application of jobshop scheduling in innoson industrial...
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Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB) An Online International Monthly Journal (ISSN: 2306-367X)
Volume:1 No.6 June 2013
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Application of Jobshop Scheduling in Innoson Industrial and
Technical Company, Enugu, Nigeria
Nwekpa Kenneth Chukwuma PhD
Senior Lecturer, Department of Business Management
Faculty of Management Sciences
Ebonyi State University, Abakaliki
Ebonyi State, Nigeria.
Email: [email protected]
______________________________________________________________________________
Abstract
The task of job shop scheduling – determining the sequence and timing of jobs on available
resources – is one of the most discussed practical problems in Management Sciences today.
There are many appropriate problem definitions for job shop scheduling due to complex and
dynamic nature of the problem with a large number of variables and constraints linked to jobs
and resources, such as specific due dates, processing times, handing/routing requirements and
capacities, not to mention alternative performance measures including maximum and mean
tardiness, mean flow time and portion of the tardy jobs. The objective of this research was to
achieve a trade-off between scheduling efficiency and delivery accuracy without compromising
cost and quality of the products necessary for satisfying customer’s needs. In order to
conceptualise this research, several articles were reviewed. A conceptual framework adduced
from expert/knowledge-based theory and theoretical framework was formulated to buttress the
study. An exploratory research design was adopted for the research. Secondary data were
collected and factored into the formulated mathematical models to generate ordinal data called
performance measures. The performance measures were subjected to statistical analytical
techniques including: Analysis of variance (ANOVA) and multivariate analysis of variance
(MANOVA) to test the formulated hypotheses. It was found that Shortest Processing Time(SPT)
among other priority dispatching rules method of jobshop scheduling was adjudged the best
performing measure in terms of operational efficiency and effectiveness.
______________________________________________________________________________
Keywords: Jobshop Scheduling, FCFS, SPT/SOT, Flow time
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1. Introduction
The industrial plastic sub-sector is one of the major components of the petroleum sector in
Nigeria. The sub-sector became very viable in the country immediately after the installation of
resin production plants in the late 1980s and mid 1990s. However, the Nigerian plastic industry
has since then been largely dominated by small and medium scale operators, with only the
technical capacity to fabricate simple and less complicated products. Plastic product, which is a
sub-sector of the petroleum product, is derived from the process of polymerization. Besides
meeting local demands, the industry also supplies to the West and Central African sub-regions
and the world at large (UNIDO, 2008).
However, demand for plastic products in Nigeria is on the increase due to rapid population
growth and changes in consumption patterns. Trends in consumption pattern has increased the
demand for the products, particularly for purposes of packaging, shopping carrier bags, containers,
agricultural tools, water sachet and household items (UNIDO, 2008).
The increasing complexities of demand for plastic items arising from population growth have
made it difficult for easy manipulation of the ordered items. This situation has made the Nigerian
plastic industry to experience cases of delayed deliveries of customized items to customers.
Delayed deliveries to customers in turn affect the cost of manufacturing. Though the cost is
difficult to measure or identify completely, important cost related measures of the system
performance like machine idle time, job flow time, job waiting time or job lateness could be
substituted for total cost. Consequently, an attempt to satisfy local demand has raised a lot of
quality issues, and has equally attracted reasonable attention to the applicable manufacturing
systems, design and analysis. This is in addition to the fact that the modern manufacturing
environment in such sectors is characterized by short product life cycle, high product diversity
(variety) and customers’ demand for both excellent quality and timely delivery. How competition
plays in the industry is thus dependent on the speed at which the dominant small and medium
scale operators react to the above challenges. Other areas that are likely to be influenced by the
rising complexities in the production and design of plastic products include the level of operating
efficiency, cost of production, and adaptation and sustainability.
There is presently a reasonable level of agreement especially among researchers, that one way
to cope with the above problems is to evolve appropriate job scheduling system and techniques.
Generally, Scheduling is concerned with allocation of resources over time and space so as to
execute the processing tasks required to manufacture a given set of products (Pinendo, 2001).
Depending on the number of resources and time/space that is available, finding a feasible or
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optimal schedule with respect to a certain objective could be trivial or very complex. Thus, a non-
continuous or intermittent production system is required, and this system could be visualized as a
system of queues or waiting time that involves jobs of varying complexities and varying due date.
