Natural language processing (NLP) is technology that allows software and computer systems to analyze, understand and act on requests and information input through normal human language.
Traditionally, we interact with machines using specialized programming languages or preset responses. NLP goes beyond these limitations and lets individuals use their normal speech and writing patterns to communicate with computer systems in a faster, easier and more convenient way.
Artificial intelligence and machine learning are both required to get the most out of NLP. The complexity of human language requires smart algorithms and self-teaching systems to parse and understand language input and provide appropriate responses and actions.
Natural language processing can remove much of the administrative overhead in managing the supply chain. This includes understanding local and global news and events, making it easier for stakeholders to query the supply chain, providing language translation, using adaptive forms and automating customer service.
Natural language processing can be used in many ways for the supply chain and logistics.
Parse, Analyze and Report on Areas That Could Impact the Global Supply Chain
Natural language processing is combined with unstructured data querying tools that will analyze and review publicly-available, published information. This data might appear in blog posts, videos, social media, news or other formats. NLP will identify and report on specific areas and keywords that might impact the supply chain. This includes issues with certain suppliers, major environmental changes, sourcing verification, monitoring of competitors, supply chain governance, ethical practices, shifts in policy and processes, reputation management and likely future trends.
Capture Information From Supply Chain Stakeholders Across Multiple Languages
Language barriers are a significant issue for global supply chains and logistics execution (e.g., pickup directions and instructions for truck drivers). NLP helps manage this issue by allowing local stakeholders to communicate in their native language. NLP will analyze, categorize and translate this data so it’s usable by everyone.
Input Supply Chain Data Using Adaptive Chatbots and Interviews
Chatbots are a natural implementation for NLP. They take user questions and provide human-readable output combined with the right context to allow for smarter supply chain information retrieval and decision-making. Chatbots request information from suppliers through adaptive interviews, tailoring questions to capture necessary information for optimized supply chains.
Query Supply Chain Data Information Using Natural Language
Because supply chains generate large datasets, optimizing the supply chain requires querying this data in the right way. NLP allows users to ask complex questions and guides them through the data, providing insights to answer those questions.
Automate Customer Service to Respond to Downstream Supply Chain Stakeholders
Natural language processing makes it much easier to automate customer service. Stakeholders ask questions and NLP responds with the right information or guides them to the appropriate area. This reduces administrative overhead in customer service centers and improves customer satisfaction throughout the supply chain.
Natural language processing provides many benefits to the supply chain:
There are four main challenges with implementing and using natural language processing in the supply chain.
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