Merging top-down and bottom-up planning approaches

This blog series discusses the complex tasks energy utility companies face as they shift to holistic grid asset management to manage through the energy transition. The first post of this series addressed the challenges of the energy transition with holistic grid asset management. The second post in this series addressed the integrated asset management platform and data exchange that unite business disciplines in different domains in one network.

Breaking down traditional silos

Many utility asset management organizations work in silos. A holistic approach that combines the siloed processes and integrates various planning management systems provides optimization opportunities on three levels:

1. Asset portfolio (AIP) level: Optimum project execution schedule

2. Asset (APMO) level: Optimum maintenance and replacement timing

3. Spare part (MRO) level: Optimum spare parts holding level

The combined planning exercises produce budgets for capital expenditures (CapEx) and operating expenses (OpEx), and set minimum requirements for grid outages for the upcoming planning period, as shown in the following figure:

Asset investments are typically part of a grid planning department, which considers expansions, load studies, new customers and long-term grid requirements. Asset investment planning (AIP) tools bring value in optimizing various, sometimes conflicting, value drivers. They combine new asset investments with existing asset replacements. However, they follow different approaches to risk management by using a risk matrix to assess risk at the start of an optimization cycle. This top-down process is effective for new assets since no information about the assets is available. For existing assets, a more accurate bottom-up risk approach is available from the continuous health monitoring process. This process calculates the health index and the effective age based on the asset’s specific degradation curves. Dynamic health monitoring provides up-to-date risk data and accurate replacement timing, as opposed to the static approach used for AIP. Combining the asset performance management and optimization (APMO) and AIP processes uses this enhanced estimation data to optimize in real time.

Maintenance and project planning take place in operations departments. The APMO process generates an optimized work schedule for maintenance tasks over a project period and calculates the optimum replacement moment for an existing asset at the end of its lifetime. The maintenance management and project planning systems load these tasks for execution by field service departments.

On the maintenance repair and overhaul (MRO) side, spare part optimization is linked to asset criticality. Failure mode and effect analysis (FMEA) defines maintenance strategies and associated spare holding strategies. The main parameters are optimizing for stock value, asset criticality and spare part ordering lead times.

Traditional planning processes focus on disparate planning cycles for new and existing assets in a top-down versus bottom-up asset planning approach. This approach leads to suboptimization. An integrated planning process breaks down the departmental silos with optimization engines at three levels. Optimized planning results in lower outages and system downtime, and it increases the efficient use of scarce resources and budget.

Source: ibm.com

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Promote resilience and responsible emissions management with the IBM Maximo Application Suite

Embracing responsible emissions management can transform how organizations impact the health and profitability of their assets. This opportunity is undeniable. An IBM CEO study, based on interviews with 3,000 CEOs worldwide, reveals that CEOs who successfully integrate sustainability and digital transformation report a higher average operating margin than their peers. Additionally, more than 80% of the interviewed CEOs say sustainability investments will drive better business results in the next five years. This study underscores the transformative potential of aligning businesses with sustainable practices.

As leaders in asset management operations, you must help your company deliver on the bottom line. Let’s explore how the IBM® Maximo® Application Suite (MAS) can help you optimize the efficiency of your assets through operational emissions management.

Unlocking the benefits of emissions management

Emissions management is not only about tracking greenhouse gases; it also involves controlling and overseeing a wide range of emissions released into the atmosphere during industrial processes. Emissions can be intentional, such as exhaust gases from a power plant, or they can be unintentional, like pollutants from manufacturing. These processes result in byproducts, including leaks, effluents, waste oil and hazardous waste.

To manage these byproducts effectively, focus on optimizing your assets and identifying emerging issues early on. Well-maintained assets produce fewer byproducts and last longer. Additionally, minimizing waste and hazardous materials promotes a safer and cleaner environment.

Besides environmental responsibility, emissions management boosts the bottom line through operational efficiency, regulatory compliance, safer working environments and an enhanced corporate image. Let’s explore each of these aspects in more detail. 

Strategic planning and operational efficiency

Strategic maintenance planning drives significant cost savings. Efficient assets have longer lifespans, improved performance and help to ensure uninterrupted production. For example, Sund & Baelt automated their inspection work to monitor and manage its critical infrastructures to help them reduce time and costs. With a better understanding of asset health and the risks to address with proactive maintenance, Sund & Baelt estimates that they can increase the lifetime of bridges, tunnels and other assets while decreasing their total carbon footprint. Establishing common sustainability goals also encourages collaboration among typically siloed departments, like operations, safety, and maintenance. Fostering this collaboration better positions you to lead future asset management programs and achieving these objectives enhances your organization’s operational health.

Compliance and fines

Regulatory bodies like the US Environmental Protection Agency (EPA) set stringent standards for companies to meet. Emissions management is pivotal in enabling compliance, as it helps organizations trace and resolve issues. This approach fosters greater accountability, driving a culture of responsibility and transparency within the company. Non-compliance leads to rapid accumulation of fines, emphasizing the importance of adhering to regulations. For example, to help protect the stratospheric ozone and reduce the risks of climate change, the EPA has levied millions of dollars of fines to companies that mismanage emissions under the Clean Air Act. In 2022, the Inflation Reduction Act amended the Clean Air Act and introduced new fines for methane leaks starting at USD 900 per metric ton of methane emissions in 2024, rising to USD 1,500 by 2026. According to the Congressional Research Service, this affects over 2,000 facilities in the petroleum and natural gas industry, and is expected to see fines of USD 1.1 billion levied in 2026, rising to USD 1.8 billion in 2028. This would result in an average facility facing annual fines totaling USD 800,000.

Operational health and working environment

Emissions management efforts can help establish a safe working environment across your organization. Reducing exposure to hazardous substances promotes better air quality reducing health risks and positively impacting the well-being of workers. Complying with occupational health and safety standards creates a workplace that prioritizes employee safety and meets regulatory requirements. Managing and monitoring emissions further reduce the likelihood of accidents and incidents. Additionally, emissions management includes developing strategies for handling emergencies related to hazardous materials. As stated by VPI, “There are always inherent dangers, but you can make them safe places to work by employing a robust and efficient maintenance strategy and safety systems of work.” Having a robust operations maintenance strategy in place enhances the organization’s ability to respond effectively to unexpected incidents, safeguarding employees.

An efficient, sustainable, and responsible enterprise reaps the benefits of a healthy culture. This approach attracts top talent and becomes appealing to investors. With more buyers favoring sustainable and responsible vendors, you’ll see an increase in sales to this market.

Enterprises demonstrating clear progress in sustainability commitments often receive more support from governing bodies. The Inflation Reduction Act, for example, offers significant tax credits for companies that can capture, and store carbon dioxide emitted from industrial operations. This external validation reinforces the need for effective emissions management. It emphasizes the multifaceted benefits to businesses, society and the environment. 

