Logistics and Supply Chain Management: Importance of Quantitative Data Analysis in Decision Making - Report Example

LOGISTICS AND SUPPLY CHAIN MANAGEMENT: IMPORTANCE OF QUANTITATIVE DATA ANALYSIS IN DECISION MAKING Student’s Name Presented to Instructor Course Date Logistics and Supply Chain Management: Importance of Quantitative Data Analysis in Decision Making Nowadays, the world of business has become highly competitive considering the rapid development of information Communication and Technology and the resultant globalization. This has posed a lot of challenges to majority of enterprises especially those involved in logistics and supply chain management. Reason being for such enterprises, logistics and supply chain management is entirely the basis upon which value for both the suppliers and customers of an enterprise is created. In addition, the value is expressed in terms of place and time (Niemi et al., 2004). It is worth noting that for every business services and products have little or no value unless they are in possession of customers when (time) and where (place) they wish to consume them. In a broader perspective, this can be incorporated into supply chain management. As such, it would therefore result into integration of crucial business processes form the customers through initial suppliers in order to provide information, services and products that add value to both the stakeholders and end users (Niemi et al., 2007). In concurrence, Toivonene et al (2006) notes that it is highly important for an enterprise to ensure there is proper management of both its logistics and supply chain. This comes in handy to ensuring effective competition on the global market. Nevertheless, logistics and supply chain managers usually make decisions based on information/data that can either be historical or present. This necessitates them to effectively determine what activities to employ within the system in order to handle materials and information flow that will improve the value of the end users as well as other stakeholders. Therefore, it is essential for a logistics manager to decide on what methods of data quantitative analysis and the ways in which such data can be obtained. Reason being it facilitates in decision making in order to enhance the overall performance of the supply chain management within an organization. In most cases, logistics managers makes their decisions based on past audits and report of regular enterprise performance in which case such information is generated throughout conventional business operations (Toivonen et al, 2006). As earlier mentioned, globalization of business activities has broadened and diversified logistics and supply chain operations. This has brought about numerous challenges because of the resultant extreme uncertainties and complexities. On the other hand, the multiplicity of international decision making environments has rendered the control and planning of logistics and supply chain processes especially in the case of multinationals to become complex. In addition, the stiff global competition has also caused the management of logistics and supply chain to become complex. This puts logistics managers in a situation where a rather holistic image of productivity of the supply chain and in particular distribution to become more important. Reason being distribution process which can as well be referred to as outbound logistics or supply chain is crucial in guaranteeing right products are available in right quantity, through right condition, at the right place, the right time, for the right end user and at the right cost. In this regard, the most efficient way of coping with such decision making dilemmas is to make use of a wide-integrated decision making system that links together quantitative and information/data from various sources collected using quantitative research approaches (Wu & Barnes, 2011). Consequently, although there is large amount of information and data available through quantitative research approaches, raw information and data is however not enough to facilitate in decision making. Such data may be in relation to inventory, products and services being offered as well as procurement schedules. In that case, the enormity of such information/data would render it difficult to determining and realizing the interdependencies of various performance factors that affects existing situations within the logistics and supply chain management. On the other hand, failure to perform analysis of the data makes it difficult to understanding the effects of a change on one factor on another. Furthermore, it is quite impossible to assess such correlated effects without having capacity to visualize and simulate different situations based on the available quantitative data. It is at such instances that statistical research methodologies for instance measuring the central tendencies, variations and standard deviations comes in handy. In evaluating performance factors, quantitative data analysis enables in inclusion of all factors so as to bring out clearer picture of the whole situation (Chen & Paulraj, 2004). In most cases, during data analysis, it is not possible to recognize relevant information within the flow of data, to visualize and as well be able to perform quick analysis in order to enable proper decision making. In that case the quantitative methodologies for instance time series and regression enable sin discovering and applying current data/information in a problem solving environment. It is important to note that most of quantitative data is collected via case studies that are carried out through employment of interviews and questionnaires. As such most data is not numerical and when it is raw it cannot be relied upon to make decisions. Besides, quite a big percentage of information collected in the context of logistics and supply chain management is rather in textual or verbal format. Through employment of various quantitative data collection methods, information in text format is applied to strengthen the numerical information so as to reach at a favourable overall understanding which is dependent on several diverse points of view (Christou, 2012). Consequently, decision making in supply chain management is quite important in order to attain both value advantage as well as competitive edge via key competencies. In nowadays business environment, there are rapid changes in terms of how various operations are done thereby introducing a lot of stiff competition. In this case there is need for businesses to reduce the cost of operations so as to remain competitive. On the other hand, it is as well important for an enterprise to develop its own key competencies to enable it distinguish from other competitors within the market and set standards. To achieve this, it is essential for management within logistics and supply chain to institute policies for creating a competitive edge. This requires efficient data analysis and quantitative research approaches comes in handy. Achieving competitive advantage also calls for diversion of resources so as to enable enterprise to focus on operations it does best and also outsource task and/or processes that are not important to its holistic objective. Employment of effective quantitative data analysis techniques in logistics and supply chain management enables an enterprise to reconsider its entire restructure and operations so as to focus on key competencies and outsource processes which are not within its