Decision Making in Supply Chain Samples †MyAssignmenthelp.com

Question: Discuss about the Decision Making in Supply Chain. Answer: Introduction Supply chain comprises of an integral functionality within organisations, which deal in variety of products(Vercellis, 2011). Organisations derive tremendous synergies from integration of supply chain functionalities. Supply chain can become value-chain for the organisation generating more revenues and opportunities for it. With globalisation and technological advent, supply chain has increasingly become an integral functionality that provide competitive advantages to the organisation. However, functionalities involving the supply chain is increasingly complex due to presence of large number of functions and participants in it. There needs to be tremendous transfer of information throughout the chain of supply chain such that it can function in an appropriate manner. At every step of information flow there is decision involvement that allows organisation to select amongst diversified strategies, costs, prices, tools and so on. Technological advancement has led to integration of RFID (radio frequency identification devices) into supply chain to enhance its efficiency and effectiveness. Hence, it can be identified that there are a number of decision making criterias that are involved in supply chain framework. The scope of this essay examines from various literatures, decision making with business intelligence criterias that are integrated into supply chain with analysis of the same(Popovi?, 2012). In the end certain recommendations are provided that can allow catering to effectiveness and efficient decision making as being a form of business intelligence into supply chain. Statement of Problem Organisations are faced with diversified range of challenges in the domain of decision making in supply chain. Business intelligence framework developed in recent years has made tremendous contributions to decision making in supply chain. This essay examines key challenges related to decision making in supply chain. Literature Review Business Intelligence has created immense impact in various domain of business functionality. Especially in the domain of supply chain business intelligence scope extends to multiple domain also allowing for development of decision making(Ballou, 2007). This literature review has incorporated and evaluated pertinent journals from business intelligence framework to understand challenges faced in the domain related to supply chain. BI in supply chain framework allows for identification of computing technologies for analysis and discovery of business supply chain related data such as inventory levels, production pave, manufacturing capabilities and so on, that can driver profitability. Z. R. Jourdan (2008) article, Business intelligence: An analysis of the literature 1. In the journal Information Systems Management, pages 121 to 131(Jourdan, 2008). The scope of this article identifies scope related to BI that can be applied in decision making in supply chain that can drive processes. As entire services related to supply chain in connected with customer delivery of products, demand forecast is an integral BI tool that are used by companies. Companies integrate supply chain BI tool for creating imapct on their seamless array of data that is available to them from warehouse management systems (WMS), TMS along with supply chain execution systems. M. Olszak (2007) article, Approach to building and implementing business intelligence systems. In the journal Interdisciplinary Journal of Information, Knowledge Management, page 2(Olszak, 2007). This article integrates ways in which companies are able to turn their integral data into key information which can be effectively be used by them. BI tools in decsion makign support for supply chain can be divided into three categories as reporting, real-time dashboards and benchmarking. Reporting is an integral functionality that allows the business to track its development and growth through various processes by obtaining data regarding Key Performance Indicators (KPIs) from the market. On-time delivery, customer acceptance rates, meeting committed capacity are all integral in makign crucial decisions in the supply chain framework(Turban, 2011). Real-time dashboards on the other hand allows interractive overview of daily happenings in transport, warehouse and other facilities that are re lated to supply chain. Benchmarking is another crucial functionality that weights companys performance against market scenarios regarding on-time deliveries, customer satisfaction rates, freigth rates and so on. Such comformance to standards allows benchmarking of the company allowing higher performance. M. Trkman (2010) article, The impact of business analytics on supply chain performance. In the journal Decision Support Systems, pages 318 to 327(Trkman, 2010). This article analyses potential of BI to contribute in the doamin of supply chain in various fields as transportation, warehousing, deliveries and so on. Through integration of BI certain key functions in supply chain can greatly be enhanced and incorporated. BI has capability to analyse smallest of mistakes in supply chain functionalities by integrating in lean logistics methodology. Application of BI tools and techniques in supply chain management framework is discussed in relation to supply chain management. An additional advantage from research in supply chain that has been added to BI proceses includes RFID tool. RFID tool acts as an additional BI tool providing data flow, which are further used for analysis at every point in supply chain processes. A.Waller (2013) article, Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. In