moving-from-preventive-to-predictive-asset-maintenance

Moving from Preventive to Predictive asset maintenance

The evolution of enterprise asset management is what makes newer business models possible. The real-time analytics and predictive maintenance allow asset manufacturers to develop entirely new business models, according to a report by the International Association of Manufacturers (IAM).

According to the IAM study, 70% of businesses are looking for new ways to generate additional revenue from their properties, with the majority citing the new business model as an appealing option.

Manufacturers are increasingly exploring asset-as-a-service business models to offer business outcomes rather than only physical assets. As a result, the responsibility for ensuring asset uptime shifts from the end-user to the manufacturer. With the manufacturer now being in charge of asset management and servicing, thereby creating more opportunities for cooperation with the customer.

Additionally, the study discovered that 50% of those surveyed were searching for ways to cut down on maintenance visits to their facilities.

While the complete realization of the asset-as-a-service economy is still evolving. The leaders are already adopting technologies and processes that will enable them to deliver asset uptime profitably. Clearly, the success of this model depends on how effectively manufacturers can shift to a more predictive state.

From predicting asset failures to predictive maintenance, Makoro™ enables businesses to identify problems before they occur. Makoro™ lets manufacturers connect to assets in the field and deliver real-time predictive recommendations. Which in turn allows them to reduce maintenance costs and deliver consistent asset uptime.

Schedule a demo to learn how Makoro™ can help you adopt new business models.

AI-Powered Utilities Asset Management Delivers Faster ROI

Asset management entails deriving the highest #roi from assets. The right Infrastructure Asset Management solution significantly impacts the reliability and return on asset investment because it enables utilities to manage assets and derive value from them at a consistent pace. Risk and compliance are key themes in 2023 – failure to keep up with asset reliability and process compliance can have a significant financial impact on #utilities.

An agile approach delivers value along the way:

The process of incorporating digital tools to support business functions and bringing the organization’s data into play is referred to as digital transformation in the context of Asset Management. Utilities currently have access to a wealth of existing data. Gathering this data, extracting relevant features, and comparing performance, condition, and other factors yields new insights and improve utilities’ ability to navigate the digital transformation. 

The knowledge gained from making data available can be used to uncover trends and previously unknown potentials. This paves the way for more advanced analytics and accelerates digital transformation, allowing utilities to realize the full value of their assets, #workforce and embark on a journey of continuous improvement. An agile approach that focuses on incremental adoption is key to the success of asset management initiatives. 

Aggregating data from all sources is important for accurate intelligence:

Following the #digitalrevolution of the last 30 years, utilities began to collect and refine their data. Most utilities now have data on almost all of their physical assets, but utilizing this information is impaired by two factors. For starters, an abundance of data can result in data overload, in which massive amounts of irregular data overwhelm any attempt to derive insights, and, relevant features are drowned out by an abundance of information. Another issue is the use of data in silos, which is common with utilities where data is isolated in different sources and formats and programs are unable to integrate and derive intelligence from disparate sources.

Impact-based approach is a critical success factor:

When we use Asset Performance Management in an impact-based approach, we consider not only the risk of asset failure but also the consequences of a possible failure. Consider a pipe failure in a wastewater network. The consequences of a pipe failure can vary greatly depending on the number and type of consumers served by this pipe (e.g., hospitals, sensitive industry), their proximity to ecologically vulnerable areas, and so on. Because the risk of major incidents is usually on par with economic considerations, an impact-based perspective provides a far better foundation for risk analysis and asset replacement and maintenance decisions.

Artificial Intelligence brings everything together:

Advances in Artificial Intelligence (AI) and Machine Learning (ML) are opening up new possibilities for utilities to gain additional insights from their data, such as estimating conditions in uninspected network pipes. Risk assessment and the balancing of operational and capital investments are critical for asset owners, as are core elements of a utility’s asset strategy. An asset strategy powered by AI uses data from all sources to create models necessary to bring assets, maintenance, and workforce together to deliver accurate recommendations on asset conditions and maintenance. But asset managers almost always have an initial “mistrust” of the #recommendations from machine-learning models because they are used to network pipe replacement either triggered by pipe age exceeding a certain number of years (time/schedule) or pipe failure (break). Using a system that learns from user interactions with the recommendations and driving continuous adoption of these recommendations by asset managers is a useful strategy to build “trust”. 

