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Modelling the distribution performance in dairy industry: a predictive analysis

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Background: Predictive analysis is a vital element to operations management as it facilitates real-time decision making and advanced planning on both strategy and performance. This paper identifies predictors to measure distribution performance in the dairy industry and to establish their importance. Methods: A distribution model is developed through exploratory structural equation modelling (SEM) techniques. The key performance predictors are marketing and distribution management, quality management, supply chain coordination, and brand management, which account for 71.5% of the variability in distribution performance. Results and conclusion: The predictors help improving the distribution performance, specifically in quality, order fill rate, and food safety. The outcomes of this research can help dairy professionals in managing their distribution channels, improving traceability, on-time delivery, and shipment accuracy. Consequently, these factors can improve distribution performance. Four predictors are elicited from the data to estimate the distribution performance and the relative importance of predictors is also established.
Czasopismo
Rocznik
Strony
425--440
Opis fizyczny
Bibliogr. 65 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Industrial and Production Engineering, National Institute of Technology, Jalandhar, 144011, India
  • Department of Industrial and Production Engineering, National Institute of Technology, Jalandhar, 144011, India
  • Department of Industrial and Production Engineering, National Institute of Technology, Jalandhar, 144011, India
  • Xuzhou University of Technology, Xuzhou, China
Bibliografia
  • 1. Ahire S.L., Golhar DY, Waller M.A., 1996. Development and validation of TQM implementation constructs. Decision Sciences 27(1): 23-56. http://doi.org/10.1111/j.1540-5915.1996.tb00842.x
  • 2. Ahumada O., Villalobos J.R., 2011. A tactical model for planning the production and distribution of fresh produce. Annals of Operations Research 190(1): 339-358. http://doi.org/10.1007/s10479-009-0614-4
  • 3. Bagozzi R., Yi Y., 1988. On the evaluation of structural equation models. Journal of Academy and Marketing Science 16(1): 74-94. http://doi.org/10.1007/BF02723327
  • 4. Behnke K., Janssen M.F., 2020. Boundary conditions for traceability in food supply chains using blockchain technology. International Journal of Information Management, 52, 101969. http://doi.org/10.1016/j.ijinfomgt.2019.05.025
  • 5. Bilgen B., Aelebi Y., 2013. Integrated production scheduling and distribution planning in dairy supply chain by hybrid modelling. Annals of Operations Research, 211(1), 55-82. http://doi.org/10.1007/s10479-013-1415-3
  • 6. Bollen K., Ting K., 1993. Confirmatory tetrad analysis. Sociological Methodology, 147-175. http://doi.org/10.2307/271009
  • 7. Bumblauskas D., Mann A., Dugan B., Rittmer J., 2020. A blockchain use case in food distribution: Do you know where your food has been? Int. Journal of Information Management, 52, 102008. http://doi.org/10.1016/j.ijinfomgt.2019.09.004
  • 8. Cagliano A.C., De Marco A., Mangano G., Zenezini G., 2017. Levers of logistics service providers efficiency in urban distribution. Operations Management Research, 10(3-4): 104-117. http://doi.org/10.1007/s12063-017-0125-4
  • 9. Collis B.A., Rosenblood L.K., 1985. The problem of inflated significance when testing individual correlations from a correlation matrix. Journal for Research in Mathematics Education 16, 52-55. http://doi.org/10.5951/jresematheduc.16.1.0052
  • 10. Cronin J.J., Taylor S.A., 1992. Measuring service quality: a re-examination and extension. Journal of Marketing 6, 55-68. http://doi.org/10.1177/002224299205600304
  • 11. Cronbach L.J., 1951. Coefficient alpha and internal structure of tests. Psychometrika 16(2): 297-334. http://doi.org/10.1007/BF02310555
  • 12. Cudeck R., Odell L.L., 1994. Applications of standard error estimates in unrestricted factor analysis: Significance tests for factor loadings and correlations. Psychological Bulletin, 115(3): 475-487. http://doi.org/10.1037/0033-2909.115.3.475
  • 13. Curkovic S., 2003. Environmentally responsible manufacturing: The development and validation of a measurement model. Journal of Operations Research, 146(2),130-155. http://doi.org/10.1016/S0377-2217(02)00182-0
  • 14. Dong K., Xu Y., 2001. Towards better coordination of the supply chain. Transportation Research Part E: Logistics and Transportation Review 37(1): 35-54. http://doi.org/10.1016/S1366-5545(00)00010-7
  • 15. Economic Times Report, New Delhi, 2018. Milk production to grow at 9 per cent annually by 2022 from 6 per cent now: Radha Mohan Singh. Retrieved from: https://economictimes.indiatimes.com/news/economy/agriculture/milk-production-togrow-at-9-per-cent-annually-by-2022-from6-per-cent-now-radha-mohansingh/articleshow/64418219.cms?from=mdr.
