Article citation information:
Łukasik, Z., Kuśmińska-Fijałkowska, A., Kozyra,
J., Olszańska, S., Roman, M. Logistics planning as an important
element of strategic decisions in production management in the company –
case study. Scientific Journal of Silesian University of Technology. Series
Transport. 2024, 122,
151-180. ISSN: 0209-3324. DOI: https://doi.org/10.20858/sjsutst.2024.122.9.
Zbigniew ŁUKASIK[1], Aldona KUŚMIŃSKA-FIJAŁKOWSKA[2], Jacek KOZYRA[3], Sylwia OLSZAŃSKA[4], Mateusz ROMAN[5]
LOGISTICS PLANNING AS AN IMPORTANT ELEMENT OF STRATEGIC DECISIONS IN
PRODUCTION MANAGEMENT IN THE COMPANY – CASE STUDY
Summary. The
authors have developed algorithms and a simulation model of the production
process in the selected enterprise, and proposed variants of emergency conduct.
An aggregated simulation model as well as simulation results will allow for the
implementation of appropriate procedures for dealing with emergencies. Through
employing these measures, audits conducted in the company will
reveal how the company is prepared for various failures and what their impact
is on the efficiency indicators of the process.
Keywords: production
logistics, management, process quality, technological transport
1.
INTRODUCTION
Proper
organization of production and logistics processes is based on the use of
decision support systems, which are intended to support the work of logistics
managers. Simulation programs enable testing of different process variants
without incurring the excessive costs of physical model building. Industry 4.0
requires precise decisions that are made in the shortest possible time. This
can, in turn, determine competitive advantage. Since the time required for
running multiple simulations is relatively short, simulation models fit
perfectly into the trend of Industry 4.0. The proper conduct of simulation must
be preceded by appropriate preparation, i.e., process mapping. The scope of
implementation must also be defined to map the real process as closely as
possible. Moving through successive procedures included in data collection, as
well as process mapping and simulation of possible solutions, translates into a
permanent improvement in the quality of the processes in place. Production is
based on a number of major decisions that must be made in a short time to avoid
bottlenecks. Quality management standards applied in production companies
provide for the introduction of appropriate contingency plans, which serve to
prevent lengthy downtimes as well as undesirable emergencies. These situations
translate into a decrease in the level of customer service and competitive
advantage in the market.
2. LITERATURE REVIEW
In
order to ensure the quality and safety of each link involved in the production
and delivery of products, enterprises must implement quality standards. The
concept of service quality in the aspect of logistics has been defined by the
logistics management council. According to Megoze Pongha, quality in logistics means that the company meets
the requirements and expectations agreed upon with the customer [1]. In terms
of service characteristics, include [2]:
- ease of getting the
information you need and placing and obtaining orders,
- timeliness and
reliability of delivery of ordered goods and communication,
- processing of orders
accurately, completely and without damage to goods and error-free
documentation,
- timely and responsive
after-sales service,
- the accurate and timely
acquisition and transfer of information between partners to support the
planning, management, and execution of the activities mentioned earlier.
In
practice, a detailed assessment of the quality of services can be problematic
because some factors that affect it may be subjective, such as customer
satisfaction [3]. According to Anneri van Zyl, increasing competitive advantage and acquiring
customers requires companies to introduce appropriate management modules
oriented toward pro-quality actions in their management systems [4].
Manufacturing
industries, which have a high degree of responsibility for tasks, have
developed industry-specific standards. Examples of these standards include the
IATF (International Automotive Task Force) for the automotive industry or the
HACCP (Hazard Analysis and Critical Control Points) for the food industry.
The
IATF 16949:2016 standards are a set of pro-quality considerations aimed at
developing a quality management system for continuous improvement. In particular,
it focuses on the prevention of errors and the reduction of variability and
losses in the supply chain [5]. When applied, these standards allow for the
elimination of undesirable behavior and conduct in manufacturing and supply
processes [6]. The implementation of quality standards is classified as a
strategic decision. Such an extensive and specified interference in the
management system should be preceded by a thorough analysis, and its
introduction should proceed in stages and in accordance with the rules of
conduct [7]. The approach adopted in the standard, which takes account of risk,
is called PDCA (Plan, Do, Check, Act) [8]. The
abbreviation PDCA originates from the Deming cycle presented in (Fig. 1.).
Fig. 1. Deming cycle [5]
The
indicated approach allows for the identification of factors that may have a
destructive impact on the process and deviate from the planned results. What is
more, the Deming cycle enables a simpler introduction of preventive supervision
measures and gives the possibility of minimizing negative effects while
maximizing the use of emerging opportunities. Quality management requires
consideration of the external and internal determinants that are relevant to
the process [9].
Leadership
represents an important aspect that is mentioned in the IATF standards. Top
managers and executives should demonstrate commitment to the whole management
system [10].
The
tasks of the management include responsibility for the effectiveness of the
management system, compliance with the quality policy and its objectives, as
well as accessing the indicated objectives. Further duties include ensuring an
integrated quality management system and promoting the process approach. The
system must be audited regularly, and its effectiveness evaluated to ensure
continuous improvement [11]. The company policy must be customer-oriented, and
the leadership should ensure that customer demands and applicable legal and
regulatory requirements are understood and followed consistently [12-13].
