The Mediating Effect of Supply Chain Agility on the Relationship Between SCOR Business Analytic Solution and Supply Chain Performance
At the global competition in the modern era, have different products to suit customer requirements contracts made available to them. As a result, companies can no longer afford to do all alone. In a competitive market, the enterprises and productive addition to the organization and the internal resources, required to manage and monitor resources and associated elements outside the organization. So today is the implementation of supply chain management as one of the foundations of e-business infrastructure around the world. This study with supply chain operation reference (SCOR) model investigated effect of supply chain agility and relationship between business analytic and supply chain performance.
Supply Chain Management, Business Analytic, Supply Chain Performance, Supply Chain Agility
[1]
Christopher, M., Logistics and supply chain management. 2012: Pearson UK.
[2]
Kohavi, R., N.J. Rothleder, and E. Simoudis, Emerging trends in business analytics. Communications of the ACM, 2002. 45(8): p. 45-48.
[3]
Fitriana, R. and T.D. Eriyatno, Progress in Business Intelligence System research: A literature Review. International Journal of Basic & Applied Sciences IJBAS-IJENS, 2011. 11(03): p. 118503-6464.
[4]
Sumathi, S. and S. Sivanandam, Emerging Trends and Applications of Data Mining. Introduction to Data Mining and its Applications, 2006: p. 165-183.
[5]
Trkman, P., et al., The impact of business analytics on supply chain performance. Decision Support Systems, 2010. 49(3): p. 318-327.
[6]
Koutsoukis, N.-S. and G. Mitra, Decision modelling and information systems: The information value chain. Vol. 26. 2003: Springer Science & Business Media.
[7]
Huan, S.H., S.K. Sheoran, and G. Wang, A review and analysis of supply chain operations reference (SCOR) model. Supply Chain Management: An International Journal, 2004. 9(1): p. 23-29.
[8]
Shepherd, C. and H. Günter, Measuring supply chain performance: current research and future directions, in Behavioral Operations in Planning and Scheduling. 2011, Springer. p. 105-121.
[9]
Shang, S. and P.B. Seddon, Assessing and managing the benefits of enterprise systems: the business manager's perspective. Information Systems Journal, 2002. 12(4): p. 271-299.
[10]
Cadez, S. and C. Guilding, An exploratory investigation of an integrated contingency model of strategic management accounting. Accounting, Organizations and Society, 2008. 33(7): p. 836-863.
[11]
Azvine, B., Z. Cui, and D. Nauck, Towards real-time business intelligence. BT Technology Journal, 2005. 23(3): p. 214-225.
[12]
Sahay, B. and J. Ranjan, Real time business intelligence in supply chain analytics. Information Management & Computer Security, 2008. 16(1): p. 28-48.
[13]
Cai, J., et al., Improving supply chain performance management: A systematic approach to analyzing iterative KPI accomplishment. Decision Support Systems, 2009. 46(2): p. 512-521.
[14]
Oliveira, M.P.V.d., K. McCormack, and P. Trkman, Business analytics in supply chains–The contingent effect of business process maturity. Expert Systems with Applications, 2012. 39(5): p. 5488-5498.
[15]
Hedgebeth, D., Data-driven decision making for the enterprise: an overview of business intelligence applications. VINE, 2007. 37(4): p. 414-420.
[16]
Cricelli, L., M. Grimaldi, and M. Hanandi, Decision making in choosing information systems: An empirical study in Jordan. VINE, 2014. 44(2): p. 1-1.
[17]
Hoole, R., Five ways to simplify your supply chain. Supply Chain Management: An International Journal, 2005. 10(1): p. 3-6.
[18]
Pettersson, A.I. and A. Segerstedt, Measuring supply chain cost. International Journal of Production Economics, 2013. 143(2): p. 357-363.
[19]
Khan, R., Business analytics and supply chain performance: An Empirical Perspective. International Journal of Operations and Logistics Management, 2013. 2(3): p. 43-56.
[20]
Li, S. and B. Lin, Accessing information sharing and information quality in supply chain management. Decision support systems, 2006. 42(3): p. 1641-1656.
[21]
Ranjan, J., Business justification with business intelligence. Vine, 2008. 38(4): p. 461-475.
