5 minute read
The Fourth Industrial Revolution is underway.
Technologies such as robotics, nanotechnology, the Internet of Things, AI and machine learning are forming "cyber-physical systems" that will transform the world as we know it.
For many industries, some of these technologies still seem remote and futuristic. Do they really affect your business? Can you hold off a bit longer, or should you look at deploying them now?
The Internet of Things (IoT) is the concept of extending internet connectivity beyond computers and mobile devices, to traditionally non-connected, everyday objects. These range from cars and household appliances to health trackers and smart sensors embedded anywhere from buildings to roads. The IoT already has billions of connected devices, and will have over 200 billion by 2020, according to Intel. All of these devices are able to collect and transmit vast amounts of data.
Managing Big Data
This harvesting of information results in Big Data. Managing it is challenging - it consists of data sets that are too large and complex for traditional analysis methods or software. You can’t put Big Data into an Excel spreadsheet and have a human crunch the numbers. It has to be handled by computers running advanced data-processing software.
Using data analytics, these computers clean up and organise data, and analyse it to show patterns and anomalies that can provide insight into a process or situation. Netflix has over 140 million subscribers, and analyses viewing patterns to see who is viewing what, and when. It can then use this to fine-tune its Recommendations system and plan new content. PepsiCo collects warehouse and POS data from all its clients, and uses this to forecast production and shipment needs.
But there are limitations to data analytics. Data initially shows you the "what", not the "why". For example, the data might reveal a problem, such as a huge fall in customers in a specific location. However it can’t give you the reason - which you need to know if you want to fix the issue. Qualitative analysis - by a human - is still needed.
The role of AI and machine learning
The next step is using artificial intelligence (AI) and machine learning (ML) to autonomously run processes based on the data analysed. There are varying definitions on what exactly constitutes AI. Researchers Andreas Kaplan and Michael Haenlein define it as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation."
Machine capabilities generally classified as AI include tasks such as understanding human speech, competing at strategic games like chess and autonomously operating cars, or being able to predict failures and trigger maintenance processes autonomously.
AI starts with human-created algorithms: instructions or rules on what to do depending on the data input. As a simple example, an email program might have rule-based filters that send certain emails to the Junk folder, e.g. specific keywords or subject headers.
Machine learning takes this a step further. Without additional human input, an app uses data to improve its algorithms and apply better rules. It might detect that a user frequently marks certain kinds of email as spam, and "learns" that these should in future be automatically sent to the Junk folder. Google uses machine learning to improve Gmail’s spam filters, looking for new patterns that might suggest an email is not to be trusted. This could be anything from the formatting of an email to the time of day it’s sent.
One example is Doxel, which uses AI-enhanced software to monitor construction sites, using robots, camera-equipped drones and LiDAR sensors. It processes visual data with deep learning algorithms, and can detect errors in construction by comparing data from everyday scans to small scale design models.
In Asset Management, machine learning may come in to play when looking at how areas with similar footfall degrade over periods of time, based on external influences as temperature and humidity. Over time, predictive modelling is improved based on previous ‘experience’ with various events.
Boeing has been combining IoT and blockchain to track the provenance and lifecycle of aircraft components. The IoT provided a quicker, deeper understanding of a machine, and the company can then apply predictive analytics to anticipate when there’s going to a problem. This helps fix issues in advance, as well as extending the life of components. It also helps with forecasting demand and rate of consumption.
Industry 4.0 is significant for every business and every industry. Big data analytics is an important investment for all organisations. As well as your own data, there are public data sets out there, such as Google Trends, various government repositories of social, economic and population data, and social media monitoring tools. Having a strategy and a roadmap for Industry 4.0 will be critical to survive and compete in this revolution.
8 minute read
Asset Management’s Opportunity
Managing asset data today can be complicated. Data sets may be stored in disparate information systems such as spreadsheets, databases and EAM or CMMS systems. The quality of the data varies and the capability to move forward may seem insurmountable. There are simple ways to unify this data and effectively leverage the business benefits of Asset Intelligence, meaning considerable gains for the Asset Management function.
A simple example of this evolution in data use and capability is weather forecasting. Previously, weather prediction relied on telegrammed observations from various points, which were interpreted to gauge how weather systems are changing. The process was slow, convoluted and the predictive power was limited. Now, by applying sensors that measure data points like humidity, air pressure and wind, and through the utilisation of sophisticated algorithms, the data can be analysed to generate predictions that have greater fidelity, far faster.