Trends of increasing customer satisfaction should be maintained, and
communication between units should be efficient. It is also necessary that the
standards introduced are presented in a documented form that is accessible to
stakeholders.
It
is also important to define precise quality objectives, which must be
consistent with the quality policy, and measurable [14]. These objectives
should take into account all requirements, ensure the conformity of products
and services, and thus increase customer satisfaction. Furthermore, it is
essential that they are monitored, properly communicated, and continuously
updated.
An
equally crucial element in the implementation of quality standards is support,
which is understood as the scope of activities aimed at maintaining continuity
of the functioning pro-quality systems as well as the implemented quality
standards [15]. The main task of the company in this respect is to
guarantee the resources necessary to establish the implementation, as well as
the maintenance and continuous improvement of quality management systems.
Analysis of the measurement system requires systematic statistical studies to
perform an analysis of the variability of the results obtained for each type of
inspection, measurement and test equipment system, defined in the control plan
[5]. The results of such tests should be consistent with those adopted in the
regulations. According to the IATF quality standards, knowledge necessary for
the operation of the processes should be established [16]. It is also key to identify
the current know-how and actions needed to achieve a higher level of expertise
[17]. Further, in the context of knowledge, the key competencies of the units
involved in the process must be indicated [18]. Appropriate training should
therefore be provided to enrich staff competencies. Importantly, the
individuals involved in the process should be aware of the quality policy, the
relevant goals, and their contribution and consequences.
Operational
activities comprise actions related to the direct production of products or the
provision of services [19]. Each operational measure should be properly planned
and supervised. The organization of operational activities includes the
definition of requirements for products and services, the establishment of criteria
for processes, and the acceptance of products and services. Further elements
involve the indication of resources, the implementation of supervision of
activities, and the establishment of guidelines for record-keeping. All
operational activities should be characterized by a high degree of
confidentiality. Planning in this respect requires taking into account:
- customer requirements
in the context of product-specific and technical requirements,
- logistical scheduling
requirements.
ISO
9001 quality standards indicate product design and development as well as
manufacturing processes with a view to preventing defects in the final
products. Prevention is a more important aspect than detection. During product
manufacturing planning and process design, it is necessary to identify the
input data required to indicate the purpose of the process and the desired
outcomes. As per the guidelines for the evaluation of performance, it is
essential to determine which effects should be monitored and measured. This
includes the type of monitoring and measurement methods, as well as methods of
analysis and evaluation of the time appropriate for measuring and monitoring.
Whenever it becomes impossible to use the process capability factor to indicate
compliance, we must reach for an alternative method based on the evaluation of
the compliance of the batch with the specification [20].
The
last key aspect addressed in the IATF standards is improvement. In terms of
manufacturing logistics, by improvement we mean continuous improvement,
enhancement, as well as gaining competence in manufacturing processes [21].
The
era of Industry 4.0 sees more pressure being put on logistics. This aims to
increase process efficiency in a broad sense. Most processes are designed
according to the customer's requirements in order to meet their expectations.
Production logistics is located between two groups of entities that have
interacting properties. The former includes factors that can influence and
exert pressure on production, i.e., a one-way action. Production must be
adapted to the standards imposed at the time. The second group consists of
cooperating actors who are oriented towards achieving a common set of goals.
The positioning of production logistics between the two absorbing groups
of cooperation, as well as the influencing factors and the continuous growth
rate of production and consumption of the offered goods, enforces the use of
modern concepts in production (Fig. 2.).
Fig. 2.
Factors influencing production logistics
Production
logistics, in addition to modern technologies and management concepts, is
mainly based on planning and production control. The former consists of forcing
such actions in the process that will bring the desired results. We can exert
control over processes by the methods shown in (Fig. 3.) [23].
Fig.
3. Types of control systems [23]
Control
of material flows depends on the choice of manufacturing system and the level
of activity of the implemented technology. The implication of adequate control
aims to reduce lead time, guarantee a relative level of customer service,
minimize inventory, shorten production cycles, and effectively utilize
production capacity.
The
standard control approach predicts the following quantities:
- tide,
- drain,
- flow (period of
stay),
- supply.
This
is reflected in the selection of two formulas for controlling the flow of
materials [22]. The first group is the so-called quantitative formula (Z -
resource, X - inflow scale, Y - flow and outflow). Group two, on the other
hand, is the temporal formula (A - entry time, start, B - exit, C - residence
time).
Group one
1.
Drainage volume and stock control (the
supplier's plan must be accepted by the recipient and should take into account
deviations). (Fig. 4) (1).
Fig. 4. Controlling outflow
volume and stock [23]
2. Controlling the size
of the outflow (the supplier’s plan is identical to the customer’s
plan) (Fig. 5) (2).
Fig. 5. Controlling
the size of the outflow
[23]
3. Controlling the size
of the stockpile (Fig. 6) (3).
Fig. 6. Controlling
the size of the stock
[23]
Group two
1.
Controlling the timing of the outflow and the
residence period (Fig. 7) (4).
Fig. 7. Controlling
the timing of outflow and residence time [23]
The implementation of an advance compensation period
will allow the manipulation of disturbances during production cycles. The start
and finish in the interdepartmental approach are derived from the cyclogram.
2. Controlling the drain
term (Fig. 8) (5).