  • 16. Eid R., 2009. Factors affecting the success of world class manufacturing implementation in less developed countries: the case of Egypt. Journal of Manufacturing Technology Management, 20(7): 989-1008. http://doi.org/10.1108/17410380910984249
  • 17. Fornell C., Larcker D.F., 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18(1): 39-50. http://doi.org/10.1177/002224378101800104
  • 18. Gandhi S., Sachdeva A., Gupta A., 2018. Developing a scale to measure employee service quality in Indian SMEs. Management Science Letters, 8(5): 455-474. http://doi.org/10.5267/j.msl.2018.3.005
  • 19. Gaskin J, Lim J., 2016. Master Validity Tool. AMOS Plugin, Gaskination's StatWiki.
  • 20. Glover J.L., Daniels D.C., Dainty A.J., 2014. An institutional theory perspective on sustainable practices across dairy supply chain. International Journal of Production Economics, 152: 102-111. http://doi.org/10.1016/j.ijpe.2013.12.027
  • 21. Georgiadis P., Vlachos D., Iakovou E., 2005. A system dynamics modeling framework for the strategic supply chain management of food chains. Journal of Food Engineering, 70(3), 351-364. http://doi.org/10.1016/j.jfoodeng.2004.06.030
  • 22. Hair J.F., Anderson R.E., Tatham R.L., Black W.C., 2005. Multivariate Data Analysis, 5th Ed. Pearson Education, New Delhi, India.
  • 23. Huang K., Wu, K.F., Ardiansyah, M.N., 2019. A stochastic dairy transportation problem considering collection and delivery phases. Transportation Research Part E: Logistics and Transportation Review, 129, 325-338. http://doi.org/10.1016/j.tre.2018.01.018
  • 24. Manzini R., Accorsi R., 2013. The new conceptual framework for food supply chain assessment. Journal of food engineering, 115(2), 251-263. http://doi.org/10.1016/j.jfoodeng.2012.10.026
  • 25. Hou D., Al-Tabbaa A., Chen H., Mamic I., 2014. Factor analysis and structural equation modelling of sustainable behaviour in contaminated land remediation. J. Cleaner Production 84: 439- 449. http://doi.org/10.1016/j.jclepro.2014.01.054
  • 26. Hsiao H.I., Van der Vorst J.G.A., Kemp R.G.M., Omta O., 2010. Developing a decision†making framework for levels of logistics outsourcing in food supply chain networks. International Journal of Physical Distribution and Logistics Management, 40(5), 395-414. http://doi.org/10.1108/09600031011052840
  • 27. Hu L., Bentler P.M., 1999. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives, SEM 6(1): 1-55. http://doi.org/10.1080/10705519909540118
  • 28. Hussey D.M., Eagan P.D., 2007. Using structural equation modelling to test environmental performance in small and medium-sized manufacturers: can SEM help SMEs? Journal of Cleaner Production 15(4): 303-312. http://doi.org/10.1016/j.jclepro.2005.12.002
  • 29. Kadipas N., Pexioto M.B., 1999. Global manufacturing practices: An empirical evaluation. Industrial Management & Data Systems, 99(3): 101-108.
