Masoprofit faces problems with accurate inventory and demand planning for meat processing equipment and accessories. Demand varies significantly according to seasonality and customer type. Insufficient prediction leads either to surpluses of expensive goods or to a lack of key components during orders.
Prediction and optimisation of warehouse stocks
Client NameMasoprofit s.r.o.
Client CountryCZ
- Client typeSME
- IndustryConsumer Goods & FMCG
- Application areasFinance & Accounting, Strategy, Planning & Decision-Making, Supply Chain & Logistics
- AI technologiesForecasting & Time Series Analysis, Machine Learning, Optimization & Planning, Reinforcement Learning, Simulation / Digital Twins
- Business impactsEmployee Enablement & Productivity, Operational Efficiency & Cost Savings
- Data typesImage Data, Structured Tabular Data, Time Series
- Delivery modelsConsulting, Product / Licensed Software, Service / Subscription
- DeploymentsCloud
- Key capabilitiesDecision Support & Augmented Analytics, Planning, Scheduling & Optimization
- Project stagesInitial Production Deployment
- Solution formsAnalysis, Recommendation, or Report, Plugin / Extension for an existing system, Web Portal / Dashboard
Solution Description
Problem description
Solution
The solution involves deploying the MyIO platform integrated with the internal K2 system, which will analyze historical data on sales, orders, and warehouse movements. Using AI predictions, it will determine the expected demand for individual products and automatically propose optimal inventory and order volumes from suppliers. The system will monitor seasonal fluctuations, trends, and anomalies and alert to situations that may affect the availability of goods.
Main Users of the Solution
Purchasers, management
Project timeframe (months)
15
Technologies used
Python, Java, React, Azure Cloud
Additional services
- AI strategy and roadmap
- Audit / feasibility study
- Identification and prioritization of suitable use cases
- Data collection and pre-processing
- Annotation / synthetic data / dataset extension
- Data governance and data quality
- Selection and customization of the AI model
- Change support and user training
- Systematic AI educational programs
- Provision of MLOps infrastructure
- Continuous maintenance and model retraining
Implementation
Project Owner on the Client's Side
Head of IT / data / technology
Participation on the Client's Side
- Business / Product Owner
- Domain / process experts
- Project and change management
- End users
Form of Supplier Involvement
Complete implementation
Operation and Maintenance
Operational Model
Joint management
Needed Competencies on the Client's Side
Head of Sales, Buyer, ERP Specialist, CTO
Other Resources or Infrastructure
In this case, the solution is deployed as SaaS. Costs include a license fee, infrastructure costs, and consumption of computational resources within a dynamic cloud infrastructure (computational jobs for predictions and optimizations). No additional investment in hardware or server management is required on the customer’s side. System support and maintenance are ensured by the supplier as part of the service.
Impact and Results
Qualitative Benefits
Improvement of the purchasing process
Quantitative Results
Expected results: 35% improvement in prediction accuracy compared to the original solution and a 15% improvement in inventory levels while simultaneously reducing the occurrence of stockouts. These results are preliminary and will be verified in production operation after full deployment of the solution in order to confirm the achievement of expected benefits.
Client Feedback
Streamlining of the purchasing and ordering processes.
Lessons Learned and Recommendations
Key Success Factors
Active client involvement during development
Biggest Challenges
Connection and linking of systems
Recommendation for Others
We recommend verifying the quality of the available data and not being afraid to pursue a similar solution. Implementing a data-driven system will not only make planning and decision-making more efficient, but will also lead to an improvement in the quality of the data itself, as it becomes transparent, controlled, and actively used in daily operations.
Promotion
Demo / Public Resources
- CompanyDNAI
- ContactJakub Szasz
- Emailjakub.szasz@dnai.ai
- Websitehttps://www.dnai.ai
- AddressU Nikolajky 3, 150 00 Praha