In short, emissions management involves taking charge, reducing waste and making your business more efficient, performant and healthy.

Better manage emissions with MAS

MAS is a comprehensive suite of applications designed to improve asset health and reliability. How can MAS help you better manage emissions?

Driving a reliability and sustainability culture starts with designing the optimal strategy for your asset operations, placing emphasis on the reliability and sustainability of your critical assets. With Maximo Reliability Strategies, cross-functional teams can accelerate failure mode and effect analysis (FMEA) for assets prone to emission issues. Asset operations teams can then apply reliability-centered maintenance strategies to ensure your assets are managed to the highest standards of reliability.

Key components in MAS, including Maximo® Health, Maximo® Manage and Health, Safety and Environmental Management (HSE), play a pivotal role in emissions management. These applications provide operational data capture, governance, safety measures and incident management. Specifically, the HSE component offers occupational health incident tracking, process safety, permits, consents, identification of environmental emissions, ISO14000 requirements compliance and investigations. 

Ensuring the optimal health of your assets needs a team with a high-performance culture empowered with the tools to realize their vision. To enhance your emissions management strategy, apply Asset Performance Management (APM) within MAS by using components like Maximo® Monitor and Maximo® Predict, which are powered by industry leading algorithms and AI through IBM watsonx services. With APM, you can take a proactive and prescriptive approach to emissions management, leading to even greater efficiency gains and asset health. 

Envizi and MAS: Better together

The IBM Envizi ESG Suite offers key capabilities for a comprehensive emissions management solution, complementing the operational excellence that MAS enables.

Envizi delivers top-down management reporting, while MAS offers bottom-up operational asset management. Envizi provides enterprise and site-level reporting, whereas MAS adds asset-level reporting for full traceability and accountability. With both solutions, you get visibility across the problem spectrum, from the enterprise to the asset level, whether through Envizi or MAS. Furthermore, you can address issues surfaced by Envizi or MAS directly at the asset level or even prevent these issues from occurring in the first place. 

With these two solutions, asset management leaders can accurately gauge their organizations’ performance and the operationalization of their sustainability goals.

Source: ibm.com

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OEE vs. TEEP: What’s the difference?

Breakdowns, equipment failure, outages and other shop floor disruptions can result in big losses for an organization. Production managers are tasked with ensuring that factories and other production lines are getting the most value out of their equipment and systems.

Overall equipment effectiveness (OEE) and total effective equipment performance (TEEP) are two related KPIs that are used in manufacturing and production environments to help prevent losses by measuring and improving the performance of equipment and production lines.

What is overall equipment effectiveness (OEE)?

OEE is a metric used to measure the effectiveness and performance of manufacturing processes or any individual piece of equipment. It provides insights into how well equipment is utilized and how efficiently it operates in producing goods or delivering services.

OEE measures the equipment efficiency and effectiveness based on three factors. The OEE calculation is simple: availability x performance x quality.

What is total effective equipment performance (TEEP)?

TEEP is also a metric used in manufacturing and production environments to measure the overall efficiency and effectiveness of equipment or a production line. It includes all the potential production time, including planned and unplanned downtime.

TEEP is calculated by multiplying four factors: availability x performance x quality x utilization.

How are OEE and TEEP different?

The main difference between these two metrics is that while OEE measures the percentage of planned production time that is productive, TEEP measures the percentage of all time that is productive. 

It’s important when making these calculations of time to use the right terminology. Here are a few common ways to measure time within a production context:

• Unscheduled time: Time when production is not scheduled to produce anything (as opposed to “scheduled time”).
• Calendar time: The amount of time spent on a job order up to its completion.
• Total operations time: The total amount of time that a machine is available to manufacture products.
• Ideal cycle time: The theoretical fastest possible time to manufacture one unit.
• Run time: The time when the manufacturing process is scheduled for production and running.

OEE primarily focuses on the utilization of available time and identifies losses due to availability, performance and quality issues. It helps identify areas for improvement and efficiency optimization.

TEEP, on the other hand, provides a broader perspective by considering all potential production time, including planned downtime for preventive maintenance or changeovers. It aims to measure the maximum potential of the equipment or production line. 

OEE is typically used to measure the performance of a specific piece of equipment or a machine. It helps you understand how effectively equipment is being utilized during actual production time. OEE is commonly used as a benchmarking tool to track and improve equipment performance over time. It helps identify bottlenecks, areas for optimization and improvement initiatives.

TEEP is used to measure the overall performance of an entire production line or multiple pieces of equipment working together. It provides a holistic view of the effectiveness of the entire system. If you are interested in understanding the maximum potential performance of your production line, including planned downtime for maintenance, changeovers or other scheduled events, TEEP is the performance metric to use. TEEP can be helpful in production capacity planning and determining the capabilities of your equipment or production line.

How can OEE and TEEP be used together?

1. Start with OEE analysis: Begin by calculating the OEE for individual machines or equipment within your production line. OEE analysis helps pinpoint the causes of losses and inefficiencies at the equipment level. A digital asset management platform can provide real-time data to help with this calculation.

2. Identify bottlenecks: Use OEE data to identify bottlenecks or areas where equipment performance is suboptimal. Look for machines with lower OEE scores and investigate the underlying issues. This can help you prioritize improvement efforts and target specific machines or processes that have the most significant impact on overall performance.

3. Evaluate TEEP for the entire line: Once you have assessed the OEE for individual machines, calculate the TEEP for your entire production line. TEEP takes into account all potential operating time—including planned and unplanned downtime—providing a broader perspective on the overall performance of the line.

4. Compare OEE and TEEP: Compare the OEE and TEEP data to gain insights into the gap between actual performance and the maximum potential performance of the production line. Identify the factors contributing to the difference between the two metrics, such as scheduled maintenance, changeovers or other planned downtime. This comparison can help you understand the overall efficiency and effectiveness of the production line.

5. Address common issues: Analyze common issues identified through OEE and TEEP analysis and devise strategies to address them. This may involve improving machine reliability, procuring new equipment, integrating continuous improvement methodologies, reducing setup or changeover times, enhancing product quality or optimizing maintenance management. Implementing targeted improvement initiatives can help bridge the performance gap and maximize the overall equipment performance.

6. Track progress over time: Continuously monitor and track both OEE and TEEP metrics over time to assess the effectiveness of your improvement efforts. Regularly evaluating these metrics allows you to measure the impact of implemented changes and identify new areas for optimization.

By combining OEE and TEEP, you can conduct a comprehensive analysis of current equipment performance at both the individual-machine and production-line levels. This integrated approach provides a deeper understanding of performance factors, helps prioritize improvement efforts, and maximizes the overall effectiveness and efficiency of your manufacturing operations, allowing production managers to achieve higher throughput and maximum uptime.

World-class observability with IBM Maximo

IBM Maximo is enterprise asset management software that delivers a predictive solution for the maximization of equipment effectiveness. Maximo is a single, integrated cloud-based platform that uses AI, IoT and analytics to optimize performance, extend the lifecycle of assets and reduce the costs of outages. 

