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Cold Chain Excellence through Data Analytics

Every decade or so, the business world invents another term for how it extracts managerial and decision-making value from computerized data. In the 1970s the favored term was decision support system, accurately reflecting the importance of a decision-centered approach to data analysis. In the early ’80s, executive information systems were the preferred nomenclature, which addressed the use of these systems by senior managers. Later in that decade, emphasis shifted to a more technical-sounding online analytical processing (OLAP). The ’90s saw the rise of business intelligence as a descriptor.

It appears, however, that another shift is taking place in the label for how we take advantage of data to make better decisions and manage organizations. The new label is analytics, which began to come into favor in the middle of this century’s first decade at least for the more statistical and mathematical form of data analysis.

Types of Data Analytics

o Descriptive Analytics (What is happening in the business?)
o Diagnostic Analytics (Why did it happen?)
o Predictive Analytics (What will happen?)
o Prescriptive Analytics (What should we do?)

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Jasmine Tan, ADLSM
Jasmine Tan has several years of experiences in the field of procurement and supply management, specifically in the frozen seafood industry. She is a member of the Singapore Institute of Purchasing and Materials Management (SIPMM), and holds the Diploma in Procurement and Supply Management. Jasmine completed the Advanced Diploma in Logistics and Supply Management (ADLSM) on October 2019 at SIPMM Institute.

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