Consequently, as orders build up at the various work centers within the plant, more efficient
method of attending to this problem are required.
It is no doubt that there are jobshop sequencing and scheduling solution technique in place.
However, the real challenge lies on efficient selection of the most appropriate technique. The
production manager, in Innoson Company is required to decide on which job to run first and to
plot the job schedule. The process of deciding the sequence and plotting the operation hours on
Gantt chart alone is very time consuming. Moreover, when a few jobs come in at the same time, it
is difficult to schedule which one to run first as it is difficult to see the actual progress of jobs
which are still in process. To decide which priority dispatching rule (PDR) to be used, such as
first come first serve (FCFS) and shortest processing time (SPT), will be evaluated for best
performance measure. Hence, the focus of this paper is to operationalize job sequencing and
scheduling in Innoson Technical and Industrial plastic company, Enugu, Nigeria, using
particularly Priority Dispatching Rules (PDRs).
1.1 Objectives of the Study
The broad objective of this study was to establish a trade-off between scheduling efficiency
and delivery accuracy without compromising cost and quality necessary to satisfy customers’
individual needs. Specifically, the objectives include:
1. To establish that FCFS job scheduling method currently used in INNOSON Technical
and Industrial plastic Company has significant influence on timely delivery of goods to
customers.
2. To determine whether SPT method of scheduling in INNOSON Technical and Industrial
plastic Company leads to customer satisfaction.
1.2 Statement of Research Hypotheses
HO1: FCFS method of job scheduling currently in use by Innoson plastic company does not have
any significant influence on the timely delivery of products to customers.
HA1: FCFS method of job scheduling currently in use by Innoson plastic company does have
significant influence on the timely delivery of products to customers.
HO2: SPT method of job scheduling does not lead to Customers’ satisfaction of Innoson Technical
and Industrial Plastic company products.
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HA2: SPT method of job scheduling does lead to Customers’ satisfaction of Innoson Technical
and Industrial Plastic company products.
2. Empirical Review
Several researchers have studied different problems of scheduling. Gupta, et al (1987)
considered the flow-shop scheduling problem with sequence-dependent additive set-up times as a
special case of the general problem, and a polynomially bounded approximate method, which was
developed to find a minimum makespan permutation schedule. The algorithm was shown to yield
optimal results for the two-machine case.
Akpan (1996) presented a technique for job shop sequencing problems via network scheduling
technique. He examined a new approach to job-shop sequencing problem that is based on a
network scheduling technique that depended on limited resource availability to achieve minimum
total processing time. The method utilized a resource allocation procedure based on random
activity (job element) selection and the method of finding the optimal solution of selecting the
trial run with the minimum time duration.
Parthasarathy and Rajendran, (1998) worked on the scheduling to minimize mean tardiness
and weighted mean tardiness in flowshop and flow line based manufacturing cell.
Although Gupta, et al. (1987), Parthasarathy, et al. (1998) and Akpan (1996) were interested
in optimal scheduling problem, the first two related their study to flowshop floor while the latter,
focused on job shop sequencing.
Mandahawi et al (2011), conducted a study on “A Max-Min Ant System to Minimize Total
Tardiness on a Single Machine with Sequence Dependent Setup Times Implementing a Limited
Budget local search”. This study presents a novel Max-Min Ant system (MMAS) algorithm for
solving sequencing problems. The MMAS algorithm adopts a new local search technique where,
a shop position is randomly chosen from the jobs’ sequence, and the job located at this shop is
either interchanged with other jobs or another job from the sequence is inserted in its position.
Vinod et al (2007) in the paper titled “Scheduling a Dynamic Job Shop Production system
with Sequence-Dependent Setups: An Experimental Study” was an experimental study for
scheduling a dynamic job shop in which the setup times are sequence dependent. They developed
a discrete event simulation model for the purpose of the experimentation. Seven scheduling rules
were incorporated in the simulation model. We five new setup-oriented scheduling rules proposed
and implemented under the factors of shop load, setup time ratios and due date tightness, the
results indicated that setup-oriented rules provide better performance than ordinary rules.
