This customer was importing and delivering products directly to local DCs. Therefore, each DC was at risk of having excess inventory or running out of stock.
The company is trying to reduce inventory and the risk of shortages by building a replenishment center and delivering products from the replenishment center to each DC.
Reduction of inventory.
Forecasting
The accuracy of the received sales plan was low and mainly used lagging indicators.
o9の支援
With o9, the company was able to improve the accuracy by creating a forecast using “Prophet” (Facebook developed), on a single integrated, cloud-native platform.
Manual Planning
Truck loading planning was done manually which was a very time-consuming process. Moreover, since the number of trucks was decided by gut-feeling, there were many cases where the setting was too large or too low.
o9の支援
With o9, the number of trucks can be estimated by automatically creating a loading plan that takes into consideration the loading rate of trucks and the number of pallets.
Number Crunching
Management of the minimum order quantity and complicated ordering conditions, considering the container’s capacity, were managed in Excel. This saved time to focus on what truly matters.
o9の支援
With o9, the grunt work was reduced by automatically creating order requests considering various ordering conditions.
This enabled the issuing of an accurate order request.
実装したコア機能
The o9 Enterprise Knowledge Graph was used to build market, demand, and supply-knowledge models. This allowed for running all key planning processes for all products and DCs, across all time horizons in a single integrated platform. o9 leveraged its open architecture to incorporate proprietary developed forecasting algorithms, safety stock logic, and truck loading logic in R and Python.
リプレースしたシステム
Homegrown solutions and Excel.
成功要因―o9を採用した3つの理由
達成したKPI
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