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Czasopismo
2021 | 17 | nr 2 | 253-269
Tytuł artykułu

Factors Affecting the Level of Supply Chain Performance and its Dimensions in the Context of Supply Chain Adaptability

Warianty tytułu
Czynniki wpływające na poziom wydajności łańcucha dostaw oraz jej wymiarów w kontekście adaptacyjności łańcucha dostaw
Języki publikacji
EN
Abstrakty
Wstęp: Ważną determinantą wydajności łańcucha dostaw jest jego adaptacyjność. Jest to jedna z istotnych cech, które przekładają się na wyniki funkcjonowania łańcucha dostaw. Adaptacyjność jest przez wielu badaczy wskazywana jako ważne źródło zdobycia i utrzymania długoterminowej przewagi konkurencyjnej, jeden z głównych czynników gwarantujących sukces łańcucha dostaw, czy też główny megatrend rozwojowy łańcuchów dostaw. Głównym celem artykułu jest zbadanie wpływu czynników, takich jak branża i stosowana strategia konkurencyjna na poziom wydajności łańcucha dostaw oraz wyniki osiągane przez łańcuch dostaw w ramach kluczowych aspektów wydajności z uwzględnieniem kontekstu adaptacyjności Metody: W artykule przeanalizowano wyniki badań przeprowadzonych techniką CATI na próbie 200 przedsiębiorstw z czterech branż: spożywczej, RTV/AGD i elektroniki, motoryzacyjnej oraz meblarskiej. Analiza zgromadzonych danych została przeprowadzona w kilku etapach. W pierwszej kolejności wykonano hierarchiczną konfirmacyjną analizę czynnikową. Opracowany model wykorzystano do pomiaru i oceny wydajności łańcuchów dostaw oraz jej wymiarów, poprzez wyznaczenie ocen czynnikowych. W ostatnim etapie zbadano wpływ takich czynników jak przynależność do branży oraz stosowana strategia konkurencyjna na poziom wydajności oraz jej czterech wymiarów. W tym etapie wykorzystano nieparametryczny test Kruskala-Wallisa. Wyniki: Wyniki przeprowadzonych badań wykazały, że poziom wydajności łańcuchów dostaw, a także jej czterech wymiarów nie jest zależny od przynależności do branży, natomiast różni się w zależności od stosowanej strategii konkurencyjnej. Wnioski: Opracowany oraz pozytywnie zweryfikowany pod względem jakości model może stanowić narzędzie użyteczne dla praktyków zarządzania do pomiaru i oceny wydajności poszczególnych łańcuchów dostaw, a także dokonywania ich porównań. Dzięki wskazaniu czynników wpływających na poziom wydajności oraz jej czterech wymiarów menedżerowie mogą także w świadomy sposób wskazywać kierunki doskonalenia łańcuchów dostaw(abstrakt oryginalny)
EN
Background: A vital determinant of supply chain performance is its adaptability. It is one of essential features that affect the results of the functioning of a supply chain. Many researchers indicate adaptability as a significant source of acquiring and maintaining a long-term competitive advantage, one of major factors that guarantee the success of a supply chain, or a major development megatrend of supply chains. The main objective of the article is to analyse the impact of such factors as industry and applied competitive strategy (cost leadership, differentiation, or focus) on the level of supply chain performance and results achieved by the supply chain with regard to the key aspects of performance in the context of adaptability. Methods: In the article the author analyses results of studies conducted with CATI method at a sample of 200 enterprises representing four industries: automotive, food, furniture as well as consumer electronics and household appliances, which are among most advanced sectors in the Polish economy (leaders of Polish export). The analysis of data gathered was carried out at a few stages. Firstly, a hierarchical confirmatory factor analysis was applied. The developed model was used for measuring and assessing the performance of supply chains and its dimensions by means of designating factor scores. The last stage involved studying the impact of such factors as type of industry or applied competitive strategy on the level of performance and its four dimensions: visibility, velocity, versatility, and responsiveness. At this stage the non-parametric Kruskal-Wallis test was used. Results: The results of the conducted studies provided evidence that the level of supply chain performance as well as its four dimensions is not affected by the type of industry, but vary in accordance to the applied competitive strategy. Conclusions: The model, developed and positively verified in terms of quality, may constitute a useful tool for management practitioners to measure and assesses the performance of specific supply chains, as well as make comparisons between them. Thanks to determining factors that affect the level of performance and its four dimensions, managers may as well consciously indicate directions in improving supply chains.(original abstract)
Czasopismo
Rocznik
Tom
17
Numer
Strony
253-269
Opis fizyczny
Twórcy
  • Bialystok University of Technology, Bialystok, Poland
Bibliografia
  • Ahimbisibwe A., Ssebulime R., Tumuhairwe R., Tusiime, W., 2016. Supply chain visibility, supply chain velocity, supply chain alignment and humanitarian supply chain relief agility. European Journal of Logistics, Purchasing and Supply Chain Management, 4(2), 34-64.
