Digital transformation of inventory management operations for specific parts at home appliance manufacturers.
Example 2: DX of inventory management operations for specific parts at a home appliance manufacturer.
We support the development and implementation of AI-powered demand forecasting models to optimize and streamline inventory management, and implement SCM (Supply Chain Management ) business reforms.
* SCM = Supply Chain Management
Background/issues
Accurate demand forecasting is difficult, and for certain parts, the degree of impact on the market necessitates prioritizing preventing stockouts, resulting in a massive amount of excess inventory.
Ordering processes consume a significant amount of time for the person in charge, and because they are done manually, they tend to become highly dependent on individual employees.
We tested and implemented statistical and AI models, but their accuracy was insufficient, making them unsuitable for practical use.
To achieve highly accurate demand forecasting and significantly reduce the workload of those responsible for ordering , we are considering using ABAEJA, which approaches SCM from the upstream.
ABEJA's Value Proposition
By building an AI system using demand forecasts that reflect actual conditions, we created a truly "usable" system. Data scientists analyze large amounts of data across the entire supply chain, enabling highly accurate demand forecasts that are true to reality.
The process where AI accuracy is not improving can be addressed by incorporating human intervention. Building a system on the ABEJA Platform and implementing it quickly will significantly reduce the burden on humans.
In the case of the customer company, a home appliance manufacturer, inventory management was handled by staff with extensive knowledge and experience. However, accurately forecasting demand was difficult, placing a heavy burden on the staff and often becoming dependent on the individual employee. In addition, the product in question was a component whose shortage would have a significant negative impact on the entire supply chain. Therefore, preventing shortages had to be the priority, resulting in a large amount of obsolete inventory that was affecting the company's financial situation.
approach
Expanding the scope to improve supply chain efficiency
Selection of business processes to be targeted for DX
Tobe formulation
requirements definition
AI system construction
Operation/accuracy improvement
We've formulated a plan to redefine highly accurate demand forecasting and incorporate human resources to address its weak points.
The scope of the AI will be narrowed down to "quarterly shipping forecasts" and "monthly orders" based on those forecasts.
We built an AI system using the ABEJA Platform.
By operating alongside humans, the AI will retrain and improve its accuracy, expanding its scope to other components and leading to the construction of new systems.
Based on the entire supply chain, demand forecasts were selected.
results
【short term】
Demand forecasting accuracy improved by more than 5%
(If the safety stock value is around 10 billion yen, this would result in a reduction of 500 million yen.)
[Medium to Long Term]
The workload of those in charge of order processing has been significantly reduced.
We will expand this to other products and improve the efficiency of the supply chain.