Using AI to Optimize Supply Chains

Over the years, the adoption of artificial intelligence (AI) in supply chain management (SCM) has increased significantly across the globe due to higher demand for transparency and visibility on supply chain data and processes, along with the need to enhance customer service. The leading industries in terms of adoption of AI in SCM are telecom (26%), high tech (23%), healthcare (21%), professional services (19%), and travel, transport and logistics (18%), among others.

Recently, UPS Supply Chain Solutions partnered with Softeon to design a warehouse network technology for making distribution centers smarter and more efficient by speeding up order intake as well as delivery. The aim is to minimize delays for customers by ensuring delivery on time.

Uber Freight has partnered with BluJay Solutions to create a robust global supply chain based on a cutting-edge freight technology. The new technology interface enables customers to obtain prices/quotes (for booking and carrier matching) in real time, leveraging a network of more than 50,000 carriers, and, thereby, enhances visibility. These partnerships underscore the use of AI in optimizing supply chain functions.

The benefits of integration of AI notwithstanding, several organizations are unable to implement it due to the following challenges:

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

Procurement experts opine that the recent disruptions in supply chain caused by the COVID-19 pandemic more than ever highlight the need to integrate AI in supply chain for optimizing the operation. To avoid critical supply chain failure, it is essential for an organization to have complete visibility on the overall ecosystem; to accurately forecast demand and supply; and to optimally plan logistics and delivery, among others. AI, along with machine learning (ML), enables organizations to accurately foresee challenges/issues in supply and accordingly take necessary (precautionary/corrective) steps beforehand.

Key AI Applications for Optimizing Supply Chain

Improving End-to-End Visibility and Response Time
With the help of AI solutions, real-time and historical data from multiple connected devices and systems (including SCM, ERP and CRM systems) can be gathered and analyzed to obtain broader and deeper operational insights that are very useful for decision-makers. Using these solutions, the procurement team can get visibility on the supply chain, foresee challenges (whether within the organization, such as breakdowns, or outside, for example, delay in shipments) and make alternative arrangements to minimize the impact on supply chain. Delay in responding promptly will adversely affect supply chain and, consequently, the bottom line.

Predicting Accurately
AI solutions enable organizations to gather information from multiple entities and functions (including suppliers, customers, inventory, and production) in real time and use it to make accurate forecasts. Traditionally, forecasting does not include real-time details and is solely based on historical data. However, with the use of AI, the accuracy of forecasting has improved substantially, enabling executives to not just plan better but also enhance efficiency. Also, employing AI to automate lower-level decision-making can free up bandwidth for managers to focus on strategizing and high-level decision-making.

Planning Supply Chain and Production Efficiently
AI tools and solutions help in analyzing huge datasets in real time, balancing demand-supply gaps, planning production efficiently, scheduling factory activities effectively, and developing error-free SCM plans and strategy. AI can help in correctly estimating the market requirement and managing production accordingly so as to avoid overproduction or shortage of product, either of which would result in loss.

Selecting Supplier and Managing Supplier Relationship
AI solutions can be applied to analyze various datasets (such as delivery performance, audits, 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 build mutually beneficial relationships.

Optimizing Logistics Route
AI solutions enable decision-makers to analyze existing routes, identify bottlenecks and zero in on the best route; this reduces both time as well as overall cost of warehousing and shipping. AI- and ML-based data crunching tools help capture details related to real-time movement of goods and correctly estimate the time of delivery.

Managing Warehouse
Employing AI solutions, both over and under-stocking can be reduced. AI analyzes big datasets much faster and eliminates errors that may creep in when analysis is done manually. Automating mundane tasks like driving forklifts, sorting, and inventory management, by using drones or autonomous ground vehicles (AGVs), transforms warehouse management.

Despite the benefits it offers, AI is yet to penetrate deeper. Conceptually strong algorithms as well as innovations in big data will not only increase processing power but also help in overcoming challenges related to data integration, contributing to expanding the application of AI in SCM.

Authored by Vipul Kumar – Specialist, Procurement Research, Aranca