Evellyn
de Morais Galvão
State
University of Maringá - Production Engineering Department, Brazil
E-mail: evelyn.mgalvao@gmail.com
Bianca
Carina Valente
State
University of Maringá - Production Engineering Department, Brazil
E-mail: biancacvalente1995@gmail.com
Syntia
Lemos Cotrim
State
University of Maringá - Textile Engineering Department, Brazil
E-mail: slcotrim2@uem.br
Gislaine
Camila Lapasini Leal
State
University of Maringá – Production Engineering Department, Brazil
E-mail: gclleal@uem.br
Edwin
Vladimir Cardoza Galdamez
State
University of Maringá - Production Engineering Department, Brazil
E-mail: evcgaldamez@uem.br
Submission: 8/8/2019
Revision: 9/18/2019
Accept: 10/2/2019
ABSTRACT
Competition among small and medium-sized enterprises has grown over the years due to the advancement of technology and the globalization of the market and operations. Production Planning, Programming and Control (PPCP) acts as a link between management and manufacturing, in these small and medium-sized enterprises. The present study, through an action research in a microenterprise manufacturer of Brooms made of PET, aimed at the implementation of a hybrid PPCP system. The model was proposed by using the concepts of Lean Manufacturing, to map and know the process eliminating wasted time and resources, Theory of Constraints to identify constraints and bottlenecks and the MRP system to act within the context of pushed production. Therefore, it is possible to obtain a Framework that presents the method used in the implementation of the work, which provided with results such as production order, security stock, and correct input definition. The model put the company in other level of organization and efficiency in the use of resources.
Keywords: Hybrid PPCP system; Lean Manufacturing; Theory of Constraints; MRP system.
1.
INTRODUCTION
With the increasing
volume of small and medium-sized enterprises, their importance has also grown
for the economic sectors of the countries, with these large jobs generators
and, therefore Beck and Kunt (2006) points out that
small and medium enterprises form a large part of private sector in many
developed and developing countries.
As observed by Giaoutzi, Nijkamp and Storey
(2016) widely believed that micro and small enterprises contain the
rejuvenation potential that is necessary for revitalizing the industrial and
service sector in our stagnating economies, and in this context, they are
consequently also often regarded as the vehicles for regional development
planning (GIAOUTZI et al., 2016).
According to Fosman and Rantanen (2011) “…
small enterprises are considered to be more innovative due to their
flexibility, higher ability to adapt and improve, and because they are
quick movers in implementing change.” Additionally, Acs,
Morck, Shaver and Yeung (1997) points out that the
modern economy, innovation remains largely the work of smaller firms.
Gunasekaran,
Rai and Griffin (2011) points out that competition among small and
medium-sized enterprises has grown over the years due to the advancement of
technology and the globalization of the market and operations, so their
survival depends on their resilience to guide their strategies and
technologies.
Planning, Programming
and Control of Production has been one of the primary publishing outlets for
operations management research for nearly three decades, according to Akmal, Podgorodnichenko, Greatbanks and Everett (2018).
That concept has become
increasingly important for the companies because it manages the flow of
materials from the production system through the flow of information and
decisions, corroborating the information of Fernandes, Azeka,
Barreto and Filho (2007). It means the resources need
to be available in the appropriate quantity, at the appropriate time, and the
appropriate quality level (FERNANDES et al., 2007).
It is in this context
that the Planning, Programming and Control of Production emerges as an
essential tool to achieve the survival and maintenance of micro and small
enterprises in the competitive market.
Among the small and
medium-sized companies in Brazil, one segment that has been showing notable
growth is the recycled PET segment. According to the Business Compromise
Recycling Association (CEMPRE, 2018) 59% of PET packaging was recycled in 2012,
totaling 331 thousand tons. The 10th edition of the PET Recycling Census in
Brazil by the Brazilian PET Industry Association showed that in 2016, 34% of
PET bales dedicated to recycling were obtained through collectors (ABIPET,
2016).
Therefore, this article
aims to propose a hybrid model to planning, programming and control of
production for micro and small enterprises with integration of tools such as
planning programming and control of production, along with Lean Manufacturing,
Theory of Constraints (TOC) and Material Requirement Planning (MRP). The model
will be evaluated through an application study in a small ecological broom
factory in the city of Maringá, Brazil. An Application Study presents an example of
application in the industrial context and serves as proof of a concept. In this
case, the study was conducted in a single company and in a specific project,
given the required time and involvement required of the entrepreneurs.
2.
LITERATURE REVIEW
2.1.
