Manufacturing

Four high-value use cases for cognitive manufacturing

Share this post:

Using cognitive to activate the next generation of production success

Cognitive computing represents an emerging technology that increases productivity, yield and quality while continuously learning, improving employee actions, processes and outcomes. When combined with manufacturing technologies, it results in cognitive manufacturing. It unites millions of data points into a source that discovers patterns and answers questions across the plant, including users, equipment, locations and streaming sensor data.

Cognitive manufacturing harnesses natural language and sensory-based capabilities. Cognitive manufacturing leverages current production technologies, such as the Industrial Internet of Things (IIoT), analytics, mobility, collaboration and robotics to provide tangible benefits at the plant level.

graphic defining cognitive computing and cognitive manufacturingFigure 1: Defining cognitive computing and cognitive manufacturing

Cognitive manufacturing creates new interactions between humans and machines. It enables a machine supervisor to assess the performance of a process or machine and receive immediate answers, preventing unplanned downtime. It allows machine technicians to access years of stored performance insight, quality assessments, manuals and repair detail, presented in context with user needs. It addresses a potential critical parts shortage by providing supplier/ecosystem detail, weather and transportation information and company expertise, all in a shared resolution room.

How the electronics industry is applying cognitive manufacturing

To better understand how cognitive manufacturing is being used in industries, the IBM Institute for Business Value surveyed 140 electronics executives around the world. The findings of the study reveal how some manufacturers have flipped the switch to cognitive manufacturing, showing greater return on investment (ROI) with increased productivity.

The report is structured in three sections which include:

  1. A set of baseline capabilities to embrace cognitive manufacturing based on current technologies – the “cognitively capable”
  2. Three stages of cognitive manufacturing maturity: Actives, Starters and Observers
  3. Obstacles and barriers aligned to an organization’s cognitive manufacturing maturity.

In today’s post, we’ve only highlighted some of the findings. You can download a copy of the full report, ‘Why cognitive manufacturing matters in electronics’ to read the The Institute of Business Value (IBV) study in its entirety.

Who is ready for cognitive manufacturing?

Organizations that have a good understanding of advanced analytics and the Industrial Internet of Things (IIoT) are prepared to embrace cognitive manufacturing more quickly than others. But what defines cognitive manufacturing maturity?

Not surprisingly, cognitively capable organizations actively use advanced analytics. This might include predictive analytics or big data approaches. A majority of respondents are actively pursuing these areas – over three-quarters were piloting or in stages of rollout.

The cognitively capable also use IoT or sensor data in the manufacturing environment. In the study, more than 70 percent of respondents had IIoT efforts underway. When an electronics manufac­turer develops these two competencies, it is cognitively capable. More than 65 percent of the study respondents fall into this category.

However being cognitively capable is just the start; developing expertise is what drives cognitive manufacturing value. To do so, an electronics company must have a defined cognitive manufacturing strategy and a portfolio of strategically aligned technology implementations. Companies with a strategy significantly outperform those without one. Companies that have both a strategy and implementations see better ROI for each technology. IBV analysis shows that an overarching strategy effectively queues the technologies for greater success.

Three stages of maturity

The Institute of Business Value (IBV) study identifies three distinct stages of cognitive manufacturing maturity which include organizations in the earliest stage, called Observers, followed by Starters and Actives.

graphic showing the three stages of cognitive manufacturing adoption

Figure 2: Three stages of cognitive maturity

Strategy is the crucial enabler of higher maturity

The three groups differ on two key characteristics: the presence of an overall strategy for cognitive manufacturing, and degree of strategic execution of multiple projects that enable higher project success and significantly fewer failed projects.

For example, Actives show greater ROI success on their technology projects. They also show markedly fewer failed projects. The other groups, Starters and Observers, show some high ROI projects, but lack the sustained benefits aligned to a complete strategic vision.

Actives also pursued a specific technology path, focusing first on cloud computing and IIoT upfront (sensoring), then looking to analytics – both predictive and big data. This path moves Actives toward more autonomous and self-learning systems. They expect to continue cognitive manufacturing investments, based on ROI and results that deliver more transparent processes and better insights.

How to activate your cognitive manufacturing competency

In the IBV report, the team first describe cognitive computing and how it gives rise to cognitive manufacturing. Then, review specific study findings, and lastly, recommend actions for executives. Overall, the study seeks to find answers to key questions such as, ‘How do manufacturers get beyond the obstacles and barriers to increase cognitive manufacturing maturity,’ and examines barriers to adoption and how study responders overcame challenges to move into a higher level of maturity.

Begin with strategy

A valuable take away from the report is a set of recommendations for organizations wanting to activate their cognitive manufacturing competency. Based on the IBV’s extensive client interactions and engagements, the research team offers readers important suggestions – tailored to an organization’s maturity level.

Research from The IBM Institute of Business Value clearly shows the benefits of cognitive manufacturing and getting started is an imperative. A well-documented cognitive manufacturing strategy will include:

  • Strategic imperatives and key drivers
  • Long-term vision
  • Business case
  • Competitive advantage
  • Targeted business and manufacturing processes
  • Technology baseline and desired future state
  • Analytics and automation skills assessment
  • Talent management and human resources
  • Executive sponsorship.

