Service-Driven Supply Chain Planning & Optimization

Predictive Models for End-to-End Planning that Maximizes Service Levels

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Official Partner

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Solution Footprint

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ToolsGroup's Service Optimizer 99+ is a market leading, demand and service-driven supply chain planning application. The technology behind SO99+ was developed over 30 years ago at the MIT and since has been continuously developed to provide cutting edge solutions as recognised by Gartner, Nucleus Research and Frost & Sullivan, who positioned us in the Leaders Quadrant for Supply Chain Planning

Supply Chain Capabilities

Demand Modeling

Frequency-based Forecasting

Frequency-based Forecasting

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SO99+ uses a proprietary composition of algorithms that’s self-learning. It automatically and continuously adjusts demand models, avoiding the manual choice of models used and over-fit risks. The demand is modeled and analysed at the lowest possible level, up to Item-customer-daily combination. This is then rolled-up to get higher level demand numbers along product, market and time hierarchies. SO99+ employs advanced Machine Learning for better accuracy and automation

Frequency-based Forecasting

Frequency-based Forecasting

Frequency-based Forecasting

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SO99+ is a stochastic solution. It uses order-line details to generate forecast with confidence interval, and thus capturing demand variability. The proprietary technology of Frequency Based Forecasting allows the automatic understanding of key characteristics of the demand. Other than the quantity, SO99+ does take into consideration the order lines frequency and order lines size in order to automatically model the customer demand behavior and, for instance, to adjust the reactive ability to the unexpected signals accordingly

Demand Signals

Frequency-based Forecasting

Demand Signals

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In SO99+ different Demand Drivers are modeled separately, i.e. Base line forecast, Trends, Patterns, Seasonality, New Product Introduction, Product Replacements, Promotions, Special Actions, Market Intelligence etc are modeled separately, thus final statistical demand is built layer by layer with respective contribution from these individual Demand Drivers. Contribution from each driver is modeled separately at item-sales area levels and gets adjusted dynamically with time to improve Demand Sensing capability..

MEIO

Dynamic Replenishment

Demand Signals

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ToolsGroup is a leader in Multi-Echelon Inventory Optimization (MEIO), with the ability to optimize inventory along mix, stage and lot size, dynamically adjusting. Proprietary Stock-to-Service Inventory models enables  strategic business decisions and interaction between Sales, Supply Chain and Finance who can agree the Business Policies on the basis of common information. Strategies agreed can be directly applied to the model, which will translate this high-level information into detailed Service Levels and Safety Stocks for each Item, at each Location and dynamically adjusting.

Dynamic Replenishment

Dynamic Replenishment

Dynamic Replenishment

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Our multi-echelon Replenishment Planning automatically  maintains optimal inventory levels for every location in 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

Machine Learning

Dynamic Replenishment

Dynamic Replenishment

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SO99+ is a 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