PPCP for Micro and Small Enterprises
According to Kuazaqui
(2013), Micro and Small Companies have an important socioeconomic role in
Brazil and in the world; they are still responsible for the generation of new
ideas, products, services and operations, since they usually result from a more
entrepreneurial vision with possibilities to use creativity and innovation more
quickly and freely.
Among the difficulties and
limitations faced by Micro and Small Companies, Galdámez
(2007) points out that factors such as competition, competitiveness of large
companies and constant technological changes in both products and production
processes end up being decisive for their performance.
Therefore, the production management
becomes a key role for this type of enterprise, because as reported by Assid, Gharb and Dhouib (2015), Wang and Liu (2013) and Fernandes, Filho and
Bonney (2009) manufacturing systems require control and monitoring, especially
in a constantly changing and increasingly complex economic environment, meeting
the quality, time and cost objectives.
2.2.
Hybrid Model for Planning,
Programming and Control of Production (PPCP)
Bertolini,
Romagnoli and Zammori
(2015) point out that PPCP systems have been the subject of research on
operations management in the last 30 years. Within this objective of planning
and controlling production are the hybrid systems of PPCP, which are called
hybrid functions, that is, because they operate with both, pulled and pushed
production.
Authors such as Korugan
and Gupta (2014) and Guan, Ma and Yin (2015) point out that in hybrid production
systems one of the challenges lies in the search for a balance in the stock
between processes. It is also related to the good levels of finished product
stocks, as well as quantity, quality and time.
Korugan and
Gupta (2014) also point out that in a hybrid model of production process there
is always the question of the uncertainty and high variability that can occur
at the ends of the process. Therefore, it is necessary that the control
mechanisms for these systems be adapted to these characteristics.
3.
RESEARCH METHODOLOGY
Research can be defined as a
scientific and systematic on a specific topic from relevant information; in
common, parlance refers to a search for knowledge or an art of scientific
investigation (KOTHARI, 2004).
The research study can be classified
from the viewpoint of: application, perspectives of objectives and enquiry mode
employed (KUMAR, 2011). From the viewpoint of enquiry model employed this study
is qualitative and quantitative. When the research is based on the measurement
of quantity or amount it is considered quantitative (KOTHARI, 2004).
Qualitative research, on the other hand, has the purpose to describe a
situation, phenomenon, problem or event with the information gathered through
the use of variables measured on nominal or ordinal scales (KUMAR, 2011).
From the viewpoint of objectives,
the research is an Application Study, allowing to evaluate the methods and
tools under the business approach (SJOBERG; DYBA; JORGENSEN, 2007). In
addition, there are a variety of data sources (interviews, observation,
qualitative data) and the unit of analysis can be a company, a project, a team,
a team member, a special event or a specific work product (EASTERBROOK, et al.,
2008). In this case, the study was conducted in a single company and in a
specific project, given the required time and involvement required of the
entrepreneurs.
3.1.
Proposed Model
Small and medium-sized businesses
often have difficulty working with a pull production system; a fact that
contributes to this is that there is not enough demand, so Just in Time must
involve negotiations with suppliers to make this model possible. In addition to
this fact, the high turnover rate causes difficulties to maintain training,
culture and discipline among the staff, factors that are essential to a pulled
system.
In this way, the proposed framework
seeks to follow the principle that a hybrid model of PPCP for small and
medium-sized enterprises should be able to operate with pulled production, to
wipe and schedule production, to reduce stock levels and identify their
bottlenecks.
The Framework of this study, which
proposes a generic hybrid model of PPCP, is represented in Figure 1.
The Framework is divided into three
stages: Planning, Programming and Control of Production, and in each of them
the concepts that support the accomplishment of this work, are: Lean
Manufacturing, Theory of Constraints (TOC) and Material Requirement Planning
(MRP).
Figure
1: Framework: Hybrid model to planning programming and control of production
for micro and small enterprises
The concept of Lean Manufacturing
gave the researcher and the company the knowledge of the productive process and
its value chain in order to reduce everything that was surplus. The MRP system
supported the verification of the demand for the company's products and
production orders of them.
Finally, the TOC made it possible to
identify production bottlenecks and other limitations of the system. Alves, Santos and Schmidt (2014) point out
that there are two assumptions addressed in TOC: the first is to consider the
company as a system whose success or failure depends on how different processes
interact with each other and the second is that a restriction is any factor
that limits the system to reach its goal.
The proposed framework is
prescriptive; it presents recommendations that guide the execution of the
activities, highlighting what should be done. The framework is structured in
stages, which are composed of activities, which have a purpose and associated
recommendations. Stages group activities according to a common goal. The
activities have a purpose, which aims to determine what it should achieve. The
recommendations aim to identify means to carry out the activity, and may take
the form of guidance, practices and / or tools.