Take a tour to see how Maximo can achieve OEE improvement while reducing the operations costs of overtime, material waste, spare parts and emergency maintenance.

Source: ibm.com

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CMMS vs. EAM: Two asset management tools that work great together

Most organizations can’t run without physical assets. Machinery, equipment, facilities and vehicles provide economic value or benefit operations. In most cases, they are fundamental to the performance of the organization, regardless of whether they are small-scale laptop portfolios or vast transportation networks. Energy companies rely on uninterrupted power supplies, airlines aim to ensure passenger safety, hospitals must provide quality patient care, haulage companies need up-to-date data on spare parts to maintain service levels.

Organizations can’t work effectively if they don’t invest to keep their assets running cost-effectively throughout their lifecycle. To do that, technicians, facility managers, maintenance teams, reliability engineers and project managers need accurate, real-time information at their fingertips.

◉ The growing complexity and scale of operations across industries and the need to track, monitor and manage assets, have been driving the evolution of advanced asset management software. Organizations are modernizing their enterprise apps, deploying more modular, intelligent systems and AI-enhanced workflows as part of broader digital transformation.

Asset management is no exception—according to IDC, 30% of organizations are strategically addressing digital transformation in enterprise asset management (EAM) solutions with a view to longer-term change management.

Two commonly used asset management and maintenance solutions are computerized maintenance management systems (CMMS) and enterprise asset management (EAM):

◉ Computerized maintenance management system (CMMS)—also called CMMS software, platforms or solutions—focuses predominantly on maintenance—helping to manage assets, schedule maintenance and track work orders.

◉ Enterprise asset management (EAM) is an asset lifecycle management solution focused on optimizing the overall lifetime performance of assets from acquisition to end-of-life.

Depending on variables like asset type, business size and the scale of operations, each solution provides different functionalities and benefits to match an organization’s maintenance requirements. Let’s explore these in more depth.

What is CMMS and what does it do?

A computerized maintenance management system (CMMS) is a type of maintenance management software that centralizes maintenance information and facilitates and documents maintenance operations. A CMMS automates critical asset management workflows and makes them accessible and auditable.

Core to CMMS is a central database that organizes and communicates information about assets and maintenance tasks to maintenance departments and teams to help them do their jobs more effectively. They typically include modules for tracking employees and equipment certifications (resource and labor management), data storage on individual assets like serial numbers and warranties (asset registry), and task-related activities like work order numbers and preventive maintenance schedules (work order management). Other features like vendor and inventory management, reporting, analysis (e.g., KPI dashboards or MRO inventory optimization) and audit trails are also included in CMMS software solutions.

CMMS evolved in the 1960s when the growing complexity of operations in large companies started to expose the limitations and inadequacies of manual and paper-based management. Data was siloed, hidden away in a multitude of spreadsheets and filing cabinets, and carrying out tasks manually was time-consuming.

During the 1980s, 1990s and 2000s, as technology became more affordable and connected, CMMS functionality expanded to include work order management, where companies assign, monitor and complete work orders and inspection checklists in one place. Other features, such as project management and spare parts purchasing, were also added as solutions advanced. Many industries depend on CMMS to improve asset and workflow visibility, streamline operations and maintenance, manage mobile field workforces, and ensure compliance in, for example, auditing and health, safety and environment reporting.

What is EAM and what does it do?

Enterprise asset management (EAM) software provides a holistic and comprehensive overview of a company’s physical assets and is used to maintain and control operational assets and equipment throughout their entire lifecycle, regardless of location.

Typically, EAM solutions cover work orders, contract and labor management, asset maintenance, planning and scheduling, condition monitoring, reliability analysis, asset performance optimization, supply chain management, and environmental, health and safety (EHS) applications. They store large amounts of data that can be analyzed and tracked, with organizations customizing their KPIs and metrics according to their specific needs. EAM solutions can also link into other enterprise management systems and workflows like enterprise resource planning (ERP), providing a single source of asset intelligence.

EAM emerged from CMMS early in the 1990s, bringing together maintenance planning and execution with skills, materials and other information spanning asset design through to decommissioning. This broadening of scope has especially benefitted industries heavily reliant on physical assets or with complex asset infrastructures where asset management effectiveness and ROI are major contributors to the bottom line.

In the oil and gas or mining industries, for example, there is a strong need to bring safety, reliability and compliance information into workflows. In defense, there are strict regulations around tracking potentially dangerous assets, and the safety of military operations depends on the operational readiness of multiple assets in disparate locations.

Organizations use EAM to save money from being wasted on preventable problems and unnecessary downtime and to enhance the efficiency, performance and lifespan of assets. Through a combination of maintenance strategies, automation and technologies like the Internet of Things (IOT) and artificial intelligence (AI), EAM can use preventive and predictive maintenance to monitor and resolve issues before they happen, maximize the use of assets, consolidate operational applications and provide in-depth cost analyses. The result is that asset management professionals make better decisions, work more efficiently and maximize investments in physical assets.

CMMS vs EAM: What’s the difference?

CMMS and EAM software have a similar purpose—to prolong and improve asset performance, boost operational efficiency and reliability, and reduce costs through more productive uptime, less downtime and longer asset lifespans. Despite some overlap, they are not the same and have key differences in functionality, approach and business context, offering different management tools and resources. In general, while most EAM systems have CMMS capabilities, only the more advanced CMMS solutions have some EAM functionality. Some of the main differences are outlined below but the extent of these varies by provider.

CMMS is dedicated to MRO (maintenance, repair and operations) for physical assets and equipment, tracking a company’s asset maintenance activities, scheduling and costs once an asset has been installed. EAM, on the other hand, provides a greater understanding of lifecycle cost and value of assets by managing the entire asset lifecycle from beginning to end. Being able to track assets, assess and monitor them, manage and optimize their quality and reliability, and gauge where inefficiencies are occurring means a business can get the most out of its assets and avoid unnecessary disruptions that could impact the smooth running of its operations.

EAM also provides data on lifetime costs—such as purchasing, maintenance, repair and servicing—which help businesses understand the total cost of ownership of individual assets. Although CMMS solutions are becoming more sophisticated, they don’t typically include additional features like high-level financial accounting or costs associated with procurement or decommissioning.

EAM also differs from CMMS in that EAM provides multi-site support across multiple worksites and geographies. Most CMMS solutions only provide single-site or limited multi-site support. That can be a substantial advantage for industries like power or mass transportation that manage vastly distributed asset portfolios.

EAM covers a wider variety of business functions than CMMS; features like contract management, fleet management, schematics, warranty tracking, energy monitoring and industry-specific apps are not typically covered in CMMS systems. EAM can also work with a broader range of other enterprise software, such as financial analysis, supply chain management and procurement, risk and compliance and sustainability. CMMS only tends to integrate with other systems to automate repetitive tasks, though a few do include purchasing capabilities.

