Engineering patterns of supply chain optimization to manage oscillation effect

  • Received November 29, 2016;
    Accepted January 12, 2017;
    Published June 8, 2017
  • Author(s)
    Thokozani Patmond Mbhele
  • DOI
    http://dx.doi.org/10.21511/ppm.15(2).2017.12
  • Article Info
    Volume 15 2017, Issue #2, pp. 124-139
  • TO CITE АНОТАЦІЯ
  • 1045 Views
  • 669 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

The cascading order variability from downstream trumping up the upstream site of the supply chain network indicates the deleterious effect to the performance of the fast moving consumer goods industry. The fundamental likelihood to optimization in this industry requires dexterous flows of quasi-real-time information, as well as reliable product availability. In this context, this study analyzes the challenges of bullwhip effect on the perspective of ingenious optimization strategies, and further contemplates to establish the engineering patterns of interrelationships on the magnitude of pooling the resources to advance supply chain capabilities. The suppression of bullwhip effect on underlying optimization strategies is sought to elevate accelerated responsiveness, improve network demand visibility and reduce volatility in frequencies to inventory replenishment. A rigorous and disciplined quantitative approach afforded the tentatively development of pattern of interrelated supply chain dimensions. The factor analysis method was used on 448 responses and insightful findings were produced from the compelling purposive sampling technique. The findings indicate that the magnitude of better ameliorating bullwhip effect, the value of competitive economic information and strength of selected optimization strategies depend on the model of unified engineering patterns. This paper provides insights to FMCG industry on using innovative strategies and modern technology to enhance supply chain visibility through integrated systems networks.