Then each activity within each of
the three stages is detailed according to its steps of execution:
The Planning stage aims to understand the production process, its
limits and predict the behavior of the demand of the products, is composed of
the following activities: Map production process, determine production
capacity, map the value stream, identify the bottlenecks and predicting demand.
Mapping the Productive Process aims
to identify the activities that make up the productive process and its
relations. To perform this activity, it is recommended to interview the
employees, collect process data using the SIPOC, map the production process,
validate the mapping with employees. It also uses concepts from the Business
Process Modeling Notation (BPMn) methodology.
The Determine Production Capacity
activity aims to identify the plant's capacity to produce its different
products. It is recommended to use the concepts provided by Lean Manufacturing
to support the calculations of productive capacity.
The Value Stream Mapping activity is
intended to classify the types of processes that exist in the system. It is
recommended to collect the description of the activities done by the employees.
Then, observe the activity performed by the employee, collect information about
inventory and waste before and after the process, collect the times that add
and do not add value in the process and finally elaborate the Value Stream Map
with the help of Visio software, supported once again by the concepts of Lean
Manufacturing.
The purpose of Identifying
bottlenecks is to identify and list system constraints. It is recommended to
develop the Value Stream Map and then, with its analysis, identify the existing
bottleneck activities, using the principles provided by TOC.
The Predicting demand activity aims
to identify seasonality and trends in order to estimate the demand for
products. It is recommended to collect the sales data of the previous periods
and to identify the seasonality and trends, to define the calculation method of
demand forecast that best suits the case, to finally carry out the forecast of
demand, based on the concept of MRP, which supports that the company should
only produce the required quantity demanded.
The Programing stage aims to define the resources that will be used
from the beginning to the end of the production flow, is composed of the
following activities: Calculate line balancing and create production order.
The Calculate line balancing
activity aims, with the help of the concepts of TOC and Lean Manufacturing, to
analyze and establish the percentage relation of sales of the product groups. I
also aim to analyze and establish the percentage relation of sales of the
existing products in each group, establish the quantity to be produced of each
product and to balance the production respecting the restrictions already
identified.
The purpose of the Production Order
activity is to produce the Production Order following the demands, as suggested
by the MRP system.
The Control stage aims to ensure that the process is performed in the
best possible way by operating with sufficient quantity of inputs and products
to meet the needs of the plant, and is composed by the activity of determining Security
stock.
The Determine Security Stock
activity is intended to define the quantities of raw materials and / or
finished products needed to meet the system's needs. It is recommended to use
the concepts Just in Time addressed in the philosophy of Lean Manufacturing to
work with the lowest security stock and thus reduce factory costs. Zahraei and Chee-Chon (2017) points security stocks is one
of the most common tactics employed to mitigate variability and used to hedge
against unexpected surges in demand.
The framework presents a systemic
view regarding PPCP for micro and small enterprises, defines the activities
that must be carried out and points out recommendations for their execution.
4.
RESULTS ANALYSIS
The company under study works in the
PET bottle recycling market for the production of domestic ecological brooms.
Its products, are destined for both domestic and industrial use, being the
industrial the largest selling point, since its differential is empirical
proven to last up to 30 times more than the same product of the conventional
line, fact that attracts industries that look for greater economy and
sustainability in its businesses.
The company operates with a pushed
production process, where the products are produced, stored and later have
their output of stock as they are sold, which sometimes causes high volume of
inventory, and others in unavailability of products for sale, due to the its
randomness of production. The implementation of a hybrid model of PPCP would
enable the company to better manage inputs, stocks and capital, making it a
strategic survival factor in the competitive market.
This study starts from the point
that an earlier study was carried out in the company, resulting in a
Standardized Production Balancing (SPB), which gave employees a routine work
and a daily production and sales target. As well as the framework already
presented, this section will be divided into three main stages: Planning,
Programming and Control of Production.
4.1.
Planning
4.1.1. Map Production Process and Determine
Production Capacity
The planning stage began with the
on-site visits to the production area, which aimed to enable the researchers to
know the production process, the activities of the operators and the resources
needed for the process. During the visits, interviews were carried out with the
operators, through a structured form, which sought to understand in detail how
the production process occurred and the flow of activities of the production of
domestic and industrial brooms.
Then, using the mapping technique in
Brown Paper, an outline of the processes mapping was elaborated based on the
information collected. The technique enabled an instant view of the process,
since all involved participated and collaborated with ideas, drawing on a large
sheet of paper all existing activities, as well as their inputs and outputs.
The operators validated the map.
With the mapping of processes, it
was possible to identify the productive capacity of the Domestic brooms, model
78 and 98 tuffs, and Industrial brooms, focus of this work. For this, the setup
time and the cycle time of the operation, were calculated and the productive
capacity per activity was calculated over a period of 1 hour. The values
obtained are shown in Figure 2.
