Business cases
Real-world success stories of AI-driven optimization, smarter forecasting, and end-to-end supply chain transformation.
Reducing inventory holding costs in the consumer goods sector
A leading German appliance manufacturer faced inefficiencies in planning, excess safety stock, and high inventory holding costs. aioneers provided transparency on overstocks and introduced targeted reduction measures.
Overcoming inventory inefficiencies
- Inaccurate planning, excess safety stocks and short product life cycles
- High inventory capital & holding due to lack of operational inventory management for slow-/non-mover and phased out stocks
How we solved it
- Tailored inventory analysis (e.g., analysis to determine overstocks in case of discontinued stocks and slow/non-movers)
- Set-up of data pipelines to SAP
- Standardized workflow for 70 warehouses offering local planners transparency on overstocks and facilitating reduction measures
- Cross-functional coordination of slow and non-mover reduction actions between logistics, supply chain and sales teams
- Tracking of activities and target achievement
Reduction of phase-out stock
Minimization of slow- & non-movers
Improving forecast accuracy for a leading appliance manufacturer
Manual planning inefficiencies and a lack of statistical forecasting methods led to inconsistent forecasts and misaligned operations. aioneers introduced AI-driven forecasting and automated planning.
Addressing planning & forecasting challenges
- High manual planning effort increased error risks and inefficiencies, resulting in inconsistent forecasts and misaligned operations
- Inaccurate forecasts, compounded by lack of statistical methods in Planning System undermined decision-making and planning quality
How we solved it
- Calculation forecast recommendation based on 50 months historical sales data
- Monthly calculation of forecast recommendations for Demand Planners on SKU and Product Group Level
- Automated of data transfer between AIO and Planning System Software
- Best-fit model selection for each forecast run and parameter optimization
- Customer-specific parameter settings, e.g. smoothing limits
- Automated dashboard updates to enable forecast controlling
Increase in forecast accuracy
Reduction in safety stocks
Enhancing VMI processes for a packaging industry leader
A consumer packaging manufacturer lacked visibility into demand and inventory, leading to inefficiencies in vendor-managed inventory (VMI). aioneers integrated customer data and implemented a control tower solution.
Enhancing supply chain collaboration
- Supply Chain Planning and Execution Processes highly manual and error-prone
- Lack of transparency along the supply chain accompanied by the difficulty to measure and maintain high performance levels
How we solved it
- Data connection to extract information on a daily basis from advanced planning system of customer’s main customer
- Forecast Data
- Customer Orders
- Call-offs
- Creation of end-to-end supply chain transparency and implementation of Control Tower cockpit, to measure service level, forecast quality and inventory levels
- Implementation of AIO’s Vendor-managed Inventory Algorithm based on defined business rules and parameters
- Calculation of production requirements and corresponding deliver plan creation
Increase in On-Time In-Full
Reduction of Finished Goods Inventory
AI-driven inventory optimization for a pet food manufacturer
Excessive inventory levels and inefficient stock management were reducing profitability. aioneers introduced AI-powered inventory transparency and autonomous recommendations.
Streamlining inventory management
- High write-offs for semi-finished and finished goods inventories
- Lack of transparency and performance measurement along the End-to-End Supply Chain
How we solved it
- Data extraction from multiple ERP systems to create a digital end-to-end representation of the physical supply chain
- Implementation of performance management dashboards for inventory optimization, e.g., Days Inventory Outstanding, 9-box model, sediment stock analysis
- Enablement of inventory simulations through assessing purchase orders and requisitions, production orders, demand forecasts and confirmed customer orders to determine optimal fulfilment steps
- Creation of alerts and findings to help users focus on critical action items to generate highest working capital impact
Reduction of Overall Inventories
Increase of Customer Service Level
Automated demand planning for a machinery & equipment leader
A global machinery manufacturer faced inefficiencies in supply chain planning, leading to increased manual efforts and misaligned decision-making. aioneers implemented digital S&OP dashboards.
Optimizing sales & operations planning
- Low SC planning maturity and high manual efforts, overall impaired S&OP process
- Severe supply bottlenecks and demand uncertainties, large backorder volume, high inventory levels
How we solved it
- Tailored S&OP Dashboards to Support S&OP Meetings
- Transparency into demand plan and actuals, analysis of demand elements such as customer orders, frame contracts and sales forecasts
- Production capacity utilization based on planned orders, production orders and planned capacities
- Evaluation of phase-in/put planning and link between predecessor/successor
- Evaluation and overview of MRP results along the bill of materials
- Projection of future inventory levels to highlight bottlenecks at the material level, and providing transparency into inventory coverages
Reduction of backorders
Decrease in overall inventory levels
Optimizing semiconductor equipment supply chains
A leading semiconductor equipment manufacturer faced supply bottlenecks and high capital tie-ups. aioneers implemented a supply chain control tower.
Improving supply chain visibility
- Supply bottlenecks and lack of material availability on the one hand and high capital tie-up due to excessive inventories on the other.
- Low service levels lead to customer dissatisfaction and ultimately to lost/ declining sales
How we solved it
- As-Is assessment and solution design in collaboration with Tier-1 consultancy
- Replacement of existing SAP service monitor into cross-functional, end-to-end Supply Chain Control
- Daily data-load and analysis of Key Performance Indicators for hit-rate, Service Level and Reliability
- Implementation of inventory performance management, embedded into Control Tower with Inventory Development, Coverage Class Analysis
- Automated identification of critical components and impact estimation of committed customer order dates / delays.