  • Arif-Uz-Zaman K., Ahsan, A.M.M.N., 2014. Lean supply chain performance measurement. International Journal of Productivity and Performance Management, 63(5), 588-612, http://doi.org/10.1108/IJPPM-05-2013- 0092
  • Barratt M., Oke A., 2007. Antecedents of supply chain visibility in retail supply chains: A resource-based theory perspective. Journal of Operations Management, 25(6), 1217-1233, http://doi.org/10.1016/j.jom.2007.01.003
  • Basu R., Wright J.N., 2008. Total supply chain management. Elsevier, United Kingdom.
  • Beamon B.M., 1999. Measuring supply chain performance. International Journal of Operations & Production Management, 19(3), 275-292, http://doi.org/10.1108/01443579910249714
  • Bozarth C., Handfield R.B., 2007. Wprowadzenie do zarządzania operacjami i łańcuchem dostaw [Introduction to operations and supply chain management]. Helion, Gliwice.
  • Brewer P.C., Speh T.W., 2000. Using the balanced scorecard to measure supply chain performance. Journal of Business Logistics, 21(1), 75-93.
  • Brown T.A., 2015. Confirmatory factor analysis for applied research. The Guilford Press, New York.
  • Caridi M., Moretto A., Perego A., Tumino A., 2014. The benefits of supply chain visibility: A value assessment model. International Journal of Production Economics, 151, 1-19, http://doi.org/10.1016/j.ijpe.2009.08.016
  • Carvalho H., Azevedo S.G., Cruz-Machado V., 2012. Agile and resilient approaches to supply chain management: Influence on performance and competitiveness. Logistics Research, 4(1), 49-62, http://doi.org/10.1007/s12159-012-0064-2
  • Chae B., 2009. Developing key performance indicators for supply chain: An industry perspective. Supply Chain Management: An International Journal, 14(6), 422-428, http://doi.org/10.1108/13598540910995192
  • Chan F.T.S., 2003. Performance measurement in a supply chain. International Journal of Advanced Manufacturing Technology, 21(7), 534-548, http://doi.org/10.1007/s001700300063
  • Chimhamhiwa D., van der Molen P., Mutanga O., Rugege D., 2009. Towards a framework for measuring end to end performance of land administration business process - a case study. Computers, Environment and Urban Systems, 33(4), 293-301, http://doi.org/10.1016/j.compenvurbsys.200 9.04.001
  • Cho D.W., Lee Y.H., Ahn S.H., Hwang M.K., 2012. A framework for measuring the performance of service supply chain management. Computers & Industrial Engineering, 62(3), 801-818, http://doi.org/10.1016/j.cie.2011.11.014
  • Clark Ch., 2007. Getting back to basics: Top five tips for accelerating supply chain velocity. Supply & Demand Chain Executive, 8(4), 26.
