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IBM Manufacturing China

MIKOPTIK

Enterprise Knowledge Search

MIKOPTIK needed to reduce the time engineers spent finding R&D knowledge and coordinating across siloed systems because access to design expertise depended too heavily on a small number of key specialists.

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40% boost in access to engineering information
50% reduction in lead times from engineering design to production planning
90% from the previous 50%

Reported impact

Business outcome

MIKOPTIK improved access to engineering information, shortened the handoff from design to production planning, and raised service quality by making critical R&D knowledge easier to find and reuse.

  • Customer Experience
  • Productivity Improvement
  • Risk Reduction

Solution

With support from XPower and IBM, MIKOPTIK built an automated knowledge-base management system for its R&D lifecycle. IBM watsonx Assistant acts as the virtual assistant interface, IBM Watson Discovery handles data discovery, classification, and content management, IBM Cloud Pak for Business Automation automates the supporting workflows and data retrieval, and IBM Cloud Pak for Integration connects the company's previously siloed R&D systems. By combining watsonx Assistant, Watson Discovery, and large language models, the solution lets engineering and production teams query technical information in natural language and receive the right product, design, and blueprinting content more quickly. The same architecture improves collaboration because it unifies access to information across multiple applications rather than leaving knowledge trapped with a handful of engineers. In effect, MIKOPTIK turned scattered R&D content into a searchable, workflow-connected engineering knowledge layer.

Data flow

R&D information from design and engineering systems is connected through Cloud Pak for Integration, classified and retrieved with Watson Discovery, exposed to users through watsonx Assistant, and coordinated with Cloud Pak for Business Automation so teams can find and act on technical knowledge faster.

Solution architecture

4 components · 2 layers
  1. Serving
    • IBM watsonx Assistant Provides the natural-language interface used by engineers to access R&D knowledge.
    • IBM Watson Discovery Discovers, classifies, and manages engineering content inside the knowledge base.
  2. Orchestration
    • IBM Cloud Pak for Business Automation Automates workflows and data-retrieval steps across the R&D knowledge process.
    • IBM Cloud Pak for Integration Connects the various R&D systems so knowledge can be shared across engineering applications.

Architecture clues

  • The assistant supports both internal engineering collaboration and technical service interactions.
  • The implementation connects previously siloed R&D systems.
  • The solution is positioned as an automated knowledge-base management system for the full R&D lifecycle.

Evidence from the source

Improved technical service correspondence and customer service experience, stabilizing at 90% from the previous 50%.
Increased efficiency of engineering information extraction, comprehension and technical review by 40%.
Shortened lead times from engineering design to production planning by 50%.
The solution consists of IBM watsonx Assistant as the virtual assistant interface, IBM Watson Discovery for data discovery, classification and management, IBM Cloud Pak for Business Automation to automate workflows and data retrieval and IBM Cloud Pak for Integration to connect the various R&D systems to each other.
The solution significantly improves efficiency and productivity within the R&D operations by providing seamless access to vital information and facilitating collaboration across teams.
Transparency Evidence & methodology 100/100 reference value · 89% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value100/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact25/25
    3 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence89%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    5 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. IBM

    MIKOPTIK customer story

    First-party
    https://www.ibm.com/case-studies/mikoptik
    Source type
    Vendor-published customer story
    Published
    May 22, 2024
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

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