How Chatbot Can Help With Knowledge Management in Manufacturing?

Massive amounts of data are fueling ideation and creativity in human’s manufacturing capacities—from product development and strategic decision-making to solving everyday problems on the factory floor. Industry 4.0 technologies have transformed technical manufacturing processes, as well as the ways teams create value and interact in manufacturing environments.

Knowledge management (KM) is foundational to these functions, where improving access to business-critical insights and information drives benefits to productivity and efficiency—and to employee understanding and growth. Achieving this level of “organizational wisdom” in manufacturing depends not only on the use of process-oriented technologies but also on employee interactions with intelligent KM tools that effectively improve human competencies. Recent developments in NLP have set technology on a course towards real-time understanding and management of both spoken and written natural human language.

Natural language processing (NLP) and its use in human-facing, artificial intelligence (AI)-driven chatbots are revolutionizing how companies and their employees apply organizational wisdom in manufacturing. As a result, the future of KM lies in AI’s ability to rapidly retrieve relevant information and insights, combined with “conversational AI” capabilities when interacting with humans via chatbots.

What Is Knowledge Management in Manufacturing?

Knowledge management consists of all processes designed to assist employees and company stakeholders in the search and retrieval of information from their company’s codified knowledge base. In modern manufacturing, this knowledge base can include vast amounts of information—everything from historical equipment data and customer use cases to standard operating procedures (SOPs) essential to employees.

Today, manufacturers face an unprecedented challenge: Their interconnected systems produce enormous volumes of data in real time, and that data must be stored, managed, and analyzed effectively to maximize its value. How can they do this at scale and at speeds that make these processes practical for everyday functions?

For years, manufacturers have used AI and machine learning (ML) to streamline data functions and back-end processes, freeing their human resources for intelligence-driven, high-value labor. Access to institutional information has been critical to employees’ success in these capacities as well. But faced with an oversaturation of information, human errors become common. As a result, companies need to equip employees with an unprecedented ability to find institutional information efficiently to succeed.

Herein lies the core advantage of employee-facing conversational AI in manufacturing environments— chatbots that can sort and process vast amounts of data, providing superior KM capabilities for internal teams. Users can retrieve any information they need at a moment’s notice through natural commands, rather than digging through electronic records manually. This single solution can be applied to any number of use cases and roles in manufacturing environments. With the right governance, chatbots can become an organization-wide solution for employee KM.

Chatbots driven by conversational AI make these capabilities accessible to any employee and scalable across the organization. Chatbots are quickly becoming a key strategic investment for companies that want to improve KM across the enterprise. Gartner notes a 160% increase in corporate interest in chatbots and similar technologies starting in 2018, driven in part by a desire to improve “knowledge management and user support.”

Examples of Knowledge Management in Manufacturing

Connected systems in manufacturing produce a constant stream of data. The most competitive companies are those that can convert this data into practical insights in as close to real time as possible. These insights allow manufacturers to excel in a number of areas, including adapting to customer demand, improving equipment maintenance, optimizing inventory tracking, and streamlining operations.

Wide-scale adoption of this “agile factory” model is already underway with the broad use of Big Data storage, processing, and analytics tools, as well as the application of automated technologies that improve technical processes today. Conversational AI represents the next evolutionary step in manufacturing, in this case because it makes possible the effective management and analysis of vast amounts of data on the part of humans.

Compared to intelligent machines, humans are notably limited in their ability to retrieve and process large volumes of data. Conversely, machines lack the ingenuity and creativity that data empowers and subsequently drives a business forward.

“What NLP does is make possible the efficient and effective management of inordinate amounts of data and information, beyond what has ever been possible by human capacity,” CIO describes. “And it would make this, for all intents and purposes, effortless for humans.”

Chatbots, in this case, represent perhaps the most straightforward method for humans to interface with these intelligent capabilities. They allow humans to interact with NLP-driven artificial intelligence as they would with any person—through a normal conversation featuring intermittent queries and responses.

In manufacturing, countless use cases exist for this type of model at multiple levels of the organization, including:

  • for factory workers retrieving and updating standard operating procedures
  • for executives seeking to understand company and employee hierarchies
  • for IT professionals seeking updates on ITSM automation/ticketing
  • for HR professionals studying or comparing policies and benefits

This “convergence of both [the] physical and digital world,” as Deloitte describes it, represents nothing short of a quantum leap in how employees engage with intelligent digital tools. And although the end results might be similar to non-NLP functions on a case-by-case basis, the cumulative time savings and the availability of information at minimal effort drive unprecedented benefits at enterprise levels.

How to Use Conversational AI for Knowledge Management in Manufacturing

Manufacturing leaders can get a better understanding of chatbots as they apply to their businesses by gaining a deeper understanding of AI. Conversational assistants like chatbots are predicated on AI, whose key function is making accurate predictions based on data available. In the case of NLP, this “data” is the information provided by humans, and the “prediction” is a machine’s understanding of the human’s intent behind the words the human has used.

In this way, conversational assistants automate tasks and human queries. Compared to advanced analytics, human’s natural language is, by its nature, light on details and context. AI uses predictive capabilities to (a) inquire further with the human if necessary, and then (b) anticipate what information the human truly needs based on its understanding of that person’s intent.

In this way, AI and KM are “two sides of the same coin,” as Deloitte describes, where “an advanced KM platform with a built-in AI engine can bring contextual knowledge and predictive models for a business problem and help practitioners discover effective solutions faster.”

Success at these everyday functional levels creates countless new business opportunities in any industry. In fact, 69% of global data and analytics decision-makers whose firms were adopting automation said they have implemented or plan to implement chatbots in the next 12 months, Forrester reports. But manufacturing stands to benefit in particular, due to its high volume of time-sensitive data and the complex nature of its operations and processes. Potential benefits include:

  • increasing the speed and success of executing key manufacturing processes
  • developing a GMP-compliant chatbot for employees to query in real-time scenarios
  • empowering employees to perform and grow, increasing satisfaction and reducing turnover
  • increasing profitability by visualizing and acting on operational improvement opportunities

Conclusion

Forrester predicts companies’ investments in self-service technologies like chatbots for knowledge management will only grow as they “increasingly adopt prescriptive AI scenarios to streamline inquiry capture [and] resolution,” among other uses. Improving and automating key aspects of knowledge management accelerates “organizational wisdom,” or the broad use and ongoing optimization of insights across the organization in real time.

In this way, enterprise knowledge management cannot be an afterthought for manufacturers because interactive KM through chatbots is essential to maintaining a competitive edge. Fortunately, these capabilities are at the center of what Botpress seeks to achieve as a company—helping machines to better understand humans is what inspires our development of conversational AI. Contact one of our manufacturing KM experts today to learn more.