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PREDICTIVEMAINTENANCETHE CORNERSTONE OF DIGITAL TRANSFORMATION

WHITE PAPER

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What if we used aviation as our inspiration to improve the reliability of our IT systems?

Introduction 5

1. IT maintenance in pursuit of the industrial ideal 9

Maintenance in industry, a long history of reliability

The slow maturing of IT maintenance

2. Predictive maintenance in IT 15

The emergence of a new paradigm: prevention rather than cure

The ingredients: data, solutions and expertise

Implementation: from risk assessment to scheduled intervention

3. Impacts and benefits 25

4. Opinions 31

Radical and necessary transformation of the IT Department – Jean‑Claude Chiche

A vision of the future in line with the challenges – Vincent Malka

Conclusion 39

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IT MAINTENANCE, THE INVISIBLE CORNERSTONE OF THE DIGITAL COMPANYDigital transformation is reshuffling the cards in terms of the performance of businesses. Faced with simultaneous changes in client expectations, business models, market conditions and working methods, every function must review its contribution to the three areas on which success now depends: operational excellence, user experience and business innovation.

All activities, including the most traditional, are now required to reinvent themselves in light of these new requirements, and even to take on an unexpected significance. For a long time, logistics has been an invisible and underestimated task, a practical aspect that seemed to have reached its limits of efficiency. Then e‑commerce exploded and it has suddenly become the backbone of success. It is thanks to its logistical excellence and continuous innovation in the field that Amazon has become what it is. Their proposition is actually entirely based on a logistical promise – delivery within 24 hours – which is its driving force in winning customers and cash flow, before even thinking of offering new services. A striking parallel can be seen between pre‑e‑commerce logistics and the IT maintenance of today, since it is becoming an activity that is crucial to the performance of the business.

With the Internet of Things, mobility, the 4.0 industry, the platform economy, and the increase in embedded, connected and smart systems, technology infrastructure is a core business. In all sectors, a company’s sales and image directly depend on the quality of service, and therefore the reliability of machines. Everything is based on “zero defect” technology, ensured specifically by its maintenance.

Just as e‑commerce has forced logistics to reinvent itself, digital requires maintenance to carry out an urgent rethink of its model. It must be able to appreciate the growth, fragmentation and diversification of infrastructures, and respond to their increasing criticality. It must be everywhere – in datacenters, factories, vehicles, everyday objects, etc. ‑ and at the same time be nowhere, being invisible, agile, never causing the slightest inconvenience or disruption. Managing to transform maintenance, by relying extensively on data and human expertise, to turn it into the cornerstone of the performance of the digital business, is the challenge we are faced with.

Philippe Borfiga Deputy Managing Director – Services – Econocom Group

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NO DIGITAL PROGRESS WITHOUT RELIABILITY

Companies are committed to digital transformation and are pushed by their customers to always innovate further. But what is the use of innovation if it doesn’t work? Users have become impatient and demanding.

As soon as a service becomes available, it must be faultless, irrespective of how new it may be. Accelerating the spread of digital progress in the company primarily means providing the assurance of its systems’ reliability, robustness and performance. In short, it means using technological excellence to further its business vision.

That is the task we have set ourselves and our maintenance activity contributes directly to this promise. But in order to guarantee maximum reliability on a long‑term basis within a constantly evolving technological environment, maintenance must itself change and increase its levels of anticipation and efficiency. Predictive maintenance, based on data, must now supplement traditional approaches.

Our experience and technological neutrality as well as the agility, creativity and expertise which characterise our universe now allow us to approach this paradigm shift with a head‑start and cutting‑edge boldness. And we firmly believe we are ideally placed to deliver all the benefits of this revolution to our clients.

Bruno Grossi Executive Director of Econocom Group

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MAINTENANCE – A MORE CRITICAL NEED THAN EVER

Primarily for reasons of security and handling regulatory developments, companies want to maintain control of their most sensitive data. To do this, they favour hybrid approaches. In spite of the cloud becoming widespread, they will still be retaining infrastructures in their own right for a long time. Maintenance remains and will therefore continue to remain a need, but a need that has now taken on increased importance.

In fact, within an environment where the user’s opinion and experience are paramount, what does an IT failure now mean? A net loss in sales, dissatisfied customers, a tarnished image, compromised security, etc. These are all unacceptable consequences. The IT system is as central to the digital company as the production plant is to the manufacturer, and it must therefore provide the same level of reliability.