That said, EAM can cost more to implement than CMMS in the first instance, largely because of its greater complexity and the additional setup costs stemming from integration with other business functions. SaaS models are changing this, bringing CMMS and EAM costs closer together, which, coupled with the additional benefits of EAM, is making it an increasingly cost-effective option.

Although modern CMMS systems can offer more than just maintenance and the line between CMMS and EAM is blurring, they remain distinct solutions. CMMS can be viewed as a subset of EAM and potentially an avenue to large-scale and more robust enterprise asset management. The two are often used together or CMMS may suffice for companies with small asset portfolios and maintenance teams. When companies are looking to scale and consolidate systems across multiple departments, however, the limitations of CMMS can impact its overall value.

Ultimately, the choice of software depends on many factors, but in general, if you are looking to understand and manage high numbers of assets in multiple locations across their entire lifecycle and incorporate other business functions like HR and finance, EAM is likely to be the way to go.

IBM Maximo Application Suite

Look no further. IBM Maximo Application Suite combines the world’s leading enterprise asset management system with all the benefits of CMMS.

Get the most value from your enterprise assets with Maximo Application Suite. It’s a market-leading single, integrated cloud-based platform that uses AI, IoT and analytics to optimize performance, extend asset lifecycles and reduce operational downtime and costs. It gives you configurable CMMS and EAM applications that can help you manage your company assets, processes and people.

Source: ibm.com

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7 steps for managing the work order process

Work orders are the driving force behind any organization’s asset management apparatus. Whenever a person or entity submits a service request, the maintenance team that receives it must create a formal paper and/or digital document that includes all the details of maintenance tasks and outlines a process for completing the tasks. That document is called a work order.

Read More: C2010-530: IBM Maximo Asset Management V7.6 Infrastructure and Implementation

The primary purpose of a work order is to keep everyone within the maintenance operation abreast of the workflow, which ultimately helps the organization organize, communicate and track maintenance work more efficiently.

Managing the work order process

The work order management process describes how a work order will move through the maintenance process, starting with maintenance task identification and wrapping up with post-completion analysis.

Phase 1: Task identification

In the first phase of the process, a person or organization identifies the tasks that the maintenance staff needs to complete. The tasks will also help the recipient determine whether the maintenance tasks qualify as planned maintenance (wherein the jobs will be easily identifiable ahead of time) or unplanned maintenance (where the scope and specifics of the job will require an initial assessment).  

Phase 2: Work request submission

Once the initiating party identifies the maintenance issues, they should lay out the details in a maintenance request form and submit it to maintenance for review and approval. Work requests can arise from any number of circumstances—from tenant requests to preventative maintenance audits.  

Phase 3: Work request evaluation

The maintenance department (or maintenance team) is responsible for evaluating work requests once they are submitted. Ideally, the department will review the details of the work request to determine the feasibility of the work and then determine personnel and resource needs. If approved, the work order request is converted to a work order.

Phase 4: Work order creation

Once the maintenance team or supervisor approves the work request and allocates the materials, equipment and staff they need to complete the jobs, they will create a new work order. The work order should include all the necessary details of the job, as well as the company contact information and an indication of the priority level and completion date. To streamline this process, organizations can standardize the work order format using a template.

In this stage, maintenance will also identify which type of work order they will need. If, for instance, a company relies on a proactive maintenance approach to anticipate and reduce equipment downtime, they will likely utilize a preventative maintenance work order. On the other hand, if a piece of equipment has already failed or the organization uses a more reactive maintenance program, the maintenance team will probably create a corrective maintenance work order or an emergency work order.

Phase 5: Work order distribution and completion

At this point, the team/supervisor will assign the necessary maintenance activities to a qualified maintenance technician who will complete the checklist of tasks on the proposed timeline. If the organization uses computerized maintenance management system (CMMS) software, the job will be automatically assigned to a technician.

Phase 6: Work order documentation and closure

Maintenance technicians are responsible for documenting and closing a work order once they complete all the assigned tasks. Technicians will need to indicate the time spent on each task, list any materials/equipment they used, provide images of their work and include notes and observations about the job. A manager may or may not need to sign off on the completed work order and provide guidance about next steps and follow-ups before moving on to the final phase.

Phase 7. Work order review/analysis

Reviewing closed work orders can provide valuable insights about maintenance operations, so organizations should try to review closed work orders as frequently as possible. Analyzing closed work orders can really help organizations identify opportunities for improvement in the work order process. Post-completion analysis also helps team members identify any tasks they missed or need to revisit.

Optimizing your work order management process

As an organization grows, it can become untenable to rely on paper work order management systems (or even Excel spreadsheets) to manage ever-evolving data needs. Larger organizations and those with more complex needs should consider investing in computerized maintenance management system (CMMS) software, a type of work order management software.

A high-quality CMMS will automatically plan, create, track and organize service requests, work orders and routine maintenance, eliminating excessive task planning duties for maintenance managers and supervisors.

Using CMMS software also allows your organization to store large amounts of data electronically, in a centralized location. With all your work order data living in one place, your management team can get real-time access to work orders as they move through the work order lifecycle. CMMS platforms with accompanying software for mobile devices push access a step farther, allowing users to track work orders and access maintenance activities remotely. 

Furthermore, a good CMMS can aggregate and display work order data according to the department’s specific needs. Maintenance teams can build and view customizable reports, visualize trend data and metrics/KPIs, and monitor asset functionality to make troubleshooting and inventory management simpler.

While adopting a CMMS can be a complex process, integrating CMMS software into your maintenance operations can help your organization reduce costs, increase data access and visibility, reduce backlog and human error, and streamline your facilities management systems. 

IBM Maximo Application Suite

Get the most value from your enterprise assets with the IBM Maximo Application Suite, a comprehensive enterprise asset management system that helps organizations optimize asset performance, extend asset lifespan and reduce unplanned downtime. IBM Maximo provides users an integrated, AI-powered, cloud-based platform with comprehensive CMMS capabilities that produce advanced data analytics and help maintenance managers make smarter, more data-driven decisions.

Source: ibm.com

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Data is key to intelligent asset management

Planning for business disruptions is the new business as usual. To get ahead in a rapidly shifting environment, industrial businesses are leaning more on integrating operational technology (OT) with IT data, which allows them to move from time-based to predictive maintenance, monitoring and management. But collecting and deriving insights from data that resides across disparate applications, legacy databases, machines, sensors and other sources is a complex affair. In fact, two-thirds of business data remains unleveraged. If companies can’t turn their data into value, it’s useless.

Read More: C1000-059: IBM AI Enterprise Workflow V1 Data Science Specialist

This is where intelligent asset management (IAM) comes in.

