Abstract
Designing product families is an enabling strategy for mass customization. In general, there are four prevalent classes of problems when designing product families: (i) product family positioning; (ii) customer preferences modeling; (iii) product family modeling; and (iv) product family configuration. Although these classes are interwoven through design problems stemming from marketing, engineering, and economic areas, they are rarely handled together in product family design methods. The lack of a systemic, integrated design perspective may lead to locally optimal solutions and ultimately result in product families not making the economic benefits of customization worthwhile. Over the years, some methods have attempted to overcome this absence of holistic design view. However, because they are restricted to theoretical levels or lack detailed applications, their practical implementation is often not possible. To bridge the pathway between theory and practical implementation, this paper uses the market-driven modularity (MDM) method to design a family of autonomous mobile palletizers economically oriented to market requirements. The empirical application of the method points out the palletizers family as being economically feasible. Furthermore, it also indicates which modules should be developed in successive design phases, as well as reveal the definition of the product family structure as the MDM’s outcome that is more sensitive to the variation of parameters/variables composing the configuration model. The main contribution of this work lies in the presentation of practical implementation details of the MDM method, which, to the best of our knowledge, has not been reported since its proposition.
Similar content being viewed by others
Notes
Design characteristics that determine the cost of a given product [95]
References
Piran FS, Lacerda DP, Sellitto MA, Morandi MIWM (2020) Influence of modularity on delivery dependability: analysis in a bus manufacturer. Prod Plan Control 1–11. https://doi.org/10.1080/09537287.2020.1776411
Park K, Gul E, Kremer O (2019) An investigation on the network topology of an evolving product family structure and its robustness and complexity. Res Eng Des 30:381–404. https://doi.org/10.1007/s00163-019-00310-y
Simpson TW, Jiao J, Siddique Z, Hölttä-Otto K (2014) Advances in product family and product platform design: methods & applications, Springer, New York. https://doi.org/10.1007/978-1-4614-7937-6
Ferguson SM, Olewnik AT, Cormier P (2014) A review of mass customization across marketing, engineering and distribution domains toward development of a process framework. Res Eng Des 25:11–30. https://doi.org/10.1007/s00163-013-0162-4
Gauss L, Lacerda DP, Cauchick Miguel PA (2021) Module-based product family design: systematic literature review and meta-synthesis. J Intell Manuf 32:265–312. https://doi.org/10.1007/s10845-020-01572-3
Shaked H, Schechter C (2017) Definitions and development of systems thinking. Syst Think Sch Leaders Holist Leadersh Excell Educ 9–22. https://doi.org/10.1007/978-3-319-53571-5
Ziaei M, Ketabi S, Ghandehari M (2021) Integrative design, production, and marketing policy for a configurable product family. Int J Manag Sci Eng Manag 1–15. https://doi.org/10.1080/17509653.2020.1852126
Hölttä-Otto K, De Weck O (2007) Degree of modularity in engineering systems and products with technical and business constraints. Concurr Eng Res Appl 15:113–126
Simpson TW, Siddique Z, Jiao J (2006) Product platform and product family design: methods and applications, Springer. https://doi.org/10.1007/0-387-29197-0
Senge PM (1990) The fifth discipline: the art and practice of the learning organization. Currency Doubleday, New York
Kumar D, Chen W, Simpson TW (2009) A market-driven approach to product family design. Int J Prod Res 47:71–104. https://doi.org/10.1080/00207540701393171