As in manufacturing, and rail and air transport, it is not a question of being able to react effectively in the event of a failure but to ensure that the failure simply no longer occurs. Consequently, the primary purpose of maintenance is no longer to make repairs, even very quickly, but to ensure the smooth operation of the technical components to protect the company from the risk of their failure.

Also, IT maintenance now falls within the logic of risk management and it is from this perspective that it must be considered. This means assessing the probability of failure of each technical component, then estimating what would be the impact in order to decide on the optimum action plan according to criticality, costs and the company’s strategic objectives.

This is the approach employed by predictive maintenance. No longer relying solely on a human response to warning signs but on a probability‑based assessment of the risk, it represents a fundamental change for all those involved. And it alone is capable of providing a response able to meet the challenges posed by digital technologies.

Stéphane Vandenborght Executive Director of Infrastructure Continuity Services – Econocom Group

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Average estimated cost of one hour of datacenter downtime

2%

48% of companies

Loss of $1m to over $5m /hr

Loss of$0.3m to $1m /hr

33% of companies

17% of companies

Loss of$0.1m to $0.3m /hr

Across all companies questioned by ITIC in their study “Cost of Hourly Downtime“, 2016.

Loss ofunder $0.1m /hr

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01IT MAINTENANCEIN PURSUIT OF THE INDUSTRY IDEAL

IT maintenance in pursuit of the industry ideal

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MAINTENANCE IN INDUSTRY, A LONG HISTORY OF RELIABILITYThe fact that aeroplane engines never break down in mid‑air is not the result of a happy coincidence but of a draconian maintenance policy that exemplifies the methods developed in the industry since the 1970s. With increasing competition, the costs and production shutdowns caused by equipment failures were considered less and less tolerable at the time. Companies were therefore structuring themselves to prevent rather than cure, beginning with the most critical sectors (nuclear, oil, aviation, etc.). Maintenance has moved from being corrective to being preventive.

Now, action and response plans for equipment are defined in order to replace worn components as a precaution, and to regularly clean and lubricate exposed mechanisms. Initially based on time‑related frequency and/or systematic usage, preventive maintenance is gradually becoming “conditional” as a result of progress in monitoring and diagnostic methods. In order to identify and schedule the required procedures, companies rely on an increasingly accurate assessment of the level of degradation of parts.

Methods (FMECA), standards (NF X 60‑000, ISO 9001) and solutions (CMMS) which ensure the relevance, quality and effectiveness of the maintenance process are gradually appearing.

With Big Data and the Internet of Things (IoT), industrial maintenance is now reaching another milestone. The opportunity to cross‑reference and analyse vast quantities of data of all kinds facilitates a far greater understanding and anticipation of faults.

Maintenance is no longer just preventive – it is now predictive.


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The non‑availability of any core equipment – rolling stock, production equipment, drilling and extraction equipment, etc., can therefore be reduced from several dozen hours to a just few seconds a year. For all major industrial groups, the challenges are considerable and predictive maintenance, which is now considered to be strategic evidence, benefits from proportional investments.Company expenditure in the field should therefore increase by almost 30% annually by 20211.

Thanks to the progress made over more than 40 years, manufacturers today have a range of solutions enabling them to differentiate and optimise their approach to maintenance depending on whether the priority is to ensure safety of operation, reduce costs or to tailor responses to operational constraints. Predictive maintenance represents the ultimate stage in this ongoing search for reliability and efficiency. That IT, in spite of its growing importance, still seems incapable of employing such an approach is, in their eyes, a source of surprise and even irritation.

1. Markets and Markets, Predictive Maintenance Market – Global Forecast to 2021, mars 2017.


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THE SLOW MATURING OF IT MAINTENANCEAt a time when industry was already firmly committed to taking the path of preventive maintenance, IT was only in its infancy in business, and it was on the sidelines of other functions that it developed and refined its own maintenance model.

IT systems are rarely closed systems that only complete planned tasks under planned conditions. On the contrary, they are most often characterised by countless hardware and software interdependencies, a wide variety of uses and demands throughout their lifecycle, and operating environments that do not always offer the strict temperature, humidity and cleanliness conditions of modern datacenters.

