How does AI in the supply chain enhance visibility and agility?
The use of AI in supply chain management is rapidly increasing. The COVID-19 crisis may also accelerate the adoption of AI technology.
Because global supply chains are becoming more complex, shippers and manufacturers are under increasing pressure to juggle massive amounts of data from disparate sources. At the same time, they must coordinate stakeholders and ensure that shipped goods are delivered on time. There is little room for error.
Artificial Intelligence is one such example of cutting-edge technologies that can easily detect anomalies and identify similar patterns in datasets. This unique ability enables supply chain managers to evaluate data analogies and subtle correlations between different data groups. They could, for example, determine what external forces cause seasonal fluctuations in demand for a specific product.
Businesses can easily integrate AI with other software solutions to track goods in transit and discover hidden patterns to improve supply chain visibility and agility.
Key takeaways
Supply chain disruption is all around us, forcing businesses to transform their supply chain and manufacturing network in order to drive resilience, relevancy, and responsibility.
The success of those efforts is dependent on putting data at the heart of the supply chain and applying AI to it at scale to create a truly connected and intelligent supply chain.
An AI strategy and roadmap, cloud, and the right talent are critical to overcoming the barriers to scaling AI and delivering genuine business value.
How AI improves supply chain visibility and agility?
In the world of supply chain management, the phrase “drowning in data and starved for insights” still applies. Most businesses have poor visibility across their supply chain, and many business leaders agree that if their organization does not have “one version of the truth,” it will struggle to meet its business objectives.
Supply Chain Intelligence Suite provides you with a unified view of your global supply chain network, as well as actionable insights, allowing you to better understand priorities, and resolve critical issues in real time. Users can make more confident decisions faster by providing common situational awareness across the supply chain.
Supply chain leaders can achieve a new level of operational agility and efficiency by automating routine decisions. Exception workflows are automatically triggered in Supply Chain Intelligence Suite to resolve any issues caused by unexpected disruption.
Most of the major companies have thousands of tier-1 partners and extended partners around the world. The open platform of Supply Chain Intelligence Suite enables customers to connect and communicate with their extended network in real time. This entails automating information sharing in place of the manual, daily process of emailing spreadsheets back and forth. Problems can be identified sooner, and cross-company teams can work more effectively together.
Benefits of AI in supply chain
A smart supply chain network can help businesses in a variety of ways.
- Increasing visibility
It can assist them in gaining visibility to late-breaking supply disruptions or demand blips, supplying the information required to resolve issues in near real time.
- Increasing agility
It aids in increasing agility so that businesses can respond to unique customer requirements with speed, specificity, and scale—increasing product availability and service levels, decreasing lost sales and inventory costs, and increasing production and fulfilment efficiency.
- Improving resiliency
It can boost resiliency (for example, maintaining On Time and In Full-service metrics) while lowering a company’s carbon footprint and overall sustainability risk.
- Reduced scope of error
Manual data entry can be time-consuming, and the margin for error is wide. These errors can cause significant problems in supply chain management, and AI reduces the risk of error to a bare minimum. “To err is human,” they say.
- Convenient Access
Data on supply chain management is widely available and can be accessed using automated technology at any time. As a result, the decision-making process is sped up.
- Client Satisfaction
Achieving higher levels of customer satisfaction is a priority for logistics companies, and providing dependable service using automated technology can help acquire new customers and retain existing ones.
- Time and effort savings
Isn’t time the most valuable resource?
The data management process in shipping is automated, which reduces the cost of manual data entry. Using AI technology for data entry speeds up the process and reduces the amount of time spent processing documents.
The troubles of investing data and supply chain AI at scale
Despite acknowledging the power and value of data and AI, businesses will likely continue to struggle to leverage their investments more broadly. In fact, 79% of COOs admit to knowing how to pilot AI but struggle to scale it across the business.
Overcoming these challenges will not be easy, but it is necessary for AI to scale and deliver genuine business value. Three things, in our experience, can help reduce roadblocks and allow AI to thrive across the enterprise.
1.Strategy and road map: determining the destination and determining how to get there
2.Cloud: utilizing data to create a single, trusted source of truth
- Talent: developing and purchasing the necessary skills
The future of Artificial Intelligent, self-driving supply chain networks
When combined and scaled, intelligent technologies and connected end-to-end data can add enormous value to any company’s supply chain. The combination unifies the supply chain, generates new efficiencies and operational capabilities, and frees up capital for reinvestment in new business models that improve customer experiences, create competitive advantage, and support profitable growth.
Longer term, this potent combination of technologies and data will fuel a shift towards truly self-driving supply chain networks, which will raise the bar for value and innovation to new heights.
AI has the potential to automate demand planning, supply planning, inventory optimization, and execution while automating decisions independently. This marks the gradual shift from humans piloting the machines to machines directed by humans.
Meanwhile, machine learning enables self-learning, prediction, prescribing, and optimizing supply chain performance across functions. In a self-driving supply chain, for example, ML-based algorithms can predict exceptions and supply chain outcomes, and if the process changes over time, cognitive computing learns and adapts to it.
AI has the potential to truly transform any supply chain—and in today’s environment, such a transformation is no longer an option. Companies can stop piloting AI and start scaling it with the right combination of people, processes, and technology, allowing the supply chain network to realize its full potential value—both in the short and long term.
Conclusion
A successful and efficient supply chain requires collaboration, agility, and visibility. Organizations can improve the flow of information and reduce the risk of disruptions by encouraging collaboration and open communication throughout the supply chain. Agility promotes the development of a flexible, responsive supply chain capable of quickly adapting to changes in demand, market conditions, and other factors.
Organizations can also improve decision-making, increase efficiency, and reduce costs by implementing real-time visibility systems. Organizations can build a supply chain that is stable, efficient, and well-equipped to meet the challenges of today’s fast-paced and ever-changing business environment by focusing on these three key elements.
