Asset lifecycle management strategy: What’s the best approach for your business?

Assets are the lifeblood of any successful business—from software programs tailored to meet an enterprise’s unique needs to a pipeline that stretches across oceans. One of the most important strategic decisions a business leader can make is how these assets are cared for over the course of their lifespans.

Whether you’re a small enterprise with only a few assets or a large-scale corporation with offices spanning the globe, asset lifecycle management, or ALM, is a fundamental part of your operations. Here’s what you need to know in order to build a successful strategy.

What is an asset?

First, let’s talk about what an asset is and why they are so important. Companies use assets to create value. There are many different types of assets, both physical and non-physical. Examples of physical assets include equipment, office buildings and vehicles. Non-physical assets include intellectual property, trademarks and patents.

What is asset lifecycle management?

Each asset a company acquires goes through six main stages over the course of its life, requiring careful maintenance planning and management tactics to provide its owners with a strong return on investment.

The following are the six stages of asset lifecycle management:

1. Planning: In the first stage of the asset lifecycle, stakeholders assess the need for the asset, its projected value to the organization and its overall cost.
2. Valuation: A critical part of the planning stage is assessing the overall value of an asset. Decision-makers must take into account many different pieces of information in order to gauge this, including the assets likely length of useful life, its projected performance over time and the cost of disposing of it. One technique that is becoming increasingly valuable during this stage is the creation of a digital twin.  
3. Digital twin technology: A digital twin is a virtual representation of an asset a company intends to acquire that assists organizations in their decision-making process. Digital twins allow companies to run tests and predict performance based on simulations. With a good digital twin, its possible to predict how well an asset will perform under the conditions it will be subjected to.
4. Procurement and installation: The next stage of the asset lifecycle concerns the purchase, transportation and installation of the asset. During this stage, operators will need to consider a number of factors, including how well the new asset is expected to perform within the overall ecosystem of the business, how its data will be shared and incorporated into business decisions, and how it will be put into operation and integrated with other assets the company owns.
5. Utilization: This phase is critical to maximizing asset performance over time and extending its lifespan. Recently, enterprise asset management systems (EAMs) have become an indispensable tool in helping businesses perform predictive and preventive maintenance so they can keep assets running longer and generating more value. We’ll go deeper into EAMs, the technologies underpinning them and their implications for asset lifecycle management strategy in another section.
6. Decommissioning: The final stage of the asset lifecycle is the decommissioning of the asset. Valuable assets can be complex and markets are always shifting, so during this phase, it’s important to weigh the depreciation of the current asset against the rising cost of maintaining it in order to calculate its overall ROI. Decision-makers will want to take into consideration a variety of factors when attempting to measure this, including asset uptime, projected lifespan and the shifting costs of fuel and spare parts.

The benefits of asset lifecycle management strategy

When you’ve invested your hard-earned capital in the acquisition of assets, it’s important to keep them running at peak levels for as long as possible. Systematizing and executing an effective asset management strategy can produce a wide range of benefits for your organization, including the following:

• Scalability of best practices: Today’s asset lifecycle management strategies use cutting-edge technologies coupled with rigorous, systematic approaches to forecast, schedule and optimize all daily maintenance tasks and long-term repair needs.
• Streamlined operations and maintenance: Minimize the likelihood of equipment failure, anticipate breakdowns and perform preventive maintenance when possible. Today’s top EAM systems dramatically improve the decision-making capabilities of managers, operators and maintenance technicians by giving them real-time visibility into equipment status and workflows.
• Reduced maintenance costs and downtime: Monitor assets in real time, regardless of complexity. By coupling asset information (thanks to the Internet of Things (IoT)) with powerful analytics capabilities, businesses can now perform cost-effective preventive maintenance, intervening before a critical asset fails and preventing costly downtime.
• Greater alignment across business units: Optimize management processes according to a variety of factors beyond just the condition of a piece of equipment. These factors can include available resources (e.g., capital and manpower), projected downtime and its implications for the business, worker safety, and any potential security risks associated with the repair.
• Improved compliance: Comply with laws surrounding the management and operation of assets, regardless of where they are located. Data management and storage requirements vary widely from country to country and are constantly evolving. Avoid costly penalties by monitoring assets in a strategic, systematized manner that ensures compliance—no matter where data is being stored.   

How to build an effective asset lifecycle management strategy

Because of the increased complexity of asset maintenance and the technologies required to build an effective maintenance strategy, many businesses utilize enterprise asset management, coupled with a strong computerized management system (CMMS) and advanced asset tracking capabilities to manage their most valuable assets.

Enterprise asset management systems (EAMs)

Enterprise asset management systems (EAMs) are a component of asset lifecycle management strategy that combines asset management software, systems and services to lengthen asset lifespan and increase productivity. Many rely on a CMMS to monitor assets in real time and recommend maintenance when necessary. Top-performing EAM systems monitor asset performance and maintain a historical record of critical activity, such as when it was purchased, when it was last repaired and how much its cost an organization over time.

Computerized maintenance management systems (CMMS)

Computerized maintenance management systems (CMMS) are software systems that maintain a database of an organization’s maintenance operations and help extend the lifespan of assets. Many industries rely on CMMS as a component of EAM, including manufacturing, oil and gas production, power generation, construction and transportation. One of the most popular features of CMMS is its ability to spot opportunities for companies to perform regular preventive maintenance on their most valuable assets.

Preventive maintenance

Preventive maintenance helps prevent the unexpected failure of an asset by recommending maintenance activities according to a historical record and current performance metrics. Put simply, it’s about fixing things before they break. Through machine learning, operational data analytics and predictive asset health monitoring, today’s top-performing asset lifecycle management strategies optimize maintenance and reduce reliability risks to plant or business operations. EAM systems and a CMMS designed to support preventive maintenance can help produce stable operations, ensure compliance and resolve issues impacting production—before they happen.

