Forecasting Use Cases
1. Financial Forecasting, Investor Relations
Industry: Enterprise Software
Functional Group: Corporate CFO
We improved the accuracy and timeliness of financial forecasts used in quarterly stockholder reports by developing machine machine learning forecasting models. Resulted in improved shareholder communications and, by increasing efficiency, allowed CFO to allocate more time to strategic planning.
2. Forecasting and Budgeting consolidation in the Cloud
Industry: Telecommunications
Functional group: Finance & Budgeting, multinational corporation
We moved forecasting and budgeting across functional groups to centralized data in the Cloud. Reduced overall workload and improved integration across subsidiaries.
3. Real-time Auto-Scaling Deployment Decision
Industry: Cloud Software
Functional Group: Product Engineering
Cloud service provider presented a problem – their customer website failed under load – and asked whether what was needed was cluster autoscaling: implementing dynamic adjustment of computer resources based on predicted demand. Developed prediction models showing auto-scaling optimization offered potential savings to $500K/year. Client was advised not to implement autoscaling after further analysis of other alternatives showed better ROI.
Supply-Chain Use Cases
1. Real-time stock management: Estimating resources on-hand to meet random “walk-in” demand
Industry: Fast Food
Functional Group: CTO
Client asked – How many items should be ready-made in anticipation of customer demand? Too many – goods will be cold and wasted. Too few – lost revenue. We built the customer a real time prediction model that trades off lost revenue with the cost of discarded goods, to maximize profit.
2. Sizing Seasonal Agricultural Product Shipping Requirements
Industry: Freight Transportation Cooperative
Functional Group: Operations
We trained a model to forecast seasonal demand for agricultural products by analyzing historical data on freight category, destination, and quantity. Client used this forecast to match suppliers with customers in shipment provisioning, maximizing utilization by reducing the number of freight transportation vessels.
3. Durable goods yield management
Industry: Consumer Electronics, Computer Hardware
Functional Group: Finance & Accounting Operations
To improve yield management, we generated markdown pricing models by estimating product price elasticities. These were used by the client for yield management that trades off forecasted revenue with inventory holding opportunity costs of existing stock, freeing up inventory of older, deprecated units, to allow room for newer, more profitable models.
4. Simulation of Electronic Health Records histories for Hospital management
Industry: Healthcare
Functional Group: Hospital Finance and Strategic Planning
We estimated hospital bed capacity needs, e.g. number of hospital beds, by detailed simulation of population health trends. The model factored in demographics, predicted disease prevalence, and seasonal trends to improve health outcomes under hospital budget constraints.
Root-cause Analysis Use Cases
1. Semiconductor Fab Root-cause Fault Analysis
Industry: Semiconductor manufacturing (fabrication plant)
Functional Group: Lean Manufacturing Operations
We piloted a causal model for ranking the root-causes of equipment faults by their probability. Built an interactive intelligent diagnostic model that codified the knowledge of repair experts, resulting in reduced level of expertise needed, using a novel AI approach for on-the-fly model adjustment and learning during diagnostic sessions. Pilot deployment reduced down time by reducing bottlenecks, and increasing throughput. Predicted revenue increase estimated to be a few million dollars per year.
2. Diagnosing “On Time and In Full” (OTIF) Failure Root-causes
Industry: Food service packaging, Multi-national Corporation
Functional Group: Corporate Accounting
We discovered actual sources of production inefficiencies for our client. They had assumed production delays caused OTIF failures. We developed a model from existing production data to predict OTIF, which revealed, surprisingly that OTIF failures did not originate in production. Existing production scheduling was driven solely to avoid presumed delivery shortages, resulting in excess inventory sitting in warehouses. Based on this analyses we successfully identified the real inefficiency and advised the firm to adopt JIT (Just-In-Time manufacturing) to optimize inventory management.
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