Towards Real Time Bottleneck Detection using Miniterms

J. Llopis

1,2 a

, A. Lacasa

1,2 b

, E. Garcia

and N. Montés

Ford Spain, Polígono Industrial Ford S/N, CP 46440, Almussafes, Valencia, Spain

Department of Mathematics, Physics and Technological Sciences, University CEU Cardenal Herrera,

C/ San Bartolome 55, Alfara del Patriarca, Valencia, Spain

Keywords: Bottleneck, Miniterm, Predictive Maintenance, Machine Effectiveness.

Abstract: The Sub-Bottleneck concept is introduced in this article for the first time. The literature defines the bottleneck

concept through the cycle time in which, as a general rule, the slowest machine with longer cycle time, is

classified as a bottleneck. Depending on the cycle time, the machine, the production line, the plant taken into

account, etc., the literature has defined the concept of bottleneck in plant, bottleneck in production line,

bottleneck in machine, etc. This article presents the Sub-Bottleneck concept for the first time. This concept

uses the mini-term, a cycle time of each component that makes up a machine to determine which is the slowest

and focus on future improvements that will optimize the efficiency of the production line. In order to validate

this proposal, the mini-terms have been implemented in a production line at the Ford factory in Almussafes

(Valencia, Spain), made up of 4 welding robots. The tests show the variable nature of the components and

that the typical bottleneck studied in the literature does not have to coincide with the Sub-Bottleneck concept.

1 INTRODUCTION

The industrial network of a country plays a very

important role in the country's own economy, this

network is nothing more than the set of processes that

are capable of transforming raw materials into a

product. These processes have their maximum

exponent in manufacturing lines where different

machines apply a series of operations to a product that

is transforming until it reaches its final condition

(Garcia, 2016).

It would be difficult to understand the successes

of any manufacturing company without taking into

account the manufacturing lines and their evolution

from the time of Henry Ford with the introduction of

chain production to the present day with the

revolution that new technologies are bringing about.

This development seeks to improve one of the most

important parameters of a production line: efficiency.

High efficiency is a competitive advantage over other

companies in the sector, the final objective is not only

to make the product, but to do it with the greatest

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benefit possible and this benefit can be understood

under one premise: maximize the time that the

machine is productive.

Seeking to improve the efficiency of production

lines, the industrial revolution in the 19th century was

the point at which a new form of production emerged

in which workers began to have specialized functions

and use machines that increasingly helped to be more

efficient both in quality and in the production itself.

The manufacturing industry is one of the

industries where technology has had a great impact,

going from having machinery that helped workers

perform operations to having machines that are

capable today of performing jobs that were previously

carried out by several workers, again improving

efficiency.

As we have seen, efficiency has always been and

will always be one of the key points within this type

of companies, which is why a measure called Overall

Equipment Effectiveness (OEE) has been

implemented, the OEE is a parameter used in the vast

majority of the industries as it takes into account

various fundamental indicators in the manufacturing

Llopis, J., Lacasa, A., Garcia, E. and Montés, N.

Towards Real Time Bottleneck Detection using Miniterms.

DOI: 10.5220/0010552901650170

In Proceedings of the 18th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2021), pages 165-170

ISBN: 978-989-758-522-7

165

process, such as availability, performance and even

quality. Getting 100% OEE would mean that the

machine has been working the entire time at full

speed and without problems.

The concept of OEE was introduced in the OEE

as part of the Total Productive Maintenance (TPM)

methodology whose main objective pursues the

efficiency of the machines of an industry (Hedman,

Subramaniyan, & Almström, 2016).

Obtaining a 100% of OEE should be the objective

to be pursued, however, this objective is difficult to

achieve, especially when we talk about industries

whose machines have been working non-stop for

years and whose probability of failure increases

considering the life span of the same. Here is another

key concept, the maintenance of these machines

whose fundamental objective is keeping them in

optimal conditions.