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Jairo et al, (2010) also conducted a study titled “Production Scheduling with Sequence-
Dependent Setups and Job Release Times”. Their study was based on a short-term production
scheduling problem inspired by real-life manufacturing systems consisting of scheduling a set of
jobs (production orders) on both single and identical parallel machines, with the objective of
minimizing the makespan or maximum completion time of all jobs. Jobs are to release dates and
there are sequence-dependent machine setup times. The problem was based on single machine
system and considered NP-hard problem. They proposed a heuristic algorithm to solve the
problem. In agreement, Montoya-torres et al (2010) in an experiment showed that heuristic
algorithm performs very well compared to optimal solution and lower bounds, and require short
computational time.
It is important to note that in the search for literature, not one study on a Nigerian situation
was found. Consequently, this study tends to bring home the problem by studying the operations
of a plastic industry. The reason is that where ever there is job shop production, scheduling
problem abounds in that place. In this work, another scheduling problem considered is scheduling
job orders (N-jobs) to M-Machines optimally. Some priority rules were evaluated alongside
performance measures such as makespan, lateness, average flow time, tardiness and average
work-in-progress inventory to determine the most efficient and effective criterion. Thus, the
problem understudy is to determine how to schedule or sequence these jobs to optimise
throughput rate of the machines (Taha 2007; Nahimas 1987).
2.1 Brief History of Innoson Technical and Industrial Plastic Industry, Enugu
Innoson technical and industrial plastic company limited, a subsidiary of Innoson group of
companies, was incorporated in 2002. It commenced full operation in October same year. It is an
indigenous blue clip Company that manufacturers plastic chairs, tables, trays, plates, spoons, cups,
jerry-cans of different sizes and many other allied plastic products.
The company ranks as the biggest plastics manufacturing company in Nigeria, and produces
the highest quality range of plastic products, has a production output of over 10,000 pieces of
chairs and tables per day with twelve (12) injection moulds machines due to the rapid increase in
demand for its products. It is believed that their effort is a direct response to the Federal
Government policy directed towards encouraging the indigenous private sector as an engine of
growth in the economy. The company employs over six hundred indigenous employees and a few
expatriate staff. The organisational structure of the company is shown as figure 1.
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Figure 1: Organisational Structure of the company
Source: Innoson Industrial and Technical Plastic Company
In the Organogram the factory units where production and maintenance department are located
are on the fourth tier of the organizational hierarchy. The jobs of sequencing and scheduling take
place in the production department.
2.2 Theoretical framework
The theory employed in this paper is expert and knowledge-based systems which were
developed by Chow and Hunng (1990), Smith (1995) developed OPIS, a Knowledge-based
system, with an interactive scheduling system that consist of two parts a knowledge base, and
inference engine to operate on that knowledge base. Formalization of the “knowledge” that
human experts use - into rules, procedures, heuristics, and other types of abstractions are captured
in the knowledge base. Three types of knowledge are usually included: procedural, declarative,
and Meta. Procedural knowledge is domain-specific problem solving knowledge. Declarative
knowledge provides the input data defining the problem domain. Meta knowledge is knowledge
about how to use the procedural and declarative knowledge to actually solve the problem. Several
data structures have been utilized to represent the knowledge in the knowledge base including
semantic nets, frames, scripts, predicate calculus, and production rules.
The inference engine selects a strategy to apply to the knowledge bases to solve the problem at
hand. It can be forward chaining (data driven) or backward chaining (goal driven). Fox, (1983)
was the first major expert system to study specifically job shop scheduling problems. He used a
constraint-directed reasoning approach with three constraint categories: organizational goals,
EXECUTIVE CHAIRMAN
GM
FACTORY MGR Acting GM
PROD. MGR HOD MAINTENANCE
H.O.D
HR/AD. H.O.D
MKTING
H.O.D ACCTS.
INTERNAL
AUDIT PRO./ PA COY SEC
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physical limitations and causal restrictions. Organizational goals considered objective functions
based on due-date and work-in-progress. Physical limitations referred to situations where a
resource had limited processing capability. Procedural constraints and resource requirements
were typical examples of the third category. Several issues with respect to constraints were
considered such as constraints in conflict, importance of a constraint, and interactions of
constraints, constraint generation and constraint obligation.
3. Methodology
3.1 Method
Generally, it is known that job shop scheduling does not have a general formula because it
belongs to Non-deterministic polynomial hard (NP-hard) problem. In other to achieve the
objectives of this research, three quantitative models were formulated.
