Tuesday, November 12, 2019

Benefits and Challenges of NLP in supply chain?

Natural language processing is a technology that provides computer systems and software to analyze, interpret and act on requests and data input through normal human language.


NLP in supply chain

Artificial intelligence and machine learning are both required to get the most out of NLP. The complexity and human language need self-teaching systems and smart algorithms to parse and understand language input and provide relevant responses and actions.

Artificial Intelligence applications utilizing natural language processing for the supply chain can benefit globally as well as local supply chains to create more reliable human-machine interfaces for not just consumers but also suppliers, manufacturers, and distributors.
With continued advancement, globalization, supply chains across the globe are going to get larger and more complex. Companies are increasingly taking the specialization approach, where all external operations and business units are divested to increase the organization's focus on their main offerings by outsourcing these operations to overseas partners. As a simplified example of this, the shoe sole making manufacturer who also makes their own shoes can outsource the shoe-making part of their operations to another business that specializes in it. This way, every organization is doing what it is best at with maximum efficiency. 

Natural Language Processing benefits to the Supply Chain

Natural language processing can assist people included in the supply chain to understand normal human communications and process information faster to drive relevant action. The following are benefits of natural language processing in the supply chain.

1. Understand and decrease potential risks with suppliers, manufacturers, and other supply chain stakeholders through analyzing reports, social media, industry news, and other areas.

2. Ensure compliance with sourcing and ethical practices through monitoring publicly-available information for potential violations by supply chain stakeholders.

3. Control the reputation of supply chain organizations to identify potential issues.

4. Diminish or remove the language barriers for uniform communications and more reliable supplier relationships.

5. Update information obtained from supply chain stakeholders through a chatbot and adaptive interview technology to assure data is captured in a reliable, consistent way.

6. Optimize the supply chain through querying complex datasets using natural language and find possibilities to enhance processes and decrease waste.

7. Improve customer satisfaction through smart, automated consumer service that gives easily-understandable supply chain information to all stakeholders.


Natural Language Processing challenges in the supply chain.

The main challenges with implementing and using natural language processing in the supply chain.

1. Training - NLP is a new way of interacting with a computer system so supply chain stakeholders will need the training to get the most out of the NLP technology.


2. Interfaces - NLP doesn't use a standard software interface to capture and manage information. Instead, users will need to ask questions and provide information using regular phrasing, grammar, and vocabulary.


3. Integration - Integrating NLP into existing technology and business processes are complex and require significant expertise and wide-ranging operational knowledge.


4. Investment - NLP is a significant investment and requires the right project management and resources to implement it effectively.


As technology continues to mature and become more functional, the applications of natural language processing for the supply chain will gain greater importance. With the help of technologies like computer vision and augmented reality, supply chain operations can become even simpler for the employees and efficient for the owners.