As a result of this complexity and diversity, no one system is exactly comparable with another, which has long been an insurmountable obstacle for preventive type approaches. It therefore seems risky to base a maintenance plan solely on a manufacturer’s guidelines measured on standard equipment, operating under optimal conditions.

To bring more proactivity to an approach that remains largely based on the excellence of its response capabilities, IT maintenance has nevertheless drawn on industry to gradually introduce a preventive dimension within the limits of its capabilities.

Systems are monitored, scrutinised and examined.

In the most advanced model, systems are monitored, scrutinised and examined to detect the slightest sign of weakness. Reliance is placed on a well‑oiled structure and expert skills in order to diagnose the cause and rectify it quickly, if possible prior to the failure or stoppage. This is conditional maintenance.

At the same time, companies have significantly improved their technical and organisational resources to ensure the continuity of service of systems that are becoming increasingly vital to the smooth running of their business.


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1REMEDIAL

MAINTENANCE A fault leads to a response

3CONDITIONAL MAINTENANCE

Based on monitoring equipment and the

condition of the parts 2PREVENTIVE

MAINTENANCEBased on manufacturer recommendations and

operational observations

How IT maintenance has matured up to today

Redundancy and business continuity and disaster recovery planning (BCP/DRP), which are all essential, do nevertheless only constitute a safety net plan that one would dread to have to roll out. Nevertheless, according to IBM, 40% of companies are forced to use them within two years. As such, the only true security remains the reliability of the main system, and therefore the ability to continuously keep it in optimum operational conditions.

Despite the progress made in recent years in the field of maintenance, the most sensitive systems and the critical and core business systems require more attention. As such, almost three quarters of companies claim to require availability in excess of 99.99%, meaning just 52 minutes of unplanned stoppage per year2. Like industrial maintenance, IT maintenance must therefore move to a higher level, going even as far as to exclude the potential of a malfunction and, in order to achieve this, also bridge the gap that separates the preventive from the predictive.

2. ITIC, « Cost of Hourly Downtime », 2016.

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02PREDICTIVEMAINTENANCEIN IT

Predictive maintenance in IT

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THE EMERGENCE OF A NEW PARADIGM: PREVENTION RATHER THAN CUREIf your PC warned you that your hard disc had more than an 80% chance of becoming seriously damaged over the next few months, you would hasten to sort out your data, perform a back‑up and replace the hardware ‑ not in a hurry but calmly, over the course of an upcoming weekend, when the procedure would not interfere with your use. Predictive maintenance is simply this logic extended to all of the infrastructure and its components.

The power of Big Data actually allows you to go much further than limited preventive maintenance models and to identify the risks of failure sufficiently in advance for it to be possible to respond in a targeted way, during programme cycles, without any impact on production. Faults are no longer fixed ‑ they are eliminated before they occur.

Since it is based on data – technical, environmental, usage, etc. – predictive maintenance can be applied to all types of technical assets: not only traditional IT infrastructures within the data center, but also hybrid configurations in the cloud, disseminated IoT infrastructures, new digital factory equipment such as 3D printers, as well as embedded smart systems in autonomous vehicles.

This approach can guarantee digital infrastructures of any kind extreme reliability at controlled costs. Operational excellence no longer relies on a series of safety nets but becomes inherent to the systems themselves.


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How IT maintenance has matured

1REMEDIAL



Based on monitoring equipment and the

condition of the parts

2PREVENTIVE



4PREDICTIVE

MAINTENANCEBased on uses that are cross-referenced with

the predictability of the causes of faults


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THE INGREDIENTS: DATA, TOOLS AND EXPERTISETo fulfil the ambitious commitment to predictive maintenance, a maintenance provider must combine several vital ingredients. Only their rigorous coordination will result in an operational and efficient service that is capable of adapting to the specific challenges of each company and each type of infrastructure.

Years of history and experienceFor decades, manufacturers, outsourcers and maintenance providers have been gathering a huge amount of high quality data of all kinds (measurements, logs, reports, etc.) on the equipment they are responsible for. With the help of administration and monitoring solutions, they record the behaviour, operating conditions and potential malfunctions of thousands of machines. Every fault and incident is the subject of an analysis, a diagnosis and a report that are all meticulously archived. By way of illustration, Econocom records the slightest blip in a base of 235,000 Wintel and Unix servers, 45,000 pieces of network hardware, 25,000 SAN bays, 1,000 AS / 400 servers, etc.