Fig. 8. Controlling
the drain term
[23]
The
planning period can be divided into fixed contractual stay units "a". The urgency number of the
processes can be determined by running the next number of the production
startup. The process marked with a lower number has a higher urgency.
Designation a is
used to denote a fixed average cycle
size for particular organizational units in interdepartmental planning.
3.
Residence period control (Fig. 9) (6).
Controlling the residence period, where p
- beginning, k - end, and
Fig. 9. Controlling the
residence period
[23]
The
increasing pace of industrial development requires flexible and functional
methods of production management. Control methods must be resistant to sudden
crises and provide complex solutions for production processes.
3. TOOLS FOR DETERMINING THE EFFICIENCY OF MACHINES
ON PRODUCTION LINES
Organizations and manufacturing companies strive to
ensure failure-free production processes, taking into account the elimination
of unnecessary activities. This is done in accordance with the Lean
Manufacturing model, with simultaneous minimization of expenses [24]. It is an
extremely difficult and responsible task to prevent possible failures in
manufacturing enterprises. Breakdowns are an undesirable state that has a
destructive effect on the production process.
Logistics managers are required to ensure continuity
of the entire supply chain, with particular emphasis on production processes.
Production processes should be monitored by checking
product quality, controlling the operating parameters of the machine,
performing audits, introducing improvements, standardizing processes and
servicing equipment. Production capacity must be adjusted in such a way that
each subsequent machine has a higher production capacity to ensure the
elimination of bottlenecks in production lines.
Production managers and planners seek savings in
every aspect of manufacturing. Proper operation of machinery parks is often
associated with the use of the OEE (Overall Equipment Effectiveness) indicator.
Calculations performed utilizing the OEE indicator reflect the effectiveness of
production machines and are performed with the use of data such as downtime,
machine changeover time, failure time, and other factors that may influence the
machine’s effectiveness. The OEE must first and foremost answer the
question of whether the selected machine, process or system is used effectively
in a specific time interval.
The equipment utilization efficiency ratio is
determined by equation (7):
where:
A –
Availability
E –
Effectiveness,
Q –
Quality.
We
calculate the availability ratio by using the planned working time in the
expected period, in relation to the real operating time of the machine in an
equivalent period (8):
where:
APT
– Actual Production Time,
PBT
– Planned Busy Time.
The
productivity index represents a normative unit of time in relation to the
actual time required to make 1 unit of the product (9):
where:
PRI
– Planned Run time per Item,
PQ –
Produced number of products,
APT
– Actual Production Time.
By
quality index, we mean the number of good products in the context of the total
number of products produced in the indicated time period (10):
where:
GQ –
number of products that comply with quality guidelines,
PQ –
number of all products manufactured.
In
practice, this indicator is influenced by many factors, which include, above
all, time. It is necessary to determine the time interval for the calculations.
Here, it is important whether this concerns a single shift, which is usually
assumed to be 8 hours of work, or if the calculations are made on a weekly
basis. We must also take note of the number of changeovers and the duration of
a single changeover. Another key component is the number of goods with losses
that do not comply with the requirements of the recipients. The last criterion
includes losses caused by failures. Each failure necessitates the cessation or
limitation of production. Figure 10 presents a diagram of possible losses in
the course of production, as well as their influence on the OEE index.
Fig. 10. The OEE
indicator
[23]
It
is also possible to indicate which area reduces the whole process efficiency.
A
group of indicators that relate directly to machine maintenance and
performance.
MTTR - mean time to repair failure (11):
where:
The
MTTR (Mean Time To
Repair) indicator determines the average time from the moment of failure to the
time of its removal, i.e., the rate of removing machine failures. The average
time from failure to repair is an important marker, especially for UR services
in manufacturing plants. It improves the monitoring and analysis of
productivity and efficiency, as well as the execution of corrective actions
that affect machine availability rate.
MTTF - mean time to failure (12):
where:
The MTTF indicator is used to determine the
average time to failure. It is very important for processes with lengthy unit
operations. The MTTF is directly connected with the MTBF, where it is one of the components. The indicator is used by
production companies in planning and organizing maintenance processes. The MTTF represents the difference of the
availability time of a machine and the failure time, divided by the number of
failures in a certain time unit.
MTBF - mean time between failures (13):
MTBF - the
performance of a machine park in terms of maintenance. The indicator informs
about the average time between one failure and another. With it, companies can
verify the time of failure as well as the availability of equipment and
machines in use.
In
the context of machine availability, in addition to the basic formula, we can
distinguish between three states. Availability represents the ability of a
machine to remain in a state that allows it to perform the desired functions,
under the designated operating conditions and for a set period of time,
assuming that all the required external resources are provided. The efficiency
of the entire production process is determined by availability, i.e., the degree
of use of the planned time, as well as the losses caused by production problems
or possible deletions. The first component of availability is related to
maintenance and depends on the reliability and maintainability of the technical
system as well as the support capacity of the maintenance organization, while
the latter depends on the time required for adjustment and tuning.
Ai (Inherent availability), which covers only the
technical system (14):
Achieved
availability Aa
also considers the organization of UR (15):
Ao (16)
(Operational availability) includes both the time for repairs and for
preventive work. Only the time during which the equipment has to be stopped is
taken into account.
where:
MMT (Mean Maintenance Time) is the sum of repair and
preventive maintenance times,
MTTM (Mean Time To Maintenance) is
the average time between them,
MLD (Mean Logistic Delay) is a measure of the
support’s capability; it represents the average waiting time for a
repair.
