professor Roberto Teti Keywords: Manufacturing, Design Method, Mass Customization.
1. Introduction Increasing market dynamics require manufacturing companies to become even more flexible: nearly unpredictable sales volumes and shorter innovation cycles require production systems that not only produce high-quality products at low cost, but also allow for rapid response to market changes and consumer needs. Responsiveness can be defined as the speed at which a production system can meet changing market and business targets in terms of volumes and product mix, and launch new products, product changes or variants [2]. However, an increase in responsiveness usually impacts resource utilization causing important cost disadvantages [3]. This particularly applies to those industrial sectors that are traditionally driven by massproduction and thus have a high level of automation. In this context, the concept of mass customization (MC) has
2212-8271 © 2013 The Authors. Published by Elsevier B.V. Selection and peer review under responsibility of Professor Roberto Teti doi:10.1016/j.procir.2013.09.075
become an important manufacturing strategy with the target to provide individually designed products and services to customers in the mass-market economy [4, 5]. Reconfigurable manufacturing systems represent nowadays one of the key responses towards the new era known as mass customization. In geographically distributed production capacities they allow quick adjustment of production capacity and functionality consenting to manufacture different products [6]. Scalable, geographically distributed and networked production facilities covering the majority of all value chain activities are located in close proximity to a particular local market [7]. Often the individuality of mass products on geographically distributed markets is given by ethnic, religious or cultural based differences like taste, body height, prohibition against eating pork and other foods, colour symbolism, tolerance of ingredients, etc. Thus, and for the reason of a higher
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logistic responsiveness due to a short shelf life, especially producers of mass customized fresh food need a well-designed network of scalable and modular manufacturing systems in their geographically distributed production sites. 2. Literature Review 2.1. Design of scalable and modular manufacturing systems Actual markets are determined by high pressure on costs, higher flexibility in volume and variants, shorter delivery time and quality [8, 9, 10]. To react quickly to this changes and challenges a manufacturing system should be designed to fulfill the needed requirements regarding flexibility and agility [11]. The term flexibility is often used in the context of flexible manufacturing systems [12] and describes different abilities of a production system to handle changes in daily or weekly volume of the same product (volume flexibility) to manufacture a variety of products without major modification of existing facilities (product mix flexibility), to process a given set of parts on alternative machines (routing flexibility), or to interchange the ordering of operations (operation flexibility) on a given part [13]. Scalable modular systems satisfy changing capacity requirements efficiently through system reconfiguration, and in the early flexible manufacturing literature this capability is called expansion flexibility [14]. According to Browne et al. [15], Sethi and Sethi [16] and AbdelMalek and Wolf [17], expansion flexibility references the capability to expand or contract production capacity of a Flexible Manufacturing System using a modular structure. Scalability is a key characteristic of reconfigurable manufacturing systems, which allows system throughput capacity to be rapidly and cost-effectively adjusted to abrupt changes in market demand. Adding or removing machines to match the new throughput requirements and concurrently rebalancing the system for each configuration, accomplishes the system reconfiguration [18]. The term agility emerged after Lean Production and can be defined as an enterprise level strategy, with manufacturing systems included as a subset. Lean system principles were aimed at minimizing operational costs by removing all unnecessary sources of cost [19]. One of the major challenges at the early design stages is to select a manufacturing system configuration that both satisfies the production functional requirements and is easy to operate and manage [20], especially in terms of its adaptability to changing environmental factors.
Diverse authors promote the concept of agility [19, 21, 22], changeability [23, 24, 25, 26] or mutability [27], mostly referring to the same or at least a very similar idea of a manufacturing system that shifts quickly between product models ideally in fast response to customer demand [22]. Many of these design approaches are focused on single facilities and elements or isolated aspects of layouts, machinery and cell manufacturing. The approach shown in this paper goes over and above that including the network characteristics as well as the scalability of such a comprehensive manufacturing system. Networks of manufacturing systems have to be agile enough to allow also qualitative growth in the form of new products and new markets [28], which is highly important for innovative and growing producer of mass customized goods and franchise companies. The costs of generating, diffusing and coordinating productive knowledge have all been neglected by the (franchising) traditional literature [29]. Therefore it seems important to give manufacturing systems designer of geographically distributed production of mass customized goods a set of guidelines ffor the right design. 2.2. Axiomatic Design as method for Manufacturing System Design To find out the requirements of a good manufacturing system, several authors propose Axiomatic Design as a useful method [11, 30, 31, 32]. The theory of Axiomatic Design is applicable to many different fields and kind of systems. This approach was developed by Nam P. Suh in the mid-1970s in the pursuit of developing a scientific, generalized, codified, and systematic procedure for design. In order to systematize the thought process and to create demarcation lines between various design activities, four domains represent the foundation of Axiomatic Design procedure: the customer domain, the functional domain, the physical domain and the process domain [33]. Customer Domain
{CA}
Customer Attributes
Functional Domain
Design
{FR}
Physical Domain
Design
Functional Requirements
{DP}
Process Domain
Design
Design Parameters
Fig. 1. The four domains of Axiomatic Design [33].