Modern customers expect same-day delivery, personalized goods and services, and transparent delivery systems. However, distinguishing your company and remaining globally competitive necessitates a level of #agility and flexibility that traditional business models are incapable of providing.

Manufacturers can improve operational efficiency and deliver goods to customers more quickly by implementing strategies such as digital quality control, location monitoring, and automated material replenishment. Asset management, as we all know, is a systematic process of developing, operating, maintaining, upgrading, and disposing of assets in the most cost-effective way possible.

Makoro™, as a result, improves asset performance for manufacturers, owners, and operators by providing #contextual recommendations to maintenance personnel to assist them in performing asset maintenance in less time, with higher quality, and consistency. Because of improved product quality, this results in a longer mean time between failures, higher workforce satisfaction, and higher customer satisfaction. Makoro™ is a recommendation-driven asset performance platform that goes beyond data, reports, and dashboards.

Schedule a call to discuss your needs and how we can assist you. It only takes 15 minutes.

makorotm-recommendations-to-improve-productivity

Makoro™ – Recommendations to improve productivity

A key success measure in the automotive industry is order-to-delivery (OTD) time. To speed up sales and to lower costs, automotive manufacturers need to reduce OTD. In order to stay competitive, manufacturers must maximize speed, efficiency and quality in how products are developed, assembled and delivered to customers.

future-of-the-smart-factory-in-2022

Future of the Smart Factory in 2022:Factories will work on these 5 verticals

Manufacturing is rapidly reviving, unfazed by substantial labor and supply chain issues. To continue this pace, producers must balance increased risks with a commitment to sustainability. Our 2022 forecast delves into five manufacturing industry trends that will assist firms in transforming risks into opportunities and capitalizing on growth.

  1. Manufacturing will transition from infrequent use of smart factories to widespread adoption: Until date, a fully smart factory with integrated solutions has remained elusive owing to gaps in offers and a lack of suppliers capable of meeting all of the technical requirements necessary to realize a smart factory. However, with the entry of hundreds of start-ups, technology has become more cost-effective. Multiple companies now provide technology and solutions like video analytics, artificial intelligence, cybersecurity, autonomous mobility robots, and command centers that formerly required unique and often in-house development. Each component of the technological puzzle has several alternatives.

Starting in 2022 we expect a significant transition from companies with a few smart factory components to those with production environments that are smart.

  1. FOMO will drive organizations.Fear of Missing Out will play a significant role in driving smart factory adoption in 2022. Organizations are quickly realizing that delaying digital transformation is not an option and that if they do not act soon, they will lose their competitive edge. These organizations will be more receptive to significant adjustments in people, processes, and technologies that will catapult them ahead of their competition.

2022 marks the year when manufacturers will start demonstrating value from their adoption of smart and sustainable manufacturing practices.

  1. Actions based on data will grab the spotlight. Manufacturers are becoming more acquainted with data from different sources, in different shapes and forms, which is necessary for the operation of a smart factory. True smart factories will include command centers — envision numerous control towers — that will bring data from throughout the company together in ways that businesses have never been able to accomplish before. Data has always been the forte of manufacturing companies, but it has been the usage of this data to drive sustainable and optimized production that has been a challenge in the past.

Through this year, we see companies making significant steps to act on data, bridging the divide between technology and people.

  1. Vision systems and autonomous mobile robots will be critical components of the smart factory. Organizations cannot afford quality faults as material prices continue to rise. Vision systems will become more important in detecting errors or faults immediately before they affect the whole manufacturing process. This will result in considerable quality and safety gains and will play a vital part in lean transformation. Another critical technology will be autonomous mobile robots (AMRs), which have the potential to significantly increase productivity by automating routine chores. Whereas a corporation may have a few dozen replenishment experts on staff today, that number may soon drop to zero.