Otto K, Hölttä-Otto K, Simpson TW, Krause D, Ripperda S, Moon SK (2016) Global views on modular design research: linking alternative methods to support modular product family concept development. J Mech Des. https://doi.org/10.1115/1.4033654
Gauss L, Lacerda DP, Cauchick Miguel PA (2022) Market-driven modularity: design method develop under a design science paradigm. Int J Prod Econ 246:108412. https://doi.org/10.1016/j.ijpe.2022.108412
Gauss L, Lacerda DP, Sellitto MA (2019) Module-based machinery design: a method to support the design of modular machine families for reconfigurable manufacturing systems. Int J Adv Manuf Technol 102:3911–3936. https://doi.org/10.1007/s00170-019-03358-1
Lee JD, Chang CH, Cheng ES, Kuo CC, Hsieh CY (2021) Intelligent robotic palletizer system. Appl Sci. https://doi.org/10.3390/app112412159
Mendoza-Calderón KD, Jaimes JAM, Maradey-Lazaro JG, Rincón-Quintero AD, Cardenas-Arias CG (2022) Design of an automatic palletizer. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/2224/1/012095
Baylis K, Zhang G, McAdams DA (2018) Product family platform selection using a Pareto front of maximum commonality and strategic modularity. Res Eng Des 29:547–563. https://doi.org/10.1007/s00163-018-0288-5
Simpson TW, Maier JR, Mistree F (2001) Product platform design: method and application. Res Eng Des Theory Appl Concurr Eng 13:2–22. https://doi.org/10.1007/s001630100002
Jiang L, Allada V (2005) Robust modular product family design using a modified Taguchi method. J Eng Des 16:443–458. https://doi.org/10.1080/09544820500287359
Jiao J, Tseng MM (1999) Methodology of developing product family architecture for mass customization. J Intell Manuf 10:3–20. https://doi.org/10.1023/A:1008926428533
Asan U, Polat S, Serdar S (2004) An integrated method for designing modular products. J Manuf Technol Manag 15:29–49. https://doi.org/10.1108/09576060410512257
Hsiao S-WS-W, Liu E (2005) A structural component-based approach for designing product family. Comput Ind 56:13–28. https://doi.org/10.1016/j.compind.2004.10.002
Kazemzadeh RB, Behzadian M, Aghdasi M, Albadvi A (2008) Integration of marketing research techniques into house of quality and product family design. Int J Adv Manuf Technol 41:1019–1033. https://doi.org/10.1007/s00170-008-1533-2
Hsiao S-W, Ko Y-C, Lo C-H, Chen S-H (2013) An ISM, DEI, and ANP based approach for product family development. Adv Eng Inform 27:131–148. https://doi.org/10.1016/j.aei.2012.10.008
Sahin-Sariisik AA, Terpenny J, Van Aken EM, Orfi N (2014) A structured approach to platform-driven product planning. Eng Manag J 26:10–23. https://doi.org/10.1080/10429247.2014.11432007
Pakkanen J, Juuti T, Lehtonen T (2016) Brownfield process: a method for modular product family development aiming for product configuration. Des Stud 45:210–241. https://doi.org/10.1016/j.destud.2016.04.004
Ma J, Kim HM (2016) Product family architecture design with predictive, data-driven product family design method. Res Eng Des 27:5–21. https://doi.org/10.1007/s00163-015-0201-4
Colombo EF, Shougarian N, Sinha K, Cascini G, de Weck OL (2020) Value analysis for customizable modular product platforms: theory and case study. Res Eng Des 31:123–140. https://doi.org/10.1007/s00163-019-00326-4
Song Q, Ni Y, Ralescu DA (2020) Product configuration using redundancy and standardisation in an uncertain environment. Int J Prod Res. https://doi.org/10.1080/00207543.2020.1815888
Thevenot HJ, Alizon F, Simpson TW, Shooter SB (2007) An index-based method to manage the tradeoff between diversity and commonality during product family design. Concurr Eng Res Appl 15:127–139. https://doi.org/10.1177/1063293X07079318
Arciniegas AJR, Kim HM (2011) Optimal component sharing in a product family by simultaneous consideration of minimum description length and impact metric. Eng Optim 43:175–192. https://doi.org/10.1080/0305215X.2010.486032
