=Paper=
{{Paper
|id=Vol-2650/paper24
|storemode=property
|title=Production Planning, Scheduling and Risk Analysis in Manufacturing Operations by Robotic Process Automation
|pdfUrl=https://ceur-ws.org/Vol-2650/paper24.pdf
|volume=Vol-2650
|authors=Michael Matonya,Balázs Kocsi,László Pusztai,István Budai
|dblpUrl=https://dblp.org/rec/conf/icai3/MatonyaKPB20
}}
==Production Planning, Scheduling and Risk Analysis in Manufacturing Operations by Robotic Process Automation==
https://ceur-ws.org/Vol-2650/paper24.pdf
Proceedings of the 11th International Conference on Applied Informatics
Eger, Hungary, January 29–31, 2020, published at http://ceur-ws.org
Production Planning, Scheduling and Risk
Analysis in Manufacturing Operations by
Robotic Process Automation∗
Michael Matonyaa , Balázs Kocsib , László Pusztaib ,
István Budaia
a
University of Debrecen, Faculty of Engineering, Department of Engineering
Management and Enterprise
matonya2008@mailbox.undeb.hu and budai.istvan@eng.unideb.hu
b
University of Debrecen, Doctoral School of Informatics.
kocsi.balazs@inf.unideb.hu and pusztai.laszlo@eng.unideb.hu
Abstract
Practical production planning and scheduling activities rely heavily on
timely information from various sources on the production and supply chain.
Within this article, the study of current planning, scheduling and risk anal-
ysis in the manufacturing sector is carried out in order to explain publicly
available challenges and opportunities. The development of a conceptual
hybrid real-time decision support system model has been accomplished to
address the challenges. The developed model involves advanced and intelli-
gent planning and scheduling techniques and performed by robotic process
automation (RPA). RPA anticipates potential risks through the use of time
and cost-oriented failure model (tcFMEA) and takes them into account dur-
ing scheduling. The aim is to reduce overall average production time by
enhancing production planning and scheduling in the manufacturing sector.
Keywords: Total cycle time, Decision support system, RPA, Takt time, Plan-
ning, Scheduling, Manufacturing operations.
∗ The work is supported by the construction EFOP-3.6.3-VEKOP-16. The project is supported
by the European Union, co-financed by the European Social Fund.
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
232
1. Introduction
The gap between the theoretical and the practical reality of planning and scheduling
development in the real world is growing. The gap is exacerbated by the increase of
compound structures and other drawbacks in industry 4.0. Therefore most of the
newly developed technologies are being evaluated on a small scale and can even only
end up being evaluated in the laboratory. Comprehensive production preparation
requires precise storage of raw materials, workstations, procedures, equipment and
workers involved in the supply chain.
In the context of the manufacturing sector, preparation should also include the
production of products most rationally and transparently possible and, during the
planning process, it should be possible to define the near-possible bottlenecks in
the supply chain and have the best possible means of completing the order on time
with titty cost margins.
Production planning is followed by a production schedule which is the most
challenging activity in any manufacturing sector, particularly one with a high low-
volume mix, which is difficult because production planning requires a highly com-
binatorial and complex decision-making process[8]. Efficient production planning
allows the use of staff, equipment, machinery, etc. Accurate and instant value-
added information is highly anticipated in the production schedule for manage-
ment to take decisions on time, especially in the digital manufacturing sector. In
the industry 4.0 era setting, the fixing of several sensors in the production system
has rapidly increased. Further real-time output information is stored in the data
storage network. The question is how much these timely data are going to support
the production schedule on issues of timely delivery of tailored high-quality goods
and lower cost of production.
1.1. Problem Statement
The idea is that there is a digital manufacturing cell that consists of more than five
machines and has more than ten workers, from which there are several activities
that need to be carried out on the machines concerned. Project scheduling is a key
issue and the idea behind it is to simplify the existing production scheduling model
with the help of robotic processing automation.
The model developed involves the extraction of information from the ERP
method, where KPI and output inputs are essential variables for the model. The
information obtained from ERP is collected by smart sensors which are mounted
across all machines and which are also capable of reading the attached work-piece
information which is to be processed in a given machine in the respective series.
The goal is to build a conceptual hybrid real-time decision support system
model, Which includes sophisticated and intelligent planning and scheduling tech-
niques and which is implemented through robotic process automation (RPA). The
RPA predicts possible risks through the use of failure mode and impact analysis
(FMEA) and takes them into account during scheduling. The goal is to reduce the
233
total average production time by improving production planning and scheduling in
manufacturing industries.
2. Related Works
2.1. Production planning
Production planning is a dynamic process requiring coordination between several
organizational units of every organization. Planning includes what to do when to
make it, how much to make it, where to make it, the material required, and the
tools needed.
The overall efficiency of the production system depends on the efficient prepa-
ration and process design at the shop flow stage. There are three planning horizons
in production planning; strategic, intermediate and tactical, but these two horizons
cover two areas of interaction with supplier relationships, costs and time markets.
