Tag: machine learning


AI in Supply Chain Management: Top 4 Reasons Procurement Companies Should invest in AI in 2019

Just as artificial intelligence (AI) has made its way into finance and healthcare, it is now creating a fourth industrial revolution in supply chain management. The use of AI in supply chain management has brought about operational improvements for companies across the globe. It has made it possible to discover patterns in supply chain data by relying on algorithms that quickly pinpoint the most important factors behind the success of supply networks. Key factors influencing supplier quality, inventory levels, demand forecasting, order-to-cash, procure-to-pay, transportation management, production planning, and more are becoming known for the first time with the aid of AI in supply chain management. This is making supply chains more efficient and result-oriented. In this article, we have discussed some of the significant benefits of AI in supply chain management for 2019.

Benefits of AI in Supply Chain Management

#1. Helps in analyzing large data sets with maximum accuracy

For supply chain management, one of the most challenging aspects is predicting future demands for production. Before any solution of demand management is implemented, the master data needs to be cleaned up. This process of normalizing and cleaning the data costs more, takes far longer, and requires an ongoing effort. The use of AI in supply chain management can help simplify this process and improve the accuracy of demand forecasting.

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#2. Improves supplier delivery performance

AI in supply chain management helps in improving supplier delivery performance, reducing freight costs, and minimizing supplier risks. These are some of the significant benefits artificial intelligence is providing in collaborative networks of the supply chain. Today, AI in supply chain management can be used to identify horizontal collaboration synergies between multiple networks of shippers.

#3. Provide better insights into improving supply chain management

Artificial intelligence helps uncover new information to improve supply chain performance.  By combining the strengths of supervised learning, unsupervised learning, and reinforcement learrequest proposalning, artificial intelligence is rapidly becoming an effective technology that continually seeks to find important factors affecting the performance of the supply chain.

#4. Helps in maintaining physical assets across the entire network of supply chain

AI in supply chain helps at visual pattern recognition, opening up several potential applications in the maintenance of physical assets and physical inspection across the entire network of the supply chain. The use of AI in supply chain is also proving to be very effective at automating inbound quality inspection throughout logistics hubs, isolating product shipments with wear and damage.

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Where Will MRO Category Management Be 5 Years from Now?

Procurement teams have a tough time tracking spends on Maintenance, Repair, and Operations (MRO) category. The problem is persistent not only in tracking but also in ascertaining savings initiatives arising out of it. A study conducted by Grainger reported that businesses spent about $110 billion on MRO materials every year. They further stated that out of the total expenditure around $12 billion of items such as light bulbs, nuts and bolts, and cleaning supplies sits on the shelves never to be used. That is a lot of money sleeping and doing nothing for you. The problems with MRO just doesn’t end there, excess inventory and special projects are causing aSE_Demo2 surge in the MRO materials cost. There surely must be a fix for all such problems today or someday in the future. So what does the future hold for MRO category management?

IBM Watson

IBM Watson has been a prominent name in the market which has made strong associations with AI and machine learning. IBM Watson is looking to solve all existing problem with MRO category management with the help of data. IBM Watson can take inputs from a wide variety of sensors, machine, and maintenance data to accurately predict a breakdown so that company can provide a preventive solution beforehand. Such predictive maintenance eliminates the need to spend money on stocking spare parts and tools. Apart from generating savings from inventory cost, it also saves the company the opportunity costs arising from downtime caused by machine breakdown. Additionally, the ability to predict breakdown in advance will eliminate spot buying activities which in turn will allow procurement professionals to negotiate competitive deals. IBM Watson looks to simplify the work of the procurement professionals in the future, with the smooth flow of operations and optimizing spends in MRO procurement. Alongside, reducing the downtime will automatically lead to an increase in overall output and efficiency.

Procurement Automation

The future of MRO procurement lies not only in predictive maintenance but also in the ability to automate purchases. Procurement teams will always have data on stocks and inventory handy, combining this data with predictive maintenance data, cognitive computers can ascertain if a specific part’s stock needs to be replenished or not. If a need for purchase is identified, then the AI system can automatically screen preferred lists of suppliers automate the actual procurement component. The process is starting from need identification to supplier screening, fetching quotations, analyzing best deals and drafting contract can be fully automated in the future.

The future for MRO category management looks bright, which multiple complexities solved by IBM Watson and procurement automation. It is clear that the future lies within data, with predictive analytics pointing out adverse situations before they arise and providing optimal solutions.

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