The
operational capability of a piece of equipment consists of availability,
maintenance, operation and operational context.
4. MODELLING PROCEDURES USING FLEXSIM
Process improvement should be preceded by process
mapping and modelling. Proper mapping is treated as a starting point for
further improvement and modeling. It is impossible to achieve maximum potential
without well-mapped processes. When a company is launched, a new department is
created in the organization, or an innovative project is introduced, it
requires mapping the most important processes.
Queuing is a very common occurrence on production
lines, particularly when processes are not properly planned or adapted to the
existing conditions. In particular, queues are formed when there are more
products to be processed than the available capacity. Queues usually occur when
machine utilization is above 70% with a simultaneously high variability of the
input flow.
In order to calculate the size of queues, we usually
apply the formula used to calculate the average waiting time in a queue (17).
where:
Queuing is caused by the high utilization of the
service station, as well as the variability of the interval between input
requests and the service time. An equally important factor is batch processing,
which results in increased handling time since the batch must be completed
before it is passed to the service station. Batch processing reduces the number
of steps between changeovers and processing. The variability of processing can
be reduced through methods that serve to cut down on the setup time required to
change the produced parts. Queues are very common and can be simulated through
discrete simulations. The importance of the queuing issue and its impact on the
performance index is shown in Figure 11.
|
|
idle time, the time during which the service station waits for an
operation |
Fig. 11. Queuing issues in
processes [24]
Each case of idle time and waiting time translates
directly into a performance indicator. When the waiting time is high, the
indicator is at a low level. This is reflected in the underutilization of the
maximum production potential, the occurrence of costs associated with lost
production, low competitive advantage, and the lack of customer confidence.
4.1. Processes
for preparing and aggregating enterprise data
The research was preceded by an analysis. From a
wide range of production or service companies, we selected a group of
enterprises focused on the automotive industry. Next, one company located in
the Podkarpackie voivodeship
was chosen. The company belongs to an international manufacturing concern, and
the main scope of its service portfolio includes the production of metal
construction elements for bodies of popular cars, with a specialization in the
production of such construction elements as vehicle frames, bumpers, transverse
beams, B-pillars, chassis, vehicle floors, and other car body parts.
The production of the company is performed on a
large scale, with the parts produced in the Podkarpackie
branch being sent to numerous car factories throughout Poland. The real essence
of this research lies in the application of the principles of sustainable
development and Corporate Social Responsibility (CSR). The activities planned
for the next few years of the company's operation consist mainly of continuous
improvement and the development of an innovative management system. Figure 12
presents the factors that determine the choice of the company and refers to
highly specialized manufacturing enterprises. They allocate a large part of the
revenues obtained revenues to permanent development of the provided services
and reliability of processes.
The analysis revealed that the company complies with
ISO and IATF quality standards, as well as
automotive quality guidelines. Production process planning managers have
indicated the need for analysis and possible emergencies in the process,
as well as the construction of emergency maintenance plans. The activities
included in process analysis, together with the design of the contingency plan,
are presented in the form of an algorithm (Fig. 13).
Fig. 12. Factors for
choosing a company
The algorithm includes individual steps that affect
the correct representation of the production process (Fig. 13). It is correct
to indicate all factors which are reflected in the realized services, but it is
also crucial to check if the data entered is correct and to analyze the
simulation results accordingly. The implementation of a faulty design can cause
errors. As a consequence, during an
emergency situation, the production will be stopped or will not proceed
according to the assumptions of the emergency plan. The manufacturing process
consists of the following steps (Tab. 1), (Tab. 2), (Tab. 3), (Tab. 4).
The date and the number of picked coils on the sheet
(Tab. 5) were used as the basis to calculate the number of manufactured semifinished products. The table below presents the number
of withdrawals of material for the production of the semifinished
product ZX01.
The data collected as part of the research was used
to identify the level of OEE and to
calculate MTTF MTTR, and MTBBF.
The machine park is characterized by the utilization
of production machines at a safe level (Fig. 14.). The highest utilization of
machine capacity occurs with the welding robot R_24 with a result of 89%. An
equally good result was achieved by the progressive press, which is used at a
level of 80%. The lowest values were found in welding machines Z_5 and Z_26.
The trend line is at 78%. In the case of the MTBF indicator (Fig. 15.), defined as the average time between
repairs, the highest level is reached by Z_26 with a result of 4,320 [min]. The
shortest time between repairs was established for R_28 and P_15 with the result
of 864 [min].