AlGeddawy T, ElMaraghy H (2013) Reactive design methodology for product family platforms, modularity and parts integration. CIRP J Manuf Sci Technol 6:34–43. https://doi.org/10.1016/j.cirpj.2012.08.001
Agard B, Bassetto S (2013) Modular design of product families for quality and cost. Int J Prod Res 51:1648–1667. https://doi.org/10.1080/00207543.2012.693963
Li Z, Cheng Z, Feng Y, Yang J (2013) An integrated method for flexible platform modular architecture design. J Eng Des 24:25–44. https://doi.org/10.1080/09544828.2012.668614
Aydin M, Ulutas BH (2016) A new methodology to cluster derivative product modules: an application. Int J Prod Res 54:7091–7099. https://doi.org/10.1080/00207543.2016.1143133
Ma S, Du G, Jiao J, Zhang R (2016) Hierarchical game joint optimization for product family-driven modular design ga. J Oper Res Soc 67:1496–1509. https://doi.org/10.1057/jors.2016.32
Hou W, Shan C, Yu Y, Hu P, Zhang H (2017) Modular platform optimization in conceptual vehicle body design via modified graph-based decomposition algorithm and cost-based priority method. Struct Multidiscip Optim 55:2087–2097. https://doi.org/10.1007/s00158-016-1629-5
Hou W, Shan C, Yu Y, Hu P, Zhang H (2018) Product-family shared-component selection based on the consistency constraint function. Proc Inst Mech Eng Part D J Automob Eng 232:838–849. https://doi.org/10.1177/0954407017707453
Borjesson F, Hölttä-Otto K (2014) A module generation algorithm for product architecture based on component interactions and strategic drivers. Res Eng Des 25:31–51. https://doi.org/10.1007/s00163-013-0164-2
Miao C, Du G, Jiao RJ, Zhang T (2017) Coordinated optimisation of platform-driven product line planning by bilevel programming. Int J Prod Res 55:3808–3831. https://doi.org/10.1080/00207543.2017.1294770
ElMaraghy H, AlGeddawy T (2012) New dependency model and biological analogy for integrating product design for variety with market requirements. J Eng Des 23:722–745. https://doi.org/10.1080/09544828.2012.709607
Simpson TW, Bobuk A, Slingerland LA, Brennan S, Logan D, Reichard K (2012) From user requirements to commonality specifications: an integrated approach to product family design. Res Eng Des 23:141–153. https://doi.org/10.1007/s00163-011-0119-4
Fan B, Qi G, Hu X, Yu T (2015) A network methodology for structure-oriented modular product platform planning. J Intell Manuf 26:553–570. https://doi.org/10.1007/s10845-013-0815-1
Dahmus JB, Gonzalez-Zugasti JP, Otto KN (2000) Modular product architecture. Proc DETC’00 ASME Des Eng Tech Conf Comput Inf Eng Conf 22:409–424. https://doi.org/10.1016/S0142-694X(01)00004-7
Zhang WY, Tor SY, Britton GA (2006) Managing modularity in product family design with functional modeling. Int J Adv Manuf Technol 30:579–588. https://doi.org/10.1007/s00170-005-0112-z
Meng X, Jiang Z, Huang GQ (2007) On the module identification for product family development. Int J Adv Manuf Technol 35:26–40. https://doi.org/10.1007/s00170-006-0712-2
Stone RB, Kurtadikar R, Villanueva N, Arnold CB (2008) A customer needs motivated conceptual design methodology for product portfolio planning. J Eng Des 19:489–514. https://doi.org/10.1080/09544820802286711
Park J, Shin D, Insun P, Hyemi H (2008) A product platform concept development method. J Eng Des 19:515–532. https://doi.org/10.1080/09544820802043583
Yan X-T, Stewart B (2010) Developing modular product family using GeMoCURE within an SME. Int J Manuf Res 5:449–463. https://doi.org/10.1504/IJMR.2010.035813
Emmatty FJ, Sarmah SP (2012) Modular product development through platform-based design and DFMA. J Eng Des 23:696–714. https://doi.org/10.1080/09544828.2011.653330
Yang Q, Yu S, Jiang D (2014) A modular method of developing an eco-product family considering the reusability and recyclability of customer products. J Clean Prod 64:254–265. https://doi.org/10.1016/j.jclepro.2013.07.030
Wei W, Liu A, Lu SCY, Wuest T (2015) A multi-principle module identification method for product platform design. J Zhejiang Univ A 16:1–10. https://doi.org/10.1631/jzus.A1400263
