It does this by collecting information from multiple sources that determines both how the current system configuration is performing and how that can be improved. Identifying complex patterns, delineating relationships in data and comparing potential models is where AI can outperform humans. OI is able to construct every possible supply chain outcome and make an informed choice as to the best configuration for your organisation from the millions of options available across multiple dimensions of cost and service.
As well as modelling the supply chain, the tool measures performance against the optimal configuration, concurrently fine-tuning and continually recalibrating to take account of new data points and changes to underlying variability patterns and trends. As it models it also learns; self-correcting so that those choices improve over time.
The OI modelling engine utilises a multi-objective optimisation algorithm, which seeks to identify the best supply chain configuration across multiple dimensions such as service level, cost and speed.
The tool continually identifies patterns and relationships from the data whilst adapting to new trends and changing circumstances.
OI uses AI to shape resilient optimal end-to-end supply chains, measures impact and improves performance continually in real time whilst tracking benefit. In doing so, it answers questions, such as:
The Oii capability models concurrently across multiple dimensions of interrelated supply chain variability considering an infinity of possible configurations as it optimises the entire network in real time.
Replenishment strategy based on dynamic segmentation and modelling of production frequency to balance cycle stocks versus efficiencies. The process runs in conjunction with other modelling components as it simulates outcomes and selects the optimal configuration.
Simulates the E2E network building multiple realities of the potential future state and the cost and service implications of each. The model identifies the optimal E2E network generating recommendations on supply chain design stock levels, factory make frequencies and decoupling points for buffers.
Real time analysis of safety stock levels based on segmented service level targets and demand and supply variability. The model uses an advanced simulation algorithm to build all possible SS levels and identifies the optimal based on service / cost targets.
The tool models current and future variability identifying patterns in the data classifying and projecting trends and using this information to forecast future demand and project supply risks onto simulations of Segmentation, SS levels, replenishment strategies and end to end network design
Dynamic product segmentation based on key variables sales, margin, volatility, volume, product characteristics and customer becomes a key Input to the modelling results.
Replenishment strategy based on dynamic segmentation and modelling of production frequency to balance cycle stocks versus efficiencies. The process runs in conjunction with other modelling components as it simulates outcomes and selects the optimal configuration.
Simulates the E2E network building multiple realities of the potential future state and the cost and service implications of each. The model identifies the optimal E2E network generating recommendations on supply chain design stock levels, factory make frequencies and decoupling points for buffers.
Real time analysis of safety stock levels based on segmented service level targets and demand and supply variability. The model uses an advanced simulation algorithm to build all possible SS levels and identifies the optimal based on service / cost targets.
The tool models current and future variability identifying patterns in the data classifying and projecting trends and using this information to forecast future demand and project supply risks onto simulations of Segmentation, SS levels, replenishment strategies and end to end network design
Dynamic product segmentation based on key variables sales, margin, volatility, volume, product characteristics and customer becomes a key Input to the modelling results.