Usage data is added to this abundance of technical data. This is crucial. Knowing which applications run on a server means knowing the demands to which it is subject.

Yet, the frequency and importance of inputs/outputs, disc access or peaks in usage significantly influence the wear and tear of the hardware, especially if it has been deviated from its primary use. Experience shows that a huge majority of faults therefore result from inappropriate usage, for example a file server reallocated to be a scheduler, which is a much more demanding task.

Lastly, environmental data is also collected (heat, humidity, dust), which also has a decisive impact on how components age.And yet, what operational manager would now know how to draft an accurate record of the peaks in temperature that occurred in their data center?

In short, we mustn’t just have technical details on aeroplane parts ‑ we must also know whether it has gone through bad weather, flown with a heavy load or landed on runways that are known to be poorly maintained.


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It will be crucial to take all these factors into account when considering its maintenance.

Real time collection tools To further enhance this knowledge base and be on the lookout for the slightest red flag in relation to a fault, we must be able to collect as much information as possible in real time relating to the systems and their use. There is a wealth of innovation in this direction, such as the advances in IoT or solutions recently developed by Econocom.

Covered in sensors, the Captain DC robot roams the data center autonomously and performs visual, sound, hygrometric and thermal monitoring in real time, 24/7, with a greater sensitivity than humans would be capable of. In the event of an incident, it can also be controlled remotely to become the eyes and contact point of the expert and speed up both diagnosis and resolution.

The Smart Event Box is an independent and non‑intrusive device designed to monitor all the datacenter components and process alerts in an automated way. Such solutions allow a dynamic and comprehensive picture of the infrastructures to be drawn up. Discover Captain DC

The latest generation robot for the monitoring and follow-up

of your datacenters

https://www.econocom.com/en/robot-ensures-data-centre-surveillance-and-security


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Unparalleled processing capabilitiesWith several hundred processors and terabytes of memory, high‑end company servers now offer calculation capacities which, until recently, were reserved for a handful of research laboratories. Thanks to this power, we are now able to process massive amounts of heterogeneous data (Big Data) within shorter time frames, even in real time, and to apply to them artificial intelligence and data mining algorithms (machine learning, factor analysis, etc).

It therefore becomes possible to exploit new or hitherto abandoned data, to trace the underlying source of a weak signal, determine correlations between malfunctions and their possible causes, or identify the probability of a failure over a distant timeline.

At the intersection of numerous disciplines, the purpose of data science is to realise these opportunities.

Like the recommendation engines of the online retail giants, data scientists are developing automated knowledge extraction methods for operational purposes. These are primarily capabilities that make predictive maintenance possible for systems as complex as IT systems.

High-end skillsAs part of a predictive approach, the purpose of maintenance will no longer be solely about changing components, or even about diagnosing the source of a fault.It must be capable of making good use of analysis tools and to interpret their results.Predictive maintenance will therefore necessitate leading candidates who are at the same time in‑depth technology experts, familiar with data analysis, and are equipped with the instincts and expertise of a sleuth.


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THE 4 INGREDIENTS OF PREDICTIVE MAINTENANCE

DATA HISTORY

REAL-TIME COLLECTION SOLUTIONS

BIG DATA

EXPERT SKILLS


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IMPLEMENTATION: FROM RISK ASSESSMENT TO SCHEDULED PROCEDURESIn practice, predictive maintenance is the result of a process in six key steps:

General frameworkBy relying on historical data and experience in the field, the maintenance provider constructs a comprehensive reference document detailing the reliability of the hardware. This wealth of knowledge means that verified, and not theoretical, MTBFs (mean times between failures) can be ascertained for the majority of hardware on the market according to purpose, conditions of use, technical combinations, etc.

Customised frameworkAn assessment of the target environment infrastructure is then carried out. A customised framework which takes into account all its parameters of use can be derived from the general framework and the information collected. An initial picture of the infrastructure and a preliminary estimate of the life expectancy of each of the components are therefore provided.

Active reassessmentAs the operating data is reported back, the customised framework is constantly being updated and the likelihoods of failure reassessed.

The ongoing improvements to the general framework, where the knowledge is being continuously consolidated, and a selection of relevant business and external information sources are also taken into account: studies and benchmarking, social networks, manufacturer sites, trade press, weather, current events, etc. Increasing the perspectives on the system helps enhance the analysis and therefore improve the ability to detect and interpret the slightest discrepancy.