Considering the figure 1 below, the production planning starts from the strategic
level as described in section 2.1 and planning is typically caused by real demand or
expected demand. Strategic planning is further broken down into incremental plan-
ning from quantitative planning, and comprehensive planning can be derived.The
preparation of the material specifications is the next step after thorough prepara-
tion. The final and challenging stage of the model is planning, which is the focal
point of discussion.
Figure 1: A typical production Planning Approach [8]
234
2.2. Production Scheduling
Scheduling is defined as the allocation of resources and the sequence of tasks for the
production of goods and services. Production scheduling specifies the time each
job starts and finishes on each machine. Often scheduling is characterized as an act
of prioritizing or organizing activities to meet individual requirements, constraints
or objectives[15].
The discovery of innovative production scheduling methods is becoming manda-
tory in today’s digitized output[6]. The Smart Scheduling layer mainly includes
advanced models and algorithms for drawing on data collected by sensors. Data-
driven approaches and advanced decision-making architecture can be used for smart
scheduling, for example, in order to achieve real-time, efficient scheduling and ex-
ecution of distributed smart models using hierarchical integrated architecture[16].
Real-time decision-making is the delivery of contextual and synchronized work-
flow information to any system anywhere at any time so that decisions can be
taken. In the sense of production planning, smart sensors are necessary when
it comes to real-time decision-making. Most manufacturing plants continue to
use different types of production Scheduling approaches to improve efficiency and
minimize production costs[6, 9].
Production planning in many manufacturing industries has been widely used in
recent years. It continues to be more prevalent in the modern era of cyber-physical
systems or the digitalized manufacturing environment. Manufacturing of modern
and innovative production scheduling methods is becoming mandatory in today’s
digital production[5, 6].
The traditional production planning and scheduling methods do not deploy
robotic process automation to anticipates potential risks through the use of time
and cost-oriented failure model (tcFMEA). The following section explains the most
common scheduling methods available.
2.3. Production Scheduling Techniques
Research on scheduling techniques has been published for many years. The most
commonly used techniques include: Kanban[13], Dispatching Rule[4], branch and
bound method[17], MRP, Analytical Methods or Exact Method comprising (N
Workers, One Machine, N Workers, Two Machines, N Jobs, Three Machines, Two
Jobs, M Machines)[12, 3, 10], Mathematical Scheduling Optimization(Linear Pro-
gramming Optimization) There is still a promising line of intensive research to come
up with productivity planning and scheduling that will characterize fast delivery
and optimized output costs[3, 10, 12].
2.4. Robotic Process Automation in Industry 4.0
Intelligent machines and raw materials are required to interact with each other in
industry 4.0 and to drive production processes cooperatively. Output should be
versatile and individualized, in particular in mass production. The cooperation
235
Figure 2: Production Scheduling techniques[Own Source]
of computers, parts and raw materials would be highly dependent on the cyber-
physical network of all items in development. Around the same time, sensors and
actuators will be part of the Internet of Things. The cyber-physical system (CPS)
is a collective network of digital elements governing physical entities. CPS are
physical and engineered systems whose operations are monitored, organized, man-
aged and integrated by the computing and communication core[2] and defined by
its three key characteristics; intelligence, connectivity and response to changes[11].
The real-time decision support system is a core component of Industry 4.0. In order
to be successful, the decision support system also depends on the configuration of
the robotic process automation (RPA).
Robotic Process Automation is a technology framework that allows computer
software to partially or completely automate human processes that are manual,
repetitive and rule-based. RPA allows a organization the opportunity to model
a business process that is definable, repeatable and rules-based, and to appoint a
"robot" program to handle the execution of that process. It is estimated that 22%
of information technology works will be replaced by robotic process automation in
the near future.[7]. Robotic is where machine mimics human actions, process is a
sequence of steps to perform a task and automation is executing any meaningful
task when done without any human interventions.
RPA does not involve any invention of software/technology for automating.
The same RPA tool can be used to automate various projects involving different
technologies and does not require any human intervention. In the market, there are
more than 12 companies that are involved in RPA but the top three companies are
Automate Anywhere Uipath and Blue Prism. Automate Anywhere is suggested to
be good for Forward Office(FOR) and Back Ward Office Robotic (BOR), Uipath
236
is doing well in FOR and Blue Prism is good at (BOR).
The main features of RPA are; Microsoft automation. Automating Mi-
crosoft Office applications may be the most used features of any RPA tool[1]. GUI
Automation. This is the process of simulating mouse and keyboard actions on
windows and controls Screen Scraping. This is the process of extracting text from
websites and win32 apps. Citrix Automation. Surface/ Citrix Automation is
used because you cannot access the elements that make up virtual machines.
2.5. Risk Analysis
Failure Mode and Effect Analysis (FMEA) is the most powerful and reliable method
for analyzing risk analysis in general. However, time and cost-oriented failure model
(tcFMEA) is used in section 3 because the application of FMEA is restricted and
weak[14].
