A cognitive supply chain is a digitally led, yet process driven one. It has the ability to analyze real-time data within the supply chain ecosystem to derive business contexts and run simulation models for predictive insights, and hence driving profitability. It can understand queries posed in natural language and analyse a large amount of data gathered through a digital transformation which, creates a self-learning environment to improve the accuracy and relevance of result on an ongoing basis.
How does cognitive analytics change the game in the supply chain?
Speed-up demand forecasting and capacity planning:
Cognitive computing improves forecast accuracy through Artificial intelligence and Semantics with other learning technologies like machine learning and deep learning over time. Cognitive implementations can get smarter and become more effective over time by learning from a large amount of data.
Optimizing Inventory Stock:
Improved forecast accuracy can reduce unbalanced inventory in the warehouse. Overstocking creates a negative impact on the reputation of the company. Demand fluctuation and the increasing need to satisfy customer demand at the earliest have only increased complexities in the supply chain, it requires large and dynamic data to understand different scenarios and their outcomes to make better decisions.
Seamless collaboration between supply chain partners:
When gathering and analysing a large amount of information from supply chain partners, creates a successful collaboration between logistics, retail, marketing, and other channel partners. A cognitive supply chain approach can benefit all by quickly making decisions in real-time and reduce error because of lag-time and miscommunication.
Increase customer satisfaction and get competitive advantage:
A highly responsive, agile, and flexible supply chain is a need for the future. It does not only give customer satisfaction to meet growing customer demand but also remain competitive in the market. The benefits are tangible, opening opportunities to dig up unused data sources, to provide personalized services, improve consistency and quality and promote and enhance information sharing.
Steps to build a cognitive supply chain:
Predict: Both internal and external data can provide a wider range of information, which can create a more holistic demand view. It can support a more forward-looking and agile-demand plan.
Plan: It is also important to balance the network of logistics partners, hubs, and warehouses. All need to be coordinated and synchronized. Cognitive capabilities can speed up the ability to track and manage large volumes of data in real time.
Control: It is possible that prediction and planning are not enough, therefore there is a need for activities to loop back themselves into the predictions and planning process to proactively manage inventory balancing.
Share: Entire process in the supply chain should be transparent. There should be a stronger link with more efficient and coordinated actions between the partners.