Fig. 13. Algorithm of conduct in the design of
emergency plans
Tab. 1
Characteristics of
production operation No. 1
|
No. of operations |
Name
of the operation |
Description
of task |
Progressive
press |
|
Operation No. 001 |
Sheet metal pressing ZX01.00 |
The sheet comes in appropriate lengths of coil
and is then cut into the appropriate size sheet. |
|
|
Name
of device/machine |
Technical
notes |
||
|
P-19 press 250 [t]. |
Process preparation and warmup 36 min. |
|
Tab. 2
Characteristics of production operation No. 2
|
No. of operations |
Name
of the operation |
Description
of task |
Hydraulic
press |
|
Operation No. 002 |
Pressing of parts ZX01.01 |
The finished sheet is fed to the next press,
which performs the pressing of ZX01.01 subassemblies. |
|
|
Name
of device/machine |
Technical
notes |
||
|
Hydraulic press |
Preparation for the process and warming up of
the press 30 min. |
|
Characteristics of
production operation No. 3
|
No. of operations |
Name
of the operation |
Description
of task |
Welder |
|
Operation No. 003 |
Spot welding of parts ZX01.02 |
The nut should be welded to the stamped pieces
ZX01.01 on a movable retainer that allows the nut to be held with sufficient
handling clearance. |
|
|
Name
of device/machine |
Technical
notes |
||
|
Welding machine Z-26 |
Process preparation and warmup 36 min. |
Intermediate
product ZX01 |
Tab. 4
Characteristics of
production operation No. 4
|
No. of operations |
Name
of the operation |
Description
of task |
|
|
Operation No. 004 |
End welding ZX01.03 |
Prepared part ZX01.02 is welded to master part
ZC05. |
|
|
Name
of device/machine |
Technical
notes |
||
|
Welding robot R-24 |
Welding time 36 [s] Preparation of the welding robot 30 min. |
|
Specifics
of material intake for production
|
Task
No. |
Name of task |
Date of task |
Quantity |
Unit |
State of material in production
|
|
101 |
Acceptance of
material |
11.01.2022 |
2 555 |
KG |
2 555 |
|
101 |
Acceptance of
material |
11.01.2022 |
2 555 |
KG |
5 110 |
|
261 |
Consumption of
production material |
11.01.2022 |
-1 236 |
KG |
3 874 |
|
261 |
Consumption of
production material |
11.01.2022 |
-1 354 |
KG |
2 520 |
|
261 |
Consumption of
production material |
11.01.2022 |
-1 353 |
KG |
1 167 |
|
262 |
Return of material
from production |
11.01.2022 |
34 |
KG |
1 201 |
|
262 |
Return of material
from production |
11.01.2022 |
1 354 |
KG |
2 555 |
|
261 |
Consumption of
production material |
11.01.2022 |
-2 421 |
KG |
134 |
|
261 |
Consumption of
production material |
11.01.2022 |
-156 |
KG |
-22 |
|
101 |
Acceptance of
material |
11.01.2022 |
3 089 |
KG |
3 067 |
|
101 |
Acceptance of
material |
12.01.2022 |
2 555 |
KG |
5 622 |
|
262 |
Return of material
from production |
12.01.2022 |
19 |
KG |
5 641 |
|
262 |
Return of material
from production |
12.01.2022 |
3 |
KG |
5 644 |
|
261 |
Consumption of
production material |
12.01.2022 |
-719 |
KG |
4 925 |
|
261 |
Consumption of
production material |
12.01.2022 |
-1 869 |
KG |
3 055 |
|
261 |
Consumption of
production material |
12.01.2022 |
-48 |
KG |
3 007 |
|
262 |
Return of material
from production |
12.01.2022 |
82 |
KG |
3 089 |
|
261 |
Consumption of
production material |
12.01.2022 |
-1 571 |
KG |
1 518 |
|
101 |
Acceptance of
material |
13.01.2022 |
2 555 |
KG |
4 073 |
|
101 |
Acceptance of
material |
13.01.2022 |
2 155 |
KG |
6 228 |
|
261 |
Consumption of
production material |
13.01.2022 |
-30 |
KG |
6 198 |
|
261 |
Consumption of
production material |
13.01.2022 |
-1 014 |
KG |
5 184 |
|
262 |
Return of material
from production |
13.01.2022 |
33 |
KG |
5 217 |
|
261 |
Consumption of
production material |
13.01.2022 |
-733 |
KG |
4 484 |
|
261 |
Consumption of
production material |
13.01.2022 |
-1 875 |
KG |
2 609 |
|
261 |
Consumption of
production material |
13.01.2022 |
-1 873 |
KG |
735 |
|
262 |
Return of material
from production |
13.01.2022 |
31 |
KG |
767 |
|
262 |
Return of material
from production |
14.01.2022 |
1 875 |
KG |
2 642 |
|
101 |
Acceptance of
material |
14.01.2022 |
2 525 |
KG |
5 167 |
|
261 |
Consumption of
production material |
14.01.2022 |
-1 237 |
KG |
3 930 |
|
261 |
Consumption of
production material |
14.01.2022 |
-1 372 |
KG |
2 558 |
|
261 |
Consumption of
production material |
14.01.2022 |
-30 |
KG |
2 528 |
|
261 |
Consumption of
production material |
14.01.2022 |
-1 375 |
KG |
1 153 |
|
262 |
Return of material
from production |
15.01.2022 |
1 375 |
KG |
2 528 |
|
262 |
Return of material
from production |
15.01.2022 |
37 |
KG |
2 565 |
|
101 |
Acceptance of
material |
15.01.2022 |
2 540 |
KG |
5 105 |
|
101 |
Acceptance of
material |
15.01.2022 |
2 540 |
KG |
7 645 |
|
261 |
Consumption of
production material |
15.01.2022 |
-2 546 |
KG |
5 100 |
|
262 |
Return of material
from production |
16.01.2022 |
21 |
KG |
5 120 |
|
261 |
Consumption of
production material |
16.01.2022 |
-2 560 |
KG |
2 560 |
Fig. 14. OEE consumption rate
Fig. 15. MTBF indicator
Fig. 16. Breakdown of production machinery
Comparative characteristics revealed the following
values in the context of the average MTTR repair time (Fig. 16.). The longest
repair time was recorded on machines Z_26 and Z_6, with a result of 150 [min].