[22]
Ziaei, S.M., Effects of financial development indicators on energy consumption and CO2 emission of European, East Asian and Oceania countries. journal of Renewable & Sustainable Energy Reviews, 2015. 42: p. 752-759.
[23]
Öztayşi, B. and Ö. Sürer, Supply Chain Performance Measurement Using a SCOR Based Fuzzy VIKOR Approach, in Supply Chain Management Under Fuzziness. 2014, Springer. p. 199-224.
[24]
Zachariadis, E.E., C.D. Tarantilis, and C.T. Kiranoudis, A guided tabu search for the vehicle routing problem with two-dimensional loading constraints. European Journal of Operational Research, 2009. 195(3): p. 729-743.
[25]
Bose, I. and R.K. Mahapatra, Business data mining—a machine learning perspective. Information & management, 2001. 39(3): p. 211-225.
[26]
Holsapple, C., A. Lee-Post, and R. Pakath, A Unified Foundation for Business Analytics. Decision Support Systems, 2014.
[27]
Sahay, B., J.N. Gupta, and R. Mohan, Managing supply chains for competitiveness: the Indian scenario. Supply Chain Management: An International Journal, 2006. 11(1): p. 15-24.
[28]
Elbashir, M.Z., P.A. Collier, and M.J. Davern, Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. International Journal of Accounting Information Systems, 2008. 9(3): p. 135-153.
[29]
Ziaei, S.M., Evaluate effects of monetary policy shocks on aggregate demand components in GCC Countries. Journal of developing areas, 2014. 48(1): p. 405-423.
[30]
Stadtler, H., Supply chain management and advanced planning––basics, overview and challenges. European journal of operational research, 2005. 163(3): p. 575-588.
[31]
Burgess, K., P.J. Singh, and R. Koroglu, Supply chain management: a structured literature review and implications for future research. International Journal of Operations & Production Management, 2006. 26(7): p. 703-729.
[32]
Lockamy III, A. and K. McCormack, Linking SCOR planning practices to supply chain performance: An exploratory study. International Journal of Operations & Production Management, 2004. 24(12): p. 1192-1218.
[33]
Ziaei, S.M., Which Is More Effective on Energy Consumption: Energy Tax Rate Or Energy Efficiency. International Journal of Energy, Environment and Economics (IJED), 2014. 21: p. 5-6.
[34]
Kasarda, J.D. and D.A. Rondinelli, Innovative infrastructure for agile manufacturers. Sloan management review, 1998. 39(2): p. 73-82.
[35]
Nagel, R.N. and P. Bhargava, Agility: the ultimate requirement for world‐class manufacturing performance. National Productivity Review, 1994. 13(3): p. 331-340.
[36]
Sharifi, H. and Z. Zhang, Agile manufacturing in practice-Application of a methodology. International Journal of Operations & Production Management, 2001. 21(5/6): p. 772-794.
[37]
Narasimhan, R., M. Swink, and S.W. Kim, Disentangling leanness and agility: an empirical investigation. Journal of Operations Management, 2006. 24(5): p. 440-457.
[38]
Ismail, H., et al., How small and medium enterprises effectively participate in the mass customization game. Engineering Management, IEEE Transactions on, 2007. 54(1): p. 86-97.
[39]
Ismail, H.S., J. Poolton, and H. Sharifi, The role of agile strategic capabilities in achieving resilience in manufacturing-based small companies. International Journal of Production Research, 2011. 49(18): p. 5469-5487.
[40]
Fisher, M.L., What is the right supply chain for your product? Harvard business review, 1997. 75: p. 105-117.
[41]
Braunscheidel, M.J. and N.C. Suresh, The organizational antecedents of a firm’s supply chain agility for risk mitigation and response. Journal of Operations Management, 2009. 27(2): p. 119-140.
[42]
Swafford, P.M., S. Ghosh, and N. Murthy, The antecedents of supply chain agility of a firm: scale development and model testing. Journal of Operations Management, 2006. 24(2): p. 170-188.
[43]
Christopher, M. and D. Towill, An integrated model for the design of agile supply chains. International Journal of Physical Distribution & Logistics Management, 2001. 31(4): p. 235-246.
[44]
Shaw, N., et al., Supply chain agility: the influence of industry culture on asset capabilities within capital intensive industries. International Journal of Production Research, 2005. 43(16): p. 3497-3516.