Announced today at MaximoWorld, IBM’s intelligent asset management brings together powerful solution suites for asset management, facilities management and environmental intelligence in one integrated place. It empowers the entire organization, from the C-suite to the frontline and all along the supply chain, to make more informed, predictive decisions across key operational and functional arenas. With IAM, all players can:

◉ Monitor and measure operations for a 360-degree view of internal and external data, using asset and sustainability performance management capabilities to help balance net income with net-zero objectives.

◉ Manage assets, infrastructure and resources to optimize and prioritize activities that improve the bottom line, including new integrations between Maximo and TRIRIGA to merge best practices across property, plant and equipment.

◉ Improve product and service quality with AI and next-gen technologies that increase customer satisfaction and cost control with intuitive visual inspection and occupancy experience solutions.

IAM breaks down the walls between these traditionally siloed data sets through a holistic approach to asset management, allowing organizations to untangle their data and bring sustainability and resiliency into their business. Here are just a few examples of how clients are utilizing IAM today.

Creating an end-to-end digital utility

One example is the New York Power Authority (NYPA). The NYPA, already the largest state public power organization in the U.S., seeks to become the nation’s first fully digital public power utility. This ambitious goal is part of the organization’s VISION2030 strategic plan, which provides a roadmap for transforming the state’s energy infrastructure to a clean, reliable, resilient and affordable system over the next decade.

To help unify its asset management system and integrate its Fleet Department, the NYPA turned to IBM Maximo®. The NYPA already uses several Maximo solutions — including the Assets, Inventory, Planning, Preventive Maintenance and Work Order modules — to help manage its generation and transmission operations. But its Fleet Department still relied on separate, standalone software for fleet management, preventing cross-organizational visibility into vehicle information. With the Maximo for Transportation solution, the Fleet Department is helping to ensure optimal management of approximately 1,600 NYPA vehicles. Using this central source reduces operational downtime and cuts costs while boosting worker productivity. It also supports the NYPA’s clean energy goals to decarbonize New York State.

Harnessing weather predictions to deliver power across India

Leading companies are also turning to IAM solutions to become more responsive to changes and to ensure business continuity. They are leveraging tools like the IBM Environmental Intelligence Suite, which provides advanced analytics to plan for and respond to disruptive weather events and avoid outages.

In recent years, India has made massive strides in ensuring that every electricity-consuming entity has access to the power they need. But the country struggled when it came to the reliability and efficiency of these services. Government officials had to calculate energy predictions manually using spreadsheets that could only consider historical energy usage. This process left much room for inefficiencies, waste, and financial loss. Officials needed a new way to understand all the factors that impact demand.

Delhi-based Mercados EMI, a leading consultancy firm that specializes in solving energy sector challenges, worked with IBM to create an AI-based demand forecasting solution to help address this problem. The model combined historical demand data with weather pattern information from The Weather Company’s History on Demand data package, which enabled officials to accurately predict when and where energy would be consumed based on environmental conditions. With this data, Mercados could provide utilities with demand forecasts with up to 98.2% accuracy rate to reduce the chances of outage and optimize their costs when it came to buying capacity. This allowed officials to make better overall decisions to balance supply, demand and costs to the consumers.

Keeping cities safe and sustainable with AI and IoT

As the economics of leveraging AI and monitoring assets remotely become more favorable than large supervisory systems, ensuring this lightweight infrastructure can also provide rapid insights from real-time situation data becomes critical. This challenge is particularly pronounced when it comes to environmental issues, where connecting city systems and infrastructure resources with real-time awareness can make all the difference.

Take Melbourne, Australia as an example. Due to climate change, Melbourne is experiencing more extreme weather such as severe rainfall events. In 2018, over 50 mm (2 in.) of rain fell in 15 minutes, resulting in flash floods and widespread power outages.

To help provide protection against flooding, the city’s water management utility, Melbourne Water, operates a vast drainage network that includes approximately 4,000 pits and grates. To function properly, the stormwater drainage system requires regular inspection and maintenance, which in turn requires thousands of hours of manpower every year, often executed during the most dangerous conditions.

That is why Melbourne water turned to AI-powered visual inspection technology in the IBM Maximo Application Suite. This allowed them to use cameras to capture real-time information about their stormwater system, then leverage AI to analyze the situation and detect blockages. And because Maximo allows easy integration between management, monitoring and maintenance data and applications, crews can focus on the areas that pose the most risk to Melbourne and its citizens.

Source: ibm.com

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IBM journey to more sustainable facilities: IBM as client zero

As IBM helps customers align sustainability goals with business objectives, we also leverage technology to achieve our own sustainability goals. Currently, IBM has a set of environmental commitments, including achieving Net Zero GHG Emissions by 2030 and diverting 90% of nonhazardous waste (by weight) from landfill and incineration by 2025. And IBM Global Real Estate (GRE) plays a critical role in meeting these targets.

Roughly 40% of the global share of annual carbon dioxide emissions comes from buildings. The UN Environment Program states that if nothing is done, greenhouse gas (GHG) emissions from buildings will more than double in the next 20 years.

To meet targets for GHG emissions, buildings and cities must shift from being part of the problem to being part of the solution. A survey from IBM and Morning Consult found that in the next two years, 62% of business leaders plan to invest in solutions that help manage assets, facilities and infrastructure to drive clean energy transition, efficient waste management and decarbonization.

Clients are looking for mature, real-world solutions that can shorten their path to sustainability, while helping improve the efficiency and performance of their operations. In this context, IBM GRE is leading a far-reaching transformation of nearly every facet of IBM’s real estate and asset management practices that demonstrates how to turn sustainability ambition into action.

Using IBM’s technology and expertise to capture data

Like other corporations, IBM is on a journey to not only reduce its GHG emissions, water consumption and waste, but also to track, analyze and report its progress toward carbon reduction goals.

For IBM, everything starts with data, so our first step was to implement solutions that capture the right data and embed the sustainability and environmental impacts of our day-to-day decisions into our operations. Two integral tools that help IBM manage its real estate operations more effectively are the IBM TRIRIGA® solution for facilities management and the IBM Maximo® solution for asset management.

Charged with the sustainable management of over 50 million square feet of space under management, across some 800 locations in 100 countries, IBM GRE had a large potential to deliver change—and a large challenge. So IBM worked with process experts from IBM Consulting to create a plan for extracting and capturing sustainability and operational data automatically and in real time to enable better decision making.

Our journey toward actionable insights

Once the plan was set, we focused on building a consistent base of data, a “single source of truth” for all underlying facilities and assets. That consideration led to two major developments to help put this foundation in place.

The first development was a close collaboration with the IBM Chief Data Office to build a new data governance program that aligns with IBM’s enterprise data standards and emphasizes data ownership. The second step was GRE’s decision to use sustainability performance management software from Envizi (now an IBM company) to help consolidate sustainability data (including key elements from TRIRIGA and Maximo) into a single, auditable system of record.

The Envizi solution was selected based on its automation capabilities, its ease of integration with core systems like TRIRIGA and Maximo, and its ability to deliver dashboard-based insights that inform business strategy.

