How is AI Changing the face of Supply Chain Management
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How is AI Changing the face of Supply Chain Management?

Supply chains across the world are in complete chaos. The COVID-19 crisis followed by the uncertain lockdowns and social distancing trends has led to significant business disruptions. It is here where artificial intelligence is acting as a savior and is also helping to modernize supply chain management

The Pivotal Role of Artificial Intelligence in Supply Chain Management 

The key reason why artificial intelligence is implemented in the supply chain industry is owing to the fact that enterprises have realized that artificial intelligence possesses the potential for solving the issues to run the global logistics network. When implemented accurately, artificial intelligence allows enterprises to be more competent, anticipating problems, and taking more agile decisions. 

Various proactive systems enabled by artificial intelligence surpass the expectations of customers for intact and on-time deliveries and augment the quality of services. It is also enhancing competence with the help of compliance processing. The outcome is lower costs and lesser problems across the logistics network. 

The most exciting part about artificial intelligence is its unlimited potential. Artificial intelligence, when combined with other technologies like machine learning, predictive analytics, and the internet of things, algorithms become more powerful. Organizations, through the access to extra data now have an improved picture of its global logistics networks. This transparency is crucial for improving the thought process regarding logistics and supply chain management that is changing. 

Key Applications of Artificial Intelligence in Supply Chain Management 

  1. Chatbots to Procure Operations– To streamline procurement-related tasks through augmented and automated chatbot requires access to smart and robust data sets. With regards to the day to the day functions present in supply chains, chatbots can be utilized for the following 
  • Converse with suppliers during trivial conversations 
  • Alert suppliers about compliance materials and governance 
  • Place purchasing orders 
  • Answer internal questions about procurement functions
  • Receive, file, and document payments and invoices and order requests. 
  1. ML for Supply Chain Planning– Supply chain planning has a crucial part in supply chain management strategies. Through smart tools for building robust and firm business plans today has become inevitable in the current-day business domain. When machine learning is applied with that of supply chain planning, this can be utilized to forecast the inventory, supply, and demand. The accurate use of supply chain management tools and machine learning can be immensely beneficial to revolutionize the agility and also optimize decision making in supply chains. The use of ML technology by supply chain management professionals can lead to finest outputs which are based on machine analysis for the big data sets and intelligent algorithms. This capability of ML can be utilized to balance supply and demand and optimize the goods delivery sans human review. 
  2. Use of Predictive Analysis to Manage Logistics– Companies today is making the most proactive approach because predictive analysis is used to forecast customer demand and customer trends. Through future analysis about demand and supply aids the supply chain management system through strategic planning, controlling inventory, procuring raw materials, performing supply analysis regarding finished goods, and developing newer products. 
  3. Manage Inventory– The most crucial task in any supply chain management is always the inventory assortment and is vital as the information regarding the availability of stock is maintained resting on the inventory which is assorted. This process of assorting is quite time consuming and is susceptible to human errors. If however automated robots are used, this can result in extreme accuracy to offer accurate inventory information along with reducing human errors and cutting down the supply chain cost. 
  4. AI in Logistics to Predict Demand– Artificial intelligence can be immensely beneficial to improve the supply chain process. This can be utilized to improve demand and enhance forecasting demand. Resting on learning and past experience, one will get an overall analysis of the different factors that influence caters to the market demands. The supplier based on this can take the most informed business decision. 
  5. Optimize Logistics Route– Artificial intelligence can be utilized to decide the finest route accessible to reduce shipping costs and make shipping faster. This will prove beneficial for any supplier who owns a large e-commerce that possesses a huge customer base. In case of such companies, artificial intelligence will prove as a helpful technology because this can help in analyzing the existing routes along with performing the optimizing of the route tracking. 
  6. Predict Peak Hours in a Logistics Center– These days, artificial intelligence is used with ML in most logistics centers for predicting and monitoring traffic together with other factors that can affect the consignment’s shipping time. Peak hours at these logistics centers are crucial with regards to shipping. Hence, artificial intelligence can be used effectively to predict and avoid such peak hours. 
  7. Automated Quality Checking– During automatic quality checks, it proves highly beneficial compared to manual methods deployed. A quality inspection that is carried out automatically backed by machine learning algorithms and computer vision programs will help scan the products in every possible dimension. As the product gets scanned across every axe, errors can be detected at about 1/4th rate in comparison to humans conducting the inspection process. 

In short, artificial intelligence has already begun to change the face of the SCM that will exacerbate the divide between the losers and winners. By culling out the uncertainties and inefficiencies deep-rooted, AI is driving enterprise-wide visibility into every facet of the supply chain system and above all, with methodologies and granularity that humans cannot mimic at scale. 

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