Applications of AI in Supply Chain Management

Supply chain processes over the globe are going through a rapid phase of globalization. It is also one of the challenges supply chain industry faces today as markets are evolving at a rapid speed with customer’s demands. Globalization has made companies work around the clock to deliver products on time.

But supply chain across industries has a lot of loopholes which if not tackled, lead to losses. Most businesses estimate a loss of 6500 hours during a work year due to manual tasks like paperwork, auditing, checking error & discrepancieslogisticsetc. which needs human intervention and ultimately, account for losses.

Now, replace this with a system which gives complete automation across supply chain where all of the tasks are handled automatically and no human resources are required to perform the same. This automation helps in saving valuable time & money which gets lost due to an inefficient supply chain systems.

Below are some of the top uses cases of AI in supply chain:

Supply Chain Process Automation Using Virtual Assistant

Chatbots are very popular among enterprises as they can automate the tasks by taking actions without the need for human intervention. These chatbots, in combination with AI, can simplify most of the day to day tasks which are human-dependentthird-party they consume huge amounts of time and money.

Now, these chatbots can help in with a wide variety of tasks like procurement, placing purchase orders, updating on the status of things, payments, and invoices etc.. These chatbots can also be personalized according to supply chain needs to focus on problems which currently requires unnecessary manpower.

These give a personalized experience to users by allowing them to customize dashboard, create workflows, schedule tasks, set alerts and take actions from within the chat. Implementing chatbots is a great way how AI in supply chain is making day to day activities effortless.

Warehouse Management Through Machine Learning

Warehouse management can also be described as the backbone of supply chain planning. For an efficient supply chain, two most important weaknesses of warehouse management, overstocking & stockouts needs to be eliminated.

Stockouts and overstocking happen due to poor management of stocks in the inventory management systems which is caused due to the inefficiency of the RFID tags.

The passive RFID tags need human intervention for scanning and providing asset location which requires logistics manual effort and cost. Thus, the assets in stock are left idle to avoid these costs.

The active RFID tags are expensive to implement and are usually avoided across large warehouses leading to no or minimal visibility

IoT devices, on the other hand, provide real-time location and environment data of an asset and work on based of ML which can be configured to send alerts and push messages when assets are moved or left idle for long. This allows the inventory managers to keep the inventory levels optimized at all times and plan demand forecasting affects.

The frequency with which these forecasting & predictive algorithms are utilized affects their accuracy. To maximize the potential of the output from the ML activity, a combination of real-time data from Bluetooth beacons & cloud deployed ML algorithms should work wonders in the long run.

Predictive Analytics For Assets in Transit

The most important factor in the success of the transportation & logistics industry is speed and predictive analytics could be the great tool for this.

Most companies are dependent on third-party logistics service providers to get an update on asset’s location & status which leads to an increase in the ETA and delivery quality, thereby negatively impacting customer experience and loyalty. In cases of mishaps, delivery of the assets gets compromised, leading to loss of customers, even though the root of the problem still persists at vendor’s end.

For a supply chain to work continuously without any obstacles, predictive analytics is a great enabler in making the whole supply chain process smarter and faster.

Predictive analytics can tackle the base problems of transportation & logistics via:

  • Improved delivery time – Analytics algorithms can provide users with expected ETAs based on live traffic & weather conditions. This data can be utilized to produce optimized route recommendations that further reduce any expected delays along the routes.
  • Eliminate spoilage – Analytics algorithms can predict & provide actionable insights about the changes/trends in the environment parameters of the assets like Temperature, Humidity or Shock. This can drastically reduce losses due to spoilage of assets that are in-transit leading to amazing customer experiences and improved NPS scores.
  • Demand forecasting – Forecasting & Predicting the demand based on the multiple parameters like order/dispatch locations, time of the year, type of product, expected ETAs is highly beneficial for all supply chains but it’s most relevant for high value or high volume goods that need high levels of accuracy in terms of delivery time & quality. The supply chain complexity for these goods is further reduced by coupling the forecasting with real-time data and cloud-based algorithm processing which, in turn, drives better planning across the supply chain.

AI is driving the race in technology and supply chain is one the first of many industries to be going through this rapid change. Applications of AI in supply chain management are bringing speed, accuracy, and visibility which in combination with machine learning and IoT based devices is allowing companies to gain a competitive edge.

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