Transforming Big Data into Supply Chain Analytics
2014; Volume: 33; Issue: 4 Linguagem: Inglês
ISSN
1930-126X
Autores Tópico(s)Big Data and Business Intelligence
ResumoEXECUTIVE SUMMARY | The emphasis of this article is how Predictive Analytics/Big Data can be used most productively in managing supply chain. They can be used to determine what happened, why it happened, and to develop a plan for change. Based on pre-defined business rules, they can identify where action is needed, they can help to prepare more accurate forecasts, and, above all, they can help to determine the best course of action with what-if analysis. The article also describes the process to transform successfully the mass of information into analytics to make better decisions in a timely manner.Analytics has been described as finding and using meaningful information in Big Data to improve business performance. Today's information technology systems gather and store a tremendous amount of supply chain related data. To take advantage of this capability, firms must transformdataintobusinessintelligence, including analytics. In supply chain, the ultimate goal is to convert the mass of unstructured into useful analytics that can help to improve service, reduce costs, improve inventory management, and increase profits. In a 2012 SAS-MIT survey with 2,500 respondents from over 20 industries, 67% indicated they are using analytics to improve overall performance.Data mining, the process of extracting information from a set and transforming it into a usable structure, supports analytics. It can be fully automatic using algorithms supported by advanced statistics, math, and software programs, or the process can be interactive, driven by the end user. For example, online analytical processing (OLAP) of multidimensional cubes (e.g., customer, location, and sales) is integrated into advanced planning software to enable reporting, and support aggregation, and drill-down slicing and dicing of data. Operationally, users can develop their own custom analytics; for example, deploying end-user defined filters or rules to find exceptions to a given rule. The data-mining tool may be programmed to do cluster analysis, detect anomalies in the data, or apply association rules.Both analyticsand data mining are growing as buzzwords that are used to describe any large-scale gathering or analysis of data. This article will focus on their application in supply chain management.APPROACHES TO USING ANALYTICSAccording to the MIT-SAS research, about 10% of firms surveyed have experts who have become Innovators who leverage advanced analytics to re-think the business and innovate processes and products. About 60% have progressed to becoming Analytic Practitioners, and 30% are still analytically challenged. In supply chain analytics, practitioners use the information gained to solve problems, improve efficiencies, increase service, and reduce inventories. The types of analytics used in the supply chain management include:1. Descriptive analytics (e.g., reports, KPIs, and dashboards) to report performance, determine what happened, why it happened, and to develop a plan for change.2. Operational level reports based on pre-determined querying logic models and end-user specified queries to improve decisions and identify the need for action.3. Predictive Analytics to improve such processes as forecasting, customer relationship management, and inventory control.4. Basic decision models that use decision logic or business rules help to optimize outputs.The term Big Data refers to the mass of information that is generated every day. In 2012, it was estimated that 2.5 exabytes of were created each day, where 1 exabyte = 1B gigabytes. The amount of available is expected to double every three years. Technology increases availability, enables communication of data, and provides the ability to analyze the information. The firms that can successfully transform this mass of information into analytics can make better decisions, act in a timely manner, and gain a competitive sustainable advantage. …
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