Figure
2: Productive Capacity of Domestic brooms and Industrial brooms
It can be observed in Figure 2 that
the Cutting in Guillotine 2 activity is the one with the highest Productive
Capacity due to its fast execution, while the Tufing
process presents the lowest Productive Capacity, becoming the production bottleneck
of the factory.
Once the process with the operators
was validated and the productive capacity was calculated, Bizagi
Modeler - Version 3.0.0.022 software was used to register the mapping of BPMn language processes.
4.1.2. Mapping the Value Stream (VSM)
For the construction of the Value
Stream Map, it was first necessary to divide the products into groups according
to the similarities in their processes. The groups defined were:
· Group A: Domestic brooms with 78
tuffs;
· Group B: Domestic brooms with 98 tuffs;
· Group C: Industrial brooms.
Then the existing processes in each
product were classified in the following categories: i)
abstract processes: outsourced processes; ii) private processes: those carried
out by the company from start to finish; iii) collaborative processes: those
that occur outside the company and part within the company.
Once the groups were defined and the
processes were classified, the data was collected. Then, the researcher had to
fill out a second form containing the information, such as the names of the
persons responsible for collecting the data, the language used, the date, the
name of the product being mapped, the name of the process the data would be
collected, and the classification of its category. The collection started with
the description of the activities by the operator while the researcher
completed the form. After that, the operator simulated the activity and
adjustments in the form were performed, when necessary.
With the form already completed, the
quantities of products that would compose the production lots were defined, and
then the time collection was started. For the collection, it was necessary for
two researchers to perform the timing, being the first one responsible for
timing the cycle time, that is, the total time of the process. And the second
researcher was responsible for collecting the operation time, or activities
that added value to the product, which means that every time the operator
performs a task that does not add value the timer should be stopped. This
activity was performed for 1 month. Thus, the the
Cycle and Operation times were collected.
For the creation of the Value Stream
Map, it was still necessary for the researchers to identify the inventories,
identify the generated residues, and identify what the suppliers and customers
of each stage of the process are. With
all data, it was possible to construct the Value Stream Map, for each group of
products.
Analyzing the Value Stream Map of
Group A, one can observe the volume of input stock between the processes, with
the highest levels being concentrated in three processes. Among the Weighing
and Separation and Screen Fill processes, the volume of reels stored allowed
the production of 969 products. In the strain Drilling process, the stock of available
strains would allow the production of 449 domestic brooms. Already in the
process of Tuffing it was possible to produce 432
products with tufts stocked.
The largest stock indices are among
the same Group B processes; however, the volume capable of being produced
differs due to different input requirements. In this group among the processes
of Weighing and Separation and Filling of Screens would be possible to produce
835 domestic brooms, in the Drilling of Strain, 391 and in the Tuffing 233 products.
For Group C, other processes such as
those with higher volumes of stocks were Cutting on Guillotine 1 and
Preparation of plates 1, with the possibility of producing 5240 and 1265
industrial brooms, respectively.
In addition, the Value Stream Map
also made it possible to analyze the times of activities that aggregate and do
not add value to the product. For Group A it was assumed that 44.73% of the
production time adds value, that is, time that the customer is willing to pay
to have the product he wants and the remaining 57.21% are times used for
activities that do not add value, such as: wait, setup, movimentation,
among others.
Group B presented 42.79% for activities
that add value and 57.21% for those that do not aggregate. Finally, in Group C,
41.63% of the time is allocated to activities that aggregate and 58.37% to
those that do not add value, as summarized in Figure 3.
Figure 3: Process times that aggregate and do not add value to the process
4.1.3. Identify the Bottlenecks (TOC)
In this step, the concepts of the
Constraint Theory were used to identify bottlenecks in the system. Firstly, it
was identified, with the aid of the Value Stream Map, the bottleneck process in
the production of each group of products.
For all three groups the same
process was identified as bottleneck: Tuffing. The tuffing process makes the production of group A in 1 hour
of work are 3 domestic brooms, group B of 2 domestic brooms and group C of 7
industrial brooms. Due to the low productive capacity of this process, it was
decided not to adjust the other processes to this bottleneck, which would cause
the production of the plant to suffer a considerable fall.
In addition to identifying
production bottlenecks, it was sought to identify the limiting factors related
to inputs, suppliers, labor, among others, since these also have an impact on
productive efficiency. As other limiting factors were listed: i) specific day in the week to receive the outsourced
products and raw material (brushes); ii) limited amount of screen size; (iii)
the number of spools divided by color; iv) types of activities performed by
employees; v) productive efficiency of approximately 80%, to consider the
worker's stops and fatigues.
