Using Insight Engines in an Industrial Environment

Data processing and analysis continue to take on an increasingly prominent role. Today, AI-based applications are already being used to a growing extent. AI-based applications support a diverse range of scenarios and process existing information clearly and concisely. As a result, companies can respond more quickly and pinpoint any potential opportunities for optimization.

The speed at which the framework conditions for organizations have increased enormously. Innovative products and services are entering the market and often disappearing again just as quickly. New market players are turning traditional industries upside down and competing with established companies.

Companies of all sizes and industries must live up to high flexibility and adaptability, also known as business process transformation. The most essential aspect of business process transformation is access to information. Employees must be able to access relevant information quickly and easily for companies to be agile and innovative.

Companies are storing less and less data in a structured way. Most data is unstructured in network drives, cloud storage, e-mail archives, or specialist applications. Lack of organization makes it increasingly difficult to maintain an overview and filter out the information required for everyday projects and tasks – small or large. To combat this challenge, robust solutions such as insight engines (i.e., intelligent knowledge management systems) are needed to filter and prepare information from the available data and make it accessible to the user.

Insight engines can find, analyze, link, and extract information from different data sources. In order to realize rapid, resource-efficient access to corporate data, insight engines automatically combine all information from various corporate data sources such as specialist applications, intranet, cloud, and CRM systems into a knowledge base.

When a query is made, the insight engine finds the content, extracts information, and makes it available to the user in an appropriately prepared format. The information provided is enriched with meaningful additional knowledge and made available to users within the scope of their access rights. This gives them a comprehensive and personalized overview (360-degree view) of the queried topic.

The main difference to conventional search engines is advanced artificial intelligence and speech recognition methods. Insight engines use artificial intelligence, such as machine learning and deep learning, as well as relevance models and rules to extract information from existing data and link it according to its correlation.

Insight engines use different technologies, such as Natural Language Processing (NLP) and Natural Language Understanding (NLU), to learn linguistic nuances, similarities, and meanings of different sentences. NLP ensures the insight engine processes human language correctly, and NLU confirms the user's intention is interpreted correctly. Despite the imprecision and ambiguity of human speech, the system correctly deduces the user's intent from the question and aligns all results accordingly. Instead of endless lists of results, users only receive answers that are relevant to them, enriched with context-specific information, and presented in personalized dashboards. To expand the knowledge base, in addition to the existing data, it is possible to integrate taxonomies, ontologies, and catalogs. Machine learning is used to identify patterns in data and derive predictive models.

In addition, intelligent knowledge management systems continuously analyze user behavior. For example, how often specific results were clicked or how often they were interacted with. These analytics calculate a model that automatically prioritizes and proactively displays the most relevant hits. They adapt answers to questions based on success or error rates from past interactions.

Use in an Industrial Environment

The manufacturing industry must operate efficiently despite the many complex and interlocking processes. One prerequisite for this is smooth exchanges of information and intelligent information consolidation. Innovative knowledge management solutions form an overview of all data from the different applications.

Insight engines help to retrieve information at a glance and to control processes accordingly. In doing so, they represent an effective tool for the entire manufacturing operation – from skills management, bill of materials management, quality management, and digital twin creation.

Leveraging Expertise: Skills Management

Today, employees with various backgrounds and career experience work in different departments of a manufacturing company. Information about previous employers, industries, areas of responsibility, etc., can be found in personnel files, but only a few know who brings which skills. Insight engines link departmental knowledge and support managers in maintaining an overview of each employee's competencies to ensure that the required know-how is available at the right time.

To properly link information, the application analyzes the data (e.g., documents created) from all the different data sources within the scope of the users' authorizations, interpret correlations between them, and prepare them in an index. These steps help generate 360-degree views of the terms they are looking for, whether specific competencies or specific employees.

When searching for a specific qualification, the system extracts those people who match the requirements. For example, someone who has already been involved in a similar project, an employee who brings background knowledge through their career, or experts with relevant competencies through advanced training. Semantic analyses enrich the search results with context-relevant additional information such as certificates and publications.

Technical Quality Assurance

Different applications in specialist departments and decentralized teams make it complicated for quality managers to obtain a consolidated view of projects, customers, or components.

To map the necessary overview of a component, insight engines link available information such as expert opinions, maintenance records, plans, and documentation in a 360-degree view and proactively make the required knowledge available to the user.

With an "Interactive Exploded View," the user receives a detailed view of the component, where detailed information can be retrieved with one click.

Bill of Materials

BOM management is a crucial part of designing and manufacturing products. In addition to sketches and plans, bills of materials form an essential basis of the operational database, especially in manufacturing. They document the parts or assemblies that make up an end product and detail correct assembly for determining requirements, procurement, and workflow planning.

Whether a product consists of ten or several hundred/thousands of individual parts, having the relevant information on the required components is often challenging.

Systems for managing bills of materials offer their own search functions (classic full-text searches), usually limited to the data in one system. Although users can find information on their search terms, these search functions reach their limits regarding further relevant information. Experts such as designers, engineers, or production planners are thus forced to search in many applications. Querying in many applications takes considerable time and often does not produce the desired result.

Insight engines include content from all relevant corporate data sources, extract the queried information, and provide it in 360-degree views.

Users immediately receive additional information, such as country-specific restrictions, availabilities, replacement suppliers, or complaints, without starting a new search query in another system.

Want to learn more? Check out our case study on BOM or our complete list of case studies.