  • Davidrajuh R, 2006. Structures for stepwise development of adaptive supply chains. Journal of Internet Commerce, 5(4), 55-72, http://doi.org/10.1300/J179v05n04_04
  • DiStefano Ch., Zhu M., Mîndrilă D., 2009. Understanding and using factor scores: Considerations for the applied researcher. Practical Assessment, Research & Evaluation, 14(20), 1-11.
  • Elking I., Paraskevas J-P., Grimm C., Corsi T., Steven A., 2017. Financial Dependence, Lean Inventory Strategy, and Firm Performance. Journal of Supply Chain Management, 53(2), 22-38, http://doi.org/10.1111/jscm.12136
  • Elrod C., Murray S., Bande S., 2013. A review of performance metrics for supply chain management. Engineering Management Journal, 25(3), 39-50, http://doi.org/10.1080/10429247.2013.1143 1981
  • Espinoza O.A., Bond B.H., Kline E., 2010. Quality measurement in the wood products supply chain. Forest Products Journal, 60(3), 249-257, http://doi.org/10.13073/0015-7473- 60.3.249
  • Estampe D., 2014. Supply chain performance and evaluation models. ISTE, London.
  • Estampe D., Lamouri S., Paris J-L., Brahim- Djelloul S., 2013. A framework for analysing supply chain performance evaluation models. International Journal of Production Economics, 142(2), 247-258, http://doi.org/10.1016/j.ijpe.2010.11.024
  • Fischer C., 2013. Trust and communication in European agri-food chains. Supply Chain Management: An International Journal, 18(2), 208-218, http://doi.org/10.1108/13598541311318836
  • Folan P., Browne J., 2005. A review of performance measurement: towards performance management. Computers in Industry, 56(7), 663-680, http://doi.org/10.1016/j.compind.2005.03.0 01
  • Ganga G.M.D., Carpinetti L.C.R., 2011. A fuzzy logic approach to supply chain performance management. International Journal of Production Economics, 134(1), 177-187, http://doi.org/10.1016/j.ijpe.2011.06.011
  • Golrizgashti S., 2014. Supply chain value creation methodology under BSC approach. Journal of Industrial Engineering International, 10(67), 1-15, http://doi.org/10.1007/s40092-014-0067-5
  • Gopal P.R.C., Thakkar J., 2012. A review on supply chain performance measures and metrics: 2000-2011. International Journal of Productivity and Performance Management, 61(5), 518-547, http://doi.org/10.1108/17410401211232957
  • Guilford J. P., 1954. Psychometric methods. McGraw-Hill, New York.
  • Gunasekaran A.,Kobu B., 2007. Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications. International Journal of Production Research, 45(12), 2819 2840, http://doi.org/10.1080/00207540600806513
  • Hines T., 2013. Supply chain strategies: Demand driven and customer focused. Routledge New York.
  • Holcomb M.C., Ponomarov S.Y., Manrodt K.B., 2011. The relationship of supply chain visibility to firm performance. Supply Chain Forum: An International Journal, 12(2), 32-45, http://doi.org/10.1080/16258312.2011.1151 7258
  • Hooper D., Coughlan J., Mullen M., 2008. Structural equation modelling: guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53-60.
  • Ivanov D., Sokolov B., 2010. Adaptive supply chain management. Springer, London
  • Ivanov D., Sokolov B., Kaeschel J., 2010. A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations. European Journal of Operational Research, 200(2), 409-420, http://doi.org/10.1016/j.ejor.2009.01.002
  • Iyer A., Seshadri S., Vasher R., 2009. Toyota supply chain management: A Strategic approach to Toyota's renowned system. McGraw-Hill Education, USA.
  • Johansson S., Melin J., 2008. Supply chain visibility. The value of information. A benchmark study of the Swedish industry. KTH Royal Institute of Technology, Stockholm.