The models are:
J(PDR) = Min (flow time) …………………………………………(1)
Where J(PDR) is priority dispatching rule method of jobshop scheduling
and Min (flowtime) is the minimum flow time
The first objective of this study intends to find out whether FCFS job scheduling method
currently in use at Innoson Industrial and Technical plastic company has significant influence on
the timely delivery of customer’s order. The flow time implies completion time of orders for
customer as a function of priority dispatching rule (method of job shop scheduling) in use. The
study intends to find whether flow time for the First come, first served – priority dispatching rule
method of job shop scheduling has influence on the delivery time.
Thus,
J(PDR) = Min (lateness, tardiness) ……………………………(2)
The second objective in tends to determine the effect on customer’s satisfaction of the SPT
method of job scheduling at Innoson plastic company. From the second model, tardiness is the
positive lateness; literally it implies earliest delivery of order to customers while lateness is about
jobs that were processed late.
However, the above model measures the customer satisfaction from the balance weight
between the two dependent variables (lateness and tardiness) and with respect to the three method
of priority dispatching rule. Consequently, this study used the – FCFS and SPT models to find the
method of job scheduling (priority dispatching rule) that minimized the lateness and tardiness of
the jobs.
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3.1.1 Procedure
First, the study needed to define the variables to enable the study collects accurate data. Thus,
in this research the independent variable is the method of job shop scheduling, and respectively
include: First come, first served (FCFS) and Shortest Processing Time (SPT). Whereas, the
dependent variable is the variable measuring an outcome of an item or trial that includes: flow
time, mean flow time, tardiness, mean lateness, lateness, maximum lateness, minimum lateness
and work-in-process inventory. The dependent variables are referred to as the performance
measures for the priority dispatching rule.
In order to factor the dependent variables data such as number of job orders from customers,
processing time and due date were collected. The collected data were simulated for each priority
dispatching rule to obtain estimates of the performance measures.
The performance measures as tabulated were subjected to Analysis of Variance (ANOVA) and
Multivariate Analysis of Variance (MANOVA) statistical test for decision-making. The reason
for the use of ANOVA and MANOVA is that they are used to test differences among sample(s) at
a time.
The first hypothesis was subjected to ANOVA statistical test because of the nature of the
objective and the model supporting the hypothesis. The other two hypotheses were subjected to
MANOVA test because the variables were more than two.
4. Findings
4.1 Data from Innoson Industrial and Technical Plastic Company
Eleven different orders placed by different customers as was recorded in the production unit of
the company were collected and studied.
The job orders collected were those for injection moulding machine, which was for production
of product lines such as, chairs of different shapes and sizes, tables, Trays, combs, Baskets,
Ammeter box, PVC covers, cloth models and so on. Also collected were the due date, the
available date and the processing time (in days). The data generated are given in table 1
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Source: Innoson Industrial and Technical Plastic Company, (2009)
4.2 Computing Due Date
From the data generated from the company (see Table 1), due date (slack) was computed in
days by subtracting the due date from available date. The intention was to know the exact time
available for production unit to deliver orders to customers according to the expectation. The data
in Table 1 were used to perform some computational procedures on some priority dispatching
rules of interest.
Table 2: Computed Due Date
Job
No.
Description Processing time
(days)
Due date
(days)
1 5,000 pieces of chairs 9 13
2 1, 000 pieces of Tables 4 10
3 1,000 pieces of Ammeter Boxes 3 7
4 10,000 pieces of PVC covers 34 28
5 100 pieces of Cloth models 1 11
6 2,000 pieces of Fan blades 3 5
7 1,000 pieces of Dustbins 2 7
8 1,000 pieces of Pallets 2 5
9 5,000 pieces of Combs 6 18
10 10, 000 pieces of Hangers 9 20
11 1,000 pieces of Baskets 2 13
Source: Innoson Industrial and Technical Plastic Company, (2009)
4.3 Priority Dispatching Rule - First come, first serve
Table 2, shows simulated jobs based on their arrival date and performance measures such as flow
time, lateness of jobs and tardiness of jobs. However, it was observed in Table 3 that 10 jobs were
late and one was produced early. Also, the average work-in-process inventory and average total
inventory were 6.88 and 6.98 respectively.
