Today’s supply chain is more reactive than proactive, which effectively negates the purpose of planning in general. The majority of supply chains are currently only reacting—reacting to the number of orders, the position of the shipment, the state of production, and so on. This is precisely why the world as a whole was unprepared to deal with a black swan event like the coronavirus outbreak. Prior to the pandemic, there were always large-scale shortages, waste, and losses in millions of dollars. The pandemic has only exacerbated the problem and highlighted the major flaws of inventory-heavy supply chains.
Predictive AI, in particular, will play an important role not only in identifying the growth in demand across different nodes in your supply chain, but also in making certain decisions and taking certain actions to reduce response time. This is a proactive approach that today’s supply chains must take in order to improve supply chain resilience in the future.
Predictive AI for a Proactive and Lean Supply Chain
Knowing demand and predicting how events will affect demand, will aid in the management of all the moving parts of the supply chain, from manufacturing to distribution. This is precisely where a more proactive supply chain has an advantage. Using Predictive AI technology to optimise the various supply chain processes will improve your Supply Chain’s ability to respond effectively across the network to any disruptions, vulnerabilities, and also help you mitigate the effects of Black Swan events.
While data analytics is based on past events, predictive analytics identifies patterns, tests assumptions, and employs Machine Learning algorithms to re-evaluate and adapt the model for the most accurate results. AI-powered predictive analytics has numerous applications in all supply chain processes, beginning with Demand Forecasting and progressing to Automated Production Planning, which is based on sensing demand at the distributor level and using that to forecast near-future demand.
This, in turn, has the potential to make production much leaner than the current inventory-heavy production model that is the norm across sectors and verticals. Companies will be able to meet 20%-30% more demand with the same or lower production capacity by applying the lean methodology throughout the supply chain, beginning with suppliers, vendors, and ending with distributors and retailers.
Applications of Predictive AI across the Supply Chain
Because of its numerous applications, AI-powered predictive analytics can assist Supply Chains in making accurate decisions in near real-time and can be widely adopted across the value chain. Predictive AI will provide you with insights that you can use to further optimise various supply chain processes.
Managing Inventory
The insights gained into granular demand can assist businesses in managing their inventory across Warehouses, Distribution Centers, and Retail Outlets. Supply and demand must be balanced to avoid waste and stockouts. AI-powered solutions can calculate safety stock levels by analysing past trends, market signals, and inventory levels.
Transportation and Logistics
The most difficult challenge for the Indian rural market is access to the vast number of kirana stores scattered across the country. Companies can also use predictive analytics to look at traffic patterns, reachability, lockdown constraints, weather, and other events to determine the best route of transportation to deliver products from Distribution Centers to Retail Stores.
Lean Supply & Production
Accurate demand predictions can assist manufacturers in prioritising and focusing their efforts on rationalising their product line and lowering production costs. AI can also be used to improve procurement and supply planning by identifying the best raw material supplier.
Promotions & Pricing
The insights gained from demand forecasts will assist you in optimising your promotional activities and pricing models, as well as shaping demand. You can run simulations for multiple scenarios with Advanced Scenario Planning to see how different promotions or pricing models affect your sales and consumer behaviour.
Implementation of AI and the future of Supply Chains
In today’s hypercompetitive and volatile market, relying on subpar predictive methods is out of the question. During the pandemic, many companies failed to adapt their predictive capabilities, resulting in excess wastage and stock-outs in manufacturing and distribution. Companies that relied on AI and ML technologies to adapt their forecasts and optimise their operations to the COVID-affected markets were the ones that survived and thrived.
According to a recent Mckinsey report, early adopters who successfully implemented AI-enabled supply-chain management were able to reduce logistics costs by 15%, inventory levels by 35%, and service levels by 65% when compared to slower-moving competitors. The best way to future-proof your organisation, making it resilient and agile, is to implement AI effectively across multiple business processes and departments.
AI Solution Providers and organisations should focus on integrating new technologies into their existing workflows in a simple and timely manner. The majority of businesses continue to invest in outdated technologies or in costly advanced solutions that take months to implement, resulting in opportunity loss for the organisation. Businesses can benefit from Predictive AI only if it does not overburden the existing process. In fact, it should simplify existing processes, stabilise forecasts, and allow businesses to identify opportunities immediately.
The best AI solutions do exactly that: they simplify, stabilise, and improve responsiveness. It will also enable the existing manpower, the sophisticated planning team, to shift their focus to shaping demand. This is how we will transition from a purely reactive to a proactive and flexible supply chain.
This article has been authored by Rahul Vishwakarma, Co-founder and CEO of Mate Labs – an Indian AI start-up developing demand forecasting solutions for supply chains globally.
