Retailers and fashion brands today face a mandate: quickly react to consumer demand and get shoppers the product they want, when and how they want it. Fast reaction time, along with a near-perfect balance of supply and demand, is essential for companies to survive and thrive.
However, companies face tough challenges that prevent them from achieving their goal of a demand-driven supply chain, including:
Complexity of the concept-to-customer process. Hundreds of different decision points and dozens of participants are involved in the process of planning, developing, sourcing, producing, shipping and selling products. Delays at any decision point have ripple effects throughout the supply chain, resulting in cost overruns, late shipments, missed opportunities and unhappy customers.
Disparate information sources. Much of the information used in the decision-making process originates with external supply chain partners such as raw material suppliers, producers and logistics providers. By the time this information is shared and incorporated into a company’s decision-making cycle, it’s too late to react, and the potential for a positive outcome is severely diminished.
Too much lag time in decision-making. In most supply chains today, information flows in an analog, linear fashion from department to department. An over-reliance on email and spreadsheets means teams are already working with old information. Disconnected systems further amplify these issues.
These challenges, combined with the pressures to respond quickly to consumer demand, put an unprecedented burden on supply chains.
Related Stories
Cognitive supply chains overcome current limitations
Artificial Intelligence, however, can help companies break through these barriers to radically improve supply chain efficiency. AI powers the cognitive supply chain, which enables better, faster decision-making and execution.
Cognitive supply chains analyze all types of data within the supply chain ecosystem, both internal (such as point-of-sale, material inventory, work-in-process, and inbound shipments) and external (weather forecasts, social sentiment and market trends). Based on integrated continuous planning and connected execution models, AI has the ability to establish network orchestration, which will exponentially increase supply chain velocity by using algorithmic decision-making and automated execution.
Companies that incorporate AI to quickly make decisions throughout the operations ecosystem will get the right products from concept to customer much faster. Unlike the analog, linear supply chain, a cognitive supply chain continually analyzes situational data, determines optimal outcomes, and automatically executes transactions.
There are three stages in the digital supply chain journey: connected, predictive and cognitive. While many companies are just now entering the connected stage, some companies are already incorporating machine learning and moving to the predictive stage of the digital supply chain. The connected and predictive stages result in significant progress for brands and retailers, but the cognitive stage is the most electrifying of all.
“Cognitive” may be one of the top buzz words in supply chains today, but cognitive supply chains are rapidly becoming reality, as retailers and fashion companies understand the transformative benefits of cognitive supply chains. AI technologies have matured to the point where they can be incorporated into supply chain decision-making.
Three keys to implementing cognitive supply chains
This year is shaping up as the year when cognitive supply chains begin to gain market adoption.
More and more companies are starting projects that will incorporate AI into their supply chains. As companies begin to implement cognitive supply chains, there are three key concepts they must understand:
For one, a cognitive supply chain is not a one-time, rip-and-replace project. Rather, it will involve layering AI into an established cloud-based platform that can integrate with legacy enterprise solutions and share information throughout the entire operations ecosystem.
Secondly, cognitive supply chains are created by a series of incremental projects. Some companies are already planning for two- or three-year timelines to enable various levels of cognitive capabilities. Some activities, like supply optimization and just-in-time (JIT) manufacturing, already incorporate data such as capacity, price, lead time and risk when selecting vendors that are capable of meeting future demand. Using this data, along with information from existing legacy systems, further broadens the capabilities of the cognitive supply chain.
Change management is extremely important. Cognitive supply chains involve a strategic corporate reorganization and a significant cultural shift. Much of the data analysis and routine decision-making will be performed without any human intervention. The workforce can then spend more time on assignments that support faster growth and higher profitability.
What are the barriers to adopting cognitive supply chains? A recent Sourcing Journal article noted that apparel brands are slow to adopt new technology, however, the article noted, “the cost of failing to innovate is even higher, and it’s growing every year.”
As cognitive supply chains become reality, retailers and brands that embrace them will quickly obtain a huge advantage over their competition and strongly position themselves for continued growth and success.