view full abstract hide full abstract
    • Figure 1. Global optimization strategies on bullwhip effect – part one
    • Figure 2. Global Optimization strategies on bullwhip effect – part two
    • Figure 3. Engineering circular patterns of supply chain optimization strategies and leagility system
    • Table 1. Descriptive and factor analysis on KMO and Bartlett’s test rotated components
    • Bartlett, J. E., II, Kotrlik, J. W., and Higgins, C. (2001). Organisational research: Determining appropriate sample size for survey research. Information Technology, Learning and Performance Journal, 19(1), 43-50.
    • Blumberg, B., Cooper, D. R., and Schindler, P. S. (2008). Business Research Methods. New York: McGraw-Hill International.
    • Borgatii, S. P., and Li, H. (2009). On social network analysis in a supply chain. Journal of Supply Chain Management, 45(2), 5-22.
    • Bowersox, D. J., Closs, D. J., Cooper, M. B., and Bowersox, J. C. (2013). Supply Chain Logistics Management. (4th Ed.). Boston: McGraw-Hill.
    • Browne, K. (2010). Trolley Psychology: Choice unlocks the psychological secrets of the supermarket and shows you how to avoid spending more than you mean to. Australian Consumer’s Association Choice Magazine, 4(60).
    • Burt, R. S. (1992). Structural Holes. Cambridge, M. A.: Harvard University Press.
    • Burt, R. S. (1997). The contingent value of social capital. Administration Science Quartley, 42(2), 339-365.
    • Cachon, G., and Terwiesch, C. (2009). Matching supply with Demand: An introduction to Operations Management. Boston: McGraw-Hill Irwin.
    • Cai, X., and Du, D. (2009). On the Effects of Risks Pooling in Supply Chain Management: Review and Extensions. Acta Methematic Applicatae, 25(4), 709-722.
    • Cao, M., and Zhang, Q. (2011). Supply chain collaboration: Impact on collaborative advantage and firm performance. Journal of Operations Management, 29, 163-180.
    • Cardi, M., Moretto, A., Perego, A., and Tumino, A. (2014). The benefits of supply chain visibility: A value assessment model. 151, 1-19.
    • Chang, T., Fu, H., Lee, W., Lin, Y., and Hsueh, H. (2007). A study of an augmented CPFR model for the 3C retail industry. Supply Chain Management: An International Journal, 12(3), 200-209.
    • Chen, I. J., Paulraj, A., and Lado, A. (2004). Strategic purchasing, supply management, and performance. Journal of Operations Management, 22(5), 505-523.
    • Chen, L. (2013). Dynamic supply chain coordination under consignment and vendor-managed inventory in retailer-centric B2B electronic markets. Industrial Marketing Management, 42, 518-531.
    • Chen, T. H., and Chang, H. M. (2010). Optimal ordering and pricing policies for deteriorating items in one-vendor multi-vendor supply chain. International Journal of Advanced Manufacturing Technology, 49(1-4), 341-355.
    • Cho, D. W., and Lee, Y. H. (2011). The value of information sharing in a supply chain with a seasonal demand process. Computers and Industrial Engineering. http://dx.doi.org/10.1016/j. cie.2011.12.004
    • Choi, T. M., and Sethi, S. (2010). Innovative quick response programs: A review. International Journal of Production Economics, 127(1), 1-12.
    • Chopra, S., and Meindl, P. (2007). Supply chain management: Strategy, planning and operations. (3rd Ed.). New Jersey: Pearson International edition.
    • Christopher, M., and Towill, D. R. (2005). An integrated model for the Design of Agile Supply Chains. International Journal of supply chain management, 6(5).
    • Christopher, M. (2011). Logistics and Supply chain management. (4th Ed.). Boston: Pearson Publicashing.
    • Claro, D. P. (2004). Managing Business Networks and the Buyer- Supplier Relationships: How Information Obtained from the Business Network Affects Trust, Transaction Specific Investments, Collaboration and Performance in the Dutch Potted Plant Industry.
    • Cooper, B. R., and Schindler, P. S. (2008). Business Research Methods. (10th Ed.). New York: McGraw- Hill International.
    • Costello, A. B., and Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research and Evaluation, 10(7), 1-9.
    • Dominguez, R., Cannella, S., and Framinan, J. M. (2014). On bullwhip-limiting strategies in divergent supply chain networks. Computers and Industrial Engineering, 73, 85-95.
    • Fitzsimmons, J. A., and Fitzsimmons, M. J. (2006). Service Management: Operations, Strategy, Information Technology. (5th Ed.), Boston: McGraw-Hill.
    • Flynn, B. B., Huo, B., and Zhao, X. (2010). The impact of Supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management, 28(1), 58-71.
    • Fu, D., Ionescu, C. M., Aghezzaf, E., and Keyser, R. D. (2014). Decentralised and centralised model predictive control to reduce the bullwhip effect in supply chain management. Computers and Industrial Engineering, 73, 21-31.
    • Gajanayake, R., Gajanayake, S., and Surangi, H. (2011). The impact of selected visual merchandising techniques on patronage intentions in supermarkets, International Conference on Business and Economic Research, 1130-1165.
    • Galaskiewicz, J. (2011). Studying supply chains from a social network perspective. Journal of Supply Chain Management, 47(1), 4-8.
    • Ganesan, S., George, M., Jap, S., Palmatier, R. W., and Weitz, B. (2009). Supply Chain Management and Retailer Performance: Emerging Trends, Issues, and Implications for Research and Practice. Journal of Retailing, 85(1), 84-94.
    • Garson, G. D. (2012). Factor Analysis. North Carolina: Statistical Associates Publishing.
    • Gronovetter, M. (2005). The impact of social structure on economic outcomes. Journal of Economic Perspective, 19(1), 33-50.
    • Gulati, R. (1998). Alliances and networks. Strategic Management Journal, 19(4), 293-317.
    • Gunasekaran, A. and Ngai, E. W. T. (2009). Modeling and analysis of build-to-order supply chains. European Journal of Operational Research, 195(2), 319-334.
    • Hair, J. J., Anderson, R. E., Tatham, R. L. and Black, W. C. (1998). Multivariate data analysis. (5th Ed.) New Jersey: Prentice-Hall International.
    • Hair, Jr, J. F., Babin, B., Money, A. H., and Samouel, P. (2003). Essentials of Business Research Methods. New York: John Wiley & Sons, Inc.
    • Heizer, J., and Render, B. (2014). Principles of Operations Management: Sustainability and Supply Chain Management. (9th Ed.), New Jersey: Pearson Education.
    • Hopp, W. J., and Spearman, M. L. (2008). Factory Physics. (3rd Ed.). Boston: McGraw-Hill International Edition.
    • Kaiser, H. F. (1970). A second generation Little Jiffy. Psychometrika, 35, 401-415.
    • Kang, J. H., and Kim, Y. D. (2012). Inventory control in a two-level supply chain with risk pooling effect. International Journal of Production Economics, 135(1), 116-124.
Related Articles

