Manufacturing is one of the hardest-hit sectors because of the country-wide lockdowns caused by the COVID-19 pandemic. As per a survey conducted in March by the Institute for Supply Chain Management, nearly 75% companies reported disruptions in one form or the other in supply chains due to coronavirus-related transportation restrictions. Another interesting statistics that emerged from the survey was the lack of a contingency plan for ~50% companies in case of a supply chain disruption. Over 50% of the companies reported sudden, unexpected delays in receiving orders. These figures depict the vulnerable state of supply chain components, including procurement, inventory, warehouses, transportation, logistics, distribution centers, retailers, and consumers.
AI in inventory and warehouse management
1) Advanced demand forecasting systems to manage inventory planning
Inventory management is an incredibly complex task to perform due to the volatile nature of demand. The absence of past data available on any pandemic of similar scope further complicates the situation. As a result, this increased uncertainty could lead to overbuying or underbuying products. Through better demand forecasting, an organization will be able to manage inventory better, increase revenue, and improve customer support.
While traditional forecasting models use past samples of inventory data, there is a need for advanced demand forecasting techniques using Artificial Intelligence to accurately forecast demand, especially during volatile situations like a pandemic. Incorporating external data modules like social media data (Twitter, Facebook), macro-economic indicators, market performance data (stocks, earnings, etc.) to the forecasting model, in addition to the past samples of the inventory data seasonality changes, best determines the product demand pattern. With this, we can have a consumer-focused manufacturing process with enhanced efficiency in inventory management to optimize inventory costs.
2) Remote predictive maintenance of equipment
During the COVID-19 crisis, manufacturers must maintain all their machines in the factories virtually(remotely) with minimal staff on-site. However, if one part of the distribution system fails, it can bring the entire warehouse to a halt. Productivity takes a sharp dive leading to customers not receiving their goods, and this risk to the business is enormous.
By developing advanced predictive maintenance software that is data-driven and powered by machine learning technology that can gather information in real-time from a variety of sources, including maintenance logs, performance logs, monitoring data, inspection reports, environmental data, and financial data, this critical situation can be resolved. The software can detect the most minor of anomalies or failure patterns from structured and unstructured data to determine the areas of highest risk. It then proactively redirects resources towards those areas before the threat becomes real.
Thus, predictive maintenance can deliver sizable cost savings, labor efficiency, better continuity in warehouse management, and increased customer satisfaction, enabling manufacturers to compete on an entirely new level.
3)Automated Visual Inspection for quality assurance
Automated visual inspections can replace validation processes in distribution centers. It involves the analysis of processes like packaging, shipping, etc. in the warehouses for quality control. Visual inspection can also be used for internal and external assessment of equipment in a production facility such as an assembly line, storage tanks, pressure vessels, piping, etc.
It has been proved repeatedly that visual inspection techniques result in the discovery of the maximum number of hidden defects during both production and warehouse management.
Visual inspection is in high demand in production lines as well, in areas such as:
- Defect identification in the automobile industry
- Defect detection in VLSI wafer fabrication
- Defect detection in products in the assembly line
- Identification of leakage in packaged foods
- Identification of wrong prints on medicines in the pharmaceutical industry
4)Employee health and social distancing in warehouses
As social distancing is mandatory to combat this viral pandemic, there is a need to develop monitoring tools by leveraging artificial intelligence and machine learning for social distancing among warehouses and other staff sections. These tools are to be developed by advanced person tracking technologies in AI according to the social distancing protocols within the organization, to zone the factory, and prohibit employees from wandering into zones where they are not needed.
5)Increased focus on robotic technology
Manufacturers should not hesitate to give a thought to robot-enabled warehouse management. As their staff falls ill, self-isolates, or quarantines, warehouses using manual picking will have to temporarily pull the shutter down, severely impacting the global supply chain. Automated robots ensure continuity of business operations and help businesses meet the growing global demand. They reduce uncertainties and mitigate risks, with a clear understanding of cost, maintenance, and capability, regardless of uncontrollable external factors like pandemics. In addition to warehouse management, robots can also play a key role in disinfecting workspaces.
Adopt a new supply chain model
There is ample scope in inventory and warehouse management in supply chains, to leverage AI. We have seen some of its use cases in production lines as well. AI can also be applied to other parts of supply chains like procurement. E.g. a cognitive selection of suppliers for procurement by doing predictive analytics to avoid risks.
By leveraging AI, along with other exponential technologies like Blockchain and IOT, companies in the manufacturing industry can anticipate and build contingency plans to adopt a new supply chain model. In addition, decentralizing supply chains by using technologies like Blockchain, organizations can dramatically improve the visibility of the end-to-end supply chain and resist external contingencies such as the coronavirus outbreak. From a public policy point of view, the focus should be on smaller manufacturing hubs in multiple cities rather than large manufacturing hubs concentrated in a few selected cities. Shortened supply chains will reduce COVID-19 impact on supply chains and improve capability and innovation.