Early signs of failure Anomalies do not pass unnoticed within the data gathered in real time – a drop in performance, noise or overheating. As soon as they have been examined in great detail by the analysis tools, which are on the lookout for the slightest sign of malfunction, they are treated as possible early signs of failure. The knowledge base enables the possible cause(s) to be identified and, where necessary, allows targeted verification to take place immediately. Extremely early detection and diagnosis of the anomaly mean that suitable precautionary measures can be taken where necessary, followed by


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GENERAL FRAMEWORK

CUSTOMISED FRAMEWORK

ACTIVE REASSESSMENT

EARLY SIGNS OF FAILURE

SCHEDULING OF WORK

MAINTENANCE OPERATIONS

The predictive maintenance process

the appropriate maintenance procedure when convenient, and if possible during the next programme cycle.

Scheduling of procedures Whether or not an alert has been sent by an early warning sign, the likelihood of failure of each component over a given timeline is now continually available. Moreover, it is known which applications would be impacted and with what consequences for the company. This information means that the action to be taken during the next period of work, with the decision taking into account the risk and criticality levels of the systems, can be identified. By way of illustration, imagine that a supermarket authorised for example a 1% chance of a significant fall in the performance of the server managing

its video screens over the next six months, but less 0.001% for the servers managing its tills.

OperationsProcedures are grouped into predefined cycles, for example on 1 May or a weekend in August, in order to minimise the impact on operations. During this reduced window, technicians carry out all the planned work. Maintenance operations now resemble less an emergency repair by a gallant mechanic and more the precision refuelling of a Formula 1 car by a winning team.

Ultimately, the system is under constant surveillance and remains in optimum operating conditions thanks to targeted and scheduled procedures: things are no longer being cured, they are being prevented.

03IMPACTS& BENEFITS

Impacts and benefits

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IMPACTS AND BENEFITSGenuinely taking a pro‑active approach that is both targeted and planned to predictive maintenance will translate into multiple technical, financial and operational benefits for companies:• guarantee of a very high level of

service quality for infrastructures• reduction in costs associated with

losses, including insurance• increased confidence in the

technological foundations of the digital company

• improved image thanks to the reliability and performance of the service provided

• ability to maintain substantial equipment bases spread over a wide area (IoT, etc.)

• increased security, efficiency and quality of maintenance operations

• in‑depth knowledge of the infrastructure, its components and their condition

• closer management of infrastructure budgets

• quicker diagnosis and faster decision making in the event (despite everything) of an incident.

It has therefore been shown that, in general, for every euro invested in predictive maintenance there is a €4‑10 saving in future repairs3. Moreover, in relation to other methods, predictive maintenance leads to a 36% fall in unplanned stoppages and a 60% reduction in the resultant costs4.

However, the major benefits of predictive maintenance should not detract from the major disruption that it represents for all those involved in the company like for its suppliers. Therefore, many impacts can be predicted and anticipated.

On the perception of maintenanceIn IT, where the habit is not to touch anything that is running – or seems to be running – predictive maintenance is a genuine cultural revolution. It will require the maintenance company to adopt a stance that is less focused on action and more on advice. The user will have to reassess the value of a service that now has only a distant connection with the repair. All those involved in IT will recognise that this activity now has a pivotal role..

3. M. Kahn et D. Levinson, The Hamilton Project, 20114. GE, “Impact of Digital on Unplanned Downtime”, 2016.


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On the client / supplier relationshipThe more a company opens its systems up, the more it informs the maintenance provider of its exact use, and the more relevant the predictive model will become. So that the approach is successful, a relationship based on trust – a genuine partnership – must therefore be created between the client and their provider. This close relationship will be particularly necessary when determining future procedures.

36 % fewer unscheduled stoppages thanks to predictive maintenance

60 % reduction in costs incurred thanks to predictive maintenance

Collaborating very far in advance, and more closely than ever, the client and maintenance provider organise the planned work together, which requires new roles and skills on both sides. It will involve identifying the best window according to operating constraints, interpreting data and evaluating risks to determine the actions to be taken, to prepare the logistics, to accommodate technicians, etc.

Lastly, since the quality of the service provision is no longer measured in terms of capacity to resolve incidents but in terms of their absence, the contractual terms will be likely to change. Will there be a move towards an insurance‑based model? Or towards a flat‑rate accompanied by a no‑claims bonus system? And how will potential non‑contractual procedures be taken into account? Or consultancy services? Here, the watchwords will be creativity and agility.


