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14 Case Studies of Manufacturing Analytics in 2024

Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Manufacturing is one of the top 3 industries which account for the biggest share of the big data and analytics revenues worldwide. Manufacturing analytics have numerous use cases which enable businesses to predict machinery’s future use, prevent failures, forecast maintenance requirements, and identify areas for enhancement.

We’ve compiled 14 success stories and case studies of deploying manufacturing analytics to help businesses identify areas for implementation.

What are the top use cases of manufacturing analytics?

Manufacturing data including machine and operator information can be processed and analyzed for:

  • Supply chain:
    • Demand forecasting
    • Inventory / asset management
    • Order management
    • Maintenance optimization
    • Risk management
  • Logistics:
    • Automation and robotics
    • Transportation allocation
  • Product development:
    • Product progress measurement
    • End user experience estimation
  • Sales:
    • Price optimization

To explore use cases in more detail, feel free to read our guide to the top 10 manufacturing analytics use cases.

What are some interesting case studies of manufacturing analytics?

Below is a list of case studies that we’ve compiled from different vendors and research resources:

VendorCustomerCountryIndustryUse CaseResults
PTCSIGSwitzerlandPackaging & ContainersAsset management & optimization
- Discovered micro-outages that hadn’t been tracked due to manual recording processes.
- Identified overconsumption of energy by certain machines.
- Increased speed of production line.
PTCColfaxUSAMechanical & Industrial Engineering- Predictive maintenance
- Asset management
- Reduced service costs through remote monitoring and predictive maintenance of equipment.
- Detect patterns and anomalies in applications.
- Increased asset utilization
Rockwell AutomationNational Engineering Industries Limited (NEI)IndiaBrownfield Factory- Predictive maintenance
- Risk management
- Increased visibility into line, shop floor, plant and enterprise performance.
- Created a connected smart plant with integrated information from supply chain for factory scheduling, energy management system, resource allocation and material visibility.
- Avoided unplanned breakdown by taking proactive actions.
Rockwell Automation-USAPolymer & Coating Materials- Asset management
- Product development
- 12% increase in production.
- 6% reduction in natural gas use/ton.
- 50% decrease in process variability.
SASKia MotorsUSAAutomotives
- Asset management
- Product development
- Predictive maintenance
- End user experience estimation
- Forecasted failure rates and maintenance costs.
- Reduced production time.
- Categorized and extracted complaint types from customer surveys to uncover quality issues.
- Proactively alerted dealers to future maintenance issues to help them optimize repairs.
SASSiemens HealthineersGermanyMedical Technology- Predictive maintenance
- Predict product failure probabilities.
- 36% less system downtime.
- Optimized service technician deployment, because they know in advance which parts are needed at a particular location.
KonuxDeutsche BahnGermanyRailway Infrastructure- Asset management
- Predictive maintenance
- Reduce 25% of maintenance costs.
- Reduce delay-causing failures.
Sight Machine--Paper Manufacturing- Predictive maintenance
- Asset management
- Identify hidden relationships between production parameters and quality issues.
- Create a digital twin of the production process
Sight Machine--Automotives- Asset management
- Risk management
- Built manufacturing data models.
- Implemented Root Cause Analysis (RCA) to identify the real source of the quality issue.
Sight Machine--Industrial Manufacturing- Asset management
- Identified $500K of potential saving costs in 3 weeks.
- Reduce scrap costs by 30%.
Augury--Infant Formula Manufacturing- Preventive maintenance
- Inventory management
- Automation
- Detected $350K worth of system errors.
- Identified opportunity for automating reliability practices.
- Early detection of fan malfunction which enabled the team to order parts without accruing expedited shipment costs.
FogHorn-ChinaEV Charger Manufacturing- Predictive maintenance
- Asset management
- Reduced false positive alerts generated by chargers.
- Anticipated maintenance needs
Mu Sigma-USAFood Manufacturing- Price optimization
- Identified stores that had the potential to increase price of a product up to $0.25 from its closest competitor without impacting sales.
- Increase revenue by 2-3%.
Mu Sigma--Energy - Inventory management
- Optimized excess inventory and was projected to save $15M-$20M annually.

To explore manufacturing analytics vendor landscape, scroll down our comprehensive list of manufacturing analytics software and vendors.

And if you believe your business will benefit from a manufacturing analytics software, contact us to help you find the right solution for your business:

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Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
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Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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