Predictive Models for End-to-End Planning and Maximize Service Levels
Our Service Optimizer algorithms are proprietary and self-learning, meaning that they continuously adjust demand models without the need for manual selection, reducing the risks of over-fitting. We model and analyze demand at the lowest level, which includes the Item-customer-daily combination, and then roll it up to obtain higher-level demand figures across product, market, and time hierarchies. Additionally, Service Optimizer incorporates advanced machine learning techniques to improve accuracy and automate processes.
Demand models are inherently stochastic in nature and capture demand variability and order-line frequencies as probabilistic distribution functions. Proprietary Frequency Based Forecasting technology enables automatic understanding of key demand characteristics. This allows for reactive adjustments to unexpected signals and improves your ability to respond to changes in demand.
We model different demand signals or drivers separately, including baseline forecasts, trends, patterns, seasonality, new product introductions, product replacements, promotions, special actions, and market intelligence. This allows to build a final statistical demand that is layered and includes contributions from each of these individual demand drivers. The contribution from each driver is modeled separately at the granular most level and is dynamically adjusted over time to improve demand sensing capability. This approach enables to more accurately capture and respond to changes in demand signals.
Our Multi-Echelon Inventory Optimization (MEIO) capabilities can optimize inventory along mix, stage, and lot size, while dynamically adjusting to changing demand patterns and supply side vectors. Our proprietary Stock-to-Service Inventory models allow for strategic business decisions by facilitating interaction between Sales, Supply Chain, and Finance teams, who can agree on business policies based on stock-to-service scenarios. The strategies agreed upon can be directly applied to the model, which then translates this high-level policy decisions into detailed Service Levels and Safety Stocks for each item and at each location. This approach enables to maintain optimal inventory levels while meeting outstanding service level commitments.
Multi-echelon Replenishment Planning automatically create proposals to ensure material movements across the network – down to the Dealer or Store level – to achieve service targets. Time-phased Replenishment Demand is updated on a daily, rolling basis in response to changing variables like promotions, seasonality and supply constraints. Time-phased Min and Max net requirements that incorporate the statistical demand and supply characteristics; automatically creates constrained replenishment and transfer proposals to balance the network
Self-learning, self-calibrating application. Demand, Inventory and Supply models are automatically generated from raw data, both structured and unstructured, and on continuous, ongoing basis. SO99+ includes unique machine learning technology able to glean market learning from Big Data and apply to the forecast (NPI, Promotion Planning, and Causal Factors). This is a unique offering
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