Conventionally, the Supply Chain teams interlock with multiple ecosystems internally and externally. It can be internal users, suppliers, partners, agencies, and customers. As a result, it generates a lot of data points and spreads across the worksheets, folders, and disconnected systems. Making data available and accessible for analysis used to be expensive. With advanced technology usage, all the data points are now available in interconnected systems with limited investment. It addressed the three critical As - Availability, Accessibility, and Affordability enabling businesses to use data to analyze and gain insights.
With 3 A of data addressed by advanced technology, the next important thing to consider is - factors affecting data. 5 Vs.
Volume - size and amount of data - Big, Medium, Small
Value - accuracy, relevence, usefulness
Variety - range-wide, narrow; structure - structured, unstructured data
Velocity - the speed at which data points are generated
Variability - changes like data types - may or may not remain the same.
With 3 As and 5 Vs of data addressed, businesses need to determine the objective of data analytics. To the minimum, they should try to address the below questions.
Which aspect of the Business situation we would like to focus on - WHAT, WHY, WOULD BE, SHOULD BE or All
Do we have the ability to GATHER, ARRANGE, VISUALIZE, ANALYSE, SIMULATE, AND RECOMMEND
What techniques do we have? - Dashboards, Reports, RCA, Fishbone Diagram, What If, Simulation, AI, and ML
Who are the Intended User teams - Operations Teams. Junior, Middle, and Senior Management, Executive leadership
What types of actions do we expect as an outcome - operational, tactical, strategic?
Basis the answers to the above questions, the below types of data analysis should be performed - Descriptive, Diagnostic, Predictive, and Prescriptive.
Descriptive - This type of analysis would cover WHAT aspect of the business situation. It usually refers to past data and provides visibility and views on what happened. It covers data gathering, cleaning, visualizing, and presenting in reports and dashboards. These are useful to Operations teams to drive day-to-day actions.
Diagnostic- This analysis covers the WHY aspect of the Business situation. It usually includes in-depth data analysis and gaining insights using different tools and Techniques. It could be Root Cause Analysis, Cause and Effect Analysis, Fish Bone Diagram, or WH- WHY Analysis. Junior to Middle-level management teams must drive operational and tactical actions using this analysis. It, too, does not cover any future data points.
Predictive - This would address the WOULD BE aspect of the Business situation. In addition to past data and related insights, it covers future data points like demand forecasting, trends, and customer preferences and predicts likely outcomes. It's accomplished through what-if analysis and simulation tools and is commonly used by Middle to Senior management to drive tactical actions mainly. Considering the characteristics of data in terms of volume, values, variety, velocity, and variability, it is not very easy to simulate missions of what-if scenarios and analyze those manually, and it would be a lot of effort.
Prescriptive - With the advancement of Artificial Intelligence and Machine Learning, the limitation of running multiple permutations and combinations simultaneously and choosing the most appropriate one for business to act upon has become possible. This analysis helps address large data sets and values changing at rapid speed and variety with ease and provides recommended scenarios for business. It addresses the SHOULD BE aspect of the Business situation.
In order to maximize the benefits of data analytics for their organization, businesses must first establish clear objectives and expected outcomes. This ensures that resources are allocated efficiently and effectively towards achieving these goals. Without a defined objective, data analytics can become a costly and time-consuming exercise without delivering meaningful insights or value. By setting specific targets and measuring success against these objectives, businesses can ensure that their data analytics initiatives are aligned with their overall strategic objectives and contribute to the organization's bottom line.
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