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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Why is more sustainable asset management for utilities important?

Modern society is dependent on power grids like never before. From cars and buses to buildings, the shift from fossil fuels to electric energy carries enormous promise for a greener future. And utilities will play an essential role in this delicate balancing act, ensuring that increased demand is met with reliable supply, while enabling customers to be active participants and accelerators of the energy transition.

Utility companies are hardly alone in this pursuit. According to the IBM 2022 CEO study, Own your impact: Practical pathways to transformational sustainability, which surveyed more than 3,000 CEOs worldwide, nearly half (48%) of respondents ranked sustainability as a top priority for their organization—an increase of 37% from 2021. Yet, despite mounting pressure from boards and investors, many CEOs have cited the lack of reliable data-driven insights as the chief impediment to their ability to act.

New demands for utilities, new approach to distribution 

For utility companies, the task of data management is even more crucial as they expand operations to keep abreast of skyrocketing demand. Further, this demand is projected to keep accelerating. IBM’s Institute for Business Value recently conducted a global study that found that 50% of surveyed consumers are considering purchasing an electric vehicle (EV) in the next three years, and that 63% of these consumers expect to use a home charging station to power them. This growth alone (without considering trends like the electrification of public transit systems and the construction of fully electric buildings) will require a significant expansion of energy and utility companies’ services, necessitating a more resilient and efficient approach to power generation and distribution.

To improve the reliability, robustness and efficiency of the power grid and other utility infrastructure, including power lines, service trucks, turbines, people lifters and earth movers, utility companies need to adopt more sustainable asset management practices and technology. This emerging area balances asset reliability and use optimization (including the best use of an asset within a company) with sustainability, such as improvements in the areas of energy and water efficiency, waste management, and environment monitoring and management.

Intelligent insights

To manage these changes, utility companies need tools that minimize their environmental impact and expand their operations efficiently. Fortunately for utility companies, innovators like IBM have been developing tools to help businesses minimize their environmental impact and expand operations with efficiency.

Take for example, the Downer Group, an integrated services company in Australia and New Zealand. Downer uses the IBM Maximo® Application Suite to power its TrainDNA platform, which provides a holistic view into its more than 200 trains across Australia. This view includes train location, timetable and status information, predictive maintenance insights, as well as remote capabilities such as the ability to remotely disconnect a carriage from the electrical grid to allow for safe maintenance work. The TrainDNA platform supports the company’s commitment to provide more timely service to passengers, getting them where they need to go when they need to get there. Downer also relies on IBM Envizi ESG Suite to capture and manage data that help them more effectively meet their sustainability goals, including data on energy consumption, waste generation and GHG emissions.

Exponential advancements in technology are helping utilities to become better prepared to accelerate the clean energy transition needed for a low carbon economy. IBM’s unified platforms harness the power of data and AI to derive real-time, predictive insights, reducing the complexities of acting on interrelated or dependent activities that support sustainability initiatives. With Maximo and Envizi, utilities can gain greater visibility of their operations as they scale up to meet the rising strain and demands on power grids.

Source: ibm.com

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Save energy, decarbonize and transition to renewables while operationalizing sustainability

Recent political and climate-related environmental events have impacted energy sourcing, supply and costs. The resulting energy crisis impacts all countries, industries, sectors and societies across Europe. Combined with imminent reporting requirements from the European Commission, saving and securing energy sustainably and moving to renewable energy sources equitably is imperative.

The immediate energy crisis coincides with the equally crucial long-term journey to sustainability. A traditional management mindset could see this imperative as an onerous obligation that could cut into profitability. But evidence shows that sustainability efforts unlock value and innovation throughout the organization. Numerous reports cite a direct correlation between sustainable practices, share prices and business performance. According to a recent IBV report, 83% of CEOs expect sustainability investments to produce improved business results in the next five years.

Meeting the needs of energy saving and security, regulatory compliance, cost savings and sustainability requires technological solutions paired with a reconsideration of business processes throughout the entire enterprise.

