Digitalisation of Supply Chain Management System for Customer Quality Service Improvement.
More details
Hide details
Faculty of Science and Technology, Institute of Computer Science, University of Tartu, Estonia
Institute of Logistics, TTK UAS, Estonia
Institute of Engineering and Circular Economy, TTK UAS, Estonia
Department of Mechanical and Industrial Engineering, TalTech, Estonia
Management Leadership & Organisations, Middlesex university, United Kingdom
Submission date: 2022-02-03
Final revision date: 2022-03-27
Acceptance date: 2022-03-29
Online publication date: 2022-04-05
Corresponding author
Eduard Shevtshenko   

Faculty of Science and Technology, Institute of Computer Science, University of Tartu, Estonia
Journal of Machine Engineering 2022;22(3):78-90
The main idea of the current research is to apply customer satisfaction level Key Performance Indicators (KPIs) for supply chain reliability improvement. The Supply Chain Operations Reference (SCOR) model based KPI metrics enable to increase the quality of product/service by monitoring, visualisation and further digitalisation of directly involved processes. In the long run, the solution will ultimately help to reduce/eliminate the number of customer reclamations in the supply chain. Industry-oriented performance measurement model based on SCOR can be easily adapted for different sectors. Approach proposed in the current research based on identification key factors of supply chain performance of SCOR model connected with predictive and diagnostic capability of Bayesian Belive Networks (BBN). The difference in performance can be reached via applying the best practices to processes, affecting the performance in larger scale.
MEDINI K., BOUREY J.P, 2012, SCOR-Based Enterprise Architecture Methodology, International Journal of Computer Integrated Manufacturing, 25/7, 594–607, DOI: 10.1080/0951192X.2011.646312.
HUGOS M., 2018, Key Concepts of Supply Chain Management, John Wiley & Sons, Inc. DOI:10.1002/9781119464495.
HANDFIELD R.B, NICHOLS E.L, 1999, Introduction to Supply Chain Management, Englewood Cliffs, Prentice-Hall,.
LIMA-JUNIOR F.R, CARPINETTI L.C.R, 2020, An Adaptive Network-Based Fuzzy Inference System to Supply Chain Performance Evaluation Based on SCOR Metrics, Computers & Industrial Engineering, 139, 106191.
GUNASEKARAN A., PATEL C., RONALD E., McGAUGHEY R.E., 2004, A Framework for Supply Chain Performance Measurement, Int. J. Production Economics, 87/3, 333–347.
TAGHIZADEH H., 2012, The Investigation of Supply Chain's The Investigation of Supply Chain's Reliability Measure: A Case Study, Journal of Industrial Engineering International, 8/1, 22–30, DOI:10.1186/2251-712X-8-22.
Supply Chain Operations Reference Model, 2017, SCOR Version 12.0, APICS.
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.
LEMGHARI R., OKAR C., SARSRI D., 2018, Benefits and Limitations of the SCOR® Model in Automotive Industries, MATEC Web of Conferences, 200, 00019, DOI: 10.1051/matecconf/201820000019.
LIMA-JUNIOR F.R,, CARPINETTI LC.R,, 2019, Predicting Supply Chain Performance Based on SCOR® Metrics and Multilayer Perceptron Neural Networks, International Journal of Production Economics, 212, 19–38, DOI: 10.1016/j.ijpe.2019.02.001.
AKKAWUTTIWANICH P., YENRADEE P., 2018, Fuzzy QFD Approach for Managing SCOR Performance Indicators, Computers & Industrial Engineering, 122, 189–201, DOI: 10.1016/j.cie.2018.05.044.
OJHA R., GHADGE A., TIWARI M.K, BITITCI U.S., 2018, Bayesian Network Modelling for Supply Chain Risk Propagation, International Journal of Production Research, 56/17, 5795–5819, DOI: 10.1080/00207543.2018. 1467059.
ABOLGHASEMI M., KHODAKARAMI V., TEHRANIFARD H., 2015, A New Approach for Supply Chain Risk Management: Mapping SCOR Into Bayesian Network, Journal of Industrial Engineering and Management, 8/1, DOI: 10.3926/jiem.1281.
DANIEL D., ISWARANI W.P., PANDE S., RIETVELD L., 2020, A Bayesian Belief Network Model to Link Sanitary Inspection Data to Drinking Water Quality in a Medium Resource Setting in Rural Indonesia, Scientific Reports, 10/1, 18867, DOI: 10.1038/s41598-020-75827-7.
SIMSEK S., DAG A., TIAHRT T., OZTEKIN A., 2021, A Bayesian Belief Network-Based Probabilistic Mechanism to Determine Patient No-Show Risk Categories, Omega, 100, 102296, DOI: 10.1016/
VAIRO T., LECCA M., TROVATORE E., REVERBERi A.P., FABIANO B., 2019, A Bayesian Belief Network for Local Air Quality Forecasting, Chemical Engineering Transactions, 74, 271–276, DOI: 10.3303/CET1974046.
