Implementation and Evaluation of MES in One-of-a-Kind Production

Giulia Bruno

, Franco Lombardi

and Mattia Orlando

Department of Management and Production Engineering,

Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129, Torino, Italy

Keywords: Industry 4.0, MES, IoT, Production Planning, Real Time, Data Collection, OKP.

Abstract: Customers are demanding more and more a product of high quality and fast delivery at a low price, while

simultaneously expecting that the product meets their individual needs and requirements. For companies

characterized by a highly customized production, it is essential to optimize the use of machines and reduce

the production cycle. The aim of this paper is to develop and evaluate how a MES is able to collect data from

the machines and use such data to perform a real time planning of production activities. The system has been

implemented in an Italian company that produces metal sheet components for prototypes and small series in

the automotive sector, which is characterized by a production with high complexity and high mix of products.

The obtained results show that the system provides several benefits in term of reduction of times.

1 INTRODUCTION

According to a McKinsey study, the benefits of

adopting new digital technologies will bring

significant gains, eg. 10-40% reduction in

maintenance costs, 10-20% reduction in quality cost,

and 30-50% reduction in total machine downtime

(McKinsey Digital, 2016). However, despite the

improvement in connectivity and computing power,

only three percent of companies are ready for large-

scale deployment of solutions for smart

manufacturing. For many of them it is not clear how

advanced analytics will streamline their operations.

This leads to many pilots in the industry and a lot of

data which is not contextualized and properly used at

all the levels in the organization. A flexible approach

to contextualize data and use it in the real-time

planning for generating automated decision-making

process could overcome this barrier.

In the market, three main groups of systems are

available, addressing different issues: (i)

manufacturing execution systems (MES), focused on

process interlocking solutions, (ii) production

planning systems, which plan activities based on

demand and availability of the resources on long time

spans, and (iii) industrial IoT solutions, which collect

real-time time data from machines with little

https://orcid.org/0000-0001-5585-647X

https://orcid.org/0000-0001-9702-6626

contextualization (in fact, machine data can’t describe

completely what is actually happening on the

production floor).

The proposed framework incorporates all the key

aspects from these different solutions. Based on a

system that is already able to deal with complex and

heavy regulated industries, such as the

pharmaceutical ones, where it provides all the data

required by regulations for electronic batch records,

the aim is to demonstrate how the real-time planning

is able to offer alternatives and intelligence in an

automated way to plan the production in the most

optimal way.

Such system is most needed especially because

the production of the 21st century is mostly focused

on the personal needs of the consumer, and the

companies that innovate and introduce new products

on the market need new approaches to quickly test the

products and reduce the time to market. The past

decades have been characterized by this trend which

can be summarized with the concept of mass

customization. This concept reaches its extreme with

the One-of-a-Kind Production (OKP): every single

product is different because it is produced for a

different customer (Wortmann et al., 1997). In this

scenario the production line must become as flexible

as possible, since an on-demand production is needed.

Bruno, G., Lombardi, F. and Orlando, M.

Implementation and Evaluation of MES in One-of-a-Kind Production.

DOI: 10.5220/0010173601170123

In Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL 2020), pages 117-123

ISBN: 978-989-758-476-3

117

Thus, a big opportunity exists now in the market,

because several of the challenges involved in

optimizing production are not well addressed or not

addressed at all. Especially the part of aggregating

data from the shop floor and use it for real-time high-

level automated decision making is not addressed.

The aim of this work is to provide intelligent

decisions in real-time in order to increase flexibility,

efficiency and predictability in manufacturing. The

expected outcome is to build a real-time planning

solution that works in a production scenario with high

complexity and high mix of products.

2 RELATED WORKS

Traditional scheduling approaches in production

involve the creation of schedules prior to beginning

of the production process. In this case, uncertainties

that are not expected nor taken into account at the

planning phase can cause delays of these schedules

(Suwa et al., 2012). Common uncertainties that occur

in a manufacturing system include machine operator

absence, material shortages, and machine failure

(Snyman et al., 2017).

In such scenarios, the manager has to react by

manually selecting a new or revised schedule to

ensure that production continues while maintaining

the required performance level. All these challenges

lead to poor utilization of resources, delays in

deliveries and sometimes chaos in production.

The innovation of real-time scheduling is to

address the shortcomings of the traditional

approaches by performing scheduling concurrently

with the production process. Furthermore, based on

the analysis of historical data, it is possible to predict

maintenance activities and include them in the

scheduling. This new approach can help industries to

better plan activities (e.g., reduce waste, improve

productivity) and mitigate the risks of non-delivering,

especially in OKP companies in which the

uncertainties are more frequent.

