Breton White Paper EN - Data Management

Welcome to interactive presentation, created with Publuu. Enjoy the reading!

Data

Management

for industrial

machines and

plants

breton white paper

Breton©2023

Milan Federico

Software Product Manager

Breton Digital Hub

Data Management for industrial machines and plants

Contents

Contents

Breton presents the second in a series of

White Papers – informative documents

intended to promote and highlight the

characteristics of various topics.

This White Paper addresses the importance of

data-drivenness, construed as the pursuit of a

data-based decisional process.

Information can be used to improve corporate

processes and achieve a competitive edge.

Data is important because it allows us to work

more efficiently, optimising production, and

can also help in making a process sustainable.

Current situation

Page 4

Customer needs

and new trends

Page 14

Proposal of

Breton software

solutions

Page 20

Data-drivenness

Page 10

Objectives

achieved

Page 18

Book a demo

Page 24

Data Management for industrial machines and plants

Introduction

Process manufacturing embraces a vast

range of sectors, such as the production

of consumer goods, chemicals, food and

pharma, all of which use machines and

systems to transform raw materials into

finished products.

In many cases the industries concerned

use advanced technology to automate and

optimise their production processes, but

sometimes the machines they use are not

connected and therefore prevent the data

generated during the process from being

collected and used. This can be problematic,

because data collected by machines during

the production process can be used to

optimise processes, boost efficiency, and

reduce waste.

Data collection in real time can help identify

and solve problems fast, thus preventing

production downtimes and reducing costs.

In addition, the use of data to optimise

processes can help to make the manufacturing

industry more sustainable, reducing its

environmental impact and increasing energy

efficiency. Without being able to collect and

use data, process manufacturing companies

simply cannot fully exploit these advantages.

To overcome this problem, some industries

are starting to invest in connectivity solutions

for their machines and equipment so they can

collect and use their data in real time in order

to optimise their production processes and

make the industry more sustainable. However,

this often calls for substantial investments in

technologies such as sensors, connectivity

gateways and data analysis software, and it

may be some time before any meaningful ROI

can be expected.

Introduction

Data Management for industrial machines and plants

current situation

Many industries are

working to improve their

global competitiveness by

exploiting the data

in their possession to

its full advantage.

1 / Current situation

Data Management for industrial machines and plants

1 / Current situation

Until now the market has offered many

solutions in the realm of data-driven

technology, many of which verticalised and

sector-specific.

The current offering however provides at best

only a partial response to the demands and

requirements of customers.

Companies in the manufacturing sector

are thus left with unmet needs and so risk

adopting a series of mutually-incompatible

micro-solutions.

We also see a proliferation of technologies

that frequently fail to give the hoped-for

results due to additional inefficiencies in terms

of time, organisation, and cost.

So, apart from acquiring multiple suppliers

there is also the cost implicit in understanding

how to make them work together.

There are many information-rich processes in

the manufacturing sector, and companies are

keen to collect data in order to monetise it and

use it to their advantage.

Context

Data Management for industrial machines and plants

1 / Current situation

“What we have

is a data glut.”

Vernor Vinge

Science Fiction Author

Professor of Mathematics and Computer Science

Data Management for industrial machines and plants

1 / Current situation

Excessive data volumes can have a negative

impact on a company's business.

For example, if a machine reads a temperature

at 10 second intervals, the result is 8,640

samples/day and 3,153,600/year.

What to do when you have to manage so

much data? Who uses them and why?

Collecting data has a cost! And the cost is high!

If we need to collect temperature data, we

need a sensor and a connection, but first of all

we need an electronics engineer to establish

where to install the sensor and where to

make the connections, not to mention cable

routing requirements.

We’ll then need an acquisition board but that’s

Problem: Data overload

The information contained within

the data do not allow value to be

imparted unless the data is first

managed, filtered, and analysed. It

follows that data is valuable not in

itself but only if it is used properly

and contextualised.

not enough, because we’ll need a software

architect to write a suitable program. Now

we only have a value, such as a value per

second... how use it?

This is where it gets difficult: each item of data

must be historicized, given meaning,

and used.

The data must therefore be useful, able to

impart value.

Data Management for industrial machines and plants

1 / Current situation

Production machines must produce. From the

perspective of the production plan (parts on

the Y axis, time on the X axis), production has

a simple formula:

N = (Parts per hour) * Time.

The more I want to produce (in the same time

period) the faster I need to work.

If we add another dimension to our analysis,

such as the energy axis, we find that energy

proceeds with speed squared.

Why do we need to change the paradigm?

“We’re entering a new world in which data may

be more important than software.” Tim O’Reilly

If correctly collected and used, data can

show, for example, that manufacturing rapidly

means increasing consumption levels. To

contextualise this concept in everyday terms,

if I drive at 100 km/h I will use far more fuel

than when travelling at 70 km/h: I follow the

same roads but in less time and at far higher

cost. Where is the sweet spot?

Data Management for industrial machines and plants

If we add another axis to our analysis, such

as the average life of machine components,

we discover that the faster we go, the higher

the stress on the machine and the faster its

components wear out.

A data-driven approach makes it possible

to find sweet spots, selecting the optimal

operating areas. If we add an axis showing

“energy cost over 24 hours”, we might

find an advantage in choosing night-time

production, for example, and the speed and

machine lifetime data may justify a drop in

hourly performance figures while maintaining

daily output unchanged and reducing costs

during the night hours by reducing machine

performance levels.

The concepts of data-drivenness thus make

it possible to apply optimisation strategies

in terms of energy, quality and process.

Productivity is no longer a linear formula,

and optimisation is possible only through the

efficient use of data.

The aims of the data-driven mindset are to

optimise, rationalise, and improve, and they

bring also concepts of sustainability.

The ability to predict future events allows

companies to automate, work longer in

unattended conditions, or reduce stress

affecting operators. In short: increase in

quality and decrease in costs (once hidden,

but today visible in sharp relief).

1 / Current situation

Data Management for industrial machines and plants

10

DATA-DRIVENness

Data-driven logic

combines a vast amount of

data and information,

merged and selected to

obtain an appropriate level

of quality and integrity of

the information that will be

used for decision-making.

2 / Data-drivenness

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Made with Publuu - flipbook maker