{PV}
Process Variables
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The Axiomatic Design methodology defines four so called Design Domains. The Customer Domain contains the so called customer-benefit attributes (CAs; customer attributes), the Function Domain is derived from this and contains the functional demands (FRs; functional requirements), the Design Domain provides Design Parameters (DPs) for the consequent implementation of the FRs, transformation into processes thereof shall be regulated by the Process Variables (PVs) in the Process Domain [33]. The core of the Theory of AD is represented by two axioms, the Independence Axiom (1st axiom) and the Information Axiom (2nd axiom), as a product or a system. For this purpose, FRs and DPs are mathematically displayed as vectors {FR} and {DP}. The Design Matrix describes the relation between these two vectors: FR
DM DP
The Information Axiom (Axiom 2) inspires the minimization of the information content of the design. The Information Axiom is defined in terms of the probability of successfully achieving FRs or DPs and states that the design with the least amount of information is the best to achieve the functional requirements of the design. 3. Best practise company case geographically distributed production of mass customized goods The best practise company in our case study is a medium-sized producer of fresh food. Section 3.1 gives a short overview of the challenges deriving from the mass customized product spectrum of the firm. In section 3.2 has been described the reason why the firm in our case study follows a decentralized production strategy in the context of a Franchise system.
(1) 3.1. Production of mass customized goods with a high level of responsiveness
where DMij
FRi DPj
(2)
The first of the two axiom demands the independence of the functional requirements (FRs). A good design is potentially achievable if exactly one Design Parameter (DP) can be identified to fulfill the allocated FR without affecting the other FRs. To fulfill the Independence Axiom, the Design Matrix must be either a diagonal or a triangle matrix. In case of the diagonal matrix, it is called an uncoupled design. This is the ideal case, as every FR can be fulfilled with exactly one DP without any interrelation to other FRs. The triangle matrices represent a so called decoupled design. These functions can only be satisfied independently from each other by respecting a certain sequence. All other cases represent a (badly) coupled design [33]. The design tasks start with the decomposition of the problem. The development of FRs and DPs hierarchy is respectively Function Domain and Design Domain. After defining the FR of the top level a design concept has to be generated. The mapping process between the domains is also driven by two fundamental axioms as basis for evaluating and selecting designs candidates in order to be a robust design [33]: The Independence Axiom (Axiom 1 recommends the independence of each functional requirement. The Independence Axiom states that when there are two or more FRs, the design solution must be such that each one of the FRs can be satisfied without affecting the other FRs [33].
The firm is a European medium-sized manufacturer of confectionery products with a distribution and sales network in many internationally markets. In the last years the management started a pilot-project with a small production unit for fresh cakes and pastries and a single point of sales (POS) based on the concept of CoffeeShop with integrated Patisserie. In the last years the success of the concept resulted in an expansion through other POS around the production unit. After building a brand the management decided to follow a Franchise strategy for international expansion. Going on international markets the decision of the management was to produce a core-spectrum of standardized and recognizable fresh cakes and pastries as well as fresh customized local products according the local taste and customer traditions and habits. To fulfill this mass customized approach, the high level of responsiveness and the closeness to customers due to the short shelf life of fresh food the firm decided to produce in geographically distributed production sites. 3.2. Geographically distributed production sites in the context of a Franchise system The Franchise system is based on different levels of Franchising and different Franchise-licenses (see Fig. 2). The firm established a Franchise-company e mission to develop the Franchise business, to develop new markets and to offer centralized services. In addition the Franchisor holds a production plant for pre-mixes to guarantee a standardized high quality and to protect the Know-how of recipes.
