Predictive Maintenance: Where to Begin?

Partnerjean-baptiste.guillaume@iacpartners.com+33 (0)6 21 62 55 99

Jean-Baptiste is IAC Partners' expert on predictive

maintenance and product development projects.

He also manages product excellence projects in a

wide range of sectors: household appliances, medical

and industrial equipment, etc.

Jean-BaptisteGuillaume

Selected references:

Co-written with:Capucine FargierSenior Consultant

capucine.fargier@iacpartners.com

2

1. Introduction

Predictive maintenance systems are the result of major

innovations in the fields of sensors and big data. These systems

are designed to anticipate failures before they occur.

Many industrial companies already benefit from major savings

on maintenance costs, but the most advanced companies rely

on predictive maintenance systems to develop differentiated

business models, including “pay-per-use” and “never fail service.”

However, it must be noted that while the general principles of

successful predictive maintenance systems are now widely

available, most players in the sector struggle to efficiently and

coordinately implement these systems.

So how should a company go about implementing a predictive

maintenance system?

We found that three common mistakes must be avoided:

• Focusing primarily on the technology (which sensors?

which algorithms?)

• Starting without support in a self-learning, trial-and-

error approach (methodological, technological and

organizational)

• Underestimating the need for organizational

transformation and change management

This type of approach will be structured around four steps:

01

03

The identification of economic savings and business opportunities

The integration of a new digital ecosystem

02

04

The implementation of technical solutions for data collection and

analysis

Transformations at process and organizational

levels

With an ambitious transformation approach, aligned with the

company's strategic objectives, manufacturers can capture all

the benefits of predictive maintenance.

3

2. Identify economic savings and business opportunities associated with a predictive maintenance system

Before defining the deployment strategy

and the resources that would be required,

the company must have a clear idea of

the benefits that company will obtain from

predictive maintenance in the short, medium

and long term. This step is approached from

two complementary angles: the reduction of

overall maintenance costs (savings) and the

identification of new business opportunities

(revenues).

While the exact cost impact differs from one

sector and organization to another, our studies

show that the impact of predictive maintenance

on basic operating metrics is generally very

significant:

• A reduction in the frequency of breakdowns

of up to 70%

• A reduction in overall maintenance costs of up

to 30% compared to preventive maintenance

• A reduction of unplanned downtime by up

to 50%

Predictive maintenance thus makes it possible

to achieve new levels of operational efficiency

by relying on the development of proprietary

technologies and predictive algorithms for the

analysis of topological data.

The second benefit of predictive maintenance

is the ability to generate new business

opportunities through the development of new,

intelligent business models.

A business case: Michelin Tire Care

In an ultra-competitive market where

the product alone is no longer a source

of value, Michelin has chosen to sell

a turnkey solution to its key accounts

(more than 100 vehicles). Their predictive

maintenance solution supports a new

service-based business model. The

earnings? For users, a forecast of the

actions to be carried out on their entire

fleet and a better use of the equipment

(e.g.; tires are used until the end of their

life cycle). For Michelin, in addition to

direct access to its end customers, this

predictive maintenance offer allows the

company to plan interventions at the

customer's site as accurately as possible.

The development of new business models is

based on a transition from the sale of traditional

products to the sale of services. This could be,

for example, new value propositions based on

operating timeframes (e.g.; number of hours/

months...) or the guarantee of a certain level of

product availability ("never fail service").

4

These benefits apply to all types of organizations,

including specialized product manufacturers

and transportation operators, as well as asset

management industries.

For example, the low-cost airline EasyJet has

implemented a predictive maintenance strategy

for its entire fleet of more than 300 aircraft,

following successful trial projects.

Thanks to the support of Airbus and its Skywise

platform, 31 adverse events were successfully

anticipated before they occurred last year.

In another industry, Nestlé has updated its

entire fleet of professional coffee machines

with the addition of sensors for predictive

maintenance services, thus optimizing use by

technicians.

Finally, the compressor company Kaeser has

implemented an business model based on

the sale of air volume rather than machines,

ensuring an optimal service rate through

predictive maintenance.

Once these strategic objectives have been determined, what technical solutions should be adopted?

5

Two types of technologies are at work:

• Smart sensors that measure relevant datas

• Big data with machine learning algorithms

which detect and define patterns: anomalies

in measured data that precede failures and

are difficult to detect using conventional

methods

Predictive maintenance involves the application

of predictive analysis to an industrial

environment. Statistics and machine learning

are at the heart of predictive maintenance.

There are two approaches to anticipating

technical failures via Machine Learning.

The first and easiest to implement is based on

Supervised Learning.

This technique consists of analyzing previous

failures in order to identify parameter variations

that could cause an incident (e.g. an exponential

increase in temperature.).

