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2021, vol. 49, br. 1, str. 233-243
Poboljšanje komisioniranja naloga kod e-trgovine korišćenjem analize WMS i BigData podataka
aCracow University of Technology, Cracow, Poland
bVilnius Gediminas Technical University, Lithuania

e-adresaalorenc@pk.edu.pl
Ključne reči: Product Allocation Problem (PAP); BigData analysis; Tableau analysis; clustering; the effectiveness of orders picking; e-commerce; warehouse logistics
Sažetak
Istražuje se poboljšanje procesa komisioniranja naloga bez dodatnih ulaganja u softver, uposlenike, alate i zalihe. U cilju rešavanja problema korišćeni su i obrađeni podaci o komisioniranju iz sistema za upravljanje skladištem (WMS). Analizirani su podaci iz izvora BigData i izvršena je klasterizacija proizvoda pomoću softvera Tableau, pri čemu je rešavan problem alokacije proizvoda (PAP). Izvršeno je izračunavanje vremena komisioniranja za referentni i Nov scenario i izvršeno je poređenje. Pokazuje se da bi standardni podaci iz WMS mogli da se koriste za rešavanje PAP problema za skraćenje ukupnog vremena komisioniranja. Metod koji autori opisuju može da se koristi za tipično skladište u kome uposlenici i viljuškari obavljaju proces komisioniranja. Posle poboljšanja plan bi mogao da se koristi za automatsko komisioniranje i primenu kod WMS. Za analizu podataka BigData softver Tableau se povezuje sa bazom podataka WMS. Dato rešenje bi moglo da se koristi za svakodnevnu analizu podataka i rešavanje problema alokacije proizvoda. Metod je lak za korišćenje, nema novih ulaganja u skup softver i automatizaciju komisioniranja da bi se postigle velike performanse procesa komisioniranja naloga. Njegova primena omogućava povećanje efikasnosti. Vlasnici prodavnica mogu da biraju više proizvoda istovremeno. Istraživanje je originalno jer koristi jednostavne metode i analizu specifičnih podataka koji su do sada korišćeni samo za izračunavanje pokazatelja performansi uposlenika.
Reference
Addo-Tenkorang, R., Helo, P.T. (2016) Big data applications in operations/supply-chain management: A literature review. Computers & Industrial Engineering, 101: 528-543
Arunachalam, D., Kumar, N., Kawalek, J.P. (2018) Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114: 416-436
Bodden-Streubuhr, M. (2016) Warehouse-Management-Systeme im Spannungsfeld von Industrie 4.0. u: Sohrt S., et al. [ur.] Handbuch Industrie 4.0: Produktion, Automatisierung und Logistik, Heidelberg: Springer, 1-13
Chan, H.K., Choi, T.M., Yue, X. (2016) Guest editorial big data analytics: Risk and operations management for industrial applications. IEEE Transactions on Industrial Informatics, 12(3): 1214-1218
Choi, T.M., Wallace, S.W., Wang, Y. (2018) Big data analytics in operations management. Production and Operations Management, Vol. 27 No. 10, 1868-1883
Chongwatpol, J., Chan, H.K. (2015) Prognostic Analysis of Defects in Manufacturing. Industrial Management & Data Systems, 115(1): 64-87
Crisp, R.D. (1968) How to Reduce Distribution Costs: A Practical, Scientific Approach to Increased Selling Efficiency. New York: Funk & Wagnalls Company
Croxton, K.L., Lambert, D.M., García-Dastugue, S.J., Rogers, D.S. (2002) The Demand Management Process. International Journal of Logistics Management, 13(2): 51-66
Davenport, T.H. (2013) Analytics 3.0. Harvard business review, 91(12): 64-72
Dremel, C., Wulf, J., Herterich, M.M., Waizmann, J.C., Brenner, W. (2017) How AUDI AG Established Big Data Analytics in Its Digital Transformation. MIS Quarterly Executive, 16(2): 80-89
Egner, C.G. (1973) Experiment in worker productivity. Montana: University of Montana, working paper, 25 May
Fang, M.Y., Lin, F. (2006) Measuring the performance of ERP system-from the balanced scorecard perspectives. Journal of American Academy of Business, 10(1): 256-263
Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B., Akter, S. (2017) Big data and predictive analytics for supply chain and organizational performance. Journal of Business Research, 70: 308-317