Embedding insights into everyday operating decisions

A consistent data baseline allowed us to embed insights into everyday operating decisions. For example, using Maximo for data center maintenance, GRE analyzes sensor data to detect and fix problems before scheduled maintenance would find them. This eliminates the extra energy consumption, waste and emissions that invisibly failing parts would cause. Maximo also enables technicians to skip unnecessary repairs and avoid incidental carbon impacts like travel and parts shipping.

This transformation is already paying off

As GRE’s transformation continues to unfold, it’s already achieving results. In 2021, IBM registered a 61.6% emission reduction against base year 2010, placing the company on track to meet its goal of a 65% reduction by 2025 (adjusted for acquisitions and divestitures). And with its recent investments, IBM is also improving its reporting capabilities. Replacing third-party tools with IBM’s Envizi ESG Suite has enabled a roughly 30% reduction in reporting costs.

Building a more sustainable organization requires strong partnerships and shared vision. At IBM, we lead by example and work with clients to turn that vision into a reality. IBM Global Real Estate is proving how it is done.

Source: ibm.com

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Six reasons you need an intelligent asset management strategy now

There is an explosion of data surrounding asset management processes. This data is invaluable, but it can only be used if it can be properly analyzed. Today more than two-thirds of data goes unused due to the complexity of integrating multiple platforms, devices and assets, and the slow, labor-intensive processes required to make it consumable. The result? Subpar operational performance and reliability issues, made most obvious in downtime and defects.

This is where intelligent asset management comes in.

Intelligent asset management (IAM) solutions put data and AI to work to optimize critical asset performance and automate enterprise operations.

Here are six reasons why your company needs intelligent asset management today:

1. Orchestrate and automate your processes

Intelligent asset management focuses on automating operational processes. This streamlines asset maintenance and management to reduce bottlenecks and manual work, improving uptime, productivity and costs.

In the world of building and space management, companies are using integrated workplace management solutions that use data, IoT and AI. These solutions help organizations design a safe, flexible workplace, increase employee engagement and drive operational efficiency.

2. Create value to grow your organization

Intelligent asset management helps grow revenue through increased asset availability and reliability.

Mining companies, for example, use autonomous vehicles for certain tasks. Equipment can be remotely monitored — sometimes from halfway across the globe — to check for proper oil pressure or temperature and to keep the asset running properly. Robots working in mines underground can operate with no downtime, mitigating the safety risks of hazardous conditions such as fire, flood, collapse, or toxic atmospheric contaminants.

3. Be more competitive

Intelligent asset management makes it easier to deploy industry best practices, such as more sustainable operations.

IAM solutions can incorporate AI, weather data, climate risk analytics, and carbon accounting capabilities, allowing organizations to spend less resources curating this complex data and more on analyzing it for insights and taking action.

4. Connect to the enterprise

Intelligent asset management means building an enterprise operations system that governs business operations, financials, and production at all levels of an organization, from the C-suite to the frontline, and along the supply chain.

Changing economic and regulatory conditions challenge oil and gas companies to constantly find better ways to monitor, manage and maintain assets while keeping employees safe.

Kuwait Oil Company needed an asset management solution that could integrate this broad range of processes under a single umbrella. The company improved its production targets through improved efficiency by deploying an IBM Maximo for Oil and Gas solution, not only in its oil extraction and drilling operations, but across operations including marine operations and the employee hospital.

5. Build AI capabilities without data scientists

Intelligent asset management integrates asset data into no-code and low-code applications for visual inspection, remote asset monitoring and predictive maintenance, eliminating content silos to provide visibility across the organization, all without the need for data scientists.

Last year IBM helped Toyota move from reactive, cycle-based maintenance to proactive, reliability centered maintenance. Now the car company can detect anomalies and measure the health of equipment at all times, while predicting and fixing failures before they occur.

6. Uncover simplicity and scalability in one package

Intelligent asset management equals a simple, secure data architecture with an open, extensible asset management platform, to act on any data, on any cloud, anywhere.

Let us help you create an intelligent asset management solution with IBM Maximo, an extendable suite with the capabilities you need.

Source: ibm.com

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What asset-intensive industries can gain using Enterprise Asset Management

Not that long ago, asset-intensive organizations took a strictly “pen and paper” approach to maintenance checks and inspections of physical assets. Inspectors walked along an automobile assembly line, manually taking notes in an equipment maintenance log. Teams of engineers performed close-up inspections of bridges, hoisting workers high in the air or sending trained divers below the water level. Risks, costs and error rates were high.

Enter the era of the smart factory floor and smart field operations. Monitoring and optimizing assets requires a new approach to maintenance, repair and replacement decisions. Work orders are now digitized and can be generated as part of scheduled maintenance, thanks to connected devices, machinery and production systems.

But challenges remain. How do enterprise-level organizations manage millions of smart, connected assets that continuously collect and share vast mountains of data? And what is the value of managing that data better? The answer lies with migrating from siloed legacy systems to a holistic system that brings information together for greater visibility across operations.

This is where Enterprise Asset Management (EAM) comes in. EAM is a combination of software, systems and services used to maintain and control operational assets and equipment. The aim is to optimize the quality and utilization of assets throughout their lifecycle, increase productive uptime and reduce operational costs.

A March 2022 IDC report detailed the business value of IBM Maximo®, one of the most comprehensive and widely adopted enterprise asset management solution sets on the market. Interviewed companies realized an average annual benefit of $14.6 million from Maximo by:

◉ Improving asset management, avoiding unnecessary operational costs, and increasing overall EAM efficiency

◉ Improving the productivity of asset management and field workforce teams using best available technology

◉ Enabling the shift from legacy/manual processes to more streamlined operations via automation and other features

◉ Supporting business needs by minimizing unplanned downtime and avoiding disruptive events and asset failure, while improving end-user productivity and contributing to better business results

◉ Supporting business transformation from scheduled maintenance to condition-based maintenance to predictive maintenance

EAM on the smart factory floor

In a smart factory setting, EAM is an integrated endeavor where shop floor data using AI and IOT can come together to reduce downtime by 50%, reduce breakdowns by 70% and reduce overall maintenance cost by 25%.

These wide-ranging benefits can be seen in action at Toyota’s Indiana Assembly, where a new car rolls off the assembly line every minute, and each process in the vehicle’s assembly must be flawless and it’s become business critical to have zero downtime and defects.

See how Toyota is using IBM Maximo Health and Predict to create a smarter, more digital factory.

Smarter infrastructure for bridges, tunnels and railways

In the U.S. alone there are more than 600,000 bridges. One in three of these bridges is crumbling and in need of repair due to structural cracks, buckled or bent steel, rust, corrosion, displacement or stress.