the Journal of Business Logistics, pages 77 to 84(Waller, 2013). The scope of this article reviews decision criterias through integration of BI into supply chain network. BI allows better decision by analysis of data, optimising performance with respect to various systems. Thus, BI tool is integral in case management wants to extend its capabilities with respect to supply chain decision making capabilities. There are wide number of journals that provides relevant insights into concpets and theories of BI that can be used in supply chain processes in decision making. The main functionality however prevails is to create a dynamic response for each step of the movemnent for the product. A challenge in respect to integrating BI for catering to decision making criteria in business is its capability to proces information. Each type of organisati on makes use of its own BI tool for generating effectiveness in its supply chain procedure. While goal for such integration is crucial to develop competency within the industry, its procedure still remains to be a challenge. Method of application of BI tools in business highly vary and differ across various domain of businesses. Hence, businesses needs to learn from their competitors regarding the various processes in BI they have integrated. Such application will allow creation of core competency and brand development in the market. Thus, BI tool is integral to success of supply chain processes. Research Gap and Critical Analysis The analysis of literatures and analysis related to business intelligence with its application in supply chain framework has been conducted in the previous section. In spite of thorough evaluation of various literatures there remains a pertinent gap in research analysis. The research has been conducted with evaluation of literatures from journal articles, hence mostly secondary analysis of data and concepts have been undertaken. Qualitative analysis has been done for the purpose of this study and quantitative analysis has mostly been rejected in this. The research is mostly theoretical in nature and practical related data has not been undertaken for this study. The scope of the study however can easily be extended in the future with further addition of quantitative research and first had data extraction. Business Intelligence is bent optimization of their performance for better decision making in supply chain framework. BI tools are used to create visible transportation, warehousing, inventory and other component integration. Supply chain requires randomness with which components of the each part of the network needs to respond, such fast response creates compatibility to provide system based functionality. While product within an organisation moves form one point to another starting with supplier of raw material, it undergoes transformations. At each stage a value addition is done to the product then it goes to its warehouse or inventory, which stores the product for final delivery to its customers(Sahay, 2008). Products moves from one point to another based on customer demands of such products, hence customer demand forecasting plays an integral in supply chain functionalities. Attaining competency through processes in supply chain is fairly easier now compared to the past. Earlier in absence of BI mechanisms and tools several products used to suffer damage, there had also been incidence of products misplacements and other mishaps. Such delays with products have not only hampered organisational brand name but also deterred expansion of businesses. Earlier instead of BI tools, data mining techniques used to be adopted that led to storing of high volumes of data. Such high volumes of data often led to confusion and misinterpretation, which resulted in nearly no effectiveness. Hence, researchers along with industrialists developed tools that techniques that allowed integrating their components of supply chain processes such that they are easily able to track products. Tracking products has been of foremost importance in this domain of supply chain that creates efficiency of processes. Along with data mining, a tool was required within industries that allowed t hem to make prediction regarding their business processes and outcomes, preventing potential losses. Thus, emergence of BI allowed immense effectiveness to existing systems prevailing in supply chain processes. BI not only gathered and processed data but also provided critical information that could value add to the organisation. Through BI processes, organisations could easily decide what to produce, how much to produce, what quantities to produce and when to produce(Ranjan, 2009). Such critical information was required to ensure organisations success and sustainability for the future. RFID formed an integral tool in SCM processes that let them integrate BI systems to products directly. Now, organisations were capable of ascertaining reasons for failure or success of their various products. They could take more prudent decisions, which were integral especially in fast moving goods. Any type of fast moving industry faces immense threats from extinction of its demand related to parti cular products. Products related to FMCG, fast fashion, trends related businesses are often faced with threats from large volumes of products lying unutilized in their inventory over long periods of time. In these industries specifically information needs to be passed rapidly throughout supply chain such that logistics managers can respond to them fast and make integral decisions related to them. Such response or decisions are not possible in case they are not sourced from authentic and reliable analysis of data from sources. Data