Jung S, Simpson TW (2016) An integrated approach to product family redesign using commonality and variety metrics. Res Eng Des 27:391–412. https://doi.org/10.1007/s00163-016-0224-5
Cheng Q, Li W, Xue D, Liu Z, Gu P, Li K (2017) Design of adaptable product platform for heavy-duty gantry milling machines based on sensitivity design structure matrix. Proc Inst Mech Eng Part C J Mech Eng Sci 23:4495–4511. https://doi.org/10.1177/0954406216670685
Wang Q, Tang D, Yin L, Ullah I, Tan L, Zhang T (2018) An optimization model for low carbon oriented modular product platform planning (MP3). Int J Precis Eng Manuf Green Technol 5:121–132. https://doi.org/10.1007/s40684-018-0013-x
Bonjour E, Deniaud S, Dulmet M, Harmel G (2009) A fuzzy method for propagating functional architecture constraints to physical architecture. J Mech Des 131:061002. https://doi.org/10.1115/1.3116253
Bejlegaard M, ElMaraghy W, Brunoe TD, Andersen AL, Nielsen K (2018) Methodology for reconfigurable fixture architecture design. CIRP J Manuf Sci Technol 23:172–186. https://doi.org/10.1016/j.cirpj.2018.05.001
Du X, Jiao J, Tseng MM (2006) Understanding customer satisfaction in product customization. Int J Adv Manuf Technol 31:396–406. https://doi.org/10.1007/s00170-005-0177-8
Krishnapillai R, Zeid A (2006) Mapping product design specification for mass customization. J Intell Manuf 17:29–43. https://doi.org/10.1007/s10845-005-5511-3
Tan C, Chung H, Barton K, Jack Hu S, Freiheit T (2020) Incorporating customer personalization preferences in open product architecture design. J Manuf Syst 56:72–83. https://doi.org/10.1016/j.jmsy.2020.05.006
Aungst S, Barton R, Wilson D (2003) The virtual integrated design method. Qual Eng 15:565–579. https://doi.org/10.1081/QEN-120018389
Jiao JR (2012) Product platform flexibility planning by hybrid real options analysis. IIE Trans (Inst Ind Eng) 44:431–445. https://doi.org/10.1080/0740817X.2011.609874
Pate DJ, Patterson MD, German BJ (2012) Optimizing families of reconfigurable aircraft for multiple missions. J Aircr 49:1988–2000. https://doi.org/10.2514/1.C031667
Hanafy M, Elmaraghy H (2015) A modular product multi-platform configuration model. Int J Comput Integr Manuf 28:999–1014. https://doi.org/10.1080/0951192X.2014.941407
Goswami M, Daultani Y, Tiwari MK (2017) An integrated framework for product line design for modular products: product attribute and functionality-driven perspective. Int J Prod Res 55:3862–3885. https://doi.org/10.1080/00207543.2017.1314039
Xiao W, Du G, Zhang Y, Liu X (2018) Coordinated optimization of low-carbon product family and its manufacturing process design by a bilevel game-theoretic model. J Clean Prod 184:754–773. https://doi.org/10.1016/j.jclepro.2018.02.240
Tucker CS, Kim HM (2008) Optimal product portfolio formulation by merging predictive data mining with multilevel optimization. J Mech Des 130:041103. https://doi.org/10.1115/1.2838336
Hilmola OP, Li W (2016) Throughput accounting heuristics is still adequate: response to criticism. Expert Syst Appl 58:221–228. https://doi.org/10.1016/j.eswa.2016.03.051
Rai R, Allada V (2003) Modular product family design: agent-based pareto-optimization and quality loss function-based post-optimal analysis. Int J Prod Res 41:4075–4098. https://doi.org/10.1080/0020754031000149248
Li L, Huang GQ, Newman ST (2008) A cooperative coevolutionary algorithm for design of platform-based mass customized products. J Intell Manuf 19:507–519. https://doi.org/10.1007/s10845-008-0137-x
Li L, Huang GQ (2009) Multiobjective evolutionary optimisation for adaptive product family design. Int J Comput Integr Manuf 22:299–314. https://doi.org/10.1080/09511920802014920
Dong M, Shao X, Xiong S (2011) Flexible optimization decision for product design agility with embedded real options. Proc Inst Mech Eng Part B J Eng Manuf 1431–1446. https://doi.org/10.1177/09544054JEM2216