The characteristics of OKP make production

scheduling and control extremely difficult (Tu et al. ,

2000). The main featuresof the OKP production are:

high customization (each product is designed and

manufactured based on customer requirements),

complicated and dynamic supply chains, great

uncertainties in production control and dynamic

production systems (Luo et al., 2011). In OKP

manufacturing, due to high customization, the

productive cycle does not repeat and the productive

tasks do not have fixed times (Tu et al., 2000).

In addition to the dynamics just mentioned, there

are also other disturbances such as stochastic

customer orders or emergency orders, and frequent

engineering changes, that make highly complex the

productive activities planning (Lu et al., 2006).

The proposed framework is a MES

(Manufacturing Execution System), i.e., a software

product able to manage factory floor material control,

and labor and machine capacity, and to track and trace

components and orders, manage inventory, optimize

production activities from order launch to finished

goods (Helo et al., 2014). A similar study was

proposed by Wang et al. (2012), who developed an

application of a RFID enabled real-time

manufacturing execution system for OKP

manufacture of radial tire mold. This study

demostrated that the atomatic workshop control

system largely improves the machines’ utilisation rate

and thus the production efficiency. In this way, the

production potentials of the company can be

exploited fully though the real-time information,

instead of being directed arbitrarly by managers.

Furthermore, our proposed system schedules

activities through the product input data and changes

the planning depending on the unexpected events to

respect, anyway, the deadlines. It also controls the

tasks status, the downtimes (due to breakdown,

maintenance, etc.), the operations in production

support (material handling, program loading, quality

control, etc.). It can also compute, through the

analysis of data, the KPIs relative to the production.

3 MES SYSTEM FRAMEWORK

The developed framework consists of several

software applications and hardware components,

produced by the Octavic PTS company

(https://octavic.dk/). The framework is useful for

bridging data from operators with machine data to

offer contextualized data (human driven data) for all

the levels in the organization. This approach gives

better insights about the root cause of the problems,

actions that have been made and provides real-time

feedback for the decision makers.

The machine data is automatically communicated

to the system (IOT technology). A practical example

of integrating operator data with machine data is

when the machine is stopped for the loading of new

equipment. In this case the operator communicates

the start and the nature of downtime to the system

while the end is automatically recognized by the

system thanks to the machine information. These last

report to the system when the spindle stops or moves.

IN4PL 2020 - International Conference on Innovative Intelligent Industrial Production and Logistics

118

The framework consists of a web application

which provides advanced analytics, real-time

feedback using flexible escalations levels, predictive

KPIs (OEE) etc.

The data is collected from machines using a

device with a touch screen that is interfaced with the

machines and guides the operator through a flexible

UI flow to input data at certain stages during the

production process. In this way the data collected

from the machines is contextualized using the

knowledge from the operator.

Data gathered is presented in a relevant format on

large screens for each level of decision making in the

organisation. The web solution responsible to

manage, present and store the data is developed using

.NET technology. For the planning solution, the

GoogleOR-Tools which as an advanced framework

for constraints programming.

The flexible UI application which is running on

the device with a touch screen is built in QT (c++).

The system is very easy to install, configure (all the

configuration is done using the web interface) and

connect to any type of machine.

4 APPLICATION

The framework has been applied to an Italian

company, which manufactures car body prototypes.

The use case company is a tier 2 supplier for

worldwide known automotive manufacturers. The

strength of the company relies in its ability of

developing complex manufacturing processes in

short time to provide prototypes and pre-series

products. The company is a perfect example of the

OKP approach to produce customized products based

on requirements of individual customers.

The OKP companies use flexible manufacturing

systems to efficiently produce unique batches (

P.R.

Dean et al., 2009).

So the production of metal sheet

prototype components for a high variety of customers

requires a flexible production system. The objective

of the paper is to estimate the improvements od

production activities planniing , before their start, by

using a MES system. According to this goal, the first

step is to analyze in detail the process of die

production.

The equipment to form metal sheet components

are made in cast iron or in resin; the last-mentioned

material is cheaper but less resistant, so it is used for

small volume orders. To minimize costs, the company

realize more equipment from a single foundry blank

(for example designs punch and blankholder

together) and only in the end separates them.

The structure of the milling cycle, necessary to

transform the foundry blank in the finished piece of

equipment, is in common for all the equipment. The

structure is composed by the sequence of three main

tasks: (i) face milling and roughing, (iii) finishing,

(iv) cutting.