Identifying the limiting factors and
the bottlenecks of the productive process, the last step of the planning
process was to predict demand.
4.1.4. Predicting Demand
This stage began with the analysis
of sales from previous years. We chose to use as data the sales volumes of the
last three, due to the fact that in the years prior to these sales were
controlled manually and, therefore, the data do not have a great reliability
and accuracy.
First, the data divided into two
major product groups were analyzed: domestic brooms and industrial brooms in
order to identify trends or seasonality’s.
When analyzing the behavior in
general, by means of the average sales of the three years, one perceives a more
constant behavior when compared to the annual values, with absence of large
ascending or descending.
In general, the analysis of the data
allowed identifying the absence of seasonality’s or trends in the behavior of
the sales of the products over the years, although some peaks and gaps were
noticed. In addition, since the data used were products sold over the months,
one can restrict as a forecast calculation method those with an approach based
on time series. Among the options with a time series approach, the most adequate
would be the Average Moving Average, since we can determine the most recent N
periods, making its behavior more assertive about the variations in demand.
For these calculations the equation
proposed by Fernandes and Godinho Filho (2010) was
used:
|
(1) |
Where:
· N: periods;
· t: present time;
· X(t+1): future point;
· X(t-N): values of the previous
periods used.
In order to carry out the demand
forecast of the domestic brooms and industrial brooms groups, the average sales
of each month were calculated based on the last three years, disregarding some
values highlighted in gray, which could cause its characterization, resulting
in a forecast farther from the reality.
Analyzing the data, it was possible
to notice a lower average sale in the periods of December, January and February
for the group of domestic brooms and a more constant behavior throughout the
rest of the year.
With the monthly averages, it was
possible to start the calculation of demand forecast for year 4. The sales
volumes of the last three months of year 3 were considered to calculate the
demand forecast for January of year 4, according to the equation mentioned
above.
4.2.
Programming
After completing the planning stage,
it was possible to start the programming stage, which was divided into two
activities, the first the production balancing and the second the elaboration
of the production orders.
4.2.1. Calculate Line Balancing
In this activity of balancing
production, it was established the production and sales goals for each product
of each group (domestic brooms and industrial brooms), using the sales data
from previous years and the demand forecast elaborated in the planning stage. I
was defined the need for the main input (brushes) and establish the balance of
existing activities in the production process.
The balancing was based on the need
to reduce inventories of finished products, products in process and inputs, in
addition to aligning suppliers with the company's production activities. As a
first step in balancing production, it was analyzed the sales percentages of
the products divided into two groups: domestic brooms and industrial brooms, as
shown in Figure 4.
Figure 4: Ratio of Sales of Domestic and Industrial brooms
From Figure 4 it can be seen that
the ratio of proportionality of sales between the two groups remained stable
between the three years, which allowed establishing as a goal the ratio of 56%
of production for industrial brooms and 44% for domestic brooms, of total volume
of 1000 already established previously.
Once the production goal of the two
groups was defined, sales for the year 1 were taken to adjust the proportion
obtained to the established sales and production target of 1000 products.
Tables 1 and 2 show the monthly output estimated by product considering the
goals of 440 domestic brooms and 560 industrial brooms.
Table 1: Estimated monthly
production of domestic brooms for year 4
Product |
Average |
% |
Estimated
Production |
Domestic 1 |
115 |
31% |
135 |
Domestic 2 |
105 |
28% |
123 |
Domestic 3 |
71 |
19% |
82 |
Industrial |
64 |
17% |
75 |
Anatomic |
11 |
3% |
13 |
Industrial 2 |
10 |
3% |
13 |
Total |
376 |
100% |
440 |
Table 1, adjusted for the total
monthly output of 440 products of the domestic brooms group, brings as an
estimated monthly output 135 Domestic 2 brooms and 123 Domestic 3, these being
both the group's leading sellers.
Table 2: Estimated monthly
production of industrial brooms for year 4
Product |
Average |
% |
Estimated
Production |
Industrial brooms 40cm |
409 |
89% |
500 |
Industrial brooms 60cm |
31 |
7% |
38 |
Industrial brooms short |
18 |
4% |
22 |
Total |
458 |
100% |
560 |
Table 2, adjusted for the total
monthly production of 560 products of the brooms industrial group, brings the
estimated monthly production of three different products, being Industrial
brooms 40cm with 89% of the group's total sales, with an estimated production of
500 products per month.
Estimated the monthly production of
each product for year 4, one can establish the monthly need for yarns for
production. To calculate the required amount of yarn, the products were divided
according to the type of yarn used (thick or thin) and then the amount per
color (green or white) was calculated. The results are shown in Table 3.