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Franchisor
Business development and central services
Master Franchisee Market A
Plant A
Distribution Center A
Centr. Plant
Production of standardized pre-mixes
Master Franchisee Market B Distribution Center B
Plant B
Master Franchisee Market C
Plant C
Distribution Center N
Franchisee A1
Franchisee B1
Franchisee N1
Franchisee A2
Franchisee B2
Franchisee N2
Franchisee An
Franchisee Bn
Franchisee Nn
Fig. 2. Franchise system structure with geographically distributed production sites.
Going in a new market the Franchisor seeks a -license as well as the production-license. The Master-franchisee is responsible ffor the development of the assigned market with new Franchisees, receives pre-mixes from the central production plant and produces fresh cakes and pastries which have to be delivered to the single POS. 4. Design Parameters for the design of networks of scalable modular manufacturing systems In several workshops and interviews with the key staff and examination of company documents could be analysed the major requirements on manufacturing systems design. In the next sections this Customer needs or Attributes (CA) were collected to obtain with the AD-approach a set of Design Parameters for the design of the in Fig. 2 shown geographically distributed Manufacturing Systems. 4.1. General expectations in a network of geographically distributed Manufacturing SSystems Asking key staff they would obtain a well-designed network of distributed Manufacturing Systems which is able to: High recognisability of the products (typical taste and naturalness through original and natural raw materials)
Protect company Know-How and secret recipes through the use of pre-mixes Guarantee a standardized high quality in every production plant over the world Minimize production costs Minimize investment in the production plant Modular and scalable manufacturing system to expand a plant step by step in relation to the demand from handcraft production, to semi-industrial production and industrialized production Simple and quick start-up of a production unit. This goal-set or these expectations can be transformed into FRs and DPs in an according decomposition. 4.2. Decomposition and multi-level-tree between Functional Requirements and Design Parameters Cochran et al. [34] developed 2001 an approach for Manufacturing Design based on the concept of Axiomatic Design. The requirement on the highest -term return in composed into lower levels (6 levels) and structured into six branches (Quality, Problem Solving, Predictable Output, Delay Reduction, Operational Costs and Investment). With the aim of a network of scalable and modular manufacturing systems the goal-set and the multi-level tree of FRs and DPs is changing. The deduced multi-level-decomposition for this goal-set in accordance with the MSDD-approach is shown in Fig. 3.
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During the decomposition process the systems designer has to follow four major steps [34]: Determination of initial set of FRs Synthesis of potential DPs Evaluation of the Independence Axiom through the dependences in the design matrix Selection of the best set of DPs (Information Axiom) Developing and decomposition of lower-level FR/DP. The re return on investment as well as the reputation of the
Manufacturing System to structure the related Design Parameters from an outgoing objective (highest level of FRs and DPs). The illustrative case from the best practise company helped to understand the requirements for designing a network of geographically distributed Manufacturing Systems in the context of a producer of mass-customized goods for a franchise sales network. This type of network of production sites becomes more AD-approach has been used in this research to define a set of critical success factors (or Design Parameters) for the design of such a network. As a result of this research has been developed a multi-level-tree of success factors that helps the systems designer following the single Design Parameters from left to right (path dependent design). In a further research the firm in the best practise will be accompanied during the design process and its implementation to prove the utility of the showed ADbased approach.
DPs on the lower level has been defined after evaluation of Axiom 1 and Axiom 2: FR11: Untouchable reputation FR12: Maximize sales FR13: Minimize manufacturing costs FR14: Minimize invested capital DP12: Maximize Quality and Image DP13: Eliminate not value adding sources DP14: Reduce working capital and tangible assets.
References The dotted arrows from a DP to a FR are indicating a decoupled or partially coupled design. In this case it is said to be path dependent decisions should be made following the multi-level-tree from left to right.
[1] Matt, D., 2010. Organization Design in geographically distributed co-operative Production, Proceedings CIRP ICME 10 7th CIRP International Conference on Intelligent Computation in Manufacaturing Engineering, Capri/Italy 23-25 June 2010. [2] Koren, Y., Shpitalni, M., 2010. Design of reconfigurable manufacturing systems, Journal of Manufacturing Systems 2010; 29: No. 4, p.130. [3] Reichwald, R., Stotko, C., Piller, F., 2005. Distributed minifactory networks as a form of real-time enterprise: concept, flexibility potential and case studies. In Kuhlin, B., Thielmann, H., Editors. The Practical Real-Time Enterprise, Berlin Heidelberg: Springer; 2005, p. 403.