Asset tracking

Asset tracking is another important component of asset lifecycle management strategy. Like EAM and CMMS, asset-tracking capabilities have also improved in recent years due to technological breakthroughs. Here are some of the most effective technologies available today for tracking assets.

• Radio frequency identifier tags (RFID): RFID tags broadcast information about the asset they’re attached to using radio-frequency signals and Bluetooth technology. They can transmit a wide range of important information, including asset location, temperature and even the humidity of the environment the asset is located in.
• WiFi-enabled tracking: Like RFIDs, WiFi-enabled tracking devices monitor a range of useful information about an asset, but they only work if the asset is within range of a WiFi network.
• QR codes: Like their predecessor, the universal barcode, QR codes provide asset information quickly and accurately. But unlike barcodes, they are two-dimensional and easily readable with a smartphone from any angle.
• Global positioning satellites (GPS): With a GPS system, a tracker is placed on an asset that then communicates information to the Global Navigation Satellite System (GNSS) network. By transmitting a signal to a satellite, the system enables managers to track an asset anywhere on the globe, in real time.

Asset lifecycle management strategy solutions

Many of today’s asset lifecycle management (ALM) solutions leverage cutting-edge technology like real-time data delivered via IoT, AI-enhanced analytics and monitoring, cloud-based capabilities and powerful automation that can help streamline workflows. Enterprise asset management (EAM) with the IBM Maximo Application Suite helps companies optimize asset performance, extend asset lifespans and reduce downtime and cost. It’s a fully integrated platform that uses advanced analytics tools and IoT data to improve operational availability and spot opportunities to perform preventive maintenance.

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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API Days Ahead For Construction

At first glance, it might be easy to dismiss a term like Application Programming Interfaces (APIs) as the kind of technical jargon that only software programmers might get excited about.  However, the implications of ‘the API economy’ could barely be less significant as organisations increasingly digitise and become data driven.  In some industries it has even disrupted whole business models and become a regular board room topic.

An API is essentially a means by which different software applications can talk to each other, like a digital glue that can bond disparate systems and services together.

If you have wondered how you can sign up for a new app or website with your Facebook ID rather than entering all of your personal details again, then it is down to APIs. Or perhaps to track a package you simply click a URL in the vendor’s e-mail and it takes you straight to the information in the delivery company’s website without any re-entry of addresses or delivery ID. The You Tube video embedded on a web page, current weather conditions beamed to the home screen on your mobile phone, and price comparison site matching your details to a host of vendor prices in seconds. All made possible by the humble, understated API.

Indeed the chances are that you have not noticed, and that is the point of APIs; that things just work automatically and effortlessly even when you move from one system or service to another.

Open the gates

Software vendors are now realising that their products need to communicate with others. APIs are now so common that anyone can create their own basic event programs without code using services like IFTTT. For example you may wish to create a recipe that automatically switches on the home central heating when your car calculates that you are 30 minutes away.

The largest benefits however are reserved for organisations, where slow and error prone manual handling of information can be replaced by seamless, automated work flows. New, external data sources can be incorporated into organisations’ decision making where, previously, intangibles had to be resolved through gut feel. And organisations can realise latent monetary value in by making it available externally.

But what does this all mean for construction?

Plug in to productivity

The construction industry is highly fragmented and this creates inefficiency. Productivity has barely moved in twenty years and if you can make profits of just 2% then you are doing well. Add to this the fact that the overhead of a major capital project can often represent 20-25% of the total cost, and there is clearly room to divert money from the desk back to the site.

Advances are already being made with the deployment of BIM; federated models such as IBM’s Asset Lifecycle Information Management platform enabling asset information to be assembled from a variety of systems and surfaced to a mobile app or other medium via APIs. This makes a wealth of information available at the user’s fingertips without the need to gather and integrate information manually.

But there are all manner of other ancillary business processes that lend themselves to automation, not least the administration of contracts, in particular the kind of standardised forms of contract found on large infrastructure projects.

Contracts are after all at their heart a series of rules in how to execute obligations and entitlements; so with the right data sources, machine logic can be used to execute certain contractual processes.

And for text based information sources that are not traditionally machine-readable, such as a project communications, we now have cognitive (artificial intelligence) APIs such as IBM’s Watson that can learn, understand and reason with natural language.

They won’t replace a human but they will help the work to be done quicker and better.

Data driven decisions

Connecting to external data can help make fast, informed decisions throughout the lifecycle. Feasibility and design processes can be streamlined through connecting to geological, land value and planning restriction data sources. Commodity price feeds may help estimate project costs more accurately whilst a weather data feed can help plan for inclement conditions.

Monetise your data

What exhaust data does your business generate and would it be of value to a third party?

Making live telematics data from your delivery vehicles available to a project manager on a busy site would help them prepare for delivery with precision, much like Uber helps you catch your taxi. Providing cement curing performance information to the supplier could help them optimise their product mix.

A supplier might provide a feed from their product or service catalogue so that cost can be incorporated dynamically into the design of an asset. And capital project benchmarking data can be made available to clients to help them estimate the cost and duration of building a new asset.

Many shared APIs will just make you a more attractive partner to work with, but some make actually generate new revenue streams.

Reasons to be API

Jealously guarding one’s information in a walled garden is on its way out whilst sharing in a controlled, selective and secure way is on its way in. It may take a while in an industry that is traditionally fragmented and adversarial. But the good news is that everyone stands to benefit.

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