We see five key functional areas IT and operations executives can assess for transformation:

1. Asset and facility management

Integrated asset management helps organizations minimize the environmental impact of their operations across the asset lifecycle and extend asset life through predictive maintenance and condition monitoring. More accurate replacement planning can help companies minimize and consolidate technician visits, reducing use and saving energy.

Intelligent integrated workplace management software can help organizations infuse sustainability into their real estate and facilities management operations. Criteria for sustainability (such as ISO 14000) can easily be specified for space acquisition or leases, and for construction and renovation projects. Carbon footprints can be reduced through space optimization and planning, and by extending asset life through improved maintenance and assessment.

2. IT: infrastructure and code

The global power capacity of data centers has grown by 43% since 2019. Data centers can account for a large portion of an organization’s energy use, making environmentally sustainable computing more critical. This year’s generation of servers can reduce energy consumption by up to 75%, space by 50% and the carbon emissions footprint by more than 850 metric tons per year, compared to x86 servers under similar conditions.

Hybrid cloud is also a critical enabler of green IT, facilitating increased visibility, greater integration and enhanced capabilities across the cloud estate. Moreover, running workloads in a container platform instead of classically deployed virtual machine environments can reduce annual infrastructure costs by 75%, thanks in part to increased energy efficiency.

The choice of code matters too. Using the right language for the right workload can reduce computing power, and therefore energy usage. Switching from one programming language to another can reduce the energy consumption of an application by up to 50%.

3. Sustainable supply chain and circularity

Through intelligent workflows and automation, companies can reduce waste and improve circularity. Making shipping and routing more efficient and trackable reduces costs and warehouse space, and thus reduces the carbon footprint. It also helps organizations ensure provenance. With 80% of consumers saying sustainability is important to them, and 60% willing to change their purchasing habits based on environmental impact, such assurance can improve consumer loyalty.

Intelligent order management software also marries sustainability to customer satisfaction by allowing companies to consolidate shipments into fewer packages. Integration with carbon-accounting engines allows carbon savings to be shown to online shoppers as they make their shipping choices.

4. Transitioning to sustainable energy sources

Becoming a sustainable enterprise requires a rigorous strategy and roadmap. It encompasses all areas of the organisation right through to enabling customers. It includes ESG reporting and finance, climate risk assessment and adaptation, responsible computing and green IT, a circular supply chain and decarbonization and clean energy transition.

Decarbonization and clean energy transition alone spans many areas including distributed energy, grid resilience, alternative fuels and emissions, flexible energy, new energy systems, mobile energy and low-carbon customers. Moving to a low carbon physical infrastructure, low carbon energy markets, EV platform enablement and digital product business for consumers are among the tactics that can be deployed.

Few organisations have a complete portfolio of these capabilities. Working with partners who can co-create these strategies using proven, repeatable methods, and leveraging extensive partner ecosystems can accelerate realization times on an organization’s journeys to net zero.

5. ESG and risk management

ESG platforms can help organizations build a single system of record that delivers auditable, finance-grade ESG and sustainability data. They enable efficient monitoring of energy demand, consumption and emissions across organizations by capturing data from utility bills, interval meters and renewable assets. When combined with weather and facility information, they can provide a granular data foundation for use with enterprise asset management software, improving predictive insights. ESG platforms also enable organizations to meet increasing regulatory reporting requirements.

Effective use of these platforms can help corporations take the right actions at the right time, reducing corporate risk and the financial and environmental impact of disasters.

Embodying sustainability

IBM leads the way for clients because we embody innovation in sustainability within our organization. We have set precedents for environmental and social commitments for over 50 years, starting with our first corporate policy on environmental affairs, enacted in 1971. Our 21 current environmental commitments include achieving net-zero GHG emissions by 2030 and diverting 90% of nonhazardous waste (by weight) from landfill and incineration by 2025, which we report on annually in our ESG Report, IBM Impact. We also promote environmental justice programs such as the IBM Sustainability Accelerator and Call For Code, enabling organizations and communities to tackle environmental issues.

