Data analytics can be a powerful driver for impactful business decisions, particular to consignment inventory levels. Using predictive analysis, we can ascertain future trends in consignment inventory levels to provide an advanced outlook and strong assumptions about future consignment inventory levels. Data analytics also allows us to look backwards to discover the trends and drivers behind movement in consignment inventory levels; being able to concisely and accurately feedback the root cause of consignment inventory values and predict with great accuracy how these levels will change over time can be important management tools for overall inventory reduction efforts.
Working with readily available data sets, determine and implement best practices for automated data collection, analysis, and reporting.
Reporting should consistent of predictive analytics and in-depth analysis of the major causes for movement in consignment inventory levels.
Using connected databases and automated reporting, and predective data analysis techniques, construct an easily digestable reporting methodology that gives a snap of the past, present, and future.
Mathematics, statistics, or computer programming educational background.
Familiar with automated data collection methods and reporting techniques
Familiar with predective analysis
Familiar with running data set scenarios