  • Inventory management, cost of capital and firm performance: evidence from manufacturing firms in Jordan

    Ashraf Mohammad Salem AlrjoubORCID , Muhannad Akram Ahmad doi: http://dx.doi.org/10.21511/imfi.14(3).2017.01
    Investment Management and Financial Innovations Volume 14, 2017         Issue #3         pp. 4-14 Views: 2854 Downloads: 2081 TO CITE АНОТАЦІЯ

    Several studies have examined the relationship between inventory management and firm performance. However, most of these studies ignore the impact of inventory types on the relationship. Moreover, the relationship is influenced by some factors such as cost of capital which has not been considered. This study examines the moderating effect of cost of capital on the relationship between inventory types and firm performance. The data of 48 firms for the period 2010-2016 which formed 279 firm-year observations were used in this study. With the use of Pearson correlation and panel Generalized Method of Moments (GMM) estimation, the findings show that inventory management with consideration of its types influence firm performance in the long term. In addition, it is also found that cost of capital moderates the relationship between inventory management and firm performance. However, the interaction between cost of capital and inventory types has different implications. It is suggested that firms should consider cost of capital when making decision on inventory types and align their inventory control to fit in to the changes in their business environment.

  • The influence of the central warehouse on the functioning of Polish small and medium-sized enterprises operating in group purchasing organizations

    Grzegorz ZimonORCID Researcher ID doi: http://dx.doi.org/10.21511/ppm.18(1).2020.03
    Problems and Perspectives in Management Volume 18, 2020         Issue #1         pp. 26-32 Views: 940 Downloads: 205 TO CITE АНОТАЦІЯ

    Growing competition in the market forces primarily small and medium enterprises (SMEs) to implement certain mechanisms that will allow them to build an advantage over the competition. Most often, SMEs try to work together as part of multi-stakeholder organizations and organize themselves into group purchasing organizations (GPOs) with the central warehouse to improve the financial state of enterprises. The article aims to assess and analyze the impact of the central warehouse on the performance of Polish SMEs operating in group purchasing organizations. The research group comprised of 172 trading enterprises operating in Polish group purchasing organizations. The investigated companies were divided into two groups. In the first group, there were companies operating within GPOs, which have a central warehouse. In the second group, there were companies in which central unit does not have a central warehouse. The analysis and research showed that the central warehouse has a positive impact on financial security of small and medium enterprises operating in group purchasing organizations. The use of central warehouse optimizes the most costly current assets component – inventories. This is confirmed by better results of inventory turnover ratios in days, reduced share of inventories in the structure of current assets, and optimization of financial liquidity ratios.

  • Influence of collateral and age on corporate capital structure

    Wanderson Heiderich Lizardo da SilvaORCID , Michele Nascimento JucáORCID Researcher ID, Anderson Luís Saber CamposORCID , Eli Hadad JúniorORCID doi: http://dx.doi.org/10.21511/imfi.16(4).2019.11
    Investment Management and Financial Innovations Volume 16, 2019         Issue #4         pp. 123-132 Views: 852 Downloads: 199 TO CITE АНОТАЦІЯ

    Being financed by third-party capital requires the companies to put up collateral or assets in guarantee, consisting of real estate, inventories, and accounts receivable that in turn depend on specific life cycles, among other aspects. The main object of this study is to analyze whether corporate debt is related to age and collateral. To do so, a sample of 194 public and private Brazilian companies was studied between 2010 and 2017. The findings indicate that more mature businesses have lower debt levels. In terms of the collateral variable and interactions between collateral and age, a negative relation was noted with financial leverage, contrary to what was expected. This fact indicates a possible lack of quality in collateral over time. Furthermore, it is noted that there is no directly proportional relationship between progression in age and collateral. The contribution of the study consists of analyzing the relationships between collateral and age in terms of the debt levels of public and private Brazilian businesses. The distinctions between these groups may throw light on organizations in the emerging countries in terms of how to handle financing decisions with financial and capital market institutions.

Download Preview
Close
Problems and Perspectives in Management
Problems and Perspectives in Management