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On the management of the installed baseThe technological framework introduced for predictive maintenance facilitates a comprehensive and up‑to‑date vision of the infrastructure, how it is ageing and the resultant risks for the company. It can therefore serve as a knowledge base to make decisions in relation to revision, renewal or the reallocation of hardware.

This asset management approach is similar to, for example, the technical management of property assets, where the maintenance of the components of a building – insulation, air‑conditioning, painting, floor coverings, etc. – is planned according to qualitative targets – security, comfort, etc. – and the costs related to their deterioration. In this way, the investment is spread and optimised across the entire lifecycle of the infrastructure.

Such as approach seems to be the natural extension of predictive maintenance, which is required to smooth the path towards a consultancy service in urbanisation and investment.

On the ecosystemFrom manufacturers to insurance companies, integrators to outsourcers, many players may be interested in predictive maintenance data.

Building on this in‑demand asset, maintenance providers are liable to reposition themselves in the value chain and create new opportunities.

The reporting and systematic analysis of data that was introduced as part of predictive maintenance will be of as much interest downstream (users) as upstream (manufacturers) for the purpose of improving the reliability of their systems yet further.In parallel, maintenance providers will have to collaborate even more closely with manufacturers and software developers in order to anticipate the sensors, probes and processes necessary for gathering relevant data on future hardware.

On functions and processes Predictive maintenance values expertise. It leads to the upskilling of technicians who must possess a comprehensive vision of the connecting chains, from the component to the business application. They are evolving towards new functions both in terms of client relations (to construct the customised framework, to schedule procedures, etc.) and of back office (to maintain and enhance the general framework, to analyse early signs of a fault, etc.) or in terms of support functions (to optimise purchases and logistics, to develop new services and partnerships, etc.).


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Data and predictive put maintenance at the heart of the IT ecosystem

PREDICTIVE MAINTENANCE

Operational processes also need to evolve, for example to carry out fewer yet more significant procedures. The priority is no longer to be capable of responding at any time as quickly as possible but to be ready to mobilise all the necessary skills with optimum efficiency on D‑Day. Similarly, parts logistics and management have been shaken up. It is no longer necessary to order emergency components or to keep an inventory of spares close to customer sites as a precaution.

Conversely, restocking and distribution must be organised and optimised based on the needs identified.

Discredited for a long time, maintenance is, thanks to becoming predictive, once again becoming an exciting activity for computer specialists as it restores all the value to technical skills.

MANUFACTURERS

USER COMPANIES

INTEGRATORS

MAINTENANCE PROVIDERS

ENGINEERING DEPARTMENTS

OUTSOURCERS INSURANCE COMPANIES

04OPINIONS

Opinions

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RADICAL AND NECESSARY TRANSFORMATION OF THE IT DEPARTMENTThe millions of pieces of behavioural data generated by machines make up the raw material of all predictive architecture. This raw material is very much part of digital transformation. That is why the magnitude of the change that predictive maintenance means for IT Departments must not be underestimated.

In the digital company, data becomes a key asset. It becomes the focus of increased attention and is often placed under the responsibility of a Chief Data Officer (CDO) or a Data Scientist who defines the rules governing it. Predictive maintenance will not therefore be limited to the addition of a few sensors or probes on hardware, far from it.

As with all other functions, the IT Department will have to upgrade its culture and upskill in order to adopt the new approach of management by data and enter the era of predictive maintenance.

That is what would help them to ask themselves the right questions prior to the introduction of a predictive maintenance platform: what type of data is needed, in what format, how often, with what dating, under what security, retention and back up conditions, in what storage and archiving systems, with what interface and what algorithms, to answer what queries, for open data or not, etc.? Artificial intelligence will be necessary to interpret all this data and to allow the results of its analysis to be integrated at the time the decision‑making process so requires.

As with all other functions, the IT Department will have to upgrade its culture and enter the era of predictive maintenance. Jean-Claude ChicheCALICEA

Opinions

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Of course, the IT department isn’t starting from scratch.It will need to rely on its expertise and existing administration, monitoring, inventory management, maintenance, and user support tools.

But changing the outlook, redefining the objectives, considering new processes, re‑evaluating relationships with suppliers and customers – all consequences of a predictive maintenance programme – all require a radical rethink. And substantial investments.