Chowdhury S, Maldonado V, Tong W, Messac A (2016) New modular product-platform-planning approach to design macroscale reconfigurable unmanned aerial vehicles. J Aircr 53:309–322. https://doi.org/10.2514/1.C033262
Phaal R, Muller G (2009) An architectural framework for roadmapping: towards visual strategy. Technol Forecast Soc Change 76:39–49. https://doi.org/10.1016/j.techfore.2008.03.018
Wheelwright SC, Clark KB (1992) Creating project plans to focus product development. Harv Bus Rev 70:70–82. https://doi.org/10.1007/s00464-008-0110-y
Meyer MH, Lehnerd AP (1997) The power of product platforms: building value and cost leadership. Free Press
Dalkey N (1969) An experimental study of group opinion: the Delphi method. Futures 1:408–426. https://doi.org/10.1016/S0016-3287(69)80025-X
Premachandra IM (2001) An approximate of the activity duration distribution in PERT. Comput Oper Res 28:443–452. https://doi.org/10.1016/S0305-0548(99)00129-X
Forza C (2002) Survey research in operations management: a process-based perspective. Int J Oper Prod Manag 22:152–194. https://doi.org/10.1108/01443570210414310
Montgomery DC, Runger GC (2011) Applied statistics and probability for engineers, 5th ed., John Wiley & Sons, Ltd
Chen W, Hoyle C, Wassenaar HJ (2013) Decision-based design: integrating consumer preferences into engineering design, Springer. London. https://doi.org/10.1007/978-1-4471-4036-8
Pahl G, Beitz W, Feldhusen J, Grote K-H (2007) Engineering design: a systematic approach, Springer. 617. https://doi.org/10.1111/dsu.12130
Malhotra NK, Birks DF (2007) Marketing research: an applied approach, 3rd ed., Prentice Hall Inc
Bardin L (1993) L’analyse de contenu [Content Analysis]. Presses Universitaires de France Le Psychologue, Paris
Yearworth M, White L (2013) The uses of qualitative data in multimethodology: developing causal loop diagrams during the coding process. Eur J Oper Res 231:151–161. https://doi.org/10.1016/j.ejor.2013.05.002
Thevenot HJ, Simpson TW (2007) A comprehensive metric for evaluating component commonality in a product family. J Eng Des 18:577–598. https://doi.org/10.1080/09544820601020014
Suh NP (2001) Axiomatic design: advances and applications. Oxford University Press, New York
Saaty TL (2008) Decision making with the analytic hierarchy process. Int J Serv Sci 1:83. https://doi.org/10.1504/IJSSCI.2008.017590
Ossadnik W, Schinke S, Kaspar RH (2016) Group aggregation techniques for analytic hierarchy process and analytic network process : a comparative analysis. Gr Decis Negot 25:421–457. https://doi.org/10.1007/s10726-015-9448-4
Eskandari H, Rabelo L (2007) Handling uncertainty in the analytic hierarchy process: a stochastic approach. Int J Inf Technol Decis Mak 6:177–189. https://doi.org/10.1142/S0219622007002356
King JR (1980) Machine-component grouping in production flow analysis: an approach using a rank order clustering algorithm. Int J Prod Res 18:213–232. https://doi.org/10.1080/00207548008919662
Kusiak A, Chow WS (1987) Efficient solving of the group technology problem. J Manuf Syst 6:117–124. https://doi.org/10.1016/0278-6125(87)90035-5
Jung S, Simpson TW (2017) New modularity indices for modularity assessment and clustering of product architecture. J Eng Des 28:1–22. https://doi.org/10.1080/09544828.2016.1252835
Haykin S (2008) Neural networks and learning machines, 3rd edn. Prentice Hall, Ontario
Jiao J, Tseng MM (1999) A pragmatic approach to product costing based on standard time estimation. Int J Oper Prod Manag 19:738–755. https://doi.org/10.1108/01443579910271692
Gümüş M (2014) With or without forecast sharing: competition and credibility under information asymmetry. Prod Oper Manag 23:1732–1747. https://doi.org/10.1111/poms.12192
Eissen K, Steur R (2007) Sketching: drawing techniques for product designers, 13th ed., BIS Publishers