Proven methods, comprehensive solutions

IBM helps customers save and secure energy sustainably while complying with increasing regulatory demands. We operationalize sustainability end-to-end with data-driven innovation through a comprehensive and growing portfolio of industry-leading consulting and technology capabilities. With an ecosystem of partners, we co-create solutions to uncover new opportunities while finding cost efficiencies, without trade-offs or compromising profitability.

Source: ibm.com

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Planned versus unplanned maintenance and the impact on spare parts stocking strategies

At a chemical plant years ago, a maintenance technician made a hasty pump repair. A required repair part wasn’t available, so the technician did the unplanned maintenance with another similar-looking part. But the materials of construction weren’t rated for the pump’s service, and the pump casing ruptured and vented 1,300-degree oil into the atmosphere.

Luckily, the chemical plant was able to mitigate environmental harm, and no one was in the immediate area when the life-threatening failure occurred. But the accident was a severe wake-up call. The plant couldn’t just hope the right parts were around. It needed an effective strategy to maintain its maintenance and repair inventory.

Maintenance technicians often feel immense pressure to get production up and running after an operations breakdown. Unfortunately, this burden can lead them to take detrimental shortcuts to complete emergency repairs. Though the risks associated with breakdowns are often financial, poor maintenance stocking can also cause environmental and safety risks.

Optimize your MRO inventory stocking strategy

Maintenance effectiveness can be expressed as a ratio of planned maintenance to breakdown maintenance. Planned maintenance is comprised of all planned and scheduled activity (PM, PdM, corrective, etc.) while breakdown maintenance consists of emergency work, reactive work and break/fix work that is unplanned and requires an immediate response. A good target follows the 80/20 rule: 80% planned, 20% breakdown.

MRO inventory refers to critical supplies, spare parts and other materials needed for routine maintenance, repair and operations. If you are optimizing MRO inventory, you will need to determine a few things before considering the overall impact of your maintenance team’s planning capabilities. Does your inventory optimization tool:

 

◉ Dynamically assign criticality to your spare parts?

◉ Have a “where-used” feature to associate spare parts with their appropriate assets?

◉ Consider the asset/equipment criticality in the overall assignment of the spare part criticality?

◉ Calculate a moving-average total lead time rather than relying only on stated lead time?

It’s important to know how critical each part is before you apply forecasting algorithms and cost models. Tools designed for the manufacturing, service and retail industries have forecasting algorithms that focus on procurement and sales data rather than asset and work order data. These algorithms don’t factor in criticality and the business risk associated with criticality, which is crucial for MRO inventory stocking strategies. I like this analogy: If a grocery store runs out of bread, business is hardly interrupted, and the store does not shut down. However, in most industries, a lack of certain parts can prolong outages or potentially shut down operations completely.

Planned versus unplanned maintenance and the impact on stocking strategies

If you are effectively planning and scheduling 80% of your maintenance activities, planned parts should be ordered and available within the stated lead time. Proper planning provides a clear demand signal for spare parts, which eliminates the need to stock for planned work. Planning thereby reduces the need for stocking, maintaining, and managing spare parts. As such, you should stock enough parts to cover the 20% of unplanned work.

It’s important to track spare parts purchases against planned and unplanned maintenance. In addition to achieving the 80% planned work ratio, businesses must consider whether the scheduling window adequately accommodates supplier lead time. If parts cannot be delivered in time, maintenance may be forced to use safety stock to continue operations. When safety stock is depleted for planned maintenance, operations are placed at risk when an emergency arises. A stockout event often leads to increased stocking levels, which creates a ripple effect that increases maintenance budgets, reduces profit margins, increases space requirements and leads to surplus. Today’s surplus is tomorrow’s potential waste.

It is important that the planners have access to an inventory optimization tool that calculates a total lead time (including requisition to order time, supplier weighted average time and delivery time). Having visibility into total lead time allows planners to fully execute their planned maintenance and have the right parts available within the lead time window. If your planning is not effective, you will end up holding more inventory for longer periods, even with an inventory optimization tool. The forecasting algorithms will adjust stocking strategies to include all consumption (unplanned and planned) rather than planning and procuring as needed and keeping stocking levels appropriate for the 20% of unplanned work. Over time, this practice leads to more obsolete, wasted inventory.