The transport (ground, air and sea), space and industry sectors have all made predictive maintenance an absolute priority for 2017. They are coming at it from the perspective of reducing downtime by 15% and making a saving of 20% on the recurring costs of traditional maintenance. One part will enable the digital transformation and roll‑out of new uses to be self‑financed.

15 % reduction in downtime in 2017

20 % saving on recurring maintenance expenses for 2017

Some may be tempted to adopt a conservative attitude and want to stick to models that have, after all, proved their worth. This would be a fundamental error and would cause IT to irreversibly fall behind other functions in terms of maturity, capabilities and quality of service. Like the rest of the company, the IT department must go through its digital metamorphosis. Without delay.

Jean-Claude Chiche Chairman and Founder of CALICEAConsultancy in Strategy and Innovations

Opinions

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A VISION OF THE FUTURE IN LINE WITH THE ADJUSTMENT CHALLENGES OF THE IT WORLDAs a result of unprecedented technological advances, all organisations are now looking to reinvent the way in which they manage and carry out their main activities, and even to create new ones. Whether this is to make scale gains by exploiting all operational and financial optimisation levers, or to develop areas that offer real promise of creating added value. Innovating is becoming a pressing need in order to stand out and face up to increasingly intense and globalised levels of competition and demands.

As a leading analyst firm in the IT services market, we are seeing a major trend that we are calling Contextual IT, meaning a move from traditional IT towards an IT that is aligned along specific environments, with their specific practices, ecosystems and their particular functional/operational needs.

Like the organisations it serves, the players in the IT ecosystem – in particular digital services companies – are carrying out extensive work and investments to support innovation and modernise their range of “solutions and services” offers.

The most innovative organisations now use analytical services to predict when and where a problem will occur, before it happens.Vincent MalkaPAC

Opinions

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Providers of outsourcing and managed services are now embedding smart / cognitive features in their services, integrating interactions with virtual agents (bots) into them, enjoying a high degree of automation and coordination, and are deploying innovations to improve the quality of the services and the experience of users. The aim is to optimise their operational efficiency and generate large scale savings that are as profitable for their clients as for their own results.When the physical world (systems, objects, machines, etc.) interacts with the virtual world (IT / digital), it goes without saying that the delivery of services and their maintenance in operational condition involves an ongoing concern to ensure compliance with the service commitments whose criticality is growing and is proportionate to the associated “business” challenges.Moreover, a change is taking place with customers in relation to the way in which operational managers and their teams view the maintenance of their installations and physical as well as virtual systems which support the key processes in their industrial activities, in transport or for the supply of energy.

The most innovative organisations now use analytical services to predict when and where a problem will occur, before it happens. They can also predict how it will occur – and how to resolve it optimally, both in terms of time and money.

To do this, the most advanced organisations are developing predictive maintenance services by using the numerous sources of internal and external data; they process these huge volumes of information provided to them efficiently and intelligently whether they are structured (database, ERP, etc.) or not (logs, machine messages, files, digital archives, etc.); lastly they roll out sophisticated analytical services, known as “business data‑driven” services, to extract their value to serve business expectations. Most of the time, the data that can be used by predictive maintenance systems or services is already available but used either poorly or not at all in organisations. Infrastructure strategies must often be redesigned to meet these new IT workloads and their development.

Opinions

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As a leader in IT maintenance activities, Econocom has rapidly seen the huge interest that could be represented by its industrial organisation, its panel of managed clients and all the historical data available, from its production outsourcing solutions, and of maintaining them in operational conditions. Econocom could quite naturally become a key player in predictive maintenance.

But it is undoubtedly the extension of its business sectors to encompass digital, security and complex infrastructure management services which will be crucial in enabling Econocom to develop the vision and delivery of services that were not accessible only a few years ago; it’s quite a clear illustration of an excellent capacity to adapt which deserves to be highlighted in the face of the challenges which continue to impact many leading IT players who often remain resistant in declining or increasingly uncertain traditional markets.

