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Conversational AI For Supply Chain Management

October 20, 2020  •  6 minute read

Supply chain management is often considered an ancillary function of the business. Even the companies that are critically dependent on logistic operations often focus on only one or two variables. That is the key reason why supply chain risks have been ignored for a long time.

A McKinsey study shows that while the global supply chain operations have crossed the $10 trillion mark in the last two decades, the interconnected supply chains have also become riskier. At least one in five companies lose over $100 million in one year due to supply chain disruptions. While that equates to 5%, how do you ensure your company is not in that small set of companies? 

Conversational AI opens a cohort of possibilities to mitigate supply chain risks. If you look closer, many supply chain processes are redundant and can be automated with even a semi-intelligent system. This is exactly the kind of problem that conversational AI is designed to solve. Here are just a few instances where industrial AI focusing on conversational systems can do wonders for logistics chain management:

Optimize Your Lead Times

Lead time is one of the strongest indicators of streamlined supply chain operations. However, it is a metric, and hence it tells only what has already happened. 

To improve it, one has to look deeper into the entire supply chain process. Several steps in the process add redundancy and resource consumption without incrementally increasing supply chain efficiency. 

For instance – tracking, reporting, and improving routes. Even with a dedicated team in place, it can become very difficult for a Supply Chain Manager to keep an eye on each moving piece in the logistics system. This often leads to delays as the manager gets to know about bottlenecks in the process quite after they have occurred. 

The issue arises primarily because Supply Chain Analysts and Managers often are dealing with live updates coming in the form of geospatial data. An analyst is hence forced to go back to the plan and see if the actual and the planned progress are matching. Having a conversational user interface plugged in with live tracking can reduce the entire process to one step. 

Analysts and managers looking after the supply chain can get the updates they have flagged as necessary and simply put a question for the bot to answer. The bot can aggregate data from multiple sources in the backend, process it, and present it conversationally. 

Having a conversational AI system in the supply chain also reduces the need for arbitrary checks & balances. Since a bot is looking after the progress and providing the updates, the overseeing process becomes more systematic and rule-based. All of this accumulates into bringing down the smaller inefficiencies across the supply chain, which aggregate and reduce the lead times. 

Provide Round the Clock, Tailored, and Profitable Customer Support.

Customer Support is one of the most overlooked parameters of the supply chain management function. Since the entire supply chain is internally operated, most companies do not make the efforts to streamline the outgoing communication.

Such practices leave the customers with only two options – drop a mail and wait for someone to respond or make a call and hope to go beyond the answering machine. Both these alternatives are arduous and provide a suboptimal customer experience. And a customer has to go through all these loops to get basic information like – is the delivery on schedule or where are we in the delivery stage?

Some companies provide a live location of the delivery. While this can work in some situations, it leaves no room for customer engagement. Besides that, the customer cannot ask any queries. A conversational AI solves this problem on multiple levels:

a. Simplified Status Tracking: The basic criteria is easily fulfilled. The conversational AI bot can easily pull data from different sources and give information on expected and current delivery status. 

b. Provide 24/7 Support: If you check the website of one of the world’s top three logistics and delivery services giants, you will see a notification on its tracking page that shows – customer service available between X and Y hours, Monday to Friday*. While this is a great initiative for its employees, the customer is left hanging. Your customers don’t have to go through the same issue. A conversational AI system can easily serve as an order tracking assistant round the clock. 

c. Cross-Sell: While supply chain conversational AI systems should not focus on selling, nothing stops them from doing so. Most human agents are divided into two sets – a customer support executive, a sales assistant. This is done to help the agents focus on one task at hand and maintain consistent performance. Bots are more adaptive. If the conversational AI bot integrates with the CRM, it can understand the customer’s buyer persona and provide product recommendations while it solves customer queries. Such augmented resource allocation helps ensure the customer is retained, engaged, and serviced—all in one go. 

3. Garner Comprehensive Insights.

The leading conversational AI systems tend to come with dashboards that allow the human analysts to have a zoomed-out look of the entire logistics operations. While this may seem convenient, it is often not enough. 

An analogy would make this clearer. Fire-fighting is a manual task, whereas finding a fire in the house is left to the sensors. This is done because fire-fighting does not have standard rules applicable to each situation. Firefighters adapt and solve problems on the ground. On the other side, detecting the fire is a standardized process – change in variables like visibility, temperature, etc. can help the sensor flag fire with reasonable confidence.

The same logic applies to supply chain operations. Instead of spending time evaluating parameters across the supply chain, analysts and managers can focus on ironing them out. This is possible only if analyzing the supply chain and flagging the issues is left to an AI system. 

A human agent who is responsible for analyzing bottlenecks in the supply chain will only be able to focus on the ones that are trending or are of large scale. Minor issues often go unnoticed. Conversational AI systems can interact with multiple ERP systems in the backend and can even pull actionable insights from third-party vendors like fleet owners, drivers, or vendors. This way, a conversational AI system has a standard process of analyzing each variable and reporting each instance of variance. 

Even in the resolution process, conversational AI can send updates across the channel to all the respective stakeholders. This helps in further making the communication transparent. Very often, you will observe that your customers would not mind a delayed delivery if you inform them in time. 

In Conclusion

Integrating AI-powered chatbots in the right places can help you get a better understanding of your supply chain and in running an automated process for optimizing it. While the benefits are many, you will have to ensure that your conversational AI system has the following features:

  1. Easy ERP integrations.
  2. Conversational abilities.
  3. Detailed analytics.
  4. Workflow integrations for automated processes.

Yellow Messenger’s Conversational AI platform for Supply Chain Capabilities has been designed, keeping in mind the pressing need for supply chain optimization and risk management. It helps you automate several processes in the supply chain cycle – supplier performance monitoring, rule-base contract awarding, order-based analytics, advanced notice generation, alerting on exceptions, and vendor management. To optimize and automate your supply chain, visit Yellow Messenger.

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