Sund & Bælt, headquartered in Copenhagen, Denmark, owns and operates some of the largest infrastructures in the world, including the Great Belt Fixed Link, an 11-mile bridge that connects the Danish islands of Zealand and Funen. To inspect bridges, the company often hired mountaineers to scale the sides and take photographs for examination. This kind of inspection could take a month, and the process had to be repeated frequently.

Seeking a next-generation EAM approach, Sund & Bælt collaborated with IBM to create an AI-powered IoT solution, IBM Maximo for Civil Infrastructure, that uses sensors and algorithms to help prolong the lifespan of aging bridges, tunnels, highways and railways. By automating more of its inspection work, the company is on track to increase productivity by 14 – 25% and reduce time-to-repair by over 30%.

These anticipated benefits mirror the strong business value of IBM Maximo displayed in the IDC report, which projects the average organization will realize an average annual benefit of $1.3 million per 100 maintenance workers, resulting in an average five-year ROI of 450%.

Source: ibm.com

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Intelligent asset management and the race to Zero D

In an earlier post, IBM industry expert Scott Campbell talked about how manufacturers are pursuing resiliency and Zero D to stop defects and improve products and service quality. In part two of our discussion, he discusses how mitigating rework can save millions and offers some insights on the value of creating citizen data scientists.

Can you explain the concept of “detect and correct” and the kind of technologies and processes you need to implement to reach that kind of efficiency and reliability?

The idea is if you can detect an issue or defect at the point of installation — using AI computer vision models — then you can correct that defect immediately without it becoming cemented into rework. The example I always use is a dashboard of a vehicle: the average vehicle has over 300 electrical connectors, and many of them reside within the dashboard. These have to be manually connected, because they’re wires and not easily managed by machinery. If a connector is not seated correctly, it’s going to short or it’s going to fail. This means that function won’t work. But if you catch the error at the point of installation — and this is where computer vision models are so important — you can determine if it is it fully connected, partially connected, or if the line technician forgot to connect it altogether.

This detection capability can also be integrated into an overall quality system and/or enterprise asset management system. In the case of Maximo Visual Inspection, it is tightly integrated into Maximo Application Suite for enterprise asset management and performance, while easily integrating into customer quality alert systems. So, when a defect is detected, it can immediately signal an alert on the manufacturing floor to ensure the worker verifies and fixes the issue before it moves to the next assembly process. This immediate alerting is what avoids expensive rework. In the case of connectors within the dashboard, if defects go undetected, the rework fix for a simple connection gets exponentially more expensive, as it often requires dashboard removal and re-installation.

Using computer vision and AI to see the errors before they turn into rework and fix them right then — and in some cases a company is willing to stop the manufacturing line to fix a problem before it gets cemented — is a pretty significant capability. Especially because scrap and defects can cost a company more than 10% of annual revenue.

When people think of AI models, they think of data scientists and the difficulty in hiring expensive resources who understand AI technologies, deep learning neural networks and specialized AI computer vision models. But what IBM has done is made it extremely easy for the subject matter experts (SMEs) — the people that know what they’re looking for defect-wise — to actually create and manage the AI models. We do it through a user interface that requires no code.

It’s literally labeling a few images within a picture as good or bad or any other decision criteria they wish to define. Then the system can provide auto-labeling based on what has been labeled thus far, greatly reducing the workload. Finally, the existing data set can be augmented to create very large data sets out of the original sample size. This provides the data to build models that give predictable outcomes — in most cases, the accuracy is high as 95% to 98%. The result: subject matter experts take control of the actual models without the need for data scientists. This makes adoption a lot faster because companies use the people who are familiar with what the manufacturer is doing on the assembly line. That expertise is also a major contributor to the high-level accuracy of the AI models.

What about the concept of predict and correct? Does that play a role in driving continuous operations?

At IBM, we asked, what if you could increase efficiency, extend asset lifecycles, reduce downtime and costs — all while building resiliency and sustainability into your business?

Predict and correct is fundamental to being able to answer that question.

We’ve made it easier to digitize operating environments by taking the sensor data coming off of assets, and understand at a point in time the condition and operational status of those assets. And it’s a lot of data! A single production line can produce more than 70 terabytes of data each day.

By understanding the asset’s total health in terms of lifecycle and leveraging historical time series data, Maximo can predict when a failure is likely to occur in the future. If you can accurately predict failure well before it happens, you can remediate it. This predict and correct capability plays a major role in delivering and facilitating continuous operations.

You start with Maximo Monitor — capturing data and gaining visibility into what your assets are actually doing. Then you add Maximo Health, which tells you from a lifecycle perspective what maintenance structure you should be looking at and allows a single view of assets across the enterprise. Finally, with Maximo Predict, you can see into the future to be much more prescriptive with your asset performance management. It’s an evolution, but Predict is where the AI models come together to allow a customer to see where there is probability for failure for all of their assets and take corrective action.

We’ve been talking about the auto industry but I’m assuming that any industry can benefit from this.

Absolutely. And it bridges beyond manufacturing. We’re talking about the pursuit of Zero D and resiliency for manufacturing because it aligns so well to Industry 4.0, but the same technology can be used, for example, in travel and transportation. Consider railways and the ability to understand the assets — which are both the railway tracks and the train itself — and looking for potential failure. Sensor data is part of it, but then AI visual inspection can also be used to visually inspect railcars, wear on couplings, wheels, and wiring as just a few examples.

Traditionally, with cargo trains, there are maintenance yards, and the train will pull in and then maintenance people manually inspect the train. They visually ensure everything is okay before they let it go back on the track. But that industry is quickly evolving to provide inspection while the train is in transit. Cameras over the tracks take pictures of the train and provide the results immediately through AI computer vision models. If there are urgent safety concerns for example, the railway operator could stop a train. If not, it could continue on, but the technicians might say, Okay, the next maintenance window we’re going to need to make these repairs. Not only is the inspection much more complete — and can happen with higher frequency — but it is also much more accurate in prediction, because it’s using sensor data as well as visual data to manage the assets. We’re also seeing this in civil infrastructure and with bridges and roadways. There’s just a lot of places where visual data and sensor data come together.

What are some of the issues and misconceptions that an organization might have when it comes to using AI to predict asset health and build a more resilient organization?

From a challenges perspective, the first one is a company that doesn’t use IBM Maximo EAM (Enterprise Asset Management) as its work order system. Often companies believe they can’t take advantage of the rest of our application suite if they don’t use Maximo EAM across their entire organizations. But IBM’s monitor, health and predict solutions can connect to other EAM systems so that companies can take advantage of their operational data. We can also connect to other systems that are gathering the sensor data and we can feed this data into Maximo Monitor. This is important because two-thirds of operational data goes unused. It also eliminates a hurdle a company might have to jump with another provider, simply because their work order system is in another vendor’s application. We can manage within that and still drive value with predictive capability by introducing monitor, health, and predict.