For years when talking about maintenance within

the operations of a factory, what is known as

corrective maintenance and preventive maintenance

were treated from two points of view (Li & Ni, 2009).

The first happens as a result of a problem during the

operation of the machine, which may be a stoppage

or deterioration in the conditions of the machine that

make it impossible to achieve the manufacturing

objectives. The second concept aims to anticipate

these stops through a temporary planning in which

different checks are carried out on the machine, thus

being able to have it in optimal conditions, generally

for this reason. One of the keys to success to ensure

that the machine is in optimal condition is the

prioritization of maintenance orders (WO), the

industry has had limited resources especially in recent

times (Subramaniyan et al., 2020), in order to be able

to prioritize the WO and therefore for many years the

criterion has been based on the experts' opinion and

the analysis of repetitive failures of the machines

themselves along the time axis. These WOs will be

carried out on a scheduled basis throughout the

machine's useful life in opportunity windows (Chang,

Ni, Bandyopadhyay, Biller, & Xiao, 2007) in

maintenance, largely avoiding stops during

production.

In the time planning of the WO we can ask

ourselves: why carry out tasks on a scheduled basis?

New technologies offer us the possibility of

obtaining information from machines in real time,

which allows us to take a further step in maintenance

by introducing the concept of predictive maintenance.

Predictive maintenance proposes to carry out

maintenance only when the machine really needs it

and not on a scheduled basis like preventive

maintenance does.

2 PARADIGM OF THE

MAINTENANCE CURSE

In order to carry out an effective predictive

maintenance system, this system should be able to

monitor all the components in real time since, no

matter how insignificant a component may be, it can

fail and therefore cause a line stoppage. With current

systems this approach would be viable only through

the massive installation of sensors, vibration,

temperature, etc. However, in an industrial

environment this approach is completely unfeasible

due to the high cost it would entail. This is what has

been named as the paradigm of the maintenance

curse. The necessary technology and algorithms are

available but its massive use is unfeasible.

2.1 Short Terms

The works aimed at improving the efficiency carried

out to date focus on implementing programmed WOs

on an experience of failures of the machines

themselves, however one of the main characteristics

of production systems is their variability (Chang, Ni,

Bandyopadhyay, Biller, & Xiao, 2006), no process

remains constant over time due to the deterioration of

the machines that make up this process. The data

feedback given by the machines in real time manages

to provide tremendously useful information so as not

to depend on programmed WOs and to carry out

maintenance when the machine really needs it, thus

improving our efficiency not only at machine level

but also at the level of resources of the company itself.

2.2 Real-time Monitoring

One of the keys to predictive maintenance lies in the

ability to obtain information from the machines

themselves. For a long time, most factories have

worked with systems called Manufacturing

Execution System (MES) that allow gathering

information about production. However, nowadays

thanks to the advances of new technologies we are

able to collect a large amount of data about machines

that could provide us with information not on

production but on the health of the machine itself.

Being able to know why a failure occurred thanks to

the data collection allows us to pay special attention

to the elements that caused the failure (Arne, Ylipää

Torbjörn, & Bolmsjö Gunnar S., 2005).

If the way in which we consider this amount of

data is changed in order to try to analyse this data

immediately after it has been generated, this will

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allow us a new starting point within the maintenance

paradigm, and for this we must consider two main

steps (Chang et al., 2006):

• Real-time data collection and analysis.

• Dynamic corrections and WO planning

according to analysed data.

2.3 Mini-terms

So far all previous studies have classified the data

under two perspectives: long and short term.

Our line of work will be based on the use of a new

paradigm introduced with the redefinition of the short

term in smaller sub-periods, the Mini-terms, (Garcia,

2016; Garcia & Montés, 2018; Garcia & Montés,

2019).

Figure 1: Pyramid of terms.

While the short term is defined as the cycle time

it takes for a line, including a station, to perform its

task, the Mini-term is defined as the cycle time it

takes for the components of the line to do their task,

as for example, pneumatic grippers, robot arms,

clamps, cylinders, see figure 1.

