Data for anticipation
We want you to analyze a set of sales data we have collected since October 2020 up until now. You should draw conclusions on trends and root causes of activity spikes in our warehouse. The analysis should eventually enable us to set the light on activity trends and/or equip us with tools to flatten out activity spikes.
At our company, we pride ourselves in being customer-driven. We believe our customers should be our priority in the present, and in the future. To prepare appropriately for the future and maintain excellent service to our customers, we must anticipate. With this in mind, we have been collecting data on the activity of our warehouse in the Netherlands. We want to receive an analysis of this data to understand trends and/or flatten out unexpected activity spikes.
Data analysis of the data that is collected and draw out appropriate conclusions. The following must be involved: draw conclusions out of the multiple warehouse systems. The data should come with substantiation like adding graphs illustrating the findings. Further on we expect answers to the following questions: What are the trends in inactivity? What is the explanation for activity spikes? Can we predict spikes? Which metrics would allow us to predict them? Can we flatten spikes out? At last is each staff member trained on one system. Could cross-training on additional systems be a standard practice to improve the efficiency and balance it all out?