Han, Q.L., Mei, Y.X. (2004) Distribution center: BP artificial neural network based site selection. China Soft Sci, 24: 140-143
Hazen, B.T., Skipper, J.B., Boone, C.A., Hill, R.R. (2018) Back in business: Operations research in support of big data analytics for operations and supply chain management. Annals of Operations Research, 270(1-2): 201-211
Hendrickson, B.A. (1999) U.S. Patent No. 5,930,784. Washington, DC: U.S. Patent and Trademark Office
Jiang, Z.Z., Wang, D.W. (2005) Model and algorithm of location optimisation of distribution centers for B2C E-commerce. Control and Decision, 20(10): 11-25
Khan, M.R. (1984) An efficiency measurement model for a computerized warehousing system. International Journal of Production Research, 22(3): 443-452
Lawrence, F.B., Jennings, D.F., Reynolds, B.E. (2005) ERP in Distribution. Nashville: South-Western Pub
Lerher, T. (2018) Warehousing 4.0 by using shuttlebased storage and retrieval systems. FME Transactions, vol. 46, br. 3, str. 381-385
Lorenc, A., Lerher, T. (2019) Effectiveness of product storage policy according to classification criteria and warehouse size. FME Transactions, vol. 47, br. 1, str. 142-150
Malmborg, C.J., Balachandran, S., Kyle, D.M. (1986) A model based evaluation of a commonly used rule of thumb for warehouse layout. Applied Mathematical Modelling, 10(2): 133-138
Matthias, O., Fouweather, I., Gregory, I., Vernon, A. (2017) Making sense of big data: Can it transform operations management?. International Journal of Operations & Production Management, 37(1): 37-55
Mineo, A.M., Plaia, A. (1998) Statistical Multivariate Techniques for the Stock Location Assignment Problem. u: Rizzi A., et al. [ur.] Advances in Data Science and Classification, Berlin-Heidelberg: Springer, 573-578
Papadopoulos, T., Gunasekaran, A., Dubey, R., Altay, N., Childe, S.J., Wamba, S.F. (2017) The role of Big Data in explaining disaster resilience in supply chains for sustainability. Journal of Cleaner Production, 142: 1108-1118
Pettit, T.J., Croxton, K.L., Fiksel, J. (2013) Ensuring supply chain resilience: Development and implementation of an assessment tool. Journal of business logistics, 34(1): 46-76
Prasad, S., Zakaria, R., Altay, N. (2018) Big data in humanitarian supply chain networks: A resource dependence perspective. Annals of Operations Research, 270(1-2): 383-413
Rushton, A., Croucher, P., Baker, P. (2014) The handbook of logistics and distribution management. Understanding the supply chain. London: Kogan Page
Salah, S., Rahim, A., Carretero, J.A. (2011) Implementation of Lean Six SIGMA (LSS) in supply chain management (SCM): An integrated management philosophy. International Journal of Transitions and Innovation Systems, 1(2): 138-162
Singh, R. (2016) Sales and distribution management: A practice-based approach. Mumbai: Vikas Publishing House
Tiwari, S., Wee, H.M., Daryanto, Y. (2018) Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Computers & Industrial Engineering, 115: 319-330
Waller, M.A., Fawcett, S.E. (2013) Data science, predictive analytics, and big data: A revolution that will transform supply chain design and management. Journal of Business Logistics, 34(2), 77-84
Wamba, S.F., Gunasekaran, A., Dubey, R., Ngai, E.W. (2018) Big data analytics in operations and supply chain management. Annals of Operations Research, Vol. 270 No. 1-2, 1-4
Wamba, S.F., Gunasekaran, A., Papadopoulos, T., Ngai, E.W. (2018) Big data analytics in logistics and supply chain management. International Journal of Logistics Management, 29(2): 478-484
Weberschlager, M. (2013) Die Rolle der Global Distribution Systems (GDS) in der Online-Flugbuchung: Alternative Distributionslösungen und Mobile Commerce als Möglichkeit die Ticketdistribution zu revolutionieren. Munchen: GRIN Verlag
Zezzatti, O., Ochoa, C.A. (2012) Logistics Management and Optimization through Hybrid Artificial Intelligence Systems. Hershey: IGI Global
 

O članku

jezik rada: engleski
vrsta rada: neklasifikovan
DOI: 10.5937/fme2101233L
primljen: 15.08.2020.
prihvaćen: 15.11.2020.
objavljen u SCIndeksu: 20.12.2020.
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