Application of Jobshop Scheduling in Innoson Industrial ...globalbizresearch.org/economics/images/files/68277_JEIEFB_Nwekpa... · Nwekpa Kenneth Chukwuma PhD Senior Lecturer, Department

Journal of Emerging Issues in Economics, Finance and Banking (JEIEFB) An Online International Monthly Journal (ISSN: 2306-367X)

Volume:1 No.6 June 2013

537

www.globalbizresearch.com

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.




541


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


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


The table above is like the key trying to explain the variables as were queue for test.




550


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


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




551


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.

References

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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


http://www.arpapress.com/Volumes/Vol6Issue%201/IJRRAS_6_1_04.PDF

http://www.arpapress.com/Volumes/Vol6Issue%201/IJRRAS_6_1_04.PDF

http://www.unido.org/events/php?id=728

http://www.sciencedirect.com/



553


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


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