Using AI in Supply Chain to Mitigate Shortages and Evaluate Demand During Corona
Reading Time: 3min 18sec
The COVID-19 pandemic has changed the business environment for many organizations around the globe. According to a survey conducted by The Institute for Supply Management, nearly three quarters of the companies it contacted in late february and early march reported some kind of supply chain disruption, and 44% of the respondents did not have a plan to deal with it.
By understanding how COVID-19 has disrupted the supply chain, organizations can establish the right plan and be able to respond, adapt and set up crisis management to face the unexpected. Below are the few ways COVID-19 has impacted the supply chain.
- Demand drops and surges by segment,
- Supply shortages,
- Inventory Management and
- Reduced productivity
Role of Machine Learning And AI In Supply Chain
To succeed in today’s competitive world, organizations must blend digital and human capabilities with proper planning and comprehensive supply chain AI. Several enterprises today are turning their trust towards machine learning in AI, the reasons are to improve capacity planning and accurate demand forecasting. According to research firm Gartner, atleast 50% of global companies would use AI-related transformational technologies in supply chain operations by 2023 and by 2025, at least 90% of new enterprise apps will have embedded AI capabilities.
Brands like Siemens and Amazon have already started implementing Artificial Intelligence into their supply chain systems. As per MHI annual industry report on Supply Chain Disruption, Robotics and Automation has jumped from fifth(previous survey) to first place(latest survey), with 67% of respondents believing it has the potential to disrupt supply chain or create a competitive advantage.
Image Taken from The 2020 MHI Annual Industry Report (source: mhi.org)
Ways with which Artificial Intelligence can help in mitigating shortages and evaluating demand:
- Demand Forecasting: Accurate forecasting of demand is one of the crucial things that an organization should focus on, gone were the days when supply chain operators used to forecast demand using conventional methods. Times have changed now, by using historical data and predictive analysis in AI, supply chain operators can better estimate all the factors which can work towards precision in demand forecasting. So that the organization can focus on producing more products in demand and thereby leading up to a:
- 65% reduction in lost sales due to inventory out-of-stock situations,
- 10-40% decrease in warehousing costs,
- 30% decrease in supply-chain administrative costs.
- Supply Prediction: Organizations can leverage ML and AI-based techniques that use complex algorithms, structured and unstructured data which can be used to interpret the arrival time of supplies, allowing the supply chain operators to take preventive measures to decrease the impact of shortages, reduce the cost, provide proper warehouse and inventory based management.
- Sales Prediction: Studies suggest that knowing what customer want before they want it, leads to organisational success. Having a well thought out sales framework is vital, as it will enhance productivity and accuracy. ML and AI-based techniques use complex algorithms that can make sales prediction through AI-based forecasting which will help the organization in efficient planning.
- Predicting Peak Hours using AI in Logistics Centers: By utilising AI and ML technology, organization will be able to monitor and predict traffic and other factors which can help them in proper planning of their shipping time.
- Warehouse Management: Proper warehouse and inventory based management is the key to succeed in todays marketplace. By using Machine learning and AI-based techniques, companies can get insights from multiple warehouses and solve the problems of under or overstocking of inventory.
- Predicting when the product arrives and leaves the warehouse: With the help of AI, supply chain operators can better estimate the order arrival and exit from the warehouse which will help them in better planning of their inventory.
Some of the other uses of Artificial Intelligence in Supply chain are:
- Cost reduction: Organisations can benefit from the insights derived from AI(Demand forecasting, supply prediction, sales prediction) to help them achieve better cost-efficiency.
- Catalogue creation: Artificial intelligence can be used to create catalogues, around 98% of the brands find it tough to create quality catalogues. The complexity in creating unique catalogues for each marketplace is high.
- Data Collection and analysis: By using Artificial Intelligence brands can find out more about competitors, marketplaces, and products which in turn will drive higher sales and enhance the customer experience.
- Automation: Brands can automate essential repetitive functions, which can save 25-40% costs on average per order on daily onboarding, account management, finance and supply chain functions.
While many companies know the importance of Artificial Intelligence in supply chain management, most do not utilise it due to a lack of understanding about its impact on customer demand and regulations. As we move into the future, having the right machine learning and AI technologies will help the organization do better planning and set up proper crisis management, and it is essential to learn from recent events to be better prepared for the future.