Keep your maintenance planning effective by understanding total lead time for required parts. Keep your inventory optimized by stocking for the unplanned, emergent work. Find an inventory optimization tool that is designed for the unique requirements of maintenance activities, and you’ll be able to put the right measurements in place to ensure success.

Source: ibm.com

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To comply with GASB 87, government organizations need a better lease management solution

Over the past few years, the corporate world has worked through achieving compliance with lease accounting standards for real estate and assets. Adherence to ASC 842 and internationally, with IFRS 16, has been a challenging journey for many. Now it’s state and local governments’ turn to shine new light on their lease obligations.

Read More: C9560-680: IBM Control Desk V7.6 Fundamentals

Asset leasing is just as widespread across state and local governments as it is with private sector enterprises. While lease accounting requirements are usually applied to real estate and facilities assets, many other asset classes are commonly leased, such as IT equipment, warehouse space, warehouse equipment, water towers, vehicle fleets, cell towers and office equipment.

In the U.S., all leases associated with these asset classes must comply with regulations from the Governmental Accounting Standards Board (GASB). GASB 87, which took effect in 2021, is the new lease accounting standard issued by GASB to more accurately portray lease obligations and improve governmental financial statements.

Successfully navigating the complexities of GASB 87 compliance requires a cross-functional effort from stakeholders across the organization. Financial and accounting executives need to stay informed about the new accounting rules and the increased complexity associated with financial forecasting in the context of lease management and accounting. Real estate directors can increase efficiencies to reduce balance sheet impacts as they oversee facilities portfolios. And IT executives need to upgrade IT systems to manage finance, real estate and other core asset optimization functions.

What’s changed with the new GASB 87?

For all public sector entities, lease accounting and reporting are being wholly redefined. Under the old GASB 13 and GASB 62 standards, there was a determination about which leases had to appear on balance sheets. That’s no longer the case. Now, with minor exceptions, all leases are considered finance leases and are required to appear on balance sheets.

Further difficulty may arise when leases are scattered across departments throughout the organization. Facility leases may be part of core infrastructure groups, while transportation departments may hold vehicle or truck leases and IT may be leasing laptops, devices or datacenter equipment. There may be other unidentified specialty leases. Finally, there may be leases embedded in service agreements, which need to be identified and accounted for.

All of these leases must comply with GASB 87 standards.

Governments need a more sophisticated lease management solution

Government organizations ultimately obtain funding from legislatures and the implied consent of citizens. The ability to accurately project near-term needs and longer-term obligations through demonstrated financial control is imperative to establishing confidence to obtain future funding. GASB 87 provides an opportunity for improved financial transparency and a better understanding of asset portfolios.

Most government entities will have more than 200 leases, large ones will have far more. It won’t be possible to comply through manual efforts or spreadsheet tracking of leases. Accounting and finance leaders will have to invest in a lease management solution to help. It is not uncommon for an organization to keep documents in file cabinets, transactions in ERP systems, critical dates and options managed in spreadsheets, and workflow managed through e-mail.

Local and state government entities can focus first on integrating all leases and contracts into a digital repository to assess the portfolio. They must identify which contracts contain a lease and which are subject to GASB 87 compliance. There can also be embedded leases in some service agreements. The complexities of these contracts and leases can only be managed effectively with the right technology.

To manage real estate and asset lease contracts, organizations need a consolidated document repository and system of record. A 360-degree view containing lease history, metadata and documents with workflow is vital for contract management and regulatory compliance.

A good lease management solution does the following:

◉ Provides pre-built data structures and processes for all lease types

◉ Integrates with financial and other critical business systems

◉ Identifies underperforming and underused facilities

◉ Models alternative planning scenarios

◉ Compares financial and non-financial returns

◉ Alerts organizations to required lease accounting reviews

◉ Automates lease accounting controls

◉ Supports audits approvals and processes

Manage lease contracts with IBM TRIRIGA

IBM TRIRIGA® provides a comprehensive system of record repository to better manage real estate and asset lease contracts. It has long been among the leading integrated workplace management (IWM) solutions helping clients with real estate and lease management, space optimization, capital projects, sustainability and maintenance needs.

