A company that deals with agricultural and construction products with more than five thousand permanent item numbers needed a module that would generate ordering proposals based on their purchasing history using a complex logic.
Data Source and Basic Logic
- The data source is from an MSSQL-based record-keeping system, where data from over five years ago is available, from which sales statistics can be extracted.
- The basic task of the module is to forecast the required quantities for all products of a selected supplier until a specified target date.
Criteria and Tasks
The sales statistics from the last three years should be considered on a monthly basis, accounting for seasonal consumer habits.
It should be estimated how long the current inventory will last when considering the previous year's consumption data for the respective months.
If the product has shown increasing sales trends in recent months, the forecasted requirements should be increased by a percentage determined by the user.
Based on criteria, a daily average should be determined, which will form the basis of the proposal, with one multiplier being the daily average and the other being the number of days between the target date and the day the current inventory runs out.
The ordering proposal should account for the product's logistical data. If the forecasted quantity must be divisible according to rounding rules based on the manufacturer's transport packaging unit, and if the number of cartons approaches a full pallet quantity by a certain percentage, the software should round up to a full pallet quantity.
The user should be ensured that the difference between rounding and the original quantities is kept in mind, as in some cases, the applied logic may not be relevant.
From the ordering proposal, an automatically prepared order form should be generated with the user's approval, which should immediately appear as an email attachment and be sent to the partner.
The project was completed within one month. For the following month, a beta version was used at the company, and based on their feedback, several refinements were made.
Since the completion of the module, the company has saved a lot of time and work, and significant results have been observed in inventory optimization.
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