[45]
Beamon, B.M., Measuring supply chain performance. International Journal of Operations and Production Management, 1999. 19(3): p. 275–292.
[46]
Stewart, G., Supply chain performance benchmarking study reveals keys to supply chain excellence. Logistics Information Management, 1995. 8(2): p. 38-44.
[47]
Gunasekaran, A., C. Patel, and R.E. McGaughey, A framework for supply chain performance measurement. International journal of production economics, 2004. 87(3): p. 333-347.
[48]
Lee, H.L. and C. Billington, Managing supply chain inventory: pitfalls and opportunities. Sloan management review, 1992. 33(3).
[49]
Cooper, M.C., D.M. Lambert, and J.D. Pagh, Supply chain management: more than a new name for logistics. International Journal of Logistics Management, The, 1997. 8(1): p. 1-14.
[50]
Abell, D.F., Competing today while preparing for tomorrow. Sloan Management Review, 1999. 40(3): p. 73-81.
[51]
Gunasekaran, A., C. Patel, and E. Tirtiroglu, Performance measures and metrics in a supply chain environment. International journal of operations & production Management, 2001. 21(1/2): p. 71-87.
[52]
Hudson, M., J. Lean, and P.A. Smart, Improving control through effective performance measurement in SMEs. Production planning & control, 2001. 12(8): p. 804-813.
[53]
Kaplan, R.S. and D.P. Norton, Balanced Scorecard-Strategien erfolgreich umsetzen. 1997.
[54]
Bhagwat, R. and M.K. Sharma, Performance measurement of supply chain management: A balanced scorecard approach. Computers & Industrial Engineering, 2007. 53(1): p. 43-62.
[55]
Tayur, S., R. Ganeshan, and M. Magazine, Quantitative models for supply chain management. Vol. 17. 1999: Springer Science & Business Media.
[56]
Camm, J.D., et al., Blending OR/MS, judgment, and GIS: Restructuring P&G's supply chain. Interfaces, 1997. 27(1): p. 128-142.
[57]
Altiok, T. and R. Ranjan, Multi-stage, pull-type production/inventory systems. IIE transactions, 1995. 27(2): p. 190-200.
[58]
Cohen, M.A. and H.L. Lee, Resource deployment analysis of global manufacturing and distribution networks. Journal of manufacturing and operations management, 1989. 2(2): p. 81-104.
[59]
Lee, H.L., C. Billington, and B. Carter, Hewlett-Packard gains control of inventory and service through design for localization. Interfaces, 1993. 23(4): p. 1-11.
[60]
Ishii, K., K. Takahashi, and R. Muramatsu, Integrated production, inventory and distribution systems. The International Journal Of Production Research, 1988. 26(3): p. 473-482.
[61]
Newhart, D.D., K.L. Stott Jr, and F.J. Vasko, Consolidating product sizes to minimize inventory levels for a multi-stage production and distribution system. Journal of the operational Research Society, 1993: p. 637-644.
[62]
Voudouris, V.T. and A. Consulting, Mathematical programming techniques to debottleneck the supply chain of fine chemical industries. Computers & chemical engineering, 1996. 20: p. S1269-S1274.
[63]
Liang, J., et al., Problem definitions and evaluation criteria for the CEC 2006 special session on constrained real-parameter optimization. Journal of Applied Mechanics, 2006. 41.
[64]
Tavana, M., et al., A new network epsilon-based DEA model for supply chain performance evaluation. Computers & Industrial Engineering, 2013. 66(2): p. 501-513.
[65]
Maskell, B.H., Performance measurement for world class manufacturing: A model for American companies. 1991: Productivity Press.
[66]
Lambert, D.M. and M.C. Cooper, Issues in supply chain management. Industrial marketing management, 2000. 29(1): p. 65-83.
[67]
Jeong, J.S. and P. Hong, Customer orientation and performance outcomes in supply chain management. Journal of Enterprise Information Management, 2007. 20(5): p. 578-594.
[68]
Min, H. and G. Zhou, Supply chain modeling: past, present and future. Computers & Industrial Engineering, 2002. 43(1): p. 231-249.
[69]
Pawlak, M. and E. Malyszek, A local collaboration as the most successful co-ordination scenario in the supply chain. Industrial Management & Data Systems, 2008. 108(1): p. 22-42.