The tool paths and all the milling support

operations (such as crane transport, fastening, metal

swarf control on blank surface, blank line up, utensil

resetting, cleaning etc..) make up the milling cycle.

The details of the activities, necessary to carry out,

depend from the piece of equipment to be produced.

The smoothing, roughing and cutting operations

are carried out on the roughing machines (suitable for

removing more material and supporting higher

stresses on the tool) while the finishing operations are

carried out on finishing machines (with high rotation

speed and feed, very accurate and with automatic tool

change).

Each developed equipment is unique. However, is

essential to find a method of classification of

equipment through which the variety of system input

data can be reduced. Thanks to this reason, the

equipment are classified according to the production

cycle. The equipment have in common the production

cycle structure but what varies for each of that is: (i)

the duration of the activities, (ii) the necessary

activities and (iii) the allocation of activities /

machines. According to how the variables

individually affect the cycle, it is possible to combine

these effects and find all the possible customizations

of the cycle. Consequently, a new equipment in

production can be classified by associating it with one

of these customizations.

Until now, the company management planned the

milling activities thanks to the experience

accumulated over the years regarding the expected

times and the recommended machines for the

equipment in production.

Relying on experience is not always the right

choice. If the production manager does not define any

planning rules and does not use a calculation tool, he

could be in serious difficulty when the amount of

activity is high.

OKP companies have highly flexible production

systems, which allows numerous chances to produce

an equipment. Choosing the best among these for a

high quantity of equipment inevitably increases

human error. In addition, the customer could request

design changes even in the production phase and a lot

of other uncertainties could be happen such as

breakdowns, maintenance, absence of operators,

delay in delivery of raw materials etc...

Implementation and Evaluation of MES in One-of-a-Kind Production

119

All these variables complicate the role of

production manager, which often has to pay workmen

overtimes thus increasing costs and reducing

earnings. Moreover, in this worry scenario the

manager stress increases and makes worse the quality

of staff work. This effect inevitably affects current

and future performance of the production process.

The aim of this work is to evaluate the benefits

that the use of a real-time planner involve already in

the design phase in a mold manufacturing company.

To this aim, the system planning results have been

compared with the manual ones through some

indicators. The details of the production process, the

method implementation and the description of the

obtained results are reported in the following

sections.

4.1 Process Description

The company manufactures and assembles sheet

metal and aluminium components for prototypes and

small series of cars and other road vehicles. The

customers are car manufacturers, that usually, in the

design phase of a new vehicle, need some models to

perform assembly and safety tests (the so-called

crash-tests) but for them the production of a small

volume of components is not convenient so they

commission these activities externally. The strength

of the company in fact relies in its ability of

developing complex manufacturing processes in

short time.

The customer imposes to the company different

progressive deadlines for components and gradually

assembles and tests them. If the tests on prototypal

components is negative, the customer can ask for

company to modify them. It is a well-known fact that

without a flexible production system, the company

could be in trouble to pass quickly these unexpected

events and respect the deadlines.

The greatest difficulties in this type of business

are the strict budgetary and production time limits and

the need of a highly flexible production system. The

earliest project deadline is 7/8 weeks form the order

of which more than 3 weeks are necessary only to

complete the equipment to forming and cutting the

metal sheet components. The remaining limited time

is used for all the other project phases.

The production process of the company is

reported in Fig.1. It starts from the acceptance of the

order of a component with the delivery by the

costumer of the CAD models needed for production.

The CAD models are received by the technical office,

where the designers define the production process and

the dies needed by the pressing machines.

Figure 1: Production process of the company.

In prototypal production the pressing machines

are mainly used for drawing and flanging metal sheet.

Drawing metal is taking a flat or partially formed

sheet metal blank and forming it into a desired shape.

Flanging metal is the act of swiping sheet metal

in a direction contrary to its previous position. These

pressing operations are performed with a punch and a

die. In a basic example of metal sheet forming, the

punch has the shape desired for the metal sheet

component and it's locked on press machine ram (the

moving of reciprocating member).The sheet metal

blank is placed over the die, which is locked on press

machine bolster plate. During the closing operation of

the pressing machine, the blankholder, that surrounds

the punch, firstly comes into contact with sheet metal

blank and applies pressure to the entire its surface

(except the area under the punch) to hold it against the

die while the punch travels towards the blank. After

contacting the sheet metal blank, the punch forces the

sheet metal into the die cavity, forming its shape.

