The Role of AI in the Global Value Chain

Introduction

A value chain describes the steps involved in taking a product from the ideation and initial design process to its delivery to the final consumer. It includes the sourcing, manufacturing, marketing and sales phases of production. Value chains are important in helping business’ efficiency so that they can deliver the most value for the least possible cost.

Michael E Porter of Harvard Business School divided a business’ activities into two categories in his concept of a value chain, namely the primary and support activities.

The primary activities further consist of five components which include:

Inbound logistics: Receiving, warehousing, and inventory management.

Operations: These are processes involved in converting raw materials into a finished product.

Outbound logistics: This involves getting the finished product to the final consumer.

Marketing and sales: These are the efforts and strategies to improve the visibility of the final product to the target consumers.

Service: These are the offerings to maintain products and enhance consumer experience e.g customer support, maintenance, repair, exchange, etc.

Support services help to improve the efficiency of the primary activities. They include:

Procurement: This is the process of obtaining raw materials for the company products.

Technological development: This involves research and development of more efficient manufacturing techniques and processes.

Human Resources Management: This involves the process of hiring, training and retaining company talents.

Infrastructure: These are the company systems and management teams e.g planning, accounting, etc.

Role of AI in Global Value Chains

This can be discussed based on the branches/divisions of the value chain. 

Inbound and Outbound Logistics: Automation of the receipt, warehousing, and delivery of finished products using artificial intelligence will improve the efficiency of the value chain. Artificial intelligence can be used for accurate inventory management to ensure efficient warehousing. Manual inventory can be exhausting and prone to error and order processing involves a huge amount of data which artificial intelligence tools can be used to analyse and interpret. These tools can also be used to forecast supply and demand based on previous data while minimising the costs and risks of unwanted inventory. In the warehouse, automation using artificial intelligence can be applied for easy documentation and updates of inventory with less time and human effort and less risks of errors.

As part of logistics, fleet management is essential in maintaining an uninterrupted progression of the supply chain. AI-enabled fleet management provides real-time logistics tracking, logistics network optimization, reduction of fleet downtime and ensuring smooth fleet operations. 

Operations: Artificial Intelligence can be used in any point of the process of converting raw materials to finished products. AI can be used in raw material selections to sort through available options and make selections based on previous productions or programmed criteria. The data can also be sorted and analysed using artificial intelligence to make predictions for future needs. The production process can also be optimised using AI to function efficiently and with minimal human intervention, preventing avoidable errors. Quality control checks can also be run using artificial intelligence to ensure an efficient process. Spend analytics and forecasting can also be done using artificial intelligence in order to ensure a smooth adaptation to market changes. 

Marketing and sales: Artificial intelligence can be used to make automated marketing decisions based on data collection and analysis, and observation of audience or economic trends. Salesforce found out that high-performing teams are 4.9 times more likely to be using artificial intelligence than under-performing ones. Artificial intelligence can be used for sales forecasting based on the analysis of historical data to determine how best to approach the target market. It can also analyse data sets to help companies understand which leads to prioritise based on the scores the AI has provided. AI powered predictive marketing content tools also enable marketing teams to be more strategic and make marketing copies more effective. 

In conclusion, in order to ensure an up-to-date and efficient global value chain, the role of artificial intelligence cannot be understated and it is important for manufacturers worldwide to take full advantage of artificial intelligence enabled tools to make their manufacturing and distribution process more effective.


References

https://throughput.world/blog/ai-in-supply-chain-and-logistics/#Benefits_of_AIPowered_Supply_Chains

https://www.mckinsey.com/industries/metals-and-mining/our-insights/building-value-chain-resilience-with-ai

https://www.supplychainbrain.com/blogs/1-think-tank/post/34972-why-ai-is-essential-to-supply-chain-transformation

https://www.investopedia.com/terms/v/valuechain.asp



Comment  0

No comments.