  • Jűttner U.,Maklan S., 2011. Supply chain resilience in the global financial crisis: An empirical study. Supply Chain Management: An International Journal, 16(4), 246-259, http://doi.org/10.1108/13598541111139062
  • Kalakota R., Robinson M., Gundepudi P., 2003. Mobile applications for adaptive supply chain: A landscape analysis, in: E-P. Lim & K. Siau (Eds), Advances in mobile commerce technologies. Idea Group Inc, Hershey, USA, http://doi.org/10.4018/978-1-59140-052- 3.ch013
  • Kaplan R.S., Norton D.P., 1996. The Balanced Scorecard: Translating Strategy into action. Harvard Business Press, Boston, Massachusetts.
  • Kohlberger R., Gerschberger M., Engelhardt- Nowitzki C., 2011. Variety in supply networks - definitions and influencing parameters, in: C.H. Fang (Ed.). Proceedings of the 6th International Congress on Logistics and SCM systems, 07-09.03.2011, Kaohsiung, Taiwan.
  • Kot S., Onyusheva I., Grondys K., 2018. Supply chain management in SMEs: evidence from Poland and Kazakhstan. Engineering Management in Production and Services, 10(3), 23-36.
  • Lee H.L., 2004. The triple-A supply chain. Harvard Business Review, 82(10), 102-112.
  • Leończuk D., Ryciuk U., Szymczak M., Nazarko J., 2019. Measuring performance of adaptive supply chains, in: A. Kawa, A. Maryniak (Eds), SMART Supply Network, EcoProduction (Environmental Issues in Logistics and Manufacturing). Springer, Cham, http://doi.org/10.1007/978-3-319-91668- 2_5
  • Lin L-Ch., Li T-S., 2010. An integrated framework for supply chain performance measurement using Six-Sigma metrics. Software Quality Journal, 18(3), 387-406, http://doi.org/10.1007/s11219-010-9099-2?
  • MacCallum R. C., Widaman K. F., Zhang S., Hong S., 1999. Sample size in factor analysis. Psychological Methods, 4(1), 84-99, http://doi.org/10.1037/1082-989x.4.1.84
  • Min S., Roath A.S., Daugherty P.J., Genchev S.E. Chen H., Arndt A.D., Richey R.G., 2005. Supply chain collaboration: what's happening? The International Journal of Logistics Management, 16(2), 237-256, http://doi.org/10.1108/09574090510634539
  • Momeni E., Tavana M., Mirzagoltabar H., Mirhedayatiane S.M., 2014. A new fuzzy network slacks-based DEA model for evaluating performance of supply chains with reverse logistics. Journal of Intelligent and Fuzzy Systems, 27(2), 793-804, http://doi.org/10.3233/IFS-131037
  • Neely A., Gregory M., Platts K., 1995. Performance measurement system design: A literature review and research agenda. International Journal of Operations & Production Management, 15(4), 80-116, http://doi.org/10.1108/01443579510083622
  • Nielsen N.P.H., Holmström, J., 1995. Design for speed: A supply chain perspective on design for manufacturability. Computer Integrated Manufacturing Systems, 8(3), 223-228, http://doi.org/10.1016/0951- 5240(95)00016-M
  • Nutt B., 2004. Infrastructure resources: Forging alignments between supply and demand. Facilities, 22(13/14), 335-343, http://doi.org/10.1108/02632770410563031
  • O'Rourke N., Hatcher L., 2013. A step-by-step approach to using SAS for factor analysis and structural equation modelling. SAS Institute Inc, Cary, NC.
  • Olugu E.U., Wong K.Y., 2009. Supply chain performance evaluation: Trends and challenges. American Journal of Engineering and Applied Sciences, 2(1), 202-211, http://doi.org/10.3844/ajeassp.2009.202.211
  • Piotrowicz W., Cuthbertson R., 2015. Performance measurement and metrics in supply chains: An exploratory study. International Journal of Productivity and Performance Management, 64(8), 1-26, http://doi.org/10.1108/IJPPM-04-2014- 0064
  • Porter M.E., 1985. Competitive advantage. The Free Press, New York.