At the end of the production process design, the

equipment CAD projects are used by the CAM

IN4PL 2020 - International Conference on Innovative Intelligent Industrial Production and Logistics

120

Figure 2: Flow chart.

department to define the tool paths both for the

polystyrene model and, subsequently, for the milling

of the foundry blank.

The foundry receives the equipment polystyrene

models and give back the cast iron ones. After this

phase, the cast iron blanks are milled and manually

overhauled by skilled workers.

Once the sheet metal forming operations are

finished in the press sector, the components are laser

cut with specific pallets (obtained previously by

copying the shape of the punch), checked by skilled

workers and send to the costumer.

4.2 Implementation

The high product customization introduces a high

planning complexity. In order to implement the real-

time planning system to the dies productive process,

a method to group the equipment by budget hours and

type of milling cycle has been defined, in order to

reduce the complexity.

The system implementation complexity is in the

input data variety. Each piece of equipment is unique

and therefore also its production cycle. According to

this reason, it looks like impossible to implement the

system in the way that it can know the production

activities depending on each equipment.

So, to reduce the input data complexity, the

equipment have been classified by means of the

production cycle. Starting with a production cycle

structure common to all the equipment, its

customization variables have been identified.

Starting from a production cycle structure

common to all the equipment its modification

variables have been identified and their effects have

been combined to find all customizations.

The variables are: (i) the duration of the activities,

(ii) the necessary activities and (iii) the allocation of

activities / machines. The changes that each of them

apports to the production cycle can be identified

through three questions:

1. How long is the milling cycle?

The answer identifies that each activity has a

different duration depending of the

equipment.

2. Is the equipment used to form a visible

component or a structural component?

The answer identifies if the roughing and

finishing activities carry out in sequence on a

roughing machine (structural component) or

if they carry out respectively on the roughing

machines and finishing ones (car-body

visible component).

3. Is the equipment simple o complex?

Implementation and Evaluation of MES in One-of-a-Kind Production

121

The equipment is complex when more

pieces are realized from the single foundry

blank (such as punch and blankholder

designed and manufactured together).

If the answer to this question is "yes" then

the last activity of the cycle is the cutting one

otherwise is the finishing one.

The combination of modification options 2 and 3

entails four production cycle types.

The last variable to manage is the duration of the

activities depending of the equipment. In order to

consider it, eleven classes have been defined, from A

to M, and a range of milling hours has associated in

ascending order to each of them. For each class there

are four production cycle types and their activities are

linked to a percentage of the class hours range: 55%

for a finishing activity, 44% for a roughing activity

and 1% for the cutting one.

The flow chart representing this process is

reported in Fig.2.

4.3 Order Loading

Upon to the arrive of a new equipment in production,

the order is loaded into the system. The new

equipment is linked to the right production cycle by

choosing one of the four types of orders: (i) the

production of a simple equipment used to form car-

body visible components, (ii) the production of a

complex equipment used to form car-body visible

component, (iii) the production of a simple equipment

used to form car-body structural component, and (iv)

the production of a complex equipment used to form

car-body structural component.

In the order loading phase, it is important to specify

one of the 11 classes in addition to the typology. In

this way, the system is going to automatically

recognize the necessary activities and their duration.

So, it is going to plan them correctly.

Regarding the allocation of activities to the machines,

the system is going to respect the following rules:

(i) For the first type of order, one change of

machines is allowed. Firstly, the face milling

and roughing activities carry out in sequence

on roughing machine then the finishing tasks

on finishing machines.

(ii) For the second type, two changes of

machines are allowed. Firstly, the face

milling and roughing activities carry out in

sequence on roughing machine, then the

finishing task on the finishing machines, and

finally the cutting task on roughing machine.

(iii) For the third type, no machine changes are

required because all the milling tasks are

carried out on a roughing machine.

(iv) For the fourth type two machine changes are

needed because the cutting activities,

although carry out on roughing machine as

the previous ones, are not immediately done

after the finishing tasks.

The company machines are divided in finishing

machines and roughing machines. In particular, the

company has four roughing machines and three

finishing machines, but some machine-activity

allocations are preferable to others depending of the

equipment. In addition to the mentioned rules, the

roughing and finishing machines have been

associated with the corresponding activities with an

increasing priority. In particular, the system is going

to firstly occupy the machines with the highest

priority and then those with the lowest priority.

During the introduction of a new order, the

operator in addition to the class and typology

(through which the system recognizes the right

milling cycle) has to report the coming date from

foundry and the deadline date from the customer.