According to the fields of application of predictive maintenance, there appear to be as many benefits as potential savings, and it is therefore quite a promising area in which to invest:• Reduce the total cost of ownership

(TCO) of IT and non‑IT assets• Provide better visibility in relation to

the obsolescence of hardware and their state of health, on processes and performances in terms of quality,

• Help to understand, monitor, predict and control process variability,

• Facilitate an in‑depth analysis of failures and risks, by identifying the initial causes and the likelihoods of occurrence,

• Optimise the scheduling and frequency of maintenance procedures,

• Improve the diagnostic capabilities for hardware and processes,

• Improve the coverage of service continuity objectives by minimising risks and also the associated budgets in order to guard against them,

• Identify inappropriate and inefficient maintenance procedures and practices,

• Identify optimum corrective procedures.

Opinions

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• Reduce the number of unscheduled (by anticipating faults rather than reacting to incidents) and costly (unnecessary preventive procedures) maintenance procedures,

• Improve the service level on the ground,

• Improve the operational efficiency of production / operating services,

• Reduce the costs of warranty claims.

Without any doubt whatsoever, predictive maintenance should experience strong growth over the next few years since it constitutes the key activity for ensuring:• the effectiveness of new generations

of infrastructures (Cloud, IoT, IA / Deep Learning, embedded systems, new networks, etc.)

• the continuity of increasingly strategic and critical digital applications: digital will continue to develop in the coming years, with exponential trends (volumes of data, technological development, etc.).

Econocom could quite naturally become a key player in predictive maintenance. Vincent Malka Consultant, Pierre Audoin Conseil (CXP Group)

In conclusion• Predictive maintenance represents

a very significant opportunity for those who will be the first to invest in this area and will be able to replicate all the benefits that have already been proven in other sectors such as aviation, manufacturing, etc. within the IT world

• This market will require players who will develop within it a perfect understanding of the management of maintenance operations processes and, probably, a specific foundation in terms of the installed base, as well as key skills in order to be able to reach a critical size necessary for profitability and ROI challenges.

.

05CONCLUSION

Conclusion

40

PREDICTIVE MAINTENANCE: GUARANTEE OF CONFIDENCE AND VEHICLE FOR VALUE CREATIONIn enabling companies to no longer be exposed to technical risks but to anticipate and address them, predictive maintenance plays a decisive role in the operational excellence of IT.It is now possible to guarantee that an essential application will not experience any failure and, more generally, to base a service quality commitment on an assessed and accepted risk. Consequently, the infrastructure becomes more than just a technology foundation: it’s a foundation of confidence on which the digital company will be able to build its future innovations.

Predictive maintenance is alone in being able to guarantee the smooth operation of connected objects at a reasonable cost.

Within an environment that is increasingly dependent on technology, the predictive maintenance of infrastructures appears to be as much a requirement as it is a key driver of value creation. As such, an application based on the deployment of thousands of connected objects – smart meters for example – can only come into being in practical and financial terms if the smooth operation of the hardware can be ensured as a reasonable cost. Only predictive maintenance is capable of doing this.

Furthermore, the capacity to assess and differentiate risks will mean that the maintenance policy can be optimised and the predictive aspect reserved for its most sensitive infrastructures. We are therefore in all likelihood heading towards combined models, where services will be proportional to criticality.

Irrespective of the approach adopted by their clients, maintenance providers will continue to be the multi‑technology custodians of the technical, user and operating data in relation to the hardware for which they are responsible..

Conclusion

41

2020 maintenancematurity model

4PREDICTIVE

MAINTENANCEBased on uses that are cross-referenced with

the predictability of the causes of faults

5COGNITIVE

MAINTENANCEBased on artificial

intelligence


Based on monitoring equipment and the condition of parts

2PREVENTIVE



1REMEDIAL


Conclusion

42

These maintenance providers will therefore continue to improve their in‑depth knowledge of increasingly rich, complex and abstract systems, which will position them as a key link in the digital value chain. Their unparalleled knowledge will become increasingly necessary for manufacturers, digital service operators and user companies, who are being continually driven to improve the reliability of their systems. This could lead to new forms of cooperation and new opportunities.

A cutting edge, rare and valued function, maintenance will become a matter for experts who will have a growing need for the support of analysis.

To minimise tasks with low added value and maximise learning from increasingly rich data, maintenance will need to further increase its levels of anticipation and automation. Thanks to artificial intelligence, it will move into its final development stage, which may be referred to as cognitive maintenance, where human expertise will be coupled with unprecedented machine expertise benefiting the entire IT ecosystem. And in this way, move successfully towards the ideal in digital uses finally free of the vagaries of underlying technologies, which from that point on will be completely invisible.

September 2017

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