AI: The Next Revolution In Supply Chain Optimisation

The adoption of artificial intelligence (AI) in supply chain management has found a new liking from people across the globe to enhance customer service. 

Artificial intelligence (AI) is being deployed by companies that have started to use these technologies across sectors and aim to explore its potential to become a major business disruptor. 

Companies through AI are exploring the possibility of applying innovative technology into their operations, in particular, organizations are striving to deploy the right combination of AI technologies to boost their efficiency and flexibility, as well as accelerate their processes and optimize their operations.

AI is transforming processes along the value chain from end to end, including supply chain management. 

What Are The Obstacles Businesses Face With AI? 

Several organizations are unable to realize the benefits of AI integration and face difficulties in implementing it due to the following challenges:

• Restricted high-quality, consistent and updated (real-time) data
• Availability of supply chain data in different silos (for example, inventory team, marketing department, purchasing manager and others have their own databases)
• Limited integration between databases and systems for accessing, cleansing and analyzing data
• Limited data governance policies related to extended supply chain

Can AI Help During A Pandemic?

The recent stalling in the supply chain caused by the COVID-19 pandemic more than ever highlights the need to integrate AI for optimising the operation.

Organisations must have a bird’s eye view of the overall ecosystem to:

• Prevent critical supply chain failure
• Accurately forecast demand and supply
• Optimally plan logistics and delivery, among others

AI enables organizations to foresee challenges/issues in supply accurately and accordingly plan necessary (precautionary/corrective) steps beforehand.

What are the Key AI applications for Optimising Supply Chain?

Improving End-to-End Visibility and Response Time

AI solutions have the ability to procure deeper and broader operational insights for decision-makers including both historical and real-time data from multiple connected devices (ERP, SCM and CRM systems)

The procurement team can get clear visibility on the supply chain, foresee challenges within the organization, such as breakdowns, or outside, delays in shipments and make alternative arrangements to minimize the impact on the supply chain.

Accuracy Prediction

With the use of AI, the accuracy of forecasting substantially, allows executives to enhance efficiency and better planning.

The application of AI can automate decision-making at the lower level while channeling the bandwidth for managers to divert their focus on strategizing and high-level decision-making.

Planning Supply Chain and Production Efficiently

• analyzing large datasets in real-time
• stabilize demand-supply gaps
• planning production efficiently
• effective scheduling of factory activities
• developing error-free software configuration management (SCM) plans and strategy
• correctly estimating the managing production and market requirements accordingly so as to avoid shortage of product or overproduction, either of which would result in loss

Selecting Supplier and Managing Supplier Relationship

Analyze various datasets (such as audits, delivery performance, evaluations, and credit scores) and obtain customized recommendations on supplier relationship management. Real-time and regular information on potential or existing suppliers can be used to create mutually beneficial relationships.

Optimising Logistics Route

Study existing routes, identify bottlenecks and identify the best route; this reduces both the time and overall cost of warehousing and shipping. With the help of a route optimization app, you can plan your route and save time before a journey. AI-based data-crunching tools help capture details related to the real-time movement of goods and accurately estimate the time of delivery.

Managing Warehouse

Reduce both over and under-stocking while analyzing big datasets much faster and eliminate errors that may arise when an analysis is done manually. Automating common tasks like driving forklifts, sorting, and inventory management through drones.

Bottomline

Despite the widespread adoption of AI in various industries, it has yet to penetrate deeper. Eventually, the evolution of stronger algorithms coupled with innovations in big data will not only lead to an increase in processing power but also help in combating challenges related to data integration. This trend will significantly contribute to expanding the application of AI in supply chain management.