5 RESULTS AND DISCUSSION

In order to show the system planning efficiency, we

compared the manual planning of activities with the

automatic planning defined by the system. An

example of the planning generated by the system is

reported in Fig.3.

Figure 3: Planning of activities on the machines.

The considered period covered the orders of 39

equipment, for which a total of 73 activities were

executed. In the original planning, the time period

started in the 48th week of 2019 and ended in the

second week of 2020.

The comparison was made accordingly to the

following three indicators:

IN4PL 2020 - International Conference on Innovative Intelligent Industrial Production and Logistics

122

1. waiting time, i.e. the time between the coming

date of the item from the foundry and the

starting date of the first activity;

2. production time, i.e. the time between the start

of the first activity and the end of the last one;

3. deadline gap, i.e., the time between the end of

the last productive activity and the deadline set

by the customer.

For each equipment, the three indicators were

computed both for the original planning and the

automatic one. The results showed that, with the

automatic planning, the 97% of the equipment present

less or equal waiting time, the 82% of equipment

present less or equal productive time, and the 77% of

equipment present a higher deadline gap.

The results show the benefits of using the system

to plan the activities before their starts. Instead, the

use during the production process allows to collect

high amount of data on it.

The company gain are several benefits dufrom e

the data collection during the production, such as (i)

the ability to know the causes of uncertainties during

the production process and so quickly react to them,

(ii) the possibility of analyzing such data at the end of

the production process to reconstruct the past events

(descriptive analysis), find the cause-effect link of the

events (diagnostic analysis), predict what will happen

with the future orders (predictive analysis) and design

the improvements that will influence the future results

(prescriptive analysis), and (iii) the possibility to

collect the product managing strategies. The built

database is essential to help managers to take the

future choices and estimate better the future quotes.

6 CONCLUSIONS

The objective of this paper is to propose a framework

to collect data and perform a real time planning of

production. The benefits of using such framework are

demonstrated in a real case of an Italian

manufacturing company.

Future works will address the further benefits of

using the developed framework also in case of

breakdowns and downtimes, to automatically

recalculate the planning of activities.

ACKNOWLEDGEMENT

The work was supported by the IoT4Industry project,

which has received funding from the European

Union’s Horizon 2020 research and innovation

programme under grant agreement No. 777455

(project website of the “Real Time Planning of

Production” project: https://www.iot4industry.eu/

project_rttp).

REFERENCES

Dean , P.R., Tu Y.L. & Xue, D., 2009. An information

system for one-of-akind production, International

Journal of Production Research, 47:4, 1071-1087.

Helo, P. Suorsa, M., Hao, Y., et al., 2014. Toward a cloud-

based manufacturing execution system for distributed

manufacturing. Computers in Industry, 4: 646-656.

Lu, B.H., et al., 2006. RFID enabled manufacturing:

fundamentals, methodology and applications.

International Journal of Agile Systems and

Management, 1 (1), 73–92.

Luo, X., et al., 2011. Operator allocation planning for

reconfigurable production line in one-of-a-kind

production. International Journal of Production

Research, 49 (3), 689–705.

McKinsey Digital, 2016. Industry 4.0 after the initial hype

- Where manufacturers are finding value and how they

can best capture it. Accessed online

from:https://www.mckinsey.com/~/media/mckinsey/b

usiness%20functions/mckinsey%20digital/our%20insi

ghts/getting%20the%20most%20out%20of%20industr

y%204%200/mckinsey_industry_40_2016.ashx

Snyman, S. & Bekker, J., 2017. Real-time scheduling in a

sensorised factory using cloud-based simulation with

mobile device access. South African Journal of

Industrial Engineering, Vol 28(4), pp 161-169.

Suwa, H. & Sandoh, H., 2012. Online scheduling in

manufacturing: a cumulative delay approach.

Tu, Y.L, Chu, X.L. and Yang, W.Y., 2000. Computer aided

process planning in virtual one-ofa-kind production.

Computer in Industry, 41, 99–110.

Tu, Y., et al., 2000. Computer-aided process planning in

virtual one-of-a-kind production. Computers in

Industry, 41 (1), 99–110.

Wortmann J.C., Muntslag D.R., Timmermans P.J.M., 1997.

Customer-driven manufacturing, Chapman & Hall,

London, UK.

M. L. Wang , T. Qu , et al., 2012. A radio frequency

identification-enabled real-time manufacturing

execution system for one-of-a-kind production

manufacturing: a case study in mould industry.

International Journal of Computer Integrated

Manufacturing, 25(1), pp. 20-34.

Implementation and Evaluation of MES in One-of-a-Kind Production

123