Supply chain management is a complex medley of processes in which even a slight lack of visibility or synchronization can lead to enormous losses and overheads. But with the recent developments in AI & machine learning, we can now harness historic and real-time supply chain data to discover patterns that help us understand what factors influence the different aspects of the supply chain network.
These insights help companies in getting a competitive edge, streamline processes, cutting down on costs and increasing profits, and leveraging recommendations to enhance the customer experience. According to Gartner, at least 50% of global companies would be using AI-related transformational technologies such as Machine Learning in supply chain operations by 2023.
5 Ways In Which ML Acts As A Game Changer In Supply Chain Management
1. Inventory Management
Ensuring the right amount of product availability in the inventory as per the future market demand has always been a constant challenge for manufacturers. With big data analytics, manufacturers can analyze different types of data including past sales demand, chanel performance, product returns, POS data, promotions data etc. to get insights around:
- What is the optimal inventory required to meet demand while ensuring stock levels are at a minimum
- How to reduce out of stock situations
- How to control the impact of product recalls
- How to enable cross-selling and improve slow-moving stock’s performance
When feeded with the latest supply and demand data, machine learning can enable a continuous improvement in a company’s efforts towards solving the over or under stocking problem.
2. Predictive And Preventive Maintenance
Equipment failures and machine breakdowns are some of the significant reasons for supply chain disruption. Unexpected and extended downtimes can result in out of stock situations and lost revenue.
In order to avoid these situations, companies are replacing the reactive and inefficient break-fix service model with proactive maintenance approaches – predictive and preventive maintenance.
This involves using machine learning to analyze data from smart parts and sensors and predicting when a machine/part will fail and determining the right time for repairs and replacements.
This allows companies to reduce excess inventory, mitigate the costs and disruption caused due to unscheduled downtime and ultimately improve customer satisfaction and brand loyalty.
In addition, machine learning can also help understand how to extend the life of the existing assets, determine common reasons for failure and take necessary proactive steps.
3. Logistics
Last mile logistics in supply chain management is prone to operational inefficiencies and costs upto 28 percent of the total cost of the delivery.
Some common challenges in this area include:
- Not able to find a parking spot for large delivery trucks near the customer’s destination and having to carry the package to its destination by walk
- Customers not being at home to sign the receipt of items and thus causing a delay in delivery
- Damages to the package during this last leg of delivery
In most cases, it’s very difficult for companies to identify exactly what’s going on during this last mile. This final step is commonly referred to as the “black box” of the supply chain.
In order to address such last-mile logistics operations and improve operational efficiency, a global brewing company recently worked with MIT Megacity Logistics Lab to leverage data and machine learning. In this scenario, the ML tools analyzed the historic route plans and delivery records, and helped identify customer-specific delivery challenges for thousands of customers across the globe. The company identified customers whose delivery constraints posed the most significant disruptions to its last mile logistics operations. From there, the company reconfigured its distribution services for a certain pool of customers.
4. Production Planning
Machine learning can simplify the complexities involved in developing production plans. For instance, CPG and Food and Beverage manufacturers are analyzing weather forecast data (temperature and sunshine data) with machine learning to more accurately predict the demand for certain product categories and plan production and inventory.
5. Supplier Relationship Management
Robust Supplier Relationship Management strategies are essential for improving supply chain resilience. Machine learning algorithms can help businesses analyze supplier data and provide insights into supplier compliance, performance patterns, and potential risks. Supply chain and procurement professionals can improve their supplier selection process and minimize supply chain disruptions by forecasting and identifying any new supplier risks.
A supply chain is a process of traversing the products right from the manufacturer to the end consumer.
Every sector comprises a dedicated team to manage the supply chain of products........ http://blockchainhints.mystrikingly.com/blog/how-can-blockchain-help-in-improving-supply-chain-management
Know the importance of supply chain management and the role of supply chain management in organization's success.
Read how SCM can help in growth of an organizationhttps://www.forceintellect.com/2018/05/14/importance-of-supply-chain-management/
Read here the how machine learning can transform the supply chain, and Machine Learning in Supply Chain and basics of machine learning ,definition of machine learning, Challenges In Logistics and Supply Chain Industry and the list of companies that using ML to improve supply chain management.
It seems like blockchain is becoming unstoppable.
Even after the burst of the Bitcoin bubble, it’s still a reliable technology.According to a recent survey, a notable portion of German Logistics managers anticipates that the blockchain technology could become the force that will revolutionize the logistics and supply chain sector.Although the survey wasn’t aimed to gather blockchain-specific stats, yet 35% of the respondents identified the vitality of the blockchain technology.
33% said that Big Data is vital for logistics, an area where blockchain is poised to rule and excel.Even though the blockchain is not the only Big Data solution, it does has the potential to be the foundation for a new world of global logistics.Big data is huge chunks of data.
It wouldn’t be possible for a human to execute such a task.
Here, the blockchain could be the database that enables global trade information to be recorded massively.Blockchain based logistics systems enhance the sectorAs per the survey, most of the respondents found that blockchain is becoming a necessity for the collaboration process.
It’s the section where there is an unprecedented amount of efficiency is waiting to be explored.For decades, the shipping management has remained opaque with records being kept on the paper, making research a difficult task, even for a large-scale company.For instance, a company’s is looking to finance a product that is in another country and has shipped it to them.With the existing system, it could take weeks to push through.On the other hand, with blockchain based supply chain solutions, where the buyer is located, who has financed the product, and how the goods are being shipped to that country, several different entities can be recorded in the transaction.How Blockchain based supply chain solutions fit in?A company could employ blockchain smart contract based trade finance platform to secure a loan for the product it wants to purchase, and the bank that’s lending the credit could use that same platform to release the payment to the seller.Instead of receiving the products at the port, a Blockchain and IoT powered scanner could verify that goods have left from the shipper’s warehouse, and release payment via a blockchain based smart contract.All the information about the whole process can be stored on one shared blockchain ledger.
venkat vajradhar Jul 29 · 4 min read Artificial intelligence is not limited to the IT or technology industry; Instead, it is widely used in other fields such as medicine, business, education, law, and manufacturing.In the following, we list the 9 most intelligent AI solutions we use today, presenting marketing machine learning as a present — not the future.SiriSiri is one of the most popular personal assistants offered by Apple on the iPhone and iPad.
The friendly female voice-activated assistant interacts with the user on a daily basis.
She helps us find information, get directions, send messages, make voice calls, open apps, and add events to the calendar.Siri uses machine learning technology to make natural language queries and requests clever and understandable.
CogitoCogito was originally co-founded by Dr. Sandy and Joshua and is the best example of behavioral reform to improve the intelligence of customer support representatives currently on the market.
The company is machine learning and behavioral science to increase customer collaboration for phone professionals.To Know About: Artificial intelligence in the manufacturing market is steadily growing at a CAGR of 49.5%to 2025 and will reach the US $ 17.2 billionCogito applies to millions of voice calls that occur every day.
PandoraPandora is one of the most popular and most demanding technology solutions.