The benefits of more accurate and farther-reaching weather reports are evident in the ability to plan better, for events, logistics and building or maintenance work. The risk of damage from extreme weather events can be quantified and better planned for.
Applying the same process of intelligent data collection and prediction to asset management can have tremendous benefits locally and globally, reducing cost and risk, and improving performance.
The Impact Worldwide
McKinsey estimates that optimised asset management could save $400 billion worldwide a year. Some of the most startling examples include:
• Canada’s needs of an investment 10 times greater than the current spend to meet demand on built assets. • A global 60% increase in infrastructure productivity is possible and could save $1 trillion a year through 2030. • Globally, current infrastructure spending is estimated at $2.5 to $3 trillion a year, only half the $6 trillion needed to meet demand to 2030.
Properly maintained infrastructure using Asset Intelligence goes some way to addressing this challenge.
Asset Management and Asset Intelligence
The term “asset management” is used in various contexts with many different meanings. In this context, asset management is the combination of management, financial, engineering, risk, personnel and asset information applied to physical assets with the objective of providing the required level of service in the most cost-effective manner.
Asset Intelligence guides many functions of Asset Management allowing evidence based decisions and strategies, and can be defined as using the data gathered from a portfolio of assets, combined with advanced technologies to make predictive decisions about the future.
Features of an Asset Intelligence platform can include the following: • Live data feeds • Lifecycle Degradation Modelling • Digital Twin Simulation • Accurate Whole-of-Lifecycle Management • Visualisation tools and reporting dashboards • Interfacing with technology ecosystem (existing CMMS, EAM or ERP systems for example)
The Benefits of Asset Intelligence
Asset Intelligence makes asset data come to life. Understanding the power of asset data in driving a strategy that is aligned with business drivers, ensures a complete line of sight from executive management through to operational delivery.
Once the Asset Intelligence platform is up and running (with good data), the benefits of better decision making will be evident almost immediately.
Planned and less frequent reactive maintenance can increase the performance and raise the capacity of the infrastructure. Take the example of hospitals – large elements of scheduled maintenance mean that frequent, disruptive work impacting doctors and patients is minimised. Additionally, planned maintenance keeps assets performing at the required level. A hospital with fit for purpose, functioning rooms means that more patients are able to be treated, increasing the number of cases that can be effectively managed each year.
Understanding the optimised disposal or repurposing of an asset is imperative to larger scale decisions as well. Whether a building is renovated, re-built or disposed of, will be based from the current performance. Asset Intelligence supplies the information to understand the performance of an asset, whatever the size.
Reduced Risk Exposure
When an organisation applies a consistent approach to risk evaluation and categorisation across the entire portfolio, understanding priorities becomes more dependable. This process allows for the prioritisation of expenditure across the asset portfolio, the ability to predict asset failure and the reduction in unplanned downtime.
Risk will always be present. It is how an organisation evaluates, manages and mitigates this risk is key. Asset Intelligence provides information that allows organisations to understand how to do this.
Many organisations use a standard framework to assess risk. Here at AssetFuture, we use a proprietary methodology called ACCRI (Asset Criticality and Condition Risk Index) which provides organisations a clear understanding of their risk profile. Using this methodology, we are able to predict the risk exposure of an asset (or a portfolio) into the future enabling decision making to mitigate future risk.
Save on Cost
Efficient asset management can mean significant cost savings for organisations. Asset maintenance and replacement relies on deciding whether it’s more effective to repair something or replace something. This is enabled though deeper consideration of the asset lifecycle curve.
Each asset has a unique replacement profile. So, a generalised replacement approach is unlikely to be efficient at allocating resources to each individual asset. One asset may degrade at a faster rate than another, or suffer a more radical performance drop-off that means replacement, rather than maintenance or repair, is a better option. Applying sophisticated algorithms based on the performance data of assets produces credible, accurate Asset Intelligence enabling cost savings across the asset’s lifecycle.
Maturing into a Strategic Approach
Since the release of ISO 55001, organisations are increasingly recognising the need for, and benefits of a strategic approach to the use of asset information. The next step in the asset management maturity journey is utilising this information to drive improved outcomes.
The NSW Department of Education currently uses AssetFuture’s Asset Intelligence platform and have seen benefits in improved asset performance, cost savings and increased budget. Read more about how the NSW DoE uses Asset Intelligence here.