Supplier network analytics triage product and supplier problems more quickly by understanding the dynamics of the underlying supplier and contract manufacturer relationships and inter-dependencies.

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.

Use scanned images - often from portable cameraphone - to identify physical properties. a key function of this is the potential for mobile apps, greatly increasing the potential specific use situations.

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 opportunities to use robotics to automate warehouses. Use of robots to support retail deliveries is one of the fastest growing use cases for the machines.

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.

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.

Consider automation of delivery and other transportation cases through drones and other automated vehicles

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.

Optimise purchasing mix across suppliers and locations should enable better pricing negotiations and reduced wastage on purchased product.

Support review and design of supplier contracts potentially including analysing key elements to focus on in contract design. This may be semi-automated with limited human involvement.

Use historic data to model supply chains to identify and predict the way potentially complex and opaque demand patterns ripple through the system under different scenarios (e.g. weather changes). This can be used to predict potential pricing.

Predict likely demand for products and model rapidly changing scenarios (e.g. weather) to limit out of stock situations.

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.