Table 1: Secondary Data from the Industry
Job
No.
Description Available
Date
Due Date Processing
Time (Days)
1 5,000 pieces of chairs 5/10/2009 19/10/2009 9 days
2 1, 000 pieces of Tables 6/10/2009 15/10/2009 4days
3 1,000 pieces of Ammeter Boxes 8/10/2009 11/10/2009 3days
4 10,000 pieces of PVC covers 5/10/2009 6/11/2009 34days
5 100 pieces of Cloth models 7/10/2009 9/10/2009 1day
6 2,000 pieces of Fan blades 9/10/2009 14/10/2009 3days
7 1,000 pieces of Dustbins 8/10/2009 12/10/2009 2days
8 1,000 pieces of Pallets 6/10/2009 10/10/2009 2days
9 5,000 pieces of Combs 10/10/2009 19/10/2009 6days
10 10, 000 pieces of Hangers 10/10/2009 29/10/2009 9days
11 1,000 pieces of Baskets 15/10/2009 17/10/2009 2days
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Table 3: Performance measures – First come, first serve
Job No. Processing
Time
(days)
Flow Time
(days)
Due date
(days)
Actual
packed date
(days)
L = Ci – Di
(days)
T = Di - Ci
(days)
1 9 9 13 13 0 4
2 4 13 10 13 3 0
3 3 16 7 16 9 0
4 34 50 28 50 22 0
5 1 51 11 51 40 0
6 3 54 5 54 49 0
7 2 56 7 56 49 0
8 2 58 5 58 53 0
9 6 64 18 64 46 0
10 9 73 20 73 53 0
11 2 75 13 75 62 0
TOTAL 516 523
Source: Computed for this work, (2010)
4.4 Priority Dispatching Rules – shortest processing Time (SPT)
Table 2 also shows shortest processing time formed by arranging the jobs from the smallest
processing time to the largest with t = 1, 2, 2, ……….34. The jobs orders were rearranged, the
performance measures such as flow time, lateness of the jobs and tardiness of jobs were obtained
for the purpose of descriptive and hypothetical analysis. The average work-in-process inventory
and average total inventory were determined to be 3.03 and 3.30 respectively. The result is shown
in Table 4.
Table 4: Performance measures - Shortest Processing Time
Job
No.
Processing Time
(days)
Flow Time
(days)
Due date
(days)
Actual
picked up
date
L=Ci – Di
(days)
T= Di - Ci
(days)
5 1 1 11 11 0 10
7 2 3 7 7 0 4
8 2 5 5 5 0 0
11 2 7 13 13 0 5
3 3 10 7 10 3 0
6 3 13 5 13 8 0
2 4 17 10 17 7 0
9 6 23 18 23 5 0
1 9 32 13 32 19 0
10 9 41 20 41 21 0
4 34 75 28 75 47 0
TOTAL 227 247
Source: Computed for this study, (2010)
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4.5 Performance measures for the priority dispatching rule
Table 5 is a summary of Tables 2, 3 and 4 which show flow time, lateness of jobs and tardiness of
jobs grouped according to the priority dispatching rule – first come, first serve; shortest
processing time and expected due date for descriptive and hypothetical analyses.
Table 5: Performance Measures for Priority Dispatching Rules
No of Jobs
Performance
Measure
1 2 3 4 5 6 7 8 9 10 11
FT 9 13 16 50 51 54 56 58 61 73 75
L 0 3 9 22 40 49 49 53 46 53 62
T 4 0 0 0 0 0 0 0 0 0 0
FT 1 3 5 7 10 13 17 23 32 41 75
L 0 0 0 0 3 8 7 5 19 21 47
T 10 4 0 5 0 0 0 0 0 0 0
*FT = FLOW TIME, *L = LATENESS, *T = TARDINESS
Computed figure for SPSS 15.0 MANOVA analysis
Source: Computed for this study, (2010)
4.6 Descriptive Presentation and analysis
Table 6 gives the descriptive statistics. In the table it could be observed that the total numbers of
jobs analyzed are 11. Analytically it was found that the mean statistics for FCFS and SPT with
regard to flow time was 46.9091 and 20.6364 respectively. These results suggest that the mean
flow time for SPT is considerable lower than that of first come first served. Also, the mean
lateness of FCFS and SPT were 35.0909 and 10.0000 respectively. It follows, that SPT has the
smallest total and mean lateness. Meanwhile, the standard derivations of these priority rules in
terms of flow time are high but the FCFS rule has the highest variance. The standard derivation
for lateness in terms of FCFS and SPT are 22.38059 and 14.34573, respectively.
