Manoeuvring Artificial Intelligence into Supply Chain Management

An article by Nachiket Dangre, PGPXP(AI & ML) Student, Woxsen University and Dr. Hemachandran K, Professor, AI & ML, Woxsen University

Supply Chain Management is defined as the management of the flow of goods and services, which involves the movement and storage of raw materials, work-in-process inventory, and finished goods as well as end to end order fulfilment from point of origin to point of consumption.

- Wikipedia

Artificial Intelligence is an intelligence displayed by machines, in which, learning and action-based capabilities mimic autonomy rather than process-oriented intelligence.

AI can be broken down into two categories:

• Augmentation: AI, which assists humans with their day-to-day tasks, personally or commercially without having complete control of the output. Such Artificial Intelligence is used in Virtual Assistant, Data analysis, software solutions; where they are mainly used to reduce errors due to human bias.
• Automation: AI, works completely autonomous in any field without the need for any human intervention. For example, robots performing key steps in manufacturing plants.

Applications of AI in Supply Chain Management

The application of AI in Supply Chain related-tasks holds high potential for boosting top-line and bottom-line values. Studies have suggested that valuable time and money is wasted on trivial supply chain related-tasks that are performed by humans.

Industries spends an average of, 55 hours in manual, paper-based processes; 39 hours  in chasing invoice exceptions, discrepancies and errors and 23 hours to respond to supplier inquiries per week

– mhlnews.com

1. Chatbots for Operational Procurement

Streamlining procurement related tasks through the automation and augmentation of chatbot requires access to robust and intelligent data sets, in which, the ‘Procuebot’ would be able to access as a frame of reference, or it’s ‘brains.’

In daily tasks, Chatbots could be utilized for-

• To speak with suppliers during trivial conversations.
• To set and send actions to suppliers regarding governance and compliance materials.
• To place purchasing requests.
• To do research and answer internal questions regarding procurement functionalities on a supplier/supplier set.
• In Receiving/filing/documentation of invoices and payments/as per order requests.

2. Machine Learning (ML) for Supply Chain Planning (SCP)

ML, applied within SCP could help in forecasting within the inventory, demand and supply. If applied correctly through SCM work tools, ML could revolutionize the agility and optimization of supply chain decision-making.

By utilizing ML technology, SCM professionals responsible for SCP would be giving best possible scenarios based upon intelligent algorithms and machine-to-machine analysis of big data sets. This kind of capability could optimize the delivery of goods while balancing supply and demand, and wouldn’t require human analysis, but rather it requires actions in setting parameters to success.

3. Machine Learning for Warehouse Management

Taking a closer look at the domain of SCP, its success is heavily reliant on proper warehouse and inventory-based management. Regardless of demand forecasting, supply flaws (overstocking or under stocking) can be a disaster for any consumer-based company/retailer.

ML provides an endless loop forecasting, which bears a constant self-improving output. This kind of capabilities could reshape warehouse management.

4. Autonomous Vehicles for Logistics and Shipping

Faster and more accurate shipping reduces lead time and transportation expenses, adds various elements of environmentally friendly operations that reduce labour costs, and most important among that is the widened gap between the competitors.

Where drivers are restricted by law from driving more than 11 hours per day without taking a break in between, a driverless truck can drive nearly 24 hours per day. That means the technology would be effectively double when ruled by AI. It is reflected at the output of the U.S. transportation network by reducing the cost by 25 percentage.

- techcrunch.com

5. ML and Predictive Analytics for Supplier Selection and Supplier Relationship Management (SRM)

Supplier selection and sourcing from the right suppliers is an increasing concern for enhancing supply chain sustainability, Corporate Social Responsibility (CSR) and supply chain ethics. Supplier-related risks have become the ball and chain for globally visible brands. One slip-up in the operations of a supplier body and bad PR is heading right towards your company.

Data sets, generated from SRM actions, such as supplier assessments, audits, and credit scoring provides an important basis for taking further decisions regarding to a supplier.

With the help of Machine Learning and intelligible algorithms, this passive data gathering could be made active.

Supplier selection would be more predictive and intelligible than ever before; by creating a platform for success from the very first collaborations. All of this information would be easily available for human inspections generated through machine-to-machine automation; by providing multiple ‘best supplier scenarios’ based on whatever parameters, the user desires.

- kodiakrating.medium.com