Table 3: Monthly yarn
requirement by type and color for each product
Product |
Weekly production
forecast |
Type of
yarn |
Yarn per unit
(Kg) |
Green yarn per unit (Kg) |
White yarn per unit (Kg) |
Total Green yarn (Kg) |
Total White yarn (Kg) |
Industrial 40cm |
125 |
Thick |
0,353 |
0,353 |
0,000 |
44,125 |
0,000 |
Domestic 2 |
33,75 |
Thin |
0,260 |
0,138 |
0,122 |
4,658 |
4,118 |
Domestic 3 |
30,75 |
Thin |
0,207 |
0,111 |
0,096 |
3,413 |
2,952 |
Domestic 1 |
20,5 |
Thick |
0,230 |
0,124 |
0,106 |
2,542 |
2,173 |
Industrial |
18,75 |
Thick |
0,230 |
0,000 |
0,230 |
0,000 |
4,313 |
Industrial 60cm |
9,5 |
Thick |
0,470 |
0,470 |
0,000 |
4,465 |
0,000 |
Industrial short |
5,5 |
Thick |
0,335 |
0,335 |
0,000 |
1,843 |
0,000 |
Anatomic |
3,25 |
Thin |
0,207 |
0,111 |
0,096 |
0,361 |
0,312 |
Industrial 2 |
3 |
Thin |
0,400 |
0,218 |
0,182 |
0,654 |
0,546 |
With the values of the required
monthly quantity of each yarn for each product, one can calculate the total
monthly quantity of each yarn, to reach the necessary monthly quantity of reels
to supply the proposed production. Figure 5 shows the total amount of yarn
divided by category.
Figure 5: Monthly yarn requirement by type and total color for the week
Figure 5 shows that the wire that is
most in need is the Thick type wire in green color, with approximately 212kg
per month, which is used by most products. Second comes the thin type yarn in
green color with 36kg and then the yarns: fine white and thick white, with
approximately 32kg and 26kg, respectively.
The total amount of each yarn and
the weight of each spool were obtained, the amount of the monthly reel
requirement for the production, as presented in Table 4.
Table 4: Monthly need for
reels
Type of wire |
Amount of
wire (Kg) |
Reel weight (Kg) |
Amoung of
reel (Unit) |
Green thick |
52,975 |
3,64 |
59 |
Green thin |
9,086 |
4,26 |
9 |
White thin |
7,928 |
4,26 |
8 |
White thick |
6,486 |
3,64 |
8 |
From Table 4 we observe a monthly
requirement of 59 reels of the green thick type yarn, followed by 9 reels of
the thin green yarn and 8 of the thin white and thick white yarn each.
After calculating the need for the
main input, we proceeded to balance the weekly activities of the operators. In
order to carry out this step, it was necessary to realign the strategy of
receiving the suppliers, as well as the dates of delivery of products in
process to third parties and to identify the new delimitations of the process.
The delimitations were:
· All Group B products that would be
pumped by third parties must arrive at the company on Monday to follow the
finishing process;
· All Group C products are due to
arrive on Tuesday to follow the finishing process;
· All yarn Tuffing
made by outsourced process should arrive on Wednesday;
· All cut Yarns must be delivered to
third parties on Wednesday.
In this way, the delimitations of
the system, the possession of the activity times and using the TOC concepts
were defined, it was possible to elaborate the balance of activities, also
respecting the limitations of each of the three operators, which are presented
in Table 5.
Table 5: Available times for
operators
|
Operator A |
Operator B |
Operator C |
Days of the week worked |
3 |
5 |
5 |
Hours worked per day |
8,8 |
8,8 |
8,8 |
Minutes worked per day |
528 |
528 |
528 |
Setup (min) |
60 |
30 |
30 |
Total Time (min) |
468 |
498 |
498 |
Operator A works 3 days a week, 8.8h
each day, totaling 528 work minutes per day. Operators B and C work 5 days a
week, totaling the same 528 minutes per day. For operator B and C was
discounted 30 minutes of setup, however for the operator A was considered 60
minutes of setup, total time for heating the oven. Therefore, as total time
available we obtained 468 minutes for operator A and 498 minutes for operators
B and C. Besides that, it was considered as a tolerance factor of 20%, knowing
that an operator cannot work an entire period without interruptions, either by
physical, physiological or for reasons beyond his control, so the established
efficiency was 80% under time of 468 and 498 minutes.
Given the mentioned information, the
balance of activities for operator was carried out. A total working time of 404
minutes was obtained, which corresponds to an efficiency of 81%, very close to
the previously established efficiency of 80%.
