107th Daily Writing Challenges

By: Bernardus Ari Kuncoro

In production subdivision, there are following data science use cases.

a. Demand Forecasting

In a way to support company’s vision and mission, production team have to make sure how many adequate results of the products. This case is a bit complicated, because not only historical data will be significant, but also external data including the buying power, season, and trend of the market. For data professionals, you need to be aware of those data sources, even ‘unknown data’.

The Demand Forecast vs Actual Sales (Photo was taken from here)

b. Strategy and Operation Planning

To become an impactful and efficient company, strategy and planning in operation are fundamental. Every step of production must meet the criteria of the optimization. In a service company such as banking, for example. There is a specific need on each branch to prepare enough cash money.

This use case is real in one of the biggest bank in Indonesia (Bank Rakyat Indonesia) whose branches located in all of the Sub regency in Indonesia Archipelago. They perform machine learning modeling by using the historical data on each branches and unit. The cash money prediction result will be reference for them to stock the cash money in the office.

Cash optimisation for BRI Branch Offices and Business Units

c. Operational KPIs Dashboard

Each divisions has goals to reach the company’s mission. Usually they use Key Performance Indicator to track the routine tasks. The dashboard will show how many products have been produced, challenges and problem lists, and so on.

d. Product Allocation

Mapping the number of produced items based on the condition in the market is very crucial. Imagine the Unilever produce the bar soaps. Usually they will use historical data, either it is based on seasonality or trend.

e. Predictive Maintenance of Manufacture Machines

Imagine you have the machines from a toaster that you use for breakfast to a computer that you utilize for working. Realize or not, if they fail, what will be the impact? We might not be productive, right? You will not have a toast bread ready. Hungry. Cannot work. Therefore, the quality of a machine is not only based on how useful and efficient, but also how reliable. To be reliable, maintain the machines is obligatory. I recommend you to read this article to jump start the business problem and solution options.

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