The accuracy of the risk analysis depends very strongly on the correct use of
the probability distributions to accurately express the complexity, randomness and
volatility of the problem. Program Evaluation and Review Technique (PERT), Four
Parameters Beta (Beta4) distributions, Triangle, Beta are the most appropriate
distributions that can represent the time and cost of production planning and
scheduling in the manufacturing sector.
2.6. Prerequisites of Production Planning and Scheduling in
industries 4.0
In order for the production planning and scheduling in real-time decision support
system to work correctly, it highly depends on the two requirements which includes;
The criteria for models: Include, the start date and finish of the job, the
workload time, the maximum completion time for each job, workload weight, the
preparation time for the work to be started, the number of job steps to be com-
pleted, the start time of the job to the respective machine.
Prerequisites for the manufacturing environment: involve, Forecast mar-
ket demand, real time technology (works, machines), commodity data, time-take
of each unit, case records, Internet of things (connectivity), captors and actuators,
Mono-free output, monitoring systems, real-time inventory levels and accessible
ERP systems. The development cycle needs the manufacturing environment.
3. Conceptual Real-Time Decision Support System
for Scheduling Optimization Process
Figure 3, shows the purely conceptual model of the Real-time decision support
system, which starts by receiving a job from the database (cloud emails) and is
documented in the ERP. The data to be entered during registration includes Job
ID, customer names, due dates, processing steps, time taken, job values and other
237
relevant information. The next step is to check the availability of all resources
(machinery, labour and logistics equipment) for jobs. In case of Machinery resources
availability the flowing two factor are used;
Machine availability factor(MAF) for job 𝑖 in machine 𝑗. MAF is a measure of
how busy are the machines at respective time of scheduling. let 𝑆𝑖𝑗 = Setup time
of job 𝑖 in machine 𝑗,𝑁 = Number of set-ups ,like wise to 𝑄𝑖𝑗 = Quantity of job 𝑖
into machine 𝑗 , 𝐶𝑇𝑖𝑗 = Cycle time of job 𝑖 in machine 𝑗. Where 𝐸𝑀 𝐶 = Effective
Figure 3: Conceptual Real time Decision Support System by
RPA.[Own Source]
238
machine capacity and 𝑁 𝑀 =Number of machines
∑︀𝐾
𝐾=1 𝑆𝑖𝑗 × 𝑁𝑖𝑗 + 𝑄𝑖𝑗 × 𝐶𝑇𝑖𝑗
(MAF) =
𝐸𝑀 𝐶 × 𝑁 𝑀
Job Operational cost factor(JOCF) for job 𝑖 in machine 𝑗 is a ratio which is
obtained by summation of all costs in a respective machine divided by total cost.
𝑀 𝐶= Material movement cost,𝑆𝐶 = Setup cost,𝑅𝐶= Run cost and 𝑇 𝐶= Total
cost of job 𝑖 and machine 𝑗
∑︀𝑛
𝑀 𝐶𝑖𝑗 + 𝑆𝐶𝑖𝑗 + 𝑅𝐶𝑖𝑗
OCF = 𝑛=1
𝑇𝐶
The most critical next step is the model itself, which must coordinate all the
jobs associated with its operations in such a way as to reduce the total cycle
time(span) of all the jobs that are the target of this project. After the arrangement
and assignment of machine employees, risk factors shall be defined, classified, and
their scenarios shall be calculated. The next step is to model the risk scheduling
framework. If the scheduling tests are successful and correct, they will be processed;
otherwise, the previous steps will have to be repeated. The RPA is also responsible
for sharing information in real-time computers and handsets during the execution
of operations. Job is registered and stored at any time in the cloud. Both feedback
and future decisions can be taken based on real-time data obtained and shared by
RPA.
4. Summaries
Through this paper, the study of current planning, scheduling, and risk analysis in
manufacturing activities have been carried out in order to highlight the challenges
and opportunities available to the public. The development of a strictly conceptual
hybrid real-time decision support system model involving sophisticated and intel-
ligent planning and scheduling techniques and applied robotic process automation
(RPA) has been accomplished in order to address the challenges.RPA forecasts
potential risks through the use of time and cost-oriented failure model (tcFMEA)
and takes them into account during preparation.
The goal is to increase the total average production time by enhancing pro-
duction planning and scheduling in the manufacturing sector.Qualitative data are
obtained from literature reviews of different academic sources (newspapers, journal
journals, lectures, books and other online platforms) along with intuitive thought.
A list of twenty-six preparation approaches is read in the literature and, separately,
more than seventeen manufacturing criteria are identified, and 11 criteria are sub-
sequently identified. Then more than 17 model requirements are described and
customized to only 17 and, finally, conceptual model steps are created.
A list of scheduling options has been compared. The design and production
requirements relevant to the selection of the best scheduling program by the AHP
239
analysis have been put in place. Furthermore, the computational model is devel-
oped, as shown in 3. To conduct the job accurately and on time in the company, it
requires hard-working workers and the appropriate information in the right period
at the right place.
5. Future research
The future study will involve an analytical, hierarchical process analysis of schedul-
ing methods. The best selected method will be advanced into industrial practical
case study.
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