JULIAN M., MÜLLER J.M., 2019, Contributions of Industry 4.0 to Quality Management – A SCOR Perspective, 9th IFAC Conference on Manufacturing Modelling, Management and Control MIM, Berlin, Germany.
JOTHIMANI D., SARMA S.P., 2014, Supply Chain Performance Measurement for Third Party Logistics, International Journal, 21/6, 944–963, DOI: 10.1108/BIJ-09-2012-0064.
HAJ SHIRMOHAMMADI A., 2002, Programming Maintenance and Repair, Technical Management in Industry, 8th edn, Ghazal Publishers, Esfahan.
TAGHIZADEH H., 2012, The Investigation of Supply Chain's Reliability Measure: a Case Study, Journal of Industrial Engineering International, 8, 22. DOI:10.1186/2251-712X-8-22.
BOZARTH C., HANDFIELD R.B., 2007, Introduction to Operations and Supply Chain Management, 2nd edn. Prentice Hall, New Jersey.
XUJIE L, 2009, Modeling and Analyzing Supply Chain Reliability by Different Effects of Failure Nodes, International Conference on Information Management, Innovation Management and Industrial Engineering, 4, 396–400.
HWANG Y-D., LIN Y-C., LYU J.R.J., 2008, The Performance Evaluation of SCOR Sourcing Process—The Case Study of Taiwan's TFT-LCD Industry, International Journal of Production Economics, 115/2, 411–423. DOI: 10.1016/j.ijpe.2007.09.014.
FISHER M.L., 1997, What is the Right Supply Chain for Your Product? Harvard Business Review, 75, 105–117.
BRAUNSCHEIDEL M.J., SURESH N.C., 2009, The Organizational Antecedents of a Firm's Supply Chain Agility for Risk Mitigation and Response, Journal of Operations Management, 27/2, 119–140.
SWAFFORD P.M., GHOSH S., MURTHY N., 2006, The Antecedents of Supply Chain Agility of a Firm: Scale Development and Model Testing, Journal of Operations Management, 24/2, 170–188.
CHRISTOPHER M., TOWILL D., 2001, An Integrated Model for the Design of Agile Supply Chains, International Journal of Physical Distribution & Logistics Management, 31/4, 235–246.
SHAW N.E., et al., 2005, Supply Chain Agility: the Influence of Industry Culture on Asset Capabilities within Capital Intensive Industries, International Journal of Production Research, 43/16, 3497–3516.
ZIAEI S., NORFIAN ALIFIAH M., ASTANEH S., 2015, The Mediating Effect of Supply Chain Agility on the Relationship Between SCOR Business Analytic Solution and Supply Chain Performance, American Journal of Business, Economics and Management, 3/4, 171–176.
MAHMOOD K., SHEVTSHENKO E., 2015, Analysis of Machine Production Processes by Risk Assessment Approach, Journal of Machine Engineering, 2015, 15/1, 112–124.
POLYANTCHIKOV I., SHEVTSHENKO E., KRAMARENKO S., 2010, Fractal Management Approach for the Manufacturing Projects in the Collaborative Networks of SME-SI, Journal of Machine Engineering, 4/9, 81–93.
AYYILDIZ E., GUMUS A.T., 2021, Interval-valued Pythagorean fuzzy AHPmethod-Based Supply Chain Performance Evaluation by a New Extension of SCOR Model: SCOR 4.0, Complex & Intelligent Systems, DOI: 10.1007/s40747-020-00221-9.
LEBAS M.J., 1995, Performance Measurement and Performance Management, Int. J. Prod. Econ. 41/1–3, DOI: 10.1016/0925-5273(95)00081-X.
CHAE B., 2009, Developing Key Performance Indicators for Supply Chain: an Industry Perspective, Supply Chain Manag 14/6, DOI: 10.1108/13598540910995192.
NEAPOLITAN R.E., 2003, Learning Bayesian Networks, Prentice Hall.
STEPHAN J., BADR Y., 2007, A Quantitative and Qualitative Approach to Manage Risks in the Supply Chain Operations Reference, 2nd International Conference on Digital Information Management, 1, 410–417.
SUNDER A., 2021, A Framework to Model Site Reliability Engineering Implementations and its Consolidation, Global Journal of Computer Science and Technology, 21/1-G, DOI: 10. 34257/GJCSTGVOL21IS1PG23.
MURUMAA L., SHEVTSHENKO E., KARAULOVA T., MAHMOOD K., POPELL J., 2021, Supply Chain Digitalisation Framework for Servive/Product Satisfaction, Modern Materials and Manufacturing, IOP Conference Series, Materials Science and Engineering; Bristol 1140, 012041, DOI:10.1088/1757-99X/1140/ 1/012041.
Journals System - logo
Scroll to top