Browning TR (2001) Applying the design structure matrix to system decomposition and integration problems: a review and new directions. IEEE Trans Eng Manag 48:292–306. https://doi.org/10.1109/17.946528
Hilier F, Lieberman G (2015) Introduction to operational research, 10th ed., McGraw-Hill, New York. https://doi.org/10.2307/2077150
Daly SR, Yilmaz S, Christian JL, Seifert CM, Gonzalez R (2012) Design heuristics in engineering. J Eng Educ 101:601–629
Rui H, Cuervo-Cazurra A, Un CA (2016) Learning-by-doing in emerging market multinationals: integration, trial and error, repetition, and extension. J World Bus 51:686–699. https://doi.org/10.1016/j.jwb.2016.07.007
Popple RA (2009) The science of palletizing: how to choose the right system. Columbia Machine Inc, Vancouver
More A (2019) Palletizer Market 2019 Research by Business Opportunities, Top Manufactures, Industry Growth, Industry Share Report, Size, Regional Analysis and Global Forecast to 2024 | Market Reports World - MarketWatch. https://www.marketwatch.com/press-release/palletizer-market-2019-research-by-business-opportunities-top-manufactures-industry-growth-industry-share-report-size-regional-analysis-and-global-forecast-to-2024-market-reports-world-2019-05-31 (Accessed 7 Dec 2019)
Ghobakhloo M (2018) The future of manufacturing industry: a strategic roadmap toward Industry 4.0. J Manuf Technol Manag 29:910–936. https://doi.org/10.1108/JMTM-02-2018-0057
Insight FB (2021) Palletizer: global market analysis, Insights and Forecast (2016 - 2027). https://www.fortunebusinessinsights.com/palletizer-market-104445
Chan L-KL-K, Wu M-LM-L (2002) Quality function deployment: a literature review. Eur J Oper Res 143:463–497. https://doi.org/10.1016/S0377-2217(02)00178-9
Jiao J, Zhang Y (2005) Product portfolio planning with customer-engineering interaction. IIE Trans (Institute Ind Eng) 37:801–814. https://doi.org/10.1080/07408170590917011
Hassanat A, Almohammadi K, Alkafaween E, Abunawas E, Hammouri A, Prasath VBS (2019) Choosing mutation and crossover ratios for genetic algorithms-a review with a new dynamic approach. Information. https://doi.org/10.3390/info10120390
Jiao J, Zhang Y, Wang Y (2007) A heuristic genetic algorithm for product portfolio planning. Comput Oper Res 34:1777–1799. https://doi.org/10.1016/j.cor.2005.05.033
Baierle I, Benitez G, Nara JSE, Sellitto M (2020) Influence of open innovation variables on the competitive edge of small and medium enterprises. J Open Innov Technol Mark Complex 6:179–196
Nara EOB, Sordi DC, Schaefer JL, Schreiber JNC, Baierle IC, Sellitto MA, Furtado JC (2019) Prioritization of OHS key performance indicators that affecting business competitiveness – a demonstration based on MAUT and Neural Networks. Saf Sci 118:826–834. https://doi.org/10.1016/j.ssci.2019.06.017
Acknowledgements
The authors do appreciate the recommendations of the reviewers and editor who dedicated their time and provided remarkable suggestions that undoubtedly enhanced our manuscript. We also thank the Coordination of Superior Level Staff Improvement (CAPES) and the Brazilian Council for Scientific and Technological Development (CNPq) for funding this research.
Funding
This work was funded by the Coordination for the Improvement of Higher Education Personnel (CAPES) and the Brazilian National Council for Scientific and Technological Development (CNPq) for funding.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Leandro Gauss and Daniel P. Lacerda. The first draft of the manuscript was written by Leandro Gauss and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Appendix
Appendix
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Gauss, L., Lacerda, D.P., Cauchick-Miguel, P.A. et al. Market-driven modularity: an empirical application in the design of a family of autonomous mobile palletizers. Int J Adv Manuf Technol 123, 1377–1400 (2022). https://doi.org/10.1007/s00170-022-10128-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-022-10128-z