FC
FS
S
PT
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Table 6: Descriptive Statistics
Mean
Std.
Deviation N
FCFS
FLOWTIME 46.9091 23.43696 11
FCFS
LATENESS 35.0909 22.38059 11
FCFS
TARDINESS .3636 1.20605 11
SPT FLOWTIME 20.6364 21.94663 11
SPT LATENESS 10.0000 14.34573 11
SPT
TARDINESS 1.7273 3.28910 11
Source: Computed from SPSS .15
Table 7: Tests of Between-Subjects Effects
FCFS
FLOWTIM
E
FCFS
LATENES
S
FCFS
TARDINE
SS
SPT
FLOWTIM
E
SPT
LATENES
S
SPT
TARDINES
S
Sum-of-
Squares and
Cross-
Products
FCFS FLOWTIME
5492.909 5005.091 -151.636 3897.636 2324.000 -499.273
FCFS LATENESS 5005.091 5008.909 -140.364 3626.364 2161.000 -544.727
FCFS TARDINESS -151.636 -140.364 14.545 -78.545 -40.000 33.091
SPT FLOWTIME 3897.636 3626.364 -78.545 4816.545 3092.000 -335.091
SPT LATENESS 2324.000 2161.000 -40.000 3092.000 2058.000 -190.000
SPT TARDINESS -499.273 -544.727 33.091 -335.091 -190.000 108.182
Covariance FCFS FLOWTIME 549.291 500.509 -15.164 389.764 232.400 -49.927
FCFS LATENESS 500.509 500.891 -14.036 362.636 216.100 -54.473
FCFS TARDINESS -15.164 -14.036 1.455 -7.855 -4.000 3.309
SPT FLOWTIME 389.764 362.636 -7.855 481.655 309.200 -33.509
SPT LATENESS 232.400 216.100 -4.000 309.200 205.800 -19.000
SPT TARDINESS -49.927 -54.473 3.309 -33.509 -19.000 10.818
Correlation FCFS FLOWTIME 1.000 .954 -.536 .758 .691 -.648
FCFS LATENESS .954 1.000 -.520 .738 .673 -.740
FCFS TARDINESS -.536 -.520 1.000 -.297 -.231 .834
SPT FLOWTIME .758 .738 -.297 1.000 .982 -.464
SPT LATENESS .691 .673 -.231 .982 1.000 -.403
SPT TARDINESS -.648 -.740 .834 -.464 -.403 1.000
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4.7 Test of hypothesis I
Table 7 gives the estimates for test of variance (Between-Subject) Effects. The study used
ANOVA statistical test to evaluate the significance of using first come, first served criterion
which Innoson Plastics Company currently uses.
It was observed from the ANOVA Table 8 that FCFS has high significant influence on method of
job scheduling. Thus, at F-value 61.491 with significant level of 0.0000, it attest that method of
job scheduling currently in use by Innoson plastics company does have significant influence on
the timely delivery of customers product. Therefore, the null hypothesis was accepted.
Table 8: ANOVA for Testing Hypothesis 1
Model Sum of Squares Df Mean Square F Sig.
Regression 4791.600 1 4791.600 61.491 .000
Residual 701.309 9 77.923
Total 5492.909 10
a Predictors: (Constant), NO OF JOBS
b Dependent Variable: FLOW TIME/FCFS
Source: Computed from SPSS .15
4.8 Testing Hypothesis II
The research studied the effect of lead-time in terms of customers’ satisfaction. The
parameters for these measurements were lateness and tardiness. The study suggested that early
delivery has an associated cost, which include, cost of holding inventory.
Table 9 is an aggregation of the effects of lateness and tardiness with the related priority
dispatching rule. The data were generated using MANOVA statistical technique.
Table 9: Within-subjects factors
Measure Factor 1 Dependent
Variable
FCFS 1
2
Lateness
Tardiness
SPT 1
2
Lateness
Tardiness
EDD 1
2
Lateness
Tardiness
Source: Computed from SPSS .15
The table above is like the key trying to explain the variables as were queue for test.