  • 30. Kamble S.S., Gunasekaran A., Gawankar S.A., 2020. Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications. International Journal of Production Economics, 219, 179-194. http://doi.org/10.1016/j.ijpe.2019.05.022
  • 31. Kumar A., Mishra A.K., Saroj S., Sonkar V.K., Thapa G., Joshi P.K., 2020. Food safety measures and food security of smallholder dairy farmers: Empirical evidence from Bihar, India. Agribusiness. http://doi.org/10.1002/agr.21643.
  • 32. Kumar A., Staal S.J., Singh D.K., 2011. Smallholder dairy farmers’ access to modern milk marketing chain in India. Agricultural Economics Research Review, 24: 243-253. http://doi.org/10.22004/ag.econ.118232
  • 33. Lau K.W., Yam C.M., Tang P.Y., 2010. Supply chain design and product modularity. International Journal of Operations & Production Management, 30(1): 20-56.
  • 34. Lee Y.H., Kim S.H., Moon C., 2002. Production-distribution planning in supply chain by using a hybrid approach. Production Planning and Control 13(1): 35-46. http://doi.org/10.1080/09537280110061566
  • 35. Lemma H., Singh R., Kaur N., 2015. Determinants of supply chain coordination of milk and dairy industries in Ethiopia: a case of Addis Ababa and its surroundings. SpringerPlus 4: 1-12. http://doi.org/10.1186/s40064-015-1287-x
  • 36. Ling E.K., Wahab S.N., 2020. Integrity of food supply chain: going beyond food safety and food quality. International Journal of Productivity and Quality Management, 29(2): 216-232. http://doi.org/10.1504/IJPQM.2020.105963
  • 37. Len-Bravo V., Caniato F., Caridi M., 2019. Sustainability in multiple stages of the food supply chain in Italy: practices, performance and reputation. Operations Management Research, 12(1-2): 40-61. http://doi.org/10.1007/s12063-018-0136-9
  • 38. Abdulai Rahaman A., Abdulai A., 2020. Vertical coordination mechanisms and farm performance amongst smallholder rice farmers in northern Ghana. Agribusiness, 36(2), 259-280. http://doi.org/10.1002/agr.21628
  • 39. Mishra P.K., Shekhar B.R., 2011. Impact of risks and uncertainties on supply chain: a dairy industry perspective. Journal of Management Research 3(2): 1-18. http://doi.org/10.5296/jmr.v3i2.651
  • 40. Mor R.S., Bhardwaj A., Singh S., Arora V., 2020. Exploring the factors affecting supply chain performance in Dairy industry using exploratory factor analysis technique. International Journal of Industrial and Systems Engineering, 36(2), 248-265. http://doi.org/10.1504/IJISE.2020.110277
  • 41. Mor R.S., Jaiswal S., Singh S., Bhardwaj A., 2019a. Demand Forecasting of the ShortLifecycle Dairy Products, In: Chahal H., Jyoti J., Wirtz J. (eds.), Understanding the Role of Business Analytics. Springer, Singapore. http://doi.org/10.1007/978-981-13-1334-9_6.
  • 42. Mor R.S., Bhardwaj A., Singh S., Nema P.K., 2019b. Framework for measuring the performance of production operations in dairy industry, In: Managing operations throughout global supply chains. http://doi.org/10.4018/978-1-5225-8157-4.ch002, IGI Global.
  • 43. Mor R.S., Bhardwaj A., Singh S., 2018. Benchmarking the interactions among barriers of dairy supply chain: An ISM approach. International Journal for Quality Research, 12(2): 385-404. http://doi.org/10.18421/IJQR12.02-06
  • 44. Nabhani F., Shokri A., 2009. Reducing the delivery lead time in a food distribution SME through the implementation of six sigma methodology. Journal of Manufacturing Technology Management 20(7): 957-974. http://doi.org/10.1108/17410380910984221
  • 45. Nardi V.A.M., Auler D.P., Teixeira R., 2020. Food safety in global supply chains: A literature review. Journal of Food Science, 85(4), 883-891. http://doi.org/10.1111/1750-3841.14999
  • 46. Nargundkar R., 2004. Marketing Research: Test and Cases, 2nd Ed., Tata McGraw Hills Pvt. Ltd: New Delhi, India.
