AI Use Cases
Determine root causes for quality issues originating outside of manufacturing eg in the supply chain
General Supply Chain
Determine root causes for quality issues originating prior to the manufacturing process. This might include supply sources or logistic process issues. Close human analyst oversight recommended.
Monitor supplier decommits and recommits
Supplier decommits/recommits analytics understand optimal production capacities of suppliers and contract manufacturers in order to properly rebalance manufacturing needs caused by supply chain disruptions (strikes, storms, wars, raw material shortages).
Identify the right match for transplant patients and donors
Optimising matches between transplant patients and donors benefits all patients across the transplant waiting list as it results in more saved lives. For example, through paired kidney donations, AI is able to identify potential donors and recipients who are biologically suited for one another and can take into account certain criteria to optimise the service such as prioritising the hardest to match patients.
Deliver anticipatory logistics through demand forecast
Anticipatory logistics are based on predictive algorithms running on big data. The practice allows logistics professionals to improve efficiency and quality by predicting demand before a consumer places an order. A cost-efficient and effective returns system is a key element in the value chain. Returns to scale are usually key to making this work economically.
Identify and predict supplier performance characteristics such as reliability
Identify and predict which suppliers meet performance characteristics such as cost effectiveness, timeliness, order completeness, quality, regulatory compliance and social responsibility. Determine most cost effective and optimal suppliers.
Capture 3rd party or internal data for price comparison and supplier relationship overview
Comparable data across multiple suppliers (or internal relationships) can allow for potentially significant negotiation power and cost reduction opportunities. Automated data aggregation and comparison from multiple sources significantly reduces the challenges to getting an aggregate and normalised view across organisations and suppliers.