Currently, the predictive maintenance approach

from the Mini-terms paradigm has its maximum

representation at Ford factory in Valencia where more

than 16,000 pieces of equipment are controlled in real

time. This control involves not only a 24-hour-a-day

surveillance of each of the equipment, but an

immediate response to the detection of deterioration

of some element of the monitored equipment thanks

to warnings generated in real time that reach the

maintenance teams, these warnings generate two

outputs simultaneously, one by email, and the other

by sending a message to any mobile device. In both

cases, an image at the time of deterioration and the

information corresponding to the line and station are

attached within these warnings, optimizing the

response time by the maintenance team (Garcia &

Montés, 2019).

3 BOTTLENECKS

As indicated in the introduction, production lines and

their components/ machines do not have an ideal

behaviour, and in addition, during their useful life

their behaviour may change. This implies that a

certain degree of imbalance is inevitable and

therefore, generating a time loss and turning the

element that suffers this time loss into what is known

as a bottleneck.

Most of the studies carried out to date try to

identify bottlenecks by associating them with

unplanned production shutdowns (Subramaniyan et

al., 2020), that is, shutdowns largely due to machine

failures. Trying to minimize these types of stops will

make the machine's OEE increase and therefore the

bottleneck will no longer happen.

Throughout the literature dedicated to the study of

bottleneck detection, different approaches have been

made (Betterton & Silver, 2012), showing two types

of states within the operation of a machine, active and

inactive state, (Subramaniyan et al., 2020) the first

being any state in which the machine is operating

without waiting, while in the second the machine is

stopped due to a wait either because it cannot

continue because the next operation is blocking it or

because it cannot start a new operation as it does not

have the necessary elements to start it: called starved,

see figure 2.

Currently, bottlenecks are always associated with

short and long-terms, therefore, the bottleneck from

the perspective of a line will be a station while from

the point of view of management it can be an entire

line or even a plant.

Figure 2: Operating states of a machine.

Towards Real Time Bottleneck Detection using Miniterms

167

In this article we propose to use the Mini-terms,

the cycle time of the components, to detect what has

been called Sub-Bottlenecks in order to determine

which component of the machine is behaving as a

bottleneck. Section 4 presents the definition of Sub-

Bottlenecks, section 5 presents the actual study

performed at a station where Sub-Bottlenecks have

been measured. Section 6 concludes with a discussion

of the preliminary and future results.

4 SUB-BOTTLENECKS

Based on the classification of cycle times defined in

Figure 1, we can define bottlenecks as shown in

Figure 3.

Figure 3: Pyramid of bottlenecks.

A Sub-Bottleneck can be defined as the

equipment that has a Mini-term longer than the rest of

the equipment in the line.

This article will focus on the analysis of the Sub-

Bottlenecks.

5 VALIDATION

To validate the relevance that Sub-Bottlenecks may

have in production, this study will focus on welding

stations. Welding units are one of the most commonly

used equipment in the automotive industry. This

usually consists of a robot arm and a welding clamp,

see figure 4. Welding lines usually consist of several

pieces of equipment that can work both in series and

in parallel. Although the welding units are made up

of the same components, their state of deterioration

may not be the same from one unit to another.

In this study, we have chosen a station of a

welding line at Ford Valencia automotive factory, this

manufacturing line called 7 includes one station

which consists of four welding robots, two placed in

the left part of part R1, R3 and two on the right side,

R2 and R4.

Figure 4: Weld line 7 – Ford Valencia.

In this station we have carried out the assembly of

a sub-set of the left side of one of the models

manufactured within the factory, this assembly begins

when a part enters the station and at that moment the

clamps close to ensure the position of the part, once

all the clamps have been closed, the work signal is

activated for the four robots. Each of the four robots

must perform a series of welding points on the part to

ensure its integrity.

We will redefine for each of these robots their

cycle time from the perspective of Mini-terms, with

which we will obtain that:

𝑇



𝑇



 𝑇



 𝑇



 𝑇



Where:

• TC

,R1

: Cycle Time is the total time the robot

takes to do the necessary work on the station.

