Lalit Das, Founder and CEO, 3SC Solutions, discusses how AI integrated business planning helps deliver an optimised output.
The world has changed at a faster pace, thanks to two prominent technologies: artificial intelligence (AI) and data analytics. It has affected many industries in the post-COVID era. These companies have adopted modern technologies to survive on a larger scale. One such industry that is deeply impacted by the pandemic is the supply chain industry.
Some studies revealed that adopting AI-integrated supply chain management solutions has yielded much better results regarding inventory management, smart manufacturing, dynamic logistic systems and real-time delivery control.
The primary goal of incorporating AI in supply chain management is to increase output in efficiency and productivity. The digitisation of supply chains has made businesses more sustainable.
How AI impacts the supply chain
AI and analytics integrated supply chain management driven by: The use of AI-based solutions means using intelligent machines capable of problem-solving. This process of smart industry manufacturing powered by Internet of Things (IoT) can fully automate things without relying on manual participation.
Furthermore, using machine-generated data and predictive analytics to make end decisions is much more prudent and efficient for businesses. This is possible with instrumented data flowing out of IoT devices. The result is an optimised workflow where large amounts of data help forecast, identify inefficiencies, and drive innovation.
Supply chain analytics is directly linked to predictive, descriptive, prescriptive and cognitive analysis. The cumulative outcome is that a business can mitigate risks and disruptions with complete transparency. They also reduce time and effort while increasing maximum business value. Then, at the consumer end, advanced analytics have the capability to provide better consumer insights, enhancing customer experience and relationships in the supply chain with data received through AI-driven systems that are analysed and executed in reports and dashboards to answer complex questions.
The fact that these technologies have such a significant impact on businesses, to survive, it is pertinent that demand planning (revolutionised by data analytics and machine learning), real-time inventory management (controlled by IoT and connected systems), and end-to-end dynamic margin optimisation within the supply chain industry (driven by AI-based solutions) are infused to make supply chains resilient.
Why you need to invest in AI and analytics-based solutions
Warehouse efficiency: With warehouse management being a core part of the supply chain, AI-based automation can help smooth transaction of goods from the retrieval of an item to the delivery at the end consumer. AI systems also provide an advantage in significant areas, such as simplifying complex procedures to speed up work. AI automation with machine learning can make faster decisions and save valuable time, ultimately reducing the cost of warehouse staff.
Enhanced safety: The AI-integrated tools come in handy to ensure the safety of warehouse management by ensuring smarter planning and material safety. AI can use data to analyse workplace safety and inform manufacturers about potential risks. It can record stocking parameters, update operations, and necessary feedback for proactive maintenance. This, in turn, helps businesses to formulate strategies to act promptly, keeping warehouses compliant with safety standards.
Reduced operations cost: One more benefit of automation for the supply chain is in the customer service segment; by automating these processes, they work error-free for much longer, reducing human error elements and workplace incidents while increasing productivity. Additionally, warehouse robots can provide greater speed and accuracy, achieving higher levels of productivity – all of which will reflect in reduced operations costs.
On-time delivery: Multiple automated systems work in synergy to accelerate traditional warehouse procedures and help remove bottlenecks with the least effort to achieve delivery targets.
AI and analytics-enabled use cases to control supply chain disruption: Today, businesses need to empower their supply chains with reliable and automated data visual analytics platforms. Mentioned below are practices to control supply chain disruption.
With algorithms and constraints-based modeling, machine learning is leveraged to recognise critical factors in supply chain and transportation data. This is a mathematical approach where a maximum and minimum range of product limits constrains the possibility of each business decision.
This data-rich methodology is the best use case of data science for supply chain forecasts that empower warehouse employees to make more informed decisions on inventory stocking. An alternative method is to take big data predictive analysis that offers deep insight to self-improve forecasting loops.
Today’s supply chain management uses AI solutions to power its inventory optimisation, where the warehouse and stock managers are informed on real-time control of parts, components, and finished goods. As machine learning ages, the AI system produces stocking recommendations based on previous purchase data and supplier deliveries.
Utilising IoT devices, machine learning and AI in the transportation and logistics industry provides an upgrade when it comes to vehicle longevity. It provides real-time insights and predictive maintenance suggestions. AI optimises the logistics and transportation processes by utilising data and improves efficiency. Cost reduction and revenue boosts are other segments that benefit from AI, negotiating shipping rates, analysing supply chain profits, and handling courier contacts in a centralised database. Additionally, AI determines important suppliers who are adding value. It also helps predict supply chain performance indicators and makes the process more transparent.
Ultimately, businesses must stay competitive and future-ready to survive in the market. These tools and services, like supply chain analytics, data visualisation and business intelligence, need to be included for the entire system to function.
ABOUT THE AUTHOR
Lalit Das, Founder and CEO, 3SC Solutions, is a supply chain veteran with over 25+ years of experience. He has gained expertise in procurement and supply planning, manufacturing execution and production planning, sales and distribution planning, and network design and optimisation. He holds expertise across a variety of industries, including automotive, industrial equipment, electronics and technology products.
Image Source: Claude AI