The ever-evolving customer expectations and the competitive nature of the supply chain industry have motivated organizations to lead with innovation, and it’s needless to say that modern technology is at the epicenter of this change. The benefits of technology in the supply chain can be categorized into three buckets:
- increased automation
- better transparency
- Generating insights to spark innovation
Software products related to logistics, inventory management, and supply chain management have quickly made their way to organizations. It’s now unimaginable to think of a supply chain function that doesn’t use one of the popular SCM software. RFID and barcode technology are heavily used in inventory management, tracking, logistics, and vehicle route tracking. Tracking every step in your supply chain pipeline creates better transparency. More recent advancements in technology are helping organizations to make incredible advancements in automation. Warehouse bots, driverless delivery vehicles, and drone delivery are among the cutting-edge applications of modern technology that are helping with supply chain automation.
While these technological advances helped organizations make significant advancements with their SCM process, they also generated a wealth of rich data. From every part or raw material ordered to every turn your delivery truck takes, big data is created for every step in your SCM operations.
The big data that is generated as part of the SCM’s digital ecosystem has transformative power. Artificial Intelligence (AI), a field of computer science, leverages big data and machine learning to generate insights and aims to perform activities that typically require human intelligence. As organizations accumulated decades’ worth of data, they are now building AI-powered applications to discover the latent trends that can help improve their operations.
While there is plenty of excitement and interest in using futuristic technologies such as Metaverse and Virtual reality to advance supply chain operations, there are examples of how AI is already helping SCM operations. In this article, I provide three practical applications of AI that can immediately spark innovations and create operational efficiencies.
New product introduction forecasting
According to Gartner, demand volatility is one of the primary pain points in their supply chain function. Predicting the demand will help organizations manufacture products accordingly and accurately. This is especially important in this global supply chain crisis. Having unsold products on the shelves and not having enough products to meet customers’ demands devastates your company’s customer experience. Demand sensing aims to solve this problem by generating forecasts based on historical data.
Time-series forecasting, a class of AI algorithms, involves modeling the temporal and seasonal nature of your historic sales cycles. Information such as previous sales history, macroeconomic conditions, marketing spend, product reviews, website visits, external competition, sales promotions, etc., is fed into the AI model to learn the latent relationships. The trained AI model can then generate forecasts for future demand.
New product introduction (NPI) forecasting is closely related to demand sensing NPI forecasting aims to understand the future orders of new products that are yet to be launched in the market. What makes NPI forecasting challenging is that we will not have any prior sales history for the product that’s yet to be launched into the market. Modern AI algorithms such as Long short-term memory networks (LSTMs) and Autoencoders are proving to be exceptionally well in generating NPI forecasts.
Every organization’s supply chain back-office deal with enormous amounts of procedural documentation. This can range from delivery orders, docking receipts, bills of landing, etc. The back-office team is expected to process and store this information, and the process can be very manual. AI algorithms in computer vision and natural language processes can read through the document and translate the images to text, which can then be stored in a database for consumption.
Another popular application of AI that’s penetrating every enterprise function is the chatbot. Chatbots can provide answers to the most commonly asked questions. Most consumer product-centric organizations have to answer questions about delivery requests, tracking, and order-related issues. Often, the questions your customers ask are repetitive and can be avoided by taking them on a guided search through your knowledge base that contains answers to frequently asked questions.
Chatbots make this search process very interactive. Instead of the customers searching through lengthy policy documents or frequently asked questions page, natural language processing can be applied to train your chatbots. Chatbots not only provide faster responses but also improves customer experience.
This use case is not entirely specific to the supply chain function. But it certainly can solve the number one problem that plagues most logistic organizations – the people problem. The latest figures from the United States Bureau of Labor Statistics indicate that annual warehouse turnover rates are 43%. The situation is no different in Indian labor markets. The attrition rates of warehouse workers across the globe are at an all-time high, and leaders must address this issue.
People analytics deals with leveraging Statistics and AI to understand the problems related to talent and predicting the reasons behind attrition is one of the widespread problems people analytics can solve.
Employee’s past performance, company benefits, employment history, employee engagement, external labor market competition, etc., can be used to effectively understand the latent factors that are causing employees to leave. This newly learned information can then be used to improve the working conditions. It’s important to note that the employee information should be anonymized and aggregated to protect the individual’s identity.
(The writer is Analytics and AI leader at Bose Corporation, who solves organizational and business problems leveraging data)
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