We see remote operations and safety requirements for frontline workers driving the adoption of smart manufacturing technologies in 2022

  1. Workforce augmentation will become crucial. For at least the next two years, the labour shortfall will endure and, in all likelihood, deteriorate before improving. This will increase the need for technology adoption in order for businesses to continue operating and meeting client expectations. Along with smart factory adoption, firms may gain a competitive edge by anticipating how important manufacturing positions will change and developing strategies for improved hiring, training, reskilling, and upskilling for these roles.

2022 is the year for empowering the frontline workforce while building upon the tribal knowledge that manufacturers have accumulated for years. Workforce augmentation is a key recipe for success in onboarding a younger and more agile workforce.

Each manufacturer should evaluate their readiness for The Year of the Smart Factory. If the answer is no, extensive due diligence on their smart factory potential should be done.  One point is clear – they need to get started in order to stay ahead.

How can Makoro™ assist?

Makoro™ optimizes industrial supply chain processes by providing continuous insights and recommendations through Makoro™ Mind, the data-driven core that leverages IoT, Digital Twin, Artificial Intelligence, and Advanced Analytics to provide operational suggestions.

Natural-language recommendations from Makoro™ are built for the frontline workers so that they can understand and act upon them.

Additionally, Makoro™ leverages customers’ existing infrastructure investments (cloud, on-premise, or hybrid) and interacts effortlessly with the customer’s existing private/public/hybrid cloud, on-premise, and edge systems.

By correlating real-time asset, maintenance, and workforce parameters with suggestion sentiment, acceptance, confidence, relevance, and originality, the Recommendations Dashboard demonstrates Makoro™’s ongoing value to manufacturers’ operations.

data-driven-decision-making-gets-better

Data Driven Decision Making Gets Better

Just came across this article (below) on balancing “reflexive” and “reflective” decision making. Since #codedataio started on #codaai to build it as a recommendation system for business decision making,

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Upskilling workforce for Industry 4.0

Despite the fact that continuously updated skills training is a well-recognized facet of the digital transformation, many industry observers see the current situation as dire. This issue is exacerbated by a wave of retirements on the one hand, and a lack of enthusiasm for manufacturing among young people on the other.

Moreover, educational programs aimed at helping new employees acquire the skills they need to enter the manufacturing field are not always as effective in addressing the need for the current workforce to continually update its skills, as digital transformation often brings rapid alterations to prevailing work processes.

Fortunately, much of the same technology facilitating Industrial Internet of Things data interchange can also be used to foster digital knowledge transfer by enabling workers to access unified databases of training materials and other content from any location.

Recommendations from Makoro™ extend beyond asset performance and operations and maintenance to the operations and maintenance workforce.

Additionally, through its #DynamicLearning capabilities, #MakoroAI helps you capture the #TribalKnowledge from your experienced and retiring workforce, which is then used to guide the younger workers entering the workforce. This facilitates worker onboarding, increases worker productivity, and drives #WorkforceEngagement.

To find out how the workforce of tomorrow – #WoT – can leverage the augmentation capabilities of Makoro™ to excel in their job responsibilities, schedule a demo today.

Connectivity is not enough, drive value through Continuous Intelligence

ROI calculation is easy with Makoro™ – the simplified, all-inclusive, per-asset-per-month pricing model makes it easy to calculate the return on investment. Unlike traditional asset performance management software, Makoro™ delivers value in days and not in months and years. Our unique Prove-Deploy-Learn-Scale cycle makes it possible.

Big bang adoption of AI technologies always fails. We recommend: start small, connect a few assets, deliver value, drive adoption. Repeat. Makoro™’s Recommendations Dashboard demonstrates value to businesses in real-time, putting them on a journey of continuous improvement.

Yet another important factor in driving digital transformation through AI is workforce adoption, which Makoro™ accelerates through natural language recommendations that are easy to understand and act upon. Connectivity is the first step, but it does not deliver value.

Makoro™ accelerates time to value through continuous intelligence derived from connected assets. Makoro™ leverages #AppliedArtificialIntelligence in solving transformative problems in the manufacturing supply chain.

#MakoroAI #CodeDataIO #PredictiveRecommendations #NaturalLanguageRecommendations #PredictiveAssetManagement #ContinuousIntelligence #Industry40 #SimplifiedROI