The importance of developing detailed use cases

The second, and perhaps more critical take away from the study is the emphasis on how important developing detailed use cases is to the overall success of a cognitive manufacturing project.

To help, the IBM Institute of Business Value recommends organizations use a template for use case detail that allows consistent and thorough documentation of elements. This enables multiple stakeholders to discuss the approach effectively. A template can expose where value is both created and lost in the current process, providing details about the core components that drive the use case.

Four initial high-value use cases for cognitive manufacturing

Manufacturing organizations face a range of challenges in maintaining desired rates of production, specifically when it comes to creating desired levels of collaboration across manufacturing operations. By focusing on high value use cases based on agreed upon key performance indicators (KPIs), it becomes easier for organizations to realize and document tangible and quantifiable benefits. The result is a clear start and finish where results are able to be measured in order to assess the real value cognitive use can play in the organization.

Here are four common example use cases that can demonstrate results, i.e., save time and money:

1. Cognitive maintenance:

Enables a machine supervisor to assess process or machine performance and receive immediate answers, preventing unplanned downtime. Using deep search and discovery, it uncovers critical patterns that improve predictive maintenance.

Cognitive approaches to maintenance offer potential solutions to organizations that face challenges related to unplanned machine downtime. Additionally, cognitive approaches can help speed time and drive flexible automation for organizations challenged in configuring machines quickly to increase flexibility.

2. Cognitive repair:

Allows machine technicians to access years of historical detail including performance, quality and repairs, plus manuals and bulletins in context. Technicians can become smarter and faster with each repair.

3. Critical parts management:

Prevents shortages with context-driven supplier/ecosystem detail, weather and transportation information, and company expertise via shared digital resolution rooms. This keeps lines up and running and increases business agility.

4. Visual inspection:

Evaluates five key defect types during in-line processes and communicates with systems that process and classify them with “go/no go” flags for monitoring and verification. It removes defective parts and devices before they get into the marketplace. Take for example, the challenge of rapidly reconfiguring production lines – a process that is critical to moving toward a low volume, high mix – and more profitable future.

More about cognitive maintenance, repair and visual inspection solutions from IBM

The average company can reduce its spend on preventive maintenance by up to 50%, according to ARC Advisory Group’s Enterprise Asset Management and Field Service Management Market Study.

Maximo Asset Management and Predictive Maintenance provides an integrated approach to managing discrete or complex assets, which can help organizations overcome challenges rooted in their aging infrastructures or human assets, and in their siloed or disconnected systems. Combining IBM Maximo and PMQ software with IoT data and cognitive computing gives enterprise the ability to manage the management of their physical infrastructure assets, while enabling them to make better decisions using real-time operational insight.

The IBM Visual Inspection for Quality offering delivers reliable results with low escape rates to reduce the dependency on specialized labor and to improve throughput of quality processes across multiple industries. The solution is being tried out successfully by several global corporations producing electronics, automotive, and industrial products.

Resources to help you continue learning

  • IoT & ManufacturingLearn more about Industry 4.0 and how to get started with Watson IoT for Manufacturing
  • Enterprise Asset Management: Download the paper to understand the impact and value of enterprise asset management.
  • Lowering costs with preventive maintenance: Learn more about employing IoT solutions to drive more up-time and lower costs with this report: Using the Internet of Things for preventive maintenance.
  • Manufacturing inspection needs: If you have manufacturing inspection needs which could benefit from IBM cognitive capabilities please take a few moments to learn more about IBM Visual Inspection for Quality.

More about the IBM Institute for Business Value

The IBM Institute for Business Value, part of IBM Global Business Services, develops fact-based strategic insights for senior business executives around critical public and private sector issues.

Please take a minute to download the full copy of the report, ‘Why cognitive manufacturing matters in electronics.’

More Manufacturing stories
By Sarah Dudley on May 30, 2018

See it in action: robots, IoT and AI unite for optimized operations

How can you improve quality, prevent downtime, and increase throughput in your manufacturing environment? The obvious answer. You bring in a robot (meet ARMonk, ARMie for short), the Internet of Things (IoT), and artificial intelligence (AI). In the series of short demo videos below, ARMie will team up with a series of experts to show you examples […]

Continue reading

By Dr. Stephan Biller on April 24, 2018

How to get started with AI-powered manufacturing

In a recent post, I examined how AI-powered manufacturing solutions enable manufacturers to aggregate data from multiple sources and apply AI for better outcomes. Now, it’s time to talk about how. Below, I have some recommendations on how to begin your AI-powered manufacturing journey. Start small, start anywhere, start now Some manufacturers are put off […]

Continue reading

By Dr. Stephan Biller on April 19, 2018

Watson and AI-powered manufacturing dominate 2018

As VP of Offering Management for IBM Watson IoT, I am often asked what Watson can do for manufacturing. As it happens, I had the chance to speak to Ralph Rio, of ARC Advisory Group about exactly this a few weeks ago at the ARC Industry Forum. The answer lies in artificial intelligence (AI). AI-powered solutions enable […]

Continue reading