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Table 10: Multivariate Analysis of Variance for hypothesis II.
Source Measure
Type III
Sum of
Squares Df
Mean
Square F Sig.
Partial Eta
Squared
Intercept FCFS 24873.818 1 24873.818 37.418 .000 .789
SPT 3840.485 1 3840.485 9.509 .012 .487
Error FCFS 6647.515 10 664.752
SPT 4038.848 10 403.885
Source: Computed from SPSS .15
In Table 9, A 2(FCFS: Lateness, Tardiness) x 2(SPT: Lateness, Tardiness) x between subjects
multivariate analysis of variance (MANOVA) was performed. The F–test from MANOVA was
37.418 with 0.000 significance at probability of 0.789 for FCFS and 9.509 with 0.012
significance at probability of 0.487 SPT, respectively. It was observed that both priority rules
have their significance levels less than 0.05 affirming that we accept the alternate hypothesis and
reject the null hypothesis. This implies that the method of job scheduling leads to customers’
satisfaction in Innoson technical and industry company products.
5. Summary and Conclusion
This works first described what is obtained in the production firms at present and particular in
plastics industries and what is lacking in Nigeria firms. An exploratory small-scale study was
done using Innoson Technical and Industrial plastic Company, Enugu. Eleven (11) orders from
customers meant for injection moulding machine were studied. The preliminary investigation
showed that presently Innoson Technical and Industrial plastic Company uses the FCFS method
of scheduling. When subjected to ANOVA test, F-static test showed that its’ method of
scheduling had a significant influence on the timely delivery at 95% level of significance.
However, the result only formed an initial solution for improvement.
Generally, the two dispatching rules (FCFS and SPT) were analysed based on other
performance measures such as flow time, mean flow time, number of late jobs, maximum
lateness, minimum lateness, average WIP inventory, average total WIP inventory and machine
utilization. It was proven that SPT performs better and this finding collaborates those by kumar et
al (2009) and Conway and Maxwell (1962).
The contribution of this work emerges from the systematic and thorough examination of the
inherently complex scheduling problems. It provides a remarkably simple yet novel platform for
evaluating the conditions for efficient coordination of priority index rules. The results were
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reported in three areas of the priority dispatching rules stages: (1) the current state of method of
job scheduling at Innoson Technical and Industrial Plastic Company, (2) Comparing the current
state of Innoson Technical and Industrial Plastic Company and SPT method of jobshop
scheduling that should be able to satisfy customers’ with relation to due date.
The current state of method of scheduling in Innoson Technical and Industrial Company is
first come, first served which in their operation could be adjudged the best but, FCFS only
favours the customers’ assuming they are there waiting for the jobs to be processed. From the
result, it is obvious that FCFS method has the longest flow time. FCFS also has a significant
influence on delivery day, but forms the bases for improvement (initial feasible solution).
The current state (FCFS) of scheduling in Innoson Technical and Industrial Plastic Company
was compared with the proposed SPT method of jobshop scheduling in terms of influencing
customer satisfaction. It was evaluated by comparing the lateness, mean lateness, tardiness and
mean tardiness of jobs in relation to FCFS and SPT. The findings suggest that it favoured the SPT
scheduling method.
The overall performance measures was evaluated such as flow-time, number of jobs lateness,
work-in-progress inventory and machine utilization in terms of operational efficiency through the
use of priority dispatching rules method of jobshop scheduling, SPT method proved to be the best.
Thus, the shortest processing time (SPT) which was the second objective was proved to be
more effective in terms of performance indexes such as flow time, number of jobs lateness, work-
in-process inventory and machine utilization and in turn satisfies customers’ curiosity for getting
their jobs faster and manufacturing optimizing their resources as well.
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Appendix A
First Come First Served (FCFS)
Average WIP inventory = sum of the flow times = 516 = 6.88
Makespan 75
Average total inventory = sum of time in system = 523 = 6.98
Makespan 75
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Shortest Processing Time (SPT)
Average WIP inventory = sum of the flow times = 227 = 3.08
Makespan 75
Average total inventory = sum of time in system = 247 = 3.30
Makespan 75