Artificial Intelligence in Supply Chain

 AI in Supply Chain

AI in Supply Chain

Artificial Intelligence is neither a new subject nor just introduced in the field of technology. But the recent research and development show that AI has a vast set of applications, can adapt processes in diverse areas including Supply Chain Management. In addition to the rising applications in the present industrial market, AI has shown its importance in the Supply chain, which is likely to gain profit from Artificial Intelligence. Along with the other promising technologies like blockchain, IoT, Cloud computing, AI is going to be the next game-changer in the field of the digital world.

What is Artificial Intelligence?

What is AI

Artificial Intelligence (AI) is the intelligence shown by machines, which are capable of doing works similar to humans. Unlike the natural intelligence demonstrated by humans and animals, this does not involve any emotionality. AI, as defined by Gartner, is a technology that interprets different kinds of events, automates and supports decisions and even takes actions by applying advanced analytics and logical techniques.

In supply chain management, artificial intelligence has gained momentum as modern industries are aggressively moving towards a more flexible and agile customer-driven market. Companies are trying to optimize their resources and freeing them up to focus on more important things like working towards the customer, solving complex problems, do creative things and many more. Hence, there is a big scope for artificial intelligence to improve the supply chain by performing assigned repetitive tasks more efficiently and accurately.

AI in Supply chain and logistics:

It is very evident that, as the variables in the overall value chain are evolving, the complexity and challenges in the supply chain are also increasing rapidly. It is now more critical to maximize productivity and therefore, the shrinkage in the margin of errors must be there. With increasing competition in the connected digital world, customer expectation related to the delivery speed, product quality, response and turnaround time has grown multifold. Here comes the need of the deployment of artificial intelligence in the supply chain.

Smart technology

The introduction of artificial intelligence and machine learning in the field of the supply chain has started to change the face of the value chain. This helps to get enterprise-wide visibility into all aspects and is helping to drive more accurate capacity planning, productivity improvement, cost optimization, better quality, improve customer satisfaction etc.

The author of the the famous book "The Customer Of The Future: 10 Guiding Principles For Winning Tomorrow's Business" Blake Morgan writes that “AI is everywhere, but perhaps its greatest impact could be felt across the supply chain.”

Pros and cons

Benefits of AI in supply chain:

1. Accurate inventory management:

With the capability of handling a high level of data, AI-driven tools help to manage accurate inventory. The inventory-related variables like stock accuracy, proper consumption, stock movement, GRN are very important and time-consuming activities. In manual management, there is a high tendency for error. The complex and intelligent algorithms in AI can analyze huge data quickly, provide guidance to the proper team, and give periodic feedback on the excess and obsolete inventory to take timely decisions. With quick and accurate forecasting AI helps to predict customer behavior, minimize the overstocking, stock-out situation and hence, generate savings for the organization.

 2.      Reduction in operational cost:

In every step of operation management, AI provides huge error-free and fast support. From customer data to dispatch and payment, the end-to-end chain is huge and a lot of communication is required to complete the loop. Due to so many touch-points, there is a chance of distortion of data. AI makes all these things fast, more accurate and with minimum communication. Thus the cost involved in every step gets reduced and the cash flow improves making the balance sheet healthier.

 3.      Better warehouse management:

A warehouse generally handles a huge amount of repetitive transactions and these data are very important to get a picture of the status of an organization. An AI-driven warehouse management system can assist quick and error-free retrieval of materials, Order processing, pick-pack-ship, materials movement, invoicing and ensures smooth delivery to customers. Along with huge time savings, AI also helps to optimize the warehouse staff.

4.      Ability to predict industry trend and Improve On-Time delivery to the customer:

To serve the customer better, it is better to understand their choice and need. AI helps to predict and forecast customer behavior which, in turn, helps to create healthy inventory, less stock-out situation, and most importantly, lesser lead time.

 5.      Controlled logistics operation:

Fleet management is considered one of the toughest areas of logistics management and here, the role of AI is increasing drastically. AI and GPS-driven data provide a handy, well-analyzed and structured output that helps to make decisions on route management, milk-run strategy, vehicle optimization, ensure correct distribution channel to improve on-time delivery to customers. Industries are moving towards autonomous vehicles to manage materials from vendors and DCs. Larger and advanced facilities have started using autonomous robots in warehouses to pick, pack, storing materials also. These all are driven by AI algorithms and programming.

 6.      Enhanced safety:

Safety is the first and foremost priority of every organization. Therefore, companies are inclining towards AI to accumulate the data and prevent any mishaps. Smarter AI-driven system help to improve human and materials safety by analyzing the workplace safety data and provides information related to possible risks, near-misses, hazards identification and risk assessment (HIRA). This helps the manufacturers work proactively and decisively to keep the workplace safe.

 7.      Reduction in administration time and errors:

We can surely say that, conventionally, the businesses are spending hundreds of weekly hours doing manual, paper-based works, supplier follow-ups, monitoring the status manually. But, through AI, many of the activities can be automated. Big technology companies like Google, Amazon, Apple are using products with artificial intelligence such as AI bots for virtual assistance, Alexa, Siri.

 Challenges of AI in the Supply chain:

AI is a revolutionary technology that has the capability to make a paradigm shift in the field of the supply chain. This growth opportunity urged businesses to invest in Artificial Intelligence and machine learning applications like automated vehicles, robots, big data analysis and many more.

But it is important to know about the limitations or challenges of this technology also. Organizations need to know what the probable obstacles are during the usage or implementation of AI.

Challenges

 1. Right data source:

As artificial intelligence works on the data points and is driven by the input data, it is very important to provide the correct set of data at the initial stage. In any organization, massive data transfer happens every moment, it is quite tough to determine the correct and usable data. If you want to get a better output, decision making and analysis from this intelligent technology, information should be absolutely perfect.

 2.      Complex system:

AI is a cloud-based system and works on complex algorithms and programming. It needs high-speed internet and bandwidth to make the information flow fast. Even the users also need complex and high-tech hardware and software to use the system. Therefore, it is very much clear that the system is pretty complex in nature and any fault can lead to disaster.

 3.      High operational cost:

The AI-operated tools and machines need hi-tech facilities and network connectivity, hardware and software. Many components are also not readily available and therefore, need to be kept in stock. As the expertise is limited, the maintenance and rectification work is also needed to be carried out by experts only. Users need to be trained and this is also a high-cost thing. Therefore, the initial investment and maintenance cost is quite high.

 4.      Data security:

AI can take intelligent steps based on the data available. And when the system is connected to the digital platform with a lot of servers and processors, there a change of data breach or data security. Moreover, when it is connected to external servers, all linked data is at high risk. Therefore, manufacturers must think of their cybersecurity systems during the implementation of AI and ML.

 5.      Integration of AI with existing system:

This is one of the most common challenges faced by any organization that has implemented AI. There must be a very good understanding of the business process by the AI solution provider so that the algorithms and programming can be designed accordingly. The close coordination between the business team and the solution provider can be the success factor of AI implementation.