- On-boarding of more than 100 users, which access Control Tower daily
Increase in service level
Reduction in component stock
Multi-Echelon Inventory Optimization in the Metals Industry
A leading steel producer needed to optimize stock levels across a complex distribution network. aioneers implemented a Multi-Echelon Inventory Optimization (MEIO) engine.
Optimizing stock levels across multiple locations
- Due to the high material value, transportation costs and competitive market situations inventories are of special interest.
- Current stocking and replenishment policies are decided locally without considering overall optimization potentials
How we solved it
- Extraction of VBAK/VBAP/MARA data for all local sales legal entities in APAC scope, as well as steel mills
- Provision of “size program” recommendation to planners as part of software-supported monthly process through
- Assessing valid replenishment policies on each echelon
- Calculating optimal parameters based on various approaches
- Evaluating outcome of each simulation based on performance indicators
- Deployment of MEIO engine to calculate optimal stock levels in local warehouses
Service level
Reduction in safety stocks
Creating end-to-end supply chain transparency for a tier-1 automotive supplier
A Tier-1 automotive supplier struggled with inventory imbalances across multiple plants, leading to inefficiencies and high stock levels. aioneers introduced AI-powered supply chain transparency.
Overcoming inventory inefficiencies
- High overall inventory position. Dead stock and slow-/non-moving inventory due to demand and supply uncertainties
- Missing end-to-end inventory transparency on global level, compounded by high manual effort to create daily inventory report
How we solved it
- AIO is configured for both, daily operational use as well as management reporting and initiation of tactical improvement measures to reduce inventories through active management by means of workflows
- Creation of end-to-end inventory transparency and projection
- Reduction of manual effort for inventory monitoring and reporting on day-to-day/monthly basis
- Possibility to perform drill-ups and -downs on material, product group, plant, etc
Reduction of sediment stock
Reduction in overall inventory position
Improving demand forecasting accuracy for a tier-1 automotive supplier
Volatile demand and poor forecast accuracy led to inefficiencies and increased supply chain costs. aioneers introduced machine-learning-based forecasting.
Enhancing demand visibility
- Short-term demand volatility impacts supply chain, with transparency on demand changes not being effectively reflected in demand forecasts
- On-time delivery, excess overstocks, expensive premium freights, production re-scheduling negatively affected
How we solved it
- (Re-)design of KPIs/metrics for demand planning and forecast performance together with client key stakeholders
- Building ML-based forecasting model including feature engineering
- Perform data cleansing, cross-validation and hyper parameter optimization to check model performance
- Forecast Performance Dashboards to visualize current forecast quality and improvement potentials
- Global rollout and adoption of the solution together with the client
Increase in forecast accuracy
Reduction of safety stock
AI-powered demand forecasting for an automotive OEM
A major automotive OEM needed better forecasting to anticipate customer demand fluctuations. aioneers deployed an AI-based forecasting system.
Enhancing demand forecasting
- Late identification of critical situations leading to reactive steering and constraints in production and logistics
- Cost intensive measures like various special air freights to secure product availability
How we solved it
- Integration of data from multiple source systems (ERP, SD, SAP BW, Anaplan)
- Machine-learning based forecasting for the entire global product portfolio, aimed at raising forecast accuracy
- Single source of truth connecting multiple plants & ERP systems
- Cross-plant & cross-functional collaboration through standardized reporting
- Forward-looking transparency and availability prediction by analyzing demand, inventories and supplies
Increase in forecast accuracy
Reduction of forecast overbias
Optimizing S&OP in pharma: reducing inventory while improving service
A pharmaceutical company struggled with high inventory levels and poor S&OP processes. aioneers deployed an advanced control tower system.
Transforming S&OP for better performance
- Immature S&OP process and lack of end-to-end supply chain transparency
- High inventory levels and continuously decreasing end customer on-time in-full performance
How we solved it
- Daily updates through data pipeline connection to Group’s SAP system
- Implementation of Supply Chain Segmentation Framework
- Enablement of S&OP concept in AIO Control Tower, with global and local views (supported by client RACI and AIO’s roles & authorization concept)
- Identification of areas of improvement and decision-making support for Supply Chain Planners & S&OP Management Team
- Creation of cross-functional, end-to-end view on the supply chain
- Deployment of comprehensive inventory performance
management system
Reduction in finished good inventories
Reduction in forecast bias
AI-powered forecasting for a specialty chemicals leader
A global specialty chemicals company needed better forecasting accuracy and inventory control. aioneers introduced AI-driven demand planning.
Optimizing demand planning
- Low forecast quality despite time-intensive, decentralized sales forecasting process
- Excess finished and semi-finished goods inventories as well as overall low customer service levels
How we solved it
- System and data integration to e.g., map product lineage, product id changes, production process related master data for semi finished SKUs to establish demand forecast baseline
- Deployment of AIO’s Machine-learning based forecasting engine to calculate demand forecast (monthly) across multiple planning levels
- Measurement of forecast quality and forecast value add along demand planning process
- Integration of forecast results into monthly business tact (S&OP)
- Automation of findings and alerts to guide planners towards
critical forecast items and enable quick issue resolution
Increase in forecast accuracy
Reduction in safety stock levels
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