Source: clickz.com
The supply chain industry is facing data flooding at an accelerated rate. And this is hampering the organization’s ability to keep up with the upcoming insights and the inflows.
The bottlenecks are becoming too prominent in the network.
While, decision-makers are trying to seek out effective ways to manage humongous amounts of data. In the end, they want the power of their data should benefit the company in a significant way.
The leaders want their supply chain to leverage the capabilities of advanced analytics that can streamline the process, make it more responsive, customer centric, and demand driven.
Why Risk Management has become a key factor in Supply Chain?
According to KPMG.US:
- 61 percent of leaders (compared with 37 percent of others) consider supply chain risk management very important. As they recognize the importance of capabilities that can enable them to gain greater visibility and predictability across their network.
- Secondly, supply chain leaders are nearly three times as likely as other companies to boost their investments in risk management by 20 percent or more in the next two years.
So, how is machine learning (ML) helping the industry in breaking through the bottlenecks?
One of the major issues that the process is facing is its over-the-top reactive approach to the risks, which most of the time disrupts the overall operations of the network.
For it a more proactive and predictive approach is required to identifying and mitigating risk before it affects operations. And also has the power to eliminate many unnecessary financial and operational losses.
Some more problems that the industry is facing
According to KPMG.US:
- No real-time reporting – 56% of supply chain executives do not have access to real-time reporting.
- Risk and compliance issues – 50% have limited knowledge of risk and compliance issues.
- End-to-end visibility – 13% do not have complete end-to-end visibility of supply chains.
- Cyber breaches – 80% of all cyber breaches occur in the supply chain.
1) Machine Learning helps gain visibility into the supply chain to determine where forthcoming bottlenecks can occur.
This implies the workforce getting visibility into the process, equipment, and inventory that comprises of an operations phase.
A lot of information can be driven to improve productivity out of supply chain, inventory management, manufacturing process, distribution, and fulfillment.
Machine learning has the capability to take into account various factors that the traditional forecasting model cannot predict.
It not only looks for patterns, but mines deeper into extremely complex data and identify the potential issues that can be the holdup on the process. ML provides better simulation models of future environments by analyzing complex data sets.
2) Another way ML is helping supply chain is by reducing costs and improving response time.
With accurate forecasting capabilities, organizations can easily optimize their processes. They can also pinpoint the challenging areas that display inefficiencies, while also projecting the roadblocks or bottlenecks in the future.
Supply chain companies with emerging technologies, such as artificial intelligence and machine learning, have inculcated the capability to respond quickly to the upcoming threats by detecting them quickly. The faster an organization has the option to respond; the more cost-effective is the solution.
3) Machine learning also has the power to better manage and maintain the assets.
When ML is integrated into asset management, it can predict the need for repairs with the help of Internet of Things (IoT) sensors. When an equipment breakdown, the IoT sensors sends an immediate notification so that the supply chain process faces very little or no downtime.
Additionally, when these sensors are paired with ML, they can predict when failure is about to occur. These forecasting can lead to prior servicing of the equipment before any issue arises, therefore reducing the cost of damages.
It has been noted that maintained equipment lasts longer with no downtime.
IoT gives an opportunity that is cost-effective in managing and maintaining the equipment that cannot be achieved with the human inspection. Also, IoT analysis can be done more frequently than human inspections.
4) Real-time monitoring with transparency.
Machine Learning provides real-time monitoring throughout the supply chain process. With the right reporting and tracking, we can monitor each and every aspect in the supply chain with ease.
This helps in identifying core inefficiencies that need to be resolved, as well as the requirement to optimize and streamline the supply chain processes.
ML also promotes transparency that provides a 360 clear view of the process. Making it easier to report any loss in the inventory within the supply chain and also reducing the chances of lost or damaged inventories.
To Conclude
By integrating machine learning along with the emerging technologies in supply chain management, companies can achieve a better understanding of the logistics and operations.
With IoT devices, organizations have collected huge volumes of data that can streamline and optimize the supply chain. Resulting in better maintenance and superior overall outcomes.
Amit Dua is the Founder of Signity Solutions. A tech-evangelist, he has an uncanny ability to synergize and build associations, thriving teams, and reputable clients. His vision is to grow his decade-old company as per global standards, and his deep analytical skills to foresee market trends, as well as global challenges.