5. Conclusions For single Manufacturing Systems we can find in the literature different approaches for their design. One of the methods Axiomatic Design was discussed and explained in this paper. AD helps the designer of a FR-12 Maximize Sales
Multi-level tree between FRs and DPs
DP-12 right product on the rigth
FR-1 Maximize ROI and reputation
FR-121 Produce masscustomized goods
DP-1 Manufacturing Systems Network Design
DP-121 Flexibility in a mass production
FR-1211 ensure flexibility in local variants DP-1211 Flexible production processes
FR-1212 maximize Flexibility in volumes DP-1212 Scalable and modular production
FR-11
FR-12
Untouchable reputation
Maximize Sales
FR-13 Minimize manufacturing costs
Minimize invested capital
DP-11 Maximize Quality and Image
DP-12 right product on the rigth
DP-13 Eliminate non value adding sources
DP-14 Reduce working capital and tangible assets
FR-111 Guarantee recognisabilty and Quality
FR-112 Standardized production of products
FR-121 Produce masscustomized goods
FR-122 Maximize availability in the Outlet
DP-111 Traditional
DP-112 Standards in production processes
DP-121
DP-122 Respond rapidly to customer demand
ingredients
FR-1112
FR-1121
FR-1122
Guarantee recognizability
Standardization of machinery
DP-1111 Centralized procurement of core materials
DP-1112 Centralized production of pre-mixes
DP-1121 Standard machines and interfaces
FR-11211
FR-12111
FR-12112
Define range of flexibility
Ensure flexibility of processes
DP-12111 Standardized obligatory and local assortment
Low grade of automation
DP-12112
Flexibility in a mass production
FR-1111 Use of original and natural raw materials
Standardization of manual work
FR-1212 maximize Flexibility in volumes
FR-1221 Maximize planning certainty
FR-1222 Flexibility in changes of demand
DP-1122 Define and implement standards
DP-1211 Flexible production processes
DP-1212 Scalable and modular production
DP-1221 Reliable production planning
Reduce time for changeover
FR-11212
FR-11221
FR-11222
FR-11223
FR-12111
FR-12112
FR-12121 Ensure reconfigurability and modularity
FR-12122
Standardized machines
produce
Standards have to be defined
Standards have to be trained
Standards have to be controlled
Define range of flexibility
Ensure flexibility of processes
Define scalable configurations
DP-11211
DP-11212
DP-11221
Standardized interfaces
production
DP-11223 Checks through Franchise Auditors
DP-12111 Standardized obligatory and local assortment
DP-12112
List of standard machines
DP-11222 Intensive and graduated trainings
DP-12121 Design modular production elements
DP-12122 Base prod. unit and configur. levels
Low grade of automation
FR-12121 Ensure reconfigurability and modularity
Define scalable configurations
FR-12111 Ensure accurate inventory situation
FR-12112 Guarantee an adequate service level
DP-12121 Design modular production elements
FR-12122
DP-12122 Base prod. unit and configur. levels
DP-12111 IT-based gathering of inventory
DP-12112 Calculation of needed safety stocks
FR-1311
FR-12113
DP-12113 Preventive maintenance
FR-132
FR-132
FR-141
FR-142
Reduce costs for space
Reduce energy consumption
Minimize working capital
Minimize tangible assets
DP-131
DP-132
DP-132
DP-141
DP-142
Minimize waste in energy
Reduce WIP and inventories
Low cost automation
Eliminate waste for waiting
FR-1312 Eliminate waste for motion and transport
DP-1311 Multiplemachine operations
DP-1312 Design of ergonomic workstations
DP-1222
Ensure reliability of machinery
FR-131 Reduce personnel costs
Minimize waste in labor
FR-1211 ensure flexibility in local variants
Minimize waste in spaces
FR-1313 Eliminate waste for rework
FR-1321 Reduce consumed floor space
FR-1322 Minimize inventory
DP-1313
DP-1321
DP-1322
Ensure stable processes
Material-flow oriented layout
One-piece-flow without buffers
FR-12121 Technical design for quick changeover
FR-12122 Organisational Design of changeovers
Minimize risk of failures
DP-12121 Design equipment for quick changeover
DP-12122 Implementation of SMED rules for changeover
DP-13131 Failure mode and effect analysis (FMEA)
Fig. 3. Multi-level tree of FRs and DPs in accordance to the MSDD approach (zoomed extract of FR-
FR-14
FR-13131
FR-13132 Reduce complexity in processes
DP-13132 simple and
.
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