“It is not the most intellectual of the species that survives, it is not the strongest that survives, but the species that survives is the one that is able to adapt to and to adjust best to the changing environment in which it finds itself.” — Charles Darwin, Origin of the Species (1859)
Today, we’re in the throes of another changing environment, one where businesses rapidly embrace digitalization. I know, I know, we all have heard all about AI before and there have been countless encounters of all of us about the subject. But what is a cognitive supply chain model and where does it fit into all this?
Simply, a cognitive supply chain model utilizes machine learning, which is a type of artificial intelligence that has been merged with advanced analytics. It helps quickly process and make sense of the vast amount of data coming from countless sources. This allows businesses to receive real-time insights from the entire integrated network, from digital data that has been captured, stored, processed, and shared from every single supply chain partner.
The benefit of a cognitive supply chain for businesses is that it eliminates noise, which in turn promotes insights and can lead to improvement. This is essential in directing decision-making for businesses and industries, and is known as “digital nudging.” The more accurate and reliable the data, the better impact it will have on leading businesses to make effective decisions for each and every component of the supply chain.
How Cognitive Learning is Applied to Supply Chain Processes
The most optimal supply chain systems will make decisions automatically, making the need for human intervention limited, which in turn increases efficiency by saving time and money. Many companies are now turning to cognitive computing and machine learning to solve challenges that come up in supply chain processes.
Here are four ways this will immediately improve your business:
1. Real-time Insights:
Allows quick responses to risks and opportunities. Planners will have access to key information, such as stock-out tolerance limits, safety stock gaps, lost sales, and audit compliance. Accordingly, there can be prompt changes made regarding short-term forecasts, marketing campaigns, and production schedules.
2. Long-term visibility on excess or unproductive inventory:
This will enable forecast accuracy improvements over time.
3. Seamless collaboration and increased productivity between supply chain partners:
Suppliers, customers, third-party manufacturers, and logistics providers work together in real-time to quickly make business decisions on the same platform. This increases the quality of interaction reduces lag time and communication.
4. Increased customer satisfaction:
Customers are getting more and more demanding, requiring billions of personalized services and they want it rapidly. The supply chains of tomorrow need to be highly responsive, agile and flexible to meet growing customer demands.
Traditional supply chain models are unable to deliver any of all this in an efficient and effective way without compromising profit. Companies do not have a solution for analyzing the enormous data scattered across various processes, sources, and system. Cognitive supply chain model overcomes these transparency and visibility hurdles by utilizing AI. Cognitive learning is now widely seen as the solution to the greatest challenges facing retail businesses, not only for the supply chain sector. It has been estimated that by 2020, 50% of all business analytics software will incorporate cognitive computing functionality in order to deliver business growth and remain competitive in today’s world.
“When we talk about supply chain visibility, it does not simply mean visibility into your own supply chain. It means visibility among partners, which enables collaborative decision making closer to the customer. This is both a science (managing the technology) and an art (using the information and metrics for competitive advantage).” –Bob Stoffel, former Senior Vice President, Engineering, Strategy and Supply Chain at UPS
Supply chains are complex because of their hybrid nature, but cognitive learning thrives on complexity. Large volume and multi-channel data means more accuracy and better cognition for the system to learn the dynamics. Hence, this makes supply chains with its multi-channel stakeholders a perfect environment to apply cognitive learning.
The drift towards smart, optimized, and automated world today is so strong that not integrating some form of AI – perhaps machine learning –means that systems have become obsolete. According to Pew Research Center, by 2025, artificial intelligence will be built into the algorithmic architecture of countless functions of business and communication, increasing relevance, reducing noise, increasing efficiency and reducing risk across everything from finding information to making transactions.
Why not make your business smarter and stay competitive in the market while others are trying to take their current AI empowered systems one step further? For example, One of Solvoyo’s e-commerce clients, has reduced its inventory by %57 per SKU and achieved 60% increase in availability. As a result, they have increased their revenue by 94% and hence strengthened their dominance in the e-commerce market. To conclude, Solvoyo utilizes cutting-edge technology to obtain remarkable improvements in an omni-channel supply chain. After all “It is not the most intellectual of the species that survives, it is not the strongest that survives, but the species that survives is the one that is able to adapt to and to adjust best to the changing environment in which it finds itself.”