TRIRIGA has helped organizations manage changes in lease accounting standards and portfolio management for over 10 years, helping clients achieve ASC and IFRS compliance since those mandates went into effect.

Beyond delivering a complete solution for GASB 87 compliance, IBM TRIRIGA enables a greater sense of overall confidence in managing asset portfolios. Lease management executives can use TRIRIGA to create a repository and achieve compliance through every step of contract management, lease accounting, and lease administration.

Managing lease contracts is a complex process involving multi-step workflows that require an intelligent set of approvals and routings to provide the necessary controls over each action in the process. Once leases are executed, the monthly payment processing, OPEX management and payment adjustments can be administered. IBM TRIRIGA helps automate the entire process and reduce manual actions by enabling alerts on contracts that may trigger reviews or re-assessment. Users can replace spreadsheets with a digitized document repository and system functionality that helps to accurately determine the carrying value of lease liabilities and right-of-use assets.

TRIRIGA handles the full scope of accounting treatments to support GASB 87 compliance, from standard adoption and activation of leases to modification, reassessment and ultimately expiration or termination.

Once contracts and leases are in order, finance directors and real estate executives can also take a fresh look at portfolio planning and transaction management. TRIRIGA also supports improved sustainability and environmental management, facilities maintenance, capital projects and facilities optimization. Executives can transform the future course of their institutions with the confidence of their stakeholders.

IBM is a leader in the industry with years of experience helping worldwide clients with lease accounting compliance and portfolio management. We are now making this experience and expertise available to state and local government clients. It’s easy to grow with TRIRIGA, using our solution beyond GASB 87 compliance to create an ideal, comprehensive solution for you.

Source: ibm.com

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Turn sustainability ambition into action

For nearly 20 years, IBM has surveyed thousands of CEOs about their biggest challenges. In the latest survey, sustainability ranked at the top, a 5-spot jump from 2021. Nearly 60% of CEOs told us they see significant demand from investors for greater transparency on sustainability. They are also feeling the pressure from multiple stakeholders. Regulators and governments in most top economies have developed corporate disclosure requirements around environmental impact. Customers want to buy from sustainable businesses. People want to work for these companies and invest in them. From the boardroom to the operations centers, all stakeholders want to play a role in making a positive difference for our world.

However, while 86% of companies say they already have a sustainability strategy in place, only 23% say they are implementing sustainability strategies across their entire organization. Many organizations with good intentions are stalled at the planning stage because implementing sustainable practices is complex and they don’t know how or where to make an impact. Despite this delay, the same IBV CEO study found that 80% of CEOs believe investments in sustainability will improve their own business results within 5 years. How can you turn strategy into results?

Take the first steps to build and operationalize sustainability

Becoming more sustainable is an opportunity to innovate, make a difference and grow. Take action by following these three steps:

Define your Sustainability goals – To succeed, your business needs to set and act on clear environmental, social and governance (ESG) goals, then execute with exceptional data discipline across the enterprise.

Establish your ESG data foundation – Create a clear baseline to underpin every goal from which to determine your current impact, track progress and implement adjustments. This requires a single system of record to integrate and manage ESG data that aligns to your goals. Collect, correlate, visualize, and analyze relevant data so you can deliver transparent, verifiable, financial-grade information and more easily identify where improvements are most needed.

Operationalize your sustainability goals – Full benefit can then be achieved by leveraging the links between this system of record for ESG data and the underlying operational systems that run across all the departments and business units of your organization. With these links in place, you can automate feedback loops that enable actions based on insights. These insights help drive sustainable transformation through intelligent facilities and assets, resilient IT Infrastructure, and circular supply chains.

Focus on three key operational areas

Intelligent facilities and assets—Monitoring and recording operational data from your organization’s physical assets and real estate facilities is a good place to start. The data you collect can fuel insights to drive significant energy savings, optimize waste management and provide predictive maintenance data to help reduce unplanned downtime.