key competencies. This is only achievable through conducting research in the market so as to establish the response of stakeholders and end-users. This is the only way in which an enterprise can survive in the existing competitive market. The decision to apply quantitative data analysis in supply chain management does not only impact positively on a company’s market positioning but as well in strategic decision making so as to chooses the right manpower, resources and partners (Christou, 2012; Dubois et al., 2004). As earlier mentioned, value advantage is key in logistics and supply chain management. Reason being it as well enables businesses to attain productivity advantage in a highly competitive market thereby creating a low cost profile. Besides, value advantage brings about a differential plus over competitive offerings. Therefore, by maximizing addition of value and reducing on cost simultaneously, it is possible to add more innovation on both the processes and products. Though efficient logistics and supply chain management it is possible to enhance productivity advantage and as well customize products and/or services being offered. On the other hand, improvement of product life cycles also requires efficient supply chain management. As such, value advantage makes a paradigm shift on the aspect of traditional offerings that majorly advocates for one-size-fits-all. In this context, quantitative data analysis in supply chain management facilitates in establishing variety of acceptable offerings by the business to the consumers. As such, it becomes easier to realize the various products to be catered for in distinct market segments as well as preferences of the clientele base. In this context, conducting an evaluation of the supply chain management facilitates in determining what are the value added activities and those that are not. Those activities that do not add value as well correlated elements are eliminated during decision making process. A case in point is in reference to activities such as overproduction, excessive delays, over processing, unnecessary transport, unnecessary movement and excess inventory among others. This comes in handy to ensuring a leaner logistics and supply chain management which is more flexible and capable of meeting the demands of both the customers and stakeholders (Ghiani et al., 2004). In respect to the above, the ability to plan resources efficiently and forecast various parameters supply chain management is imperative so as to ensure optimal performance. The capacity to assess and predict is of the key aspect that quantitative data analysis provides in decision making process in the context of logistics and supply chain management. this has always been the major strength of relying of quantitative data and other than just facilitating to predict scenarios within the supply chain, it as well convinces the decision makers during the decision making process. In today’s supply chain management, the capacity to accurately forecast medium-term and near-term future events for instance demands of new and existing products is one among most of the key capacities of supply chain management. This goes a long way to enabling in planning the logistics in order to ensure consistent supply of products and/or services. Consequently, forecasting techniques may be economic in nature whereby they the p measures and predicts macro-economic quantities that may in future affect supply chain management. Such macroeconomic quantities are for instance foreign exchange rates, money supply, inflation rates and business cycle. All this come in handy to facilitate in effective planning within logistics and supply chain management so as to ensure that an enterprise attains competitive advantage over other competitors within the industry. In this regard, apart from the emphasis that can be placed on pull-based production models within the production chain, effective data analysis and forecasting still stands out as an invaluable tool foe enabling proper planning of an activities within supply chain. In this regard, some of the key successful analysis and forecasting techniques that can be employed in supply chain and management in the context of supply chain management are statistical quantitative methodologies; these include time-series analysis that are used in an attempt to narrow down an issue into a number of various components for instance cycles, trends and seasonality in order to accurately determine them. Besides, other includes quantitative analytical techniques such as simple linear regression and multiple linear regressions (Christou, 2012). In summary, as described in the above argumentative essay, it is clearly evident that logistics and supply chain requires precise data and or information in order to make decisions. This is putting into consideration the key role that both plays in ensuring efficient delivery or products and services so as to ensure satisfaction of the customers and the stakeholders. As such, deciding on what data to use and how it can be obtained is imperative if an enterprise aims at cutting out a niche in the highly competitive global market. On the other hand, it is with noting that with the rapid technological changes, there is increased complexity in logistics and supply chain management and this may hinder achievement of competitive edge of any enterprise. Therefore, it is for that reason in which case decision makers are supposed to institute quantitative data measurement and forecasting techniques so as to be able study the changing business climate. Through employment of such strategies, it is possible to effectively and efficiently manage various issues by defining, measuring and analysing them. Issues that are found not to add value or contribute towards achievement of competitive advantage are eliminated. Reason being employment of lean strategies in logistics and supply management come s in handy to reducing costs and enhancing the type of service offered to customers. Therefore, it is important for decision makers within supply chain management to decide on the type of and how data has been collected. Bibliography Chen, I.J. & Paulraj, A., 2004. Towards a theory of supply chain management: the constructs and measurements. Journal of Operations Management, 10(2), pp. 27-39. Christou, I. T., 2012. Quantitative Methods in Supply Chain Management: Models and algorithms. London: Springer. Dubois, A., Hulthen, K. & Pedersen, A.C., 2004. Supply chain and interdependence: a theoretical analysis. European Journal of Purchasing & Supply Management, 10, pp. 3-9. Ghiani, G,, Laporte, G. & Musmanno, R., 2004. Introduction to logistics systems planning and control. Chicester: Wiley. Niemi, P., Pekkanen, P., Huiskonen, J., 2007. Improving the impact of quantitative analysis on supply chain policy making. Int. J. Production Economics, 108, pp.165–175. Niemi, P., Pekkanen, P., Huiskonen, J., 2004. Understanding the strategic supply chain decision-making—when solving a model is not enough. Proceedings of the EUROMA 2004, France. Toivonen, J., Kleemola, A., Vanharanta, H. & Visa, A., 2006. Improving logistical decision making—applications for analyzing qualitative and quantitative information. Journal of Purchasing & Supply Management, 12, pp. 123–134. Wu, C. & Barnes, D., 2011. A literature review of decision-making models and approaches for partner selection in agile supply chains. Journal of Purchasing & Supply Management, 17, pp. 256–274. Read More