4.2.2. Create Production Order
With balanced production, it was
possible to prepare the Production Order, which presents information such as:
date of production, product to be produced and quantity to be produced.
The production order was elaborated
based on the monthly sales target already defined, 1000 products in total,
being divided according to demand forecast. It is important to highlight that
the production orders were elaborated respecting the restrictions defined by
the TOC, and considering the individuality of each operator, respecting their
working hours and capacities.
An important change here is the fact
that the BPP used by the company previously divided production only between
domestic brooms and industrial brooms, not specifying which model of each
product should be produced. The Production Order developed after this study
identifies which model should be produced, offering greater assertiveness in
forecasting demand.
4.3.
Control of Production
4.3.1. Determining the Security Stock
The security stock will be of great
importance to the company, since in addition to offering protection to
variations in demand; it will also absorb the failures of its suppliers. In
addition, with the right amount of input stocks, opportunities for improvements
in layout may arise, and before this work, the company made its purchases of
inputs according to the suppliers' offer, causing high levels of inventories in
the factory.
In order to determine the security
stock of the main inputs: reels, strains and plaques, it was first established
that the level of security stock should be sufficient to cover production in a
period of one week. After that, the calculations of the quantities needed to be
kept in stock were carried out.
In order to calculate the stock of
plastic broom strain it was necessary to identify which plastic broom strain
models each product used, since there are more than one size. Then, the
required weekly quantity was calculated using the quantity of each type of
product to be produced, obtaining the safety stock. It was obtained that for
the safety stock it would be necessary to maintain 251 plastic broom strain,
divided into 5 different types.
Finally, the security stock of
plates was calculated, which are used only in industrial brooms to attach them
to wooden cables. As well as the calculation of the strains, their quantity was
obtained according to the quantity of product that uses this input. At the end
of the security inventory, it was established that the following would be kept
in the factory: 22 spools, distributed among the different types of yarn, as
shown in Table 5, 251 strains, distributed among the 5 existing sizes and 280
iron rods for industrial production brooms.
5.
CONCLUSIONS
Production Planning, Programming and
Control has proven to be an efficient tool in the pursuit of production
excellence. Increased productivity and assertiveness in demand forecasting,
lower inventories and production costs and efficient use of resources are some
of the results presented when this tool is used correctly.
The objective of this work was to
propose a hybrid model of PPCP implementation in a small company, allowing its
development step by step. However, the model was generic enough to serve as a
guide and replicated in other small companies.
The model was constructed using the
concepts of Lean Manufacturing, Push Production (MRP and MRP II) and Theory of
Constraints in each of its three phases: planning, programming and control. By
exploring the advantages of each of these concepts and adapting the reality of
the company under study, it was possible to create an adaptable model that
offered notable contributions to the company.
Among the model's main gains were
the planning phase, which was knowledge of the productive capacity,
identification of the processes that aggregate and do not add value to the
consumer. In addition, identification of production bottlenecks, identifying
the processes that should receive the most efforts in their optimization, and
finally, a more assertive forecast of demand, which considered the different
models of product existing in the company.
In the Production Scheduling phase
with the production balancing and the concepts of the production pushed it was
possible to establish a change in the relations with suppliers and third
parties adapting them to the needs of the company, which later allowed the construction
of the production orders offering the operators activities.
Finally, in the Production Control
phase, the security stocks were defined within the concept of Lean
Manufacturing, which preaches the maintenance of minimum inventories that can
absorb any faults in the process or suppliers, enabling the company to
significantly reduce costs. It is noteworthy that in this phase a spreadsheet
was developed that offers the company better visual management and decision
support, allowing the company to become more and more assertive in its
processes through continuous improvement.
In general, the implementation of
the Planning, Programming and Production Control tool brought the company a
better management of its material and human resources, allowing its better
prospection and advantage in the existing competitive market.
REFERENCES
ACS,
Z. J.; MORCK, R.; SHAVER, J. M.; YEUNG, B. (1997) Small Business Economics,
v. 9, p. 7-20. doi: 10.1023/A:1007991428526
ALVES, R.; SANTOS, J. A. A.; SCHMIDT, C. A. P. (2014)
Aplicação dos princípios da teoria das restrições e de técnicas de simulação na
gestão da dinâmica operacional de um pequeno restaurante: um estudo de caso. Espacios,
v. 35, n. 7,
p. 21- 29.
AKMAL, A.; PODGORODNICHENKO, N.; GREATBANKS, R.; EVERETT, A. M. (2018). Bibliometric analysis of production planning and control (1990–2016). Production Planning and Control, v. 29, n. 4, p. 333-351.
