Successful management of the supply chain is an important factor which gives a competitive edge to any company. An efficient, optimized and well managed supply chain leads to cost reduction, faster production cycle and fulfillment of customer orders. Considering the fact that the supply chain is made up of many interconnected and important blocks like manufacturing factories, warehouses, inventory, transportation and order fulfillments, establishing an efficient process and ensuring the mitigation of risks to maintain uninterrupted operations is a herculean task. Added to this is the ripple effect of widespread globalization, fluctuating customer demand and a myriad of products of varying complexities.

AI in supply chain management

More than a decade ago, IBM had envisioned a smart supply chain model and predicted that the supply chains of the future would be instrumented, interconnected, and intelligent. Machine generated data, using sensors to track goods irrespective of locations, enabling global supplier networks through enhanced connectivity and applying advanced analytics to evaluate their risks and constraints were some of the highlights of that prediction, which we now witness in real life.

There are countless examples which showcase the importance of Artificial Intelligence (AI) and how it has been used to manage supply chains across verticals. AI algorithms can be used to perform advanced analytics on the information collected along the various blocks of supply chain. Machine learning and Natural Language Processing can be applied to the huge amount of manufacturing and operational data to extract useful insights, which can contribute to better planning and delivery.

These powerful models can predict potential disruptions in manufacturing or logistics. They can suggest alternative recommendations in case of unplanned events as well as optimal routes for transportation. By constantly learning over time from data, the AI model can become better and better in predicting these recommendations. For example, data from other sources like weather and climate can be combined with operational data to predict possible disruptions and can be used to inform the logistics department of possible alternatives. Companies have started using AI based tools for layout planning and optimization as well.

Benefits of using AI

AI enabled processes can deliver incredible value to supply chain and logistics. Inventory management is a part of supply chain where the possibility of manual error is very high. Inadequate stock, overstocking and stock-outs are a few examples which are the outcomes of inaccurate inventory management. Using AI, it is possible to analyze the inventory related data to learn the patterns for predicting customer behavior, forecast demand and anticipate customer trends.

Warehouse management is another important part of supply chain which can benefit by the application of AI and automation. Robots and computer vision based AI applications can help in identifying the location of items in the warehouse automatically. This helps in fast retrieval of items and timely delivery. Video analytics solutions can be used to monitor the warehouse, prevent thefts, ensure compliance, alert workers about restricted and dangerous zones thereby ensuring safety of workforce through voice assistance and conversational AI.

Vision based AI solutions can be used to improve the quality of packaging and palletization. Edge AI solutions can use the images captured by camera to check the quality of packaging, printing, material on the pallets etc. This helps in the faster rejection of packages which do not meet the quality expectations.

The advantage of AI solutions is that they help to bring down the dependency on manual processes, thereby reducing the occurrence of errors. This results in fast and smart operations which facilitates on time delivery to the customer.

Best practices for using AI

There could be challenges which the companies need to be aware of while planning to adopt AI solutions for supply chain management. Before any advanced analytics can be applied, steps should be taken to appropriately consolidate and engineer the data available in disintegrated siloed form in different databases. This calls for the organizations to devote time and effort to study and finalize the breaking down of silos, as they are tightly coupled with company culture and deeply rooted business processes. This is one of the startup activities which any organization has to plan as part of digital transformation.

The development of the AI based solutions can be done by internal R&D teams or by service providers. The final deployment platform for the AI solutions – whether on cloud or edge or hybrid, should be decided based on factors like response time, network bandwidth etc. Scalability and cost effectiveness of the end-to-end solution should be evaluated. Security is another important aspect to be considered in cloud based systems. Efficient handling of real time data coming from IoT enabled physical sensors across supply chain, which needs to be analyzed for various use cases like fleet management, driver behavior analytics, predictive maintenance etc should also be considered.

What the future holds

Gartner predicts that “The rise of IIoT will allow supply chains to provide more differentiated services to customers, more efficiently”. Clearly AI adoption redefines supply chain and logistics, by empowering organizations with efficient decision making capability through prediction of disruptions and unplanned events. Ultimately this helps in planning solutions well in advance to achieve streamlined production scheduling.

Written by Sindhu Ramachandran

on 06 Dec 2021

CoE leader (AI), QuEST Global

Sindhu Ramachandran leads the Deep Learning (DL) and Image Analytics Practice as well as the Artificial Intelligence (AI) Center of Excellence for Electronics & Embedded Systems at QuEST. Her primary responsibility is to provide the best of Artificial Intelligence solutions to customers and end-users. She and her team strive to provide quality solutions in Natural Language Processing, Machine Learning (ML) and Deep Learning (DL) through consultancy services, proof of concepts, pilots, onsite AI workshops and proactive proposals.