The longer repair time is due to the technical specification of the machine;
the presented welding machines are very precise and difficult to repair. In
terms of MTTF, the longest time between failures was found for machine Z_26.
Presses P_19 and P_40 are characterized by a similar MTTF of 1320 [min].
4.2. Flow of
the production line testing process on the simulation plane
The analyzed manufacturing process was represented
by a simulation model by means of the FlexSim tool.
The process takes place in two production halls. In (Fig. 17.), production hall
No. 1 has been visualized with a blue color, and the first production operation
No. 0001 is performed in the indicated area. Production hall No. 2 has been
marked with a yellow color, and it is where operations No. 002, 003 and 004 are
performed.
Alternative machines were proposed for the
production machines indicated in the model. The following factors were taken
into account in the selection of the machines: production capacity higher or
similar to the original model, distance from the existing production line,
technical parameters, appropriate compression force in the press, welding time
of the elements, compatibility of the robots, use of the machines on the shop
floor, functionality of the machine allowing mutual substitution, and
performance of the indicated operations. The list of alternative production
machines is presented in the table (Tab. 6.).
Production hall 1, shown in (Fig. 18), has been
updated by plotting on the simulation plane alternative machines proposed from
the list of available production machines.
The machinery shown in (Fig. 18) consists of
equipment that can replace each other. Each machine in relation to the serial
process can be assigned an alternative production machine, or a completely new
production line that performs analogous operations can be started up. If
production is transferred to an alternative machine, the necessary operating
time associated with running the task on the indicated machine must be added.
Fig. 17. Diagram of the base
production line in hall no. 1
Alternative
machinery
|
No. |
Series process machine |
Alternative machine |
Reprogramming time |
|
1 |
P-19 press 250 [t]. |
Press P-42 400 [t]. |
Piston change 45 min
|
|
2 |
Hydraulic press P-15
63 [t] |
Hydraulic press P-16
63 [t] |
Piston change 36 min
|
|
3 |
Welding machine Z-26 |
Welding machine Z-5 |
Reprogramming 20 min
|
|
4 |
Welding robot R-24 |
Welding Robot R-28 |
Reprogramming 45 min |
Fig. 18. Scheme of the
simulation model hall no. 2
All devices were programmed in the simulation model
according to the actual technical parameters of the production machines in the
studied enterprise. Repeated reproduction of the simulated production process
demonstrated the correct representation of the real process in the FlexSim program. The simulation plane contains the objects
included in the table (Tab. 7) with the number assigned according to the order.
The time and number of realized processes were comparable by 95%. With such
accuracy, the results of the simulations offer a very precise examination of
the validity of introducing alternative solutions.
Objects
available on the simulation plane
|
No. |
Name of object in the
simulation plane |
Description of functionalities |
|
1 |
Steel_[No.] |
Source supplying the
steel required to cut the sheet |
|
2 |
P_[No.] |
Progressive and
hydraulic presses |
|
3 |
Pallets_[No.] |
Pallet feeder |
|
4 |
Pak_[No.] |
Space for the
packaging of components |
|
5 |
K_[No.] |
Storage area |
|
6 |
O_[No.] |
Activity operator |
|
7 |
TE_[No.] |
Transport trolleys |
|
8 |
Pallet field EUR_[No.] |
Storage area for
pallets |
|
9 |
RZ_[No.] |
Unpacking area |
|
10 |
S_[No.] |
Source of transport
containers |
|
11 |
Z_[No.] |
Welding machines |
|
12 |
R_[No.] |
Sealing robots |
|
13 |
E_[No.] |
The area of removal
of redundant elements on the simulation plane that does not affect the time
and manner of implemented processes |
4.3. Findings on variants of production operations
The initial phase of the research project refers to
analyzing and extracting the results from a simulation model where all
processes run smoothly and no machine failures occur. In subsequent phases, the
downloaded outcomes will enable a comparison of how the indicated process
affects the overall services performed.
The planned production schedule is to manufacture
20,280 units of ZX01 parts on the base production line, shown in green in the
simulation plane, and, in addition, 11,016 units of ZXM01 parts on the
alternative production line, shown in blue in the simulation model.
Fig.
19. Utilization of production machines on the baseline
The presented results of operating parameters, taken
directly from the objects on the simulation plane, indicate the presence of
idle times on progressive and hydraulic presses, which can be optimized by
appropriate scheduling of deliveries (Fig. 19). At the same time, the
machine’s operating status must be controlled so that changes do not
cause blocking of the machines in the process. The welding machines are
characterized by a high degree of blocking. On the other hand, welding robots
are utilized at 75%, which means a good machine utilization rate. The start
time of the simulation adopted in the project was set to 11.01.2022 at 8:00:00
a.m. and each subsequently executed simulation variant refers to the indicated
hour. The end time of the entire process, under proper conditions and without
the emergence of a failure, is 16:43:35 on 18.01.2022. This is a continuous operation
without breaks and shifts, all to ensure the readability of results. The total
time to complete the task is 176 h and 43 min.