  • Qrunfleh S., Tarafdar M., 2014. Supply chain information systems strategy: Impacts on supply chain performance and firm performance. International Journal of Production Economics, 147, 340-350, http://doi.org/10.1016/j.ijpe.2012.09.018
  • Ross T.J., Holcomb M.C., Fugate B.S., 2004. Connectivity. Enabling visibility in the adaptive supply chain. http://web.utk.edu/~mholcomb/Report2004. pdf, Accessed December 14, 2018.
  • Ruhi U., Turel O., 2005. Driving visibility, velocity and versatility: The role of mobile technologies in supply chain management. Journal of Internet Commerce, 4(3), 95- 117, http://doi.org/10.1300/J179v04n03_06
  • Ryciuk U., 2016. Zaufanie międzyorganizacyjne w łańcuchach dostaw w budownictwie [Inter-organizational trust in construction supply chains]. Wydawnictwo Naukowe PWN, Warszawa.
  • Schmidt Ch.G., Foerstl K., Schaltenbrand B., 2017. The Supply Chain Position Paradox: Green Practices and Firm Performance. Journal of Supply Chain Management, 53(1), 3-25, http://doi.org/10.1111/jscm.12113
  • Scholten K., Schilder S., 2015. The role of collaboration in supply chain resilience. Supply Chain Management: An International Journal, 20(4), 471-484, http://doi.org/10.1108/SCM-11-2014-0386
  • Shaw S., Grant D.B., Mangan J., 2010. Developing environmental supply chain performance measures. Benchmarking: An International Journal, 17(3), 320-339, http://doi.org/10.1108/14635771011049326
  • Swaminathan J.M., Tayur S.R., 2003. Models for supply chains in e-business. Management Science, 49(10), 1387-1406, http://doi.org/10.1287/mnsc.49.10.1387.173 09
  • Szymczak M., 2015a. Elastyczność, wrażliwość i odporność jako cechy adaptacyjnych łańcuchów dostaw [Flexibility, Responsibility and Resiliency as Features of Adaptive Supply Chains]. Studia Oeconomica Posnaniensia, 3(6), 39- 54.
  • Szymczak M., 2015b. Ewolucja łańcuchów dostaw [The Evolution of Supply Chains]. Wydawnictwo Uniwersytetu Ekonomicznego w Poznaniu, Poznań.
  • Tarasewicz R., 2014. Jak mierzyć efektywność łańcuchów dostaw? [How to measure the performance of supply chains?]. Szkoła Główna Handlowa - Oficyna Wydawnicza, Warszawa.
  • Tsironis L.K., Matthopoulos P.P., 2015. Towards the identification of important strategic priorities of the supply chain network. Business Process Management Journal, 21(6), 1279-1298, http://doi.org/10.1108/BPMJ-12-2014-0120
  • Uluman M., Doğan C.D., 2016. Comparison of factor score computation methods in factor analysis. Australian Journal of Basic and Applied Sciences, 10(18), 143-151.
  • Whitten G.D., Green K.W., Zelbst P.J., 2012. Triple-A supply chain performance. International Journal of Operations & Production Management, 32(1), 28-48, http://doi.org/10.1108/01443571211195727
  • Ying J., Li-jun Z., 2011. The quantitative research on the index system of supply chain performance measurement based on SCOR, in: J. Jiang (Ed.), Proceedings of the 2011 International conference on informatics, cybernetics, and computer engineering (ICCE2011) November 19-20, 2011. Melbourne, Australia. Advances in Intelligent and Soft Computing, 112, Springer, Berlin, Heidelberg
  • Zailani S., Jeyaraman K., Vengadasan G., Premkumar R, 2012. Sustainable supply chain management (SSCM) in Malaysia: A survey. International Journal of Production Economics, 140(1), 330-340, http://doi.org/10.1016/j.ijpe.2012.02.008
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