Resilient IT infrastructure—Data centers provide multiple opportunities for improving sustainability. Upgrading IT infrastructure with newer, more energy-efficient equipment can help you reduce energy consumption and eliminate wasteful, outdated hardware. What’s more, the same steps you take to improve business resiliency across your organization also help you improve customer experiences and productivity while you work towards meeting your sustainability goals.

Circular supply chains—Encouraging reuse and providing customers with transparent sourcing data for the products they buy is something more consumers are demanding. Deploying intelligent workflows and taking advantage of automation opportunities can not only reduce waste but also optimize fulfillment and delivery paths with lower carbon footprints. Solutions powered by AI and backed by blockchain can help you progress toward a net zero supply chain.

Advance your journey with IBM

As you embark on your sustainability journey and tackle each of our 3 recommended steps, IBM and its ecosystem of partners are on hand to support and help you along the way. We recognize that no one can do it alone, but together we can turn sustainability ambition into action.

Define your sustainability goals with IBM Consulting™ — IBM experts, strengthened by our strategic alliances and partnerships, are helping clients embed sustainability into the fabric of their business to increase operational efficiencies, expose innovation opportunities and drive competitive differentiation. Many businesses rely on IBM Consulting as their partner for the new rules of modern business. We believe open ecosystems, open technologies, open innovation, and open cultures are the key to creating opportunities and the way forward for modern business and our world. Our expert advice helps you leverage sustainability as a catalyst for profitable business transformation.

Establish your ESG data foundation and a clear baseline with IBM® Envizi — Envizi provides a single system of record to manage ESG data that aligns to your goals in a verifiable and auditable way. Automate the collection and consolidation of more than 500 data types, with support for internationally recognized ESG reporting frameworks, to manage environmental goals, identify efficiency opportunities, and assess sustainability risks. Proactively plan and manage the economic impact of weather and climate change events with IBM® Environmental Intelligence Suite®.

Operationalize sustainability goals with IBM technology — At IBM, we are building a comprehensive portfolio of capabilities in collaboration with our network of partners, including AWS, EY, Microsoft, Salesforce, SAP, and WBCSD among others. We help you realize sustainable transformation by leveraging the connections between your ESG data and your daily operations. Many of our clients choose IBM® Maximo® as their foundation for operational insights on physical assets to drive clean energy transition, efficient water and waste management, and decarbonization using AI, analytics and IoT. If your goal is to automate workflows and establish resilient IT infrastructure, IBM Turbonomic® is a no-fuss solution that helps you improve data center efficiency, performance optimization and cloud workflow tracking. IBM LinuxONE can also bring sustainability to mission-critical workflows, helping you manage server sprawl and contain your corporate carbon footprint. And if you seek to improve responsible sourcing and supply chain transparency, IBM Supply Chain Intelligence Suite provides insights and intelligent workflows to enable equitable, net zero supply chains through AI and blockchain. No matter where you decide to act, IBM and its partners have the capabilities to help you achieve your sustainability goals.

Find out more about sustainability solutions from IBM

Industry leaders rely on IBM solutions to help meet sustainability goals

BestSeller—Reducing return rates, driving supply chain efficiencies, and stepping forward to sustainable fashion with the help of IBM Garage, by developing “Fashion.ai” to help customers purchase items that fit their physical dimensions and preferred style.

Celestica—Consolidating, streamlining, and automating ESG data reporting with IBM Envizi Sustainability Performance Management to produce finance-grade, auditable data and transform sustainability reporting across the manufacturing portfolio.

Sund & Baelt—Digitizing maintenance and asset management processes using IBM Maximo to extend the life of critical infrastructure (like bridges and tunnels) by 100 years and avoid 750,000 tons of carbon dioxide emissions.

Carhartt—Deploying cloud-first strategy with IBM Turbonomic® Application Resource Management to ensure 24×7 performance while reducing application needs and energy consumption.

BBVA—Adopting IBM z15’s central processing equipment to reduce carbon dioxide emissions and energy consumption of BBVA Data Center processors by 50%.

Partnership for Carbon Transparency—Partnering with the World Business Council for Sustainable Development (WBCSD) and others to enable the secure exchange of verified, standardized product emissions data across organizations.

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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