Fig. 20. Efficiency of production machines on the
simulation plane
The highest utilization rate in terms of preparation
was found for P_19 with 74% (Fig. 20). The scope of the preparation work
includes proper sheet metal positioning, securing the part, and similar
activities. The highest rate of completed processes was recorded for robot R_24
with a score of 61%, process preparation 18%, and break 21%.
The next stage of the project consisted of
simulating an emergency, whereby progressive press P_19 had to be stopped, and
all operations transferred to machine P_42.
Fig. 21. Comparison of results for P_19 and P_42
Fig.
22. Activity of machines with variant I
In the
first variant of the planned failure simulation, there is a large increase in
the break, which is necessary for component change and reprogramming (Fig. 22).
Press P_15 recorded an interruption of 82 %, also, robots R_24 and R_28 are
characterized by a large number of interruptions at 60 %. Production in the
first press P_42 runs without interruption due to the operations assigned from
press P_19 (Fig. 22).
The
presented results refer to a comparison of the work efficiency of both machines
throughout 16 working hours (Fig. 21). It should be noted that press P_42 has
an adequate reserve of time in which no activity is performed. The indicated
parameters, e.g., the idle time of the machine and its location, should be
considered in the context of the relocation of production in case of failure.
The
simulation revealed the possibility of using the indicated alternative machine
without significantly extending the process. Fig. 23 shows the results for
increasing throughput up to the maximum performance of the indicated machine.
The total task execution time for the selected alternative machine is 190 h and
34 min.
Fig.
23. Press utilization for P_42 Fig.
24. Press utilization for P_16
In the
event of a failure on hydraulic press P_15, an alternative press P_16 with
comparable parameters was selected. The behavior during the failure of the
pressing process indicates that all the parts that arrive from production hall
1 are redirected from press P_15 to press P_16. The aggregate data shown in
Fig. 25 indicate that the redirection of operations to press P_ 16 has not
blocked its production capacity, for which there is a safety reserve. The time
necessary for completing the job in an emergency situation was 177 h and 38
min, with a machine workload of 55% operation and 30% preparation.
The
last variant of the tested simulations involved a complete suspension of
production on the "green" line and the assignment of all tasks to the
"blue" line. To ensure that the simulation was conducted correctly, a
new production plan was arranged, and a division was made into orders resulting
from the previously planned operations on the "blue" line as well as
orders that had been rewritten from the "green" line. It was also
necessary to consider appropriate breaks for reprogramming the production
machines.
Fig. 25. Results of the use of alternative machines
Changes
introduced in the production series decreased the utilization of production
capacity in the whole machine park and caused an idle state (Fig. 25). Welding
machine Z_5 has an idleness level of 60%; there is also a high idle state in
robot R_28 with a result of 68%. Blockage of the whole production line
significantly increased the time necessary to perform individual tasks. In the
case of starting production on one "blue" line, the total time was
272 h and 59 min.
For
visualization purposes, emergency machines are shown in red (Fig. 26). These
machines do not perform operational activities. All tasks have been performed
by the "blue" line.
The
presented raw material delivery plans demonstrate the number of delivered loads
for standard production (Fig. 27). On the other hand, Fig. 28 presents a
situation where operations must be combined on the blue production line due to
the third variant of the failure. Production was extended from 123 h to 269 h.
It was also necessary to include technical interruptions in the new production
plan, which are essential to reprogram machines.
Emergency
situations must be handled according to accepted standards. In order to
standardize processes and improve quality, an algorithm has been developed
(Fig. 29).
Not
every emergency situation is associated with the use of an alternative machine.
If repairs occur within 72 hours, production can be stopped and the breakdown
reported to the maintenance team for rectification. The decision to introduce
an alternative machine, and thus to change the production process, must be
approved by the production manager, whose job requires, among others, the
selection of the machine envisaged in the list of alternative machinery and
introducing appropriate adjustments to the existing production plans.
Fig.
26. Simulation model during the execution of operations
Fig. 27. Number of raw materials delivered
Fig. 28. Plan of raw materials delivered II
Fig. 29. Algorithm of proceeding in emergency
situations
5. CONCLUSION
Considering a holistic view of production logistics,
the end customer plays a significant role. A particularly important factor in
on-time delivery is the company's assurance of a continuous production process.
Emergency situations require the management of many variants of production
operations, which have an important function. With the sudden appearance of
technical problems, decision-making must be quick, especially in the automotive
industry. However, it is still important that management decisions are analyzed
thoroughly.
The research and analysis performed on a real object
indicate the validity of the use of the compiled indicators, algorithms and
simulation models in the context of determining the accuracy of emergency
plans. Therefore, the posed hypotheses have been confirmed. A simulation model
offers the possibility of experimentation without losses for the company, as
well as helps to develop an optimal solution. Furthermore, simulations allow us
to instantly visualize emergencies, as shown by the authors. On the other hand,
modeling production processes brings benefits such as a quick response to
emerging threats. The scope of the research included the selection of
alternative machines, taking into account their technical and operational
parameters. The collected data were used as the basis to program three variants
of emergencies, which are characterized by the highest probability of
occurrence in the studied enterprise. Appropriate machines were plotted on the
simulation plane, including technical parameters and emergency situations.