Unique but relatively simple algorithms can

be applied to develop these models. However,

this technique can only predict types of failure

which have previously occurred.

The second technique involves the application

of an ‘Unsupervised Learning’-based model.

The goal here is to detect systematic changes

in the data that would be a precursor to an

incident that has not occurred in the past. The

advantage is that this model does not require

to be "trained" on past incident data and will

be able to predict failures that have never

occurred.

On the other hand, it requires further

development and a better understanding of

the technology of the products or machines

concerned.

Ability to predict new types of incidents

Development difficulty

Supervised Learning Unsupervised Learning

Optimization and maintenance of algorithms

Requires past incident data Yes No

No Yes

Medium High

High Low

3. Implement technical solutions based on data collection and analysis

Predictive maintenance systems identify early signs of failure based on historical data analysis, real-time monitoring of product behavior and machine learning.

6

Although the technical level (the installation of relevant sensors and algorithms) is essential in the employment of predictive maintenance, it is not enough for a successful implementation.

Predictive maintenance is based on four key technologies:

• Intelligent sensors, to collect relevant data

• IoT (Internet of Things) platforms, to store the collected data

• Machine Learning algorithms, to define patterns: anomalies in measured data prior to failures that are difficult to detect by conventional methods

• Applications, enabling users to view key results on different types of electronic media (smartphone, tablet, PC, etc.)

Sensors

IoT Platforms

Predictive algorithms

Applications

77

4. The required integration of a new digital ecosystem

Both IT giants and start-ups are integrating predictive maintenance platforms in their operations. It is crucial to join this new, fast-growing ecosystem - however, few organizations have experience with smart sensors and predictive algorithms.

Some of these skills will be acquired with experience, and some must come from specialized players.

There are four main types of players in the digital ecosystem of predictive maintenance:

Leaders in the software industryIncluding IBM, SAP, SAS, who benefit from

their historical expertise and existing business

relationships in other sectors

Leaders in industrial analysis specialized in predictive maintenance Such as Predikto, Falkonry and Augury. Smaller in

size compared to previous major groups, these

players are experts in the rapid and simplified

deployment of predictive maintenance but with

a less visible sales force

Industry leadersIncluding GE, Siemens and Bosch, who benefit

from a precise knowledge of customer needs

from their core business, but with more limited

software experience

Start-ups With the support of investment funds based on

ambitious market forecasts in terms of value

creation. These new players seek to distinguish

themselves in order to enter the market with

more powerful algorithms, specializations by

industrial sector or more intuitive interfaces

01

03

02

04

In order to smoothly shift to this new system, organizations must first partner with specialists in the

sector, before acquiring these new digital skills through recruitment or training programs.

However, skills alone are not enough: the organization must transform itself in order to reap all the

benefits of a predictive maintenance system.

8

5. Reaping the benefits of predictive maintenance requires a redesign of service processes and organization

Manufacturers face three significant cultural and organizational changes with the implementation of predictive maintenance:

• A new way of thinking about service and

maintenance, even new business models

• Integration of new technologies: smart

sensors and big data

• Collaboration within a new, constantly evolving

ecosystem, made up of a myriad of players

These cultural and organizational changes are the

main obstacles to successful implementation,

along with difficulty in achieving technological

breakthroughs

According to a 2018 study by GE Digital (when

predictive maintenance structures were first

implemented), the two main difficulties (as

perceived by manufacturers) were:

• Related to the creation of algorithms adapted

and dedicated to the data specific to the

product in question & data collection (in 80%

of cases)

• Related to the deployment and

implementation of the solution, disruptive by

definition and therefore requiring structured

change management (in 72% of cases)

Success therefore also requires the delineation

of a dedicated change strategy at the group

level, which will support the implementation of

new, more agile processes and the development

of a digital business culture.

This change strategy is built around successful

first Proofs of Concept (POC), joint sessions

on digital issues and training to overcome the

hesitation associated with a teams’ inexperience

in these subjects.

9

What’s next?The race is on, and most major manufacturers

are conducting pilot projects for more global

expansion on a group-wide scale.

As we have seen, we must avoid three

mistakes:

• Starting with technology

• Launching alone without support from the

ecosystem

• Underestimate the role of transformation

and need for change management.

The approaches that work are coordinated,

ambitious and go well beyond the technology

with a focus on transforming organizations

and internal culture.

So where do we start? The first step is to put

the predictive maintenance in a good position

on the company's strategic roadmap!

Predictive maintenance in aerospace: Where are we at?

Learn more

Which players, which issues and above all... which outcomes?

By Olivier Saint-Esprit, Partner

Contact us to receive our study

10