Another typical issue is that each asset has siloed data into its own repository. Getting data across all the assets collected into a single pool can be very difficult and time-consuming. But we can bring connectors via APIs or solutions like IBM App Connect and help customers consolidate data into a single repository. This repository can capture time series data, and then you have a starting point for building resiliency and sustainability into your business by extending asset lifecycles and reducing downtime and costs. Once you’re positioned for intelligent asset management — and building resiliency and sustainability into your business — you can reduce operational costs up to 25% and increase uptime and availability by 20%. Those are results that no one objects to.

Source: ibm.com

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Industry 4.0 and the pursuit of resiliency

Downtime can cost a manufacturer upwards of USD 21,000 per minute. Fortunately, AI has evolved to accurately identify issues and take action. This advanced technology allows companies to easily add intelligent “eyes” to their operations with standard mobile devices — the same smartphones and tablets that you’re using right now. All to quickly identify defects in production outputs as well as remotely monitor assets for potential disruptions.

I talked with IBM expert Scott Campbell about this AI evolution and his current focus: helping clients intelligently manage their assets with Zero D, which stands for zero defects and zero downtime. Scott has had numerous product management roles within IBM. And almost all of them centered around some type of AI technology. First in financial environments, then with Red Bull racing (where his team used AI simulation to understand race dynamics), and now as the lead product manager for IBM Maximo and IBM TRIRIGA.

What’s the biggest challenge manufacturers face right now?

Every manufacturer knows that there’s a tremendous value if you can eliminate defects and stop rework. If you can keep your manufacturing facility running 24×7 without any downtime, it’s almost a given that there is ROI there. The challenge is how do you actually transition from a reactive environment — which is where most manufacturers are — to a proactive environment. So instead of thinking, we have a problem to fix, how do you think instead, we’re anticipating problems to fix before they actually become problems. The cool thing is, IBM has AI technology that is sophisticated enough to let a company do that effectively. But we have to make sure that it’s trustworthy. When a company looks at all this data, they have to believe in it. Otherwise they’ll go right back to reactive maintenance.

A lot of people talk about Industry 4.0, but I think the big challenge for many manufacturers is how do you even get started? How do you take something that’s transformational and evolve it over time? Because you can’t do this in a big bang approach, or a forklift upgrade approach. You have to evolve it. And you have to start somewhere.

Starting with defect detection is a good way to get introduced into an AI environment that’s fairly easy to understand. It’s pictures, it’s images. You can see the system is doing a better job than an individual can do, and that makes it easier to expand use of that technology. Once you begin to build trust in those results, it’s easier to use machine learning and AI technology for maintaining the assets running on the manufacturing floor. Then you understand the health of an asset, you know hey the odds are really high — a probability of 85 to at 95% — that this asset is going to fail sometime in the next 45 days, so let’s do something about it. 

And manufacturers are moving toward this?

Oh, yeah, you’re seeing it across the board. There’s a big North American auto manufacturer using AI visual detection and predictive monitoring, and they saw immediate results. It’s incredible how quickly they were successful just running a simple pilot. They found 30 defects in the first 30 days, which isn’t that big of a deal. But they were looking at one single connector in one point of their installation, tied to one specific problem for them. When they expanded that to multiple locations on their assembly line, they found up to 200 defects a day. So in the very first month they gained a USD 1.8 million savings on that one manufacturing line.

There are two parts to the Zero D story. Visual inspection and asset performance management (APM). Visual inspection uses computer vision models focused on quality inspection. APM uses machine learning models based on time series data to determine health of assets and probable failures in the future. Toyota is using Maximo Visual Inspection, and now they are also using the Maximo Asset Performance Management (APM) suite. They tested Maximo APM on some of their machinery that does liquid cooling and found that was another problem area for them. By implementing the software into this pilot, they are now able to monitor the asset health 24×7 and predict probability of failure in the future. It is the foundation for them to shift from being reactive and cycle-based, to practicing a proactive, reliability-centered maintenance strategy. This will be transformational for their entire organization.

Those are just two examples of where Industry 4.0 and how intelligent asset management has started to gain traction. Of course, there are lots of others, but those two examples are true showcases for transformational manufacturing processes.

Does adopting Industry 4.0 bear out all the way down the line to the customer?

Yes, it does, especially on two fronts: quality and meeting demand. For Toyota, quality is mission one. Fewer recalls and less warranty work (compared to other vehicle brands) drives customer loyalty, not to mention reduced costs for rework.

Then when it comes to meeting demand, it’s estimated that downtime costs on average about USD 21,000 per minute. That means within an hour, you have a million-dollar problem. And if you can’t meet the demand, somebody else will. There’s loyalty in car buying, but there is also availability, especially with the chip shortage.

When it comes to defects and downtime each topic seems big enough on its own. Why not tackle them separately? Why do you advocate handling them both at once?

Either one of them is critical. But you achieve true transformation when you attack them both at the same time. Because no matter how high your quality is, you can’t meet demand if you have downtime. Conversely, even if you’re super effective in your manufacturing processes but your quality inspection is poor, you’re just adding to your scrap heap or your rework at a tremendous pace.

That’s why it’s the combination of AI-based visual inspection for quality and asset performance management for predictive repair that lets you increase quality and production efficiency at the same time — and that helps build a sustainable and resilient business.

Do you have any other hard numbers around the savings that an intelligent asset management program could bring to a manufacturer?

Of course, this approach is applicable beyond the auto industry. It’s just a very good use case that folks understand. If you look at the rework of a defect — and it’s important to distinguish between a defect and rework — a defect can occur in the production line, but it only becomes rework if it goes undetected through final production. If you catch it, and fix it, before it gets to the next stage of the line, it’s no longer a defect. We emphasize detecting and correcting at the point of installation.

If a defect turns into rework work — which means if it’s either caught in final inspection or somewhere down the line, even potentially by the customer — then it’s about USD 300 per incident. So, if you think of that North American auto manufacturer who found 200 defects a day, they saved USD 300 multiplied by 200 defects multiplied by 365 days. That’s how you hit very large numbers very quickly in terms of saving.

Can you also talk about the savings from not over-maintaining an asset and only performing maintenance when it’s actually needed?

When our customers understand “$21,000 a minute,” they tend to create very rigid maintenance schedules. The problem is they have no knowledge of what actually needs to be fixed. It becomes hey we’re going to check everything once a week. There’s the idea that frequent maintenance schedules are cheaper even though it’s overkill.

But with an APM platform, you can reduce your maintenance and improve uptime — all at the same time! It is prescriptive in terms of understanding where you need to actually apply resources. It provides 24×7 monitoring of the health of assets, can detect anomalies before they become critical issues, and can predict the probability of failure in the future. Technicians are no longer tied to calendar-based scheduling. Because now a company has the data that indicates these assets are just fine and they’re not going to fail for another month, several months, or even years. This means technicians, who are becoming a scarce resource, can better schedule their time. And companies can utilize technicians much more effectively in the areas that have the highest value, based on data they can trust.

Source: ibm.com

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