Multiple simulations without stopping production and incurring additional
losses allowed to find the optimal solution, i.e., transferring tasks to other
objects, which takes place in cases of repairs lasting longer than 72 hours. An
analysis of the exploitation parameters allowed us to compare the use of the
machine during continuous operation and upon failure.
An important element related to machine failure rate
is rate analysis. MTTR has made it possible to determine the
average time from failure to recovery. Furthermore, it has a fundamental impact
on the measurement and analysis of trends in the context of efficiency and the
rate of removal of machine failures. The lowest MTTR value was characteristic
for press P_16, with a result of 20 min, which informs us that the repair of a
given machine usually proceeds very quickly. The highest MTTR value, and at the same time the longest repair time, is
registered on machine Z_5 with a value of 150 min. In this case, it is
necessary to improve the repair process or to diagnose the failure faster.
Another indicator considered in the selection of the
machines was the MTTF index, which
represents the average time to failure. Welding machine Z_26 achieved the
highest score of 4,200 min, and the lowest value was found for press P_15 and
robot R_24 with a result of 792 min. MTTF
is especially significant
for processes with lengthy unit operations. With the help of the results
aggregated and analyzed in the company, we also diagnosed areas where failures
or process disturbances could occur. The simulation model was used to test
variants of operations in order to obtain the optimal solution.
A detailed analysis of the entire production process
performed by calculating common indicators of OEE is essential for simulation. OEE makes it possible to indicate the effectiveness of machines.
The use of the indicator in the study provides the answer to the question
whether the chosen machine, process or system is used effectively in a certain
time interval. In the case of OEE, efficiency
should be increased for machine Z_5 with a score of 55% and for Z_26 with a
score of 60%. In further processes, improvement opportunities can be sought for
machines that have a high score but also room for further improvement.
Additional enhancements can be realized on press P_19 with a score of 80%,
welding robot R_24 with an OEE of 89%,
and robot R_28 with a value of 78%. The time differences resulting from the
tested variants are as follows: in the case of the failure of press P_19, the
production was extended by 13 h and 34 min. Variant 2 during the failure of
press P_15 resulted in a production extension of only 55 min. The last
simulation of variant 3 showed an extension of the realized tasks by 96 h and
16 min. The third variant is characterized by a significant difference in the
time necessary for performing services. The longer time results from the
complete stoppage of operations on one production line and redirecting the performed
tasks to the neighboring line.
The utilization of the capacity of alternative
machines is at an acceptable level. No additional bottlenecks are created as a
result of task redirection. Furthermore, individual machines are not blocked.
An appropriate selection of machines made it possible to perform tasks
interchangeably. A changeover of operations on different components requires a
proper changeover time, which was taken into account during the design of the
new production plan.
The proper operation and use of alternative machines
requires the construction of an algorithm for emergency situations. The use of
an alternative machine is the last resort, as it requires appropriate
procedures for shifting task priorities and creating a new schedule. Due to the
specificity of the tasks, a limit of 72 hours was set for the company as the
maximum time for repairing the machine. If the company cannot carry out the
repair in the set time, it is necessary to implement a contingency plan, which
is decided by the production manager or other management staff.
The results indicate an increase in the use of the
production potential to its maximum capacity. During the changes in operations,
there were also technical interruptions, which meant that the machine was idle at
a given moment, but the modifications were necessary to reprogram or replace
technical components. Such actions also served to compile instructions for the
procedure and standardize processes. The simulations conducted made it possible
to introduce an appropriate algorithm to handle failures. In such a case, the
management will dispose of the necessary means, such as the tested contingency
plans, and thus react quickly to the emerging threat. The use of an analytical
approach to aggregate data and parameters from individual machines, as well as
to record individual indicators, together with mapping the simulation process,
makes it possible to test emergencies and is essential to the overall view of
the implemented processes.
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Scientific Journal of Silesian University of Technology. Series
Transport is licensed under a Creative Commons Attribution 4.0
International License
[1] Casimir Pulaski Radom University, Faculty of
Transport, Electrical Engineering and Computer Science, Malczewskiego 29, 26-600
Radom, Poland. Email: z.lukasik@uthrad.pl.
ORCID: https://orcid.org/0000-0002-7403-8760
[2] Casimir Pulaski Radom University, Faculty of
Transport, Electrical Engineering and Computer Science, Malczewskiego 29, 26-600
Radom, Poland. Email: a.kusminska@uthrad.pl.
ORCID: https://orcid.org/0000-0002-9466-1031
[3] Casimir Pulaski Radom University, Faculty of
Transport, Electrical Engineering and Computer Science, Malczewskiego 29, 26-600
Radom, Poland. Email: j.kozyra@uthrad.pl.
ORCID: https://orcid.org/0000-0002-6660-6713
[4]
University of Information Technology and Management in Rzeszow, Chair of
Logistics and Process Engineering, Sucharskiego 2,
35-225 Rzeszow, Poland. Email: solszanska@wsiz.edu.pl.
ORCID: https://orcid.org/0000-0002-0912-4726
[5]
Cracow University of Economics, Rakowicka 27, 31-510
Cracow, Poland. Email: s224729@student.uek.krakow.pl. ORCID:
https://orcid.org/0000-0002-8358-9679