from storage are compiled and analysed utilizing BI technologies that have capabilities to recognize key integral information. With BI systems integrated into business systems, an organisation can not only have control over their resources, financial primarily rather they can take useful decisions regarding their inventory. The most useful invention of dynamic responsive supply chain system is lean manufacturing processes, that only triggers production once needed to. Lean manufacturing can deliver efficiency and high profitability to business by reducing amounts of working capital that gets blocked due to inventory. RFID techniques are similar that creates a response system that triggers information once a product gets exhausted. This can allow SCM processes in backward integration. At every point in supply chain a decision has to be made as to whether to transport or hold inventory. SCM processes that integrates BI often includes ERP (Enterprise Resources Planning) systems as well. ERP system can provide information regarding resource facility that are present with the organisation, such that SCM can trigger its procurement. Another integral aspect of SCM is its transportation systems. An ineffective transportation system can claim significant amount of resources and put burden on the business. It is the liability of SCM to plan its transportation management processes as well such that it can function in cost reductions. Transportations for the organisation uses diesel which needs to be optimized such that it does not become burdensome on the business. While every business is aware regarding the multiple benefits that can incur from BI integration into supply chain, there prevails confusion regarding its application capability. A methodology for application of BI tools for obtaining a procedural decision making needs to be devised. Such strategies for integration of decision making capabilities according to industry standards will help create competency and leadership position for the organisations. Recommendations and Conclusion Analysis of framework related to BI integration into SCM processes for decision making can enable development of better and more efficient framework. With globalisation, corporations now needs to reach out to global customer bases with their products hence SCM forms a key to their sustenance. Certain recommendations, which will allow corporations to gain maximum advantage from integration of BI into SCM framework, includes the following; Organisations in order to have a responsive supply chain system needs to integrate BI tools to transform data into information for the organisation. Once data transforms into information and is passed onto layers of supply chain delivery then it can act as useful data for taking integral decisions. Decision making in supply chain is restricted to comprehending analytical data available through BI systems. While BI functionality acts as a key component in providing information, scope related to such information needs to be carefully evaluated prior to their application as they are purely mechanically computed. BI systems might provide information that are integral for taking decisions but it is not able to provide filtered information, which it users needs to. While BI might be a mechanical process, decision support systems are not hence makers of decisions needs to carefully evaluate such data and information prior to arriving at decisions. Customer demand is a highly dynamic field that changes continuously and is affected by a plethora of variables. With new trends and corporations catering to similar products, it might be nearly impossible to arrive at decision regarding customer demand. Thus, in customer demand forecast, decision makers has to carefully evaluate the various variables in connection to past trends prior to instructing their production processes. BI might be capable of generating information from wide variety of data, but its implications has to be attempted physically with such information. Meaning movement of goods in an efficient manner is possible only in case information is passed on rapidly throughout such supply chain systems. An integrated framework of supply chain systems is more effective compared to one that is dependent on external systems. Thus, organisations needs to integrate their crucial functions with respect to their product delivery at each and every point of the supply chain including transportation to derive efficiency from the process. References Ballou, R. H. 2007. Business logistics/supply chain management: planning, organizing, and controlling the supply chain. Pearson Education India. Jourdan, Z. R. 2008. Business intelligence: An analysis of the literature 1. Information Systems Management, 121-131. Olszak, C. M. 2007. Approach to building and implementing business intelligence systems. Interdisciplinary Journal of Information, Knowledge Management, 2. Popovi?, A. H. 2012. Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 729-739. Ranjan, J. 2009. Business intelligence: Concepts, components, techniques and benefits. Journal of Theoretical and Applied Information Technology, 60-70. Sahay, B. S. 2008. Real time business intelligence in supply chain analytics. Information Management Computer Security, 28-48. Trkman, P. M. 2010. The impact of business analytics on supply chain performance. Decision Support Systems, 318-327. Turban, E. S. 2011. Decision support and business intelligence systems. Pearson Education India. Vercellis, C. 2011. Business intelligence: data mining and optimization for decision making. John Wiley Sons. Waller, M. A. 2013. Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management. Journal of Business Logistics, 77-84.