• THtW: Home to Wait Time is the time that the

robot uses from the first position, called Home

Position to the waiting position before starting

to work, this position is a safe position to

which the robot always goes back and it

happens before the part is correctly positioned.

•

: Move On Time is the time in which the

robot is in motion from the start of the job until

it is finished.

•

: Welding Time is the time in which the

robot is performing its work, in this case

making welding points on the part.

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• TSB: Starved

Blocked Time is the time in

which the robot is stopped waiting to be able

to carry out its work either due to lack of parts

(Starved) or because it cannot continue since

subsequent stations prevent the part from

coming out (Blocked).

The diagram in Figure 5 shows the distribution of

sub-cycles times that make up the cycle time of a

welding station.

All Mini-terms are programmed in the line's PLC,

and the values obtained are sent to a NoSQL database

called Miniterm4.0 database. In table 1 we can see the

exact value of sub-cycle times of the station in line 7

in a specific cycle.

Table 1: Division of sub-cycle times of station in line 7. The

time marked in red indicates that this robot is the Sub-

Bottleneck.

With the current bottleneck methods, we would

only have the first column as data to analyse and

therefore the efforts would be focused on R3,

however, if we continue checking table 1 we can draw

conclusions that we could not even come up with

when using the current methods of bottlenecks.

By using each column as an independent variable

of the system, instead of analysing the R3 as the only

bottleneck to analyse we could analyse the different

bottlenecks according to the Mini-terms, that is, to

analyse the Sub-Bottlenecks so we could determine

that:

• Home To Wait: The R1 is the bottleneck.

• Robot Move On: The R2 is the bottleneck.

• Welding Time: The R3 is the bottleneck.

• Waits: The R4 is the bottleneck.

Figure 5: Sub-cycle time diagram.

Another characteristic we can observe throughout

several executions of the system is that the Sub-

Bottleneck is dynamic, so when depending on the set

of operations that take place in the station, it can vary

from cycle to cycle, however, despite the fact that this

variability exists we will consider as Sub-Bottleneck

the maximum time repeated. Next, we can see the

execution of several cycles of the station in line 7 and

the evolution of the Sub-Bottleneck throughout those

cycles, see table 2.

6 CONCLUSIONS

In this article, the Sub-Bottleneck concept, the

bottleneck component, has been defined for the first

time. The detection of Sub-Bottlenecks is a

fundamental step in predictive maintenance, it is not

only capable of identifying, thanks to the

fundamentals of Miniterms, which elements of a

Table 2: Evolution of SubBottleneck over several execution cycles. The red marking indicates that it is the SubBottleneck.

Towards Real Time Bottleneck Detection using Miniterms

169

manufacturing line are affecting the OEE in real time,

but it is also capable of opening a unique range of line

optimizations.

One of the conclusions that we can draw from the

present study is that the bottleneck examined in the

literature so far does not have to coincide with the

same one identified by the Sub-Bottlenecks, in fact in

a station we will only find the typical bottleneck and

a Sub-Bottleneck per Mini-Term analysed.

Being able to subdivide the times of each element

of the line as we have seen allows us to know, for

example, which robots have a higher workload,

welding time, despite not being the bottleneck, and

this could allow us to rebalance operations. We could

also identify which elements have longer waiting

times, to which we could apply speed reduction

algorithms in real time with the aim of providing a

lower consumption.

Our future work will be focused on two lines.

Firstly, the use of the information provided by the

Sub-Bottlenecks in order to improve the efficiency of

the production lines. Secondly, the improvement of

the subdivision of the welding line in mini-terms in

order to detect the Sub-Bottlenecks more efficiently.

For example, the times that there could be during the

welding process due to failure of this.

ACKNOWLEDGEMENTS

The authors wish to thank Ford España S.L. and in

particular the Almussafes Factory for the support in

the present research.

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