ASSID, M.; GHARBI, A.; DHOUIB, K. (2015) Joint
production and subcontracting planning of unreliable multi-facility
multi-product production systems. Omega, n. 50, p. 54-69.
ASSOCIAÇÃO BRASILEIRA DA
INDÚSTRIA DO PET (ABIPET) Panorama do
Setor. Available in: http://www.abipet.org.br, access in: Junho/2019.
ASSOCIAÇÃO BRASILEIRA DA
INDÚSTRIA DO PET (ABIPET) Indústria do
Pet no Brasil: Mercado, Perspectivas e Reciclagem. Available
in: http://www.abipet.org.br, access in: Junho/2019.
BECK, T.; DEMIRGUC-KUNT, A. (2006) Small and medium-size enterprises: Access to finance as a growth constraint. Journal of Banking & Finance, v. 30, n. 11, p. 2931-2943.
BERTOLINI, M.; ROMAGNOLI, G.; ZAMMORI, F. (2015) Simulation of two hybrid production planning and control systems: A comparative analysis. In: Industrial Engineering and Systems Management (IESM), International Conference on. IEEE, p. 388-397.
EASTERBROOK, S.; SINGER, J.; STOREY, M. A.;
DAMIAN, D. (2008) Selecting Empirical Methods for Software Engineering
Research. In: Shull F., Singer J., Sjøberg D.I.K. (eds) Guide to Advanced
Empirical Software Engineering. Springer,
London.
FERNANDES, F. C. F.;
AZEKA, F.; BARRETO, M. C. M.; FILHO, M. G. (2007) Identificação dos principais
autores em planejamento e controle da produção por meio de um survey mundial com pesquisadores da área. Gestão
& Produção, v. 14, n. 1, p. 83-95.
FERNANDES, F. C. F.; FILHO, M. G.; BONNEY, M. (2009) A proposal for integrating production control and quality control. Industrial Management & Data Systems, v. 109, n. 5, p. 683-707.
FORSMAN, H.; RANTANEN, H. (2011) Small manufacturing and service enterprises as innovators: a comparison by size. European Journal of Innovation Management, v. 14, n. 1, p. 27-50. doi:10.1108/14601061111104689
GALDAMEZ, E. V. C. (2007) Proposta
de um Sistema de Medição de Desempenho para Clusters Industriais de Pequenas e
Médias Empresas. Tese de
Doutorado. Universidade de São Paulo (Usp), São
Carlos-SP.
GIAOUTZI, M.; NIJKAMP, P.; STOREY, D. J. (2016) Small and medium size neterprises and regional development. Routledge, London.
GUAN, X.; MA, S.; YIN, Z. (2015) The impact of hybrid push–pull contract in a decentralized assembly system. Omega, v. 50, p. 70-81.
(2011) Resilience and competitiveness of small and medium size enterprises: an empirical research. International Journal of Production Research, v. 49, n. 18, p. 5489-5509, doi: 10.1080/00207543.2011.563831
HSIEH, Y.; CHOU, Y. (2017) Modeling the impact of service innovation for small and medium enterprises: A system dynamics approach. Simulation Modelling Practice and Theory, v. 82, p. 84-102.
KORUGAN, A.; GUPTA, S. M. (2014) An adaptive CONWIP mechanism for hybrid production systems. The International Journal of Advanced Manufacturing Technology, v. 74, n. 5-8, p. 715-727.
KOTHARI, C. R. (2004) Research Methodology, methods and techniques. 3 ed. New Age International (P) Ltd.
KUAZAQUI, E. (2013) Brazilian micro-enterprises: an exploratory study on marketing strategies. China-USA Business Review, v. 12, n. 10.
KUMAR, R. (2011) Research Methodology, a step-by-step guide for beginners. 3 ed. SAGE Publications Ltd.
SALEEM, J. J.; MILITELLO, L. G.; RUSS, A. L.; WILCK, N. R. (2016) The need for better integration between applied research and operations to advance health information technology. Healthcare, v. 4, n. 2, p. 80-83.
SJOBERG, D.; DYBÅ, T.; JORGENSEN, M. (2007) The Future of Empirical Methods in Software Engineering Research. FoSE 2007: Future of Software Engineering, p. 358-378. 10.1109/FOSE.2007.30.
WANG, C.; LIU, X. B. (2013). Integrated production planning and control: A multiobjective optimization model. Journal of Industrial Engineering and Management, v. 6, n. 4, p. 815–830.
ZAHRAEI, S. M.; CHEE-CHONG, T. (2017) Optimizing
a supply network with production smoothing, freight expediting and safety
stocks: An analysis of tactical trade-offs. European Journal of
Operational Research, v. 262, n. 1, p. 75-88.