  • 47. Ngai E.W.T., Cheng T.C.E., Ho S.S.M., 2004. Critical success factors of web-based supply-chain management systems: an exploratory study. Production Planning & Control, 15(6): 622-630. http://doi.org/10.1080/09537280412331283928
  • 48. Nunnally J.C., 1978. Psychometric Theory, 2nd Ed. New York: McGraw Hill.
  • 49. Okano M.T., Vendrametto O., Santos O.S., 2014. How to improve dairy production in Brazil through indicators for the economic development of milk chain. Modern Economy 5: 663-669. http://doi.org/10.4236/me.2014.56062
  • 50. Pitt L.F., Watson R.T., Kavan C.B., 1995. Service quality: A measure of information systems effectiveness. MIS Quarterly, 19, 173-187. http://doi.org/10.2307/249687
  • 51. Robson C., 2002. Real World Research, 2nd Ed. Oxford: Blackwell.
  • 52. Sahin F., Robinson E.P., 2002. Flow coordination and information sharing in supply chains: review, implications, and directions for future research. Decision Sciences 33(4): 505-536. http://doi.org/10.1111/j.1540-5915.2002.tb01654.x
  • 53. Saunders M., Lewis P., Thornhill A., 2009. Research Methods for Business Students, 5th Ed. Essex: Person Education.
  • 54. Safaei A.S., Husseini M., Farahani R.Z., Jolai F., Ghodsypour S.H., 2010. Integrated multi-site production distribution planning in supply chain by hybrid modelling. International Journal of Production Research 48(14): 4043-4069. http://doi.org/10.1080/00207540902791777
  • 55. Selim H., Ozkarahan I., 2008. A supply chain distribution network design model: an interactive fuzzy goal programming-based solution approach. Int. J. Advance Manufacture & Technology 36, 401-418. http://doi.org/10.1007/s00170-006-0842-6
  • 56. Singh N., Javadekar P., 2011. Supply chain management of perishable food products: a strategy to achieve competitive advantage through knowledge management. Indian J. of Marketing 41(10).
  • 57. Sitek P., Wikarek J., 2015. A hybrid framework for the modelling and optimisation of decision problems in sustainable supply chain management. Int. J. Production Research 53(21): 6611-6628. http://doi.org/10.1080/00207543.2015.1005762
  • 58. Smith B.G., 2007. Developing sustainable food supply chains. Philosophical Transactions of the Royal. Society B: Biological Sciences 363(1492): 849-861. http://doi.org/10.1098/rstb.2007.2187
  • 59. Tanaka J.S., 1987. How big is big enough? Sample size and goodness of fit in structural equation models with latent variables. Child Development, 58(1): 134-146. http://doi.org/10.2307/1130296
  • 60. Thirupathi R.M., Vinodh S., 2016. Application of interpretive structural modelling and structural equation modelling for analysis of sustainable manufacturing factors in Indian automotive component sector. International Journal of Production Research 54(22): 6661-6682. http://doi.org/10.1080/00207543.2015.1126372
  • 61. Thomas A.V., Mahanty B., 2018. Dynamic assessment of control system designs of information shared supply chain network experiencing supplier disruption. Operational Research, 21, 425-451, http://doi.org/10.1007/s12351-018-0435-9.
  • 62. Trochim W.M., 2007. Research Methods, Biztantra, New Delhi.
  • 63. Vinodh S., Joy D., 2012. Structural equation modelling of lean manufacturing practices. International Journal of Production Research 50(6): 1598-1607. http://doi.org/10.1080/00207543.2011.560203
  • 64. Vosooghidizaji M., Taghipour A., CanelDepitre B., 2020. Supply chain coordination under information asymmetry: a review. International Journal of Production Research, 58(6): 1805-1834. http://doi.org/10.1080/00207543.2019.1685702
  • 65. Zhang X., Yousaf H.A.U., 2020. Green supply chain coordination considering government intervention, green investment, and customer green preferences in the petroleum industry. Journal of Cleaner Production, 246, 118984. http://doi.org/10.1016/j.jclepro.2019.118984
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Bibliografia
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