Mathematical Problems in Engineering

Volume 2012 (2012), Article ID 731092, 23 pages

doi:10.1155/2012/731092

Research Article

Statistical Analyses and Modeling of the Implementation of Agile Manufacturing Tactics in Industrial Firms

Mohammad D. AL-Tahat¹ and Khaled M. Bataineh²

¹Industrial Engineering Department, The University of Jordan, Amman 11942, Jordan

²Department of Mechanical Engineering, Jordan University of Science and Technology, Irbid 22110, Jordan

Received 5 January 2012; Revised 22 April 2012; Accepted 4 June 2012

Academic Editor: Sri Sridharan

Copyright © 2012 Mohammad D. AL-Tahat and Khaled M. Bataineh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

This paper provides a review and introduction on agile manufacturing. Tactics of agile manufacturing are mapped into different production areas (eight-construct latent): manufacturing equipment and technology, processes technology and know-how, quality and productivity improvement, production planning and control, shop floor management, product design and development, supplier relationship management, and customer relationship management. The implementation level of agile manufacturing tactics is investigated in each area. A structural equation model is proposed. Hypotheses are formulated. Feedback from 456 firms is collected using five-point-Likert-scale questionnaire. Statistical analysis is carried out using IBM SPSS and AMOS. Multicollinearity, content validity, consistency, construct validity, ANOVA analysis, and relationships between agile components are tested. The results of this study prove that the agile manufacturing tactics have positive effect on the overall agility level. This conclusion can be used by manufacturing firms to manage challenges when trying to be agile.

1. Introduction

Agile manufacturing (AM) is described as new tactics of manufacturing. It emerged after lean production (LP). It represents pattern shifts from mass production (MP). It originated from the 21st century manufacturing enterprise study that was conducted at Lehigh University in the early 1990s [1]. Following that, a book entitled “Agile Companies and Virtual Enterprise” recognized as the state-of-the-art work on AM was published in 1995.

According to Groover [1] “agile manufacturing can be defined as: (1) an enterprise level manufacturing strategy of introducing new products into rapidly changing markets, (2) an organizational ability to thrive in a competitive environment characterized by continuous and sometimes unforeseen change”. Pham et al. [2] defined agile manufacturing as the ability to thrive in a competitive environment of continuous unpredictable change and respond quickly to rapidly changing market driven by customer-based value of products and services.

The international CAM-I [3] addressed the capabilities of an enterprise to reconfigure itself quickly in response to sudden changes, but in ways that are cost effective, timely, robust, and of a broad scope. Agility theory seeks to provide matrices for business processes, physical operations, and human resources to respond to rapid and unpredictable changes.

Agile companies tend to reveal the following agile principles: (1) rapid configuration of resources to meet dynamic change of market opportunities; (2) managerial personnel needs and knowledge should be distributed to all level of enterprise on trust base; (3) building business relationships to effectively enhance competitiveness; (4) considerable attention on innovation and entrepreneurship should be highly considered; (5) considerable attention on the value of solutions to customers' problems rather than on the product cost and price.

Important aspects and tactics of AM are mapped into different production areas as shown in Table 1. The main focus of this study is to investigate and measure an agility index that represents the overall implementation of AM tactics in Jordanian industrial firms. After a Structural Equation Model (SME) is proposed, related hypotheses are formulated. Necessary statistical analysis is carried out using the proper tools. Finally, results are presented and discussed.

Table 1: Mappings of AM tactics to eight impact areas.

Impact area
Identifier
Agile tactic
Identifier

(1) Manufacturing equipment and technology
MET
Group technology, cellular layouts, continuous flow
GTC

Production process reengineering
PPR

Flexible manufacturing system
FMS

CNC and DNC
CNC

Robotics and PLC’s
PLC

CAD/CAM, CAPP, and CIM
CIM

(2) Processes technology

and know-how
PTK
Removal of waste methods
RWM

Reconfigurable, and continuously changeable system
RCS

Rapid machine setups and changeovers
RSC

Standardized operating procedures
SOP

Rapid prototyping, remote, and e-manufacturing
E-M

(3) Quality and productivity improvement and measures
QPIM
Fast identification of in-process defects
FID

Strategic focus on long-term productivity performance
LPP

Modular production facilities
MPF

Fast production cycle times
FPT

(4) Production planning

and control
PPC
Effective information system
EIS

Make-to-order strategy
MTO

Decision making at functional knowledge levels
DMK

Manufacturing resource planning
MRP

(5) Shop floor management
SFM
High flexibility approaches
HFS

General purpose equipments
GPE

Effective communication technology
ECT

Removal of waste methods
RWM

Make-to-lot size
MLT

(6) Product design and development
PDD
Quick introduction of new products
QIN

Rapid changes to control software
RCC

Rapid prototyping, remote, and e-manufacturing
E-M

CAD/CAM, CAPP, and CIM
CIM

(7) Supplier relationship management
SRM
Effective communication technology
ECT

Long-term supplier relationship
LTR

Supplier evaluation and selection
SES

(8) Customer relationship management
CRM
Immediate and quick delivery
IQD

Effective communication technology
ECT

Make-to-order strategy
MTO

Competitive unit cost
CUC

Product customization
PCU

2. Comparison of Agile Manufacturing and Lean Production

LP and AM are complement to each other and should not be viewed as competitive. They are mutually supportive. On the other hand, LP and AM use different statements of principles. The emphasis in LP seems to be more on technical and operational issues, while emphasis in AM is on enterprise and people issues. AM is broader in scope and more applicable to the enterprise level. On the other hand, LP tries to smooth out the production schedule and reduce batch sizes [1, 4].

AM uses flexible production technology to minimize disruptions due to design changes. By contrast, the philosophy behind AM is to embrace unpredictable changes. The capacity of an agile company to adapt to changes depends on its capabilities to minimize the time and the cost of setup and changeover, to reduce inventories of finished products, and to avoid other forms of waste. Table 2 summarizes some differences between LP and AM in many different business dimensions [1, 2]. The products are customized in both AM and LP. AM and LP want to have continuous relationships with their customers. Agile principles focus on the enhancement of enterprise's ability to respond quickly to rapidly changing market driven by customer-based value of products and services. On the other hand, lean principles focus on the elimination of sources of different types of waste. Agile enterprise can be described as lean, while the reverse is not necessary true.

Table 2: Comparison of agile manufacturing and lean production.

Dimension
Lean production
Agile manufacturing

Principles
Eliminate source of waste

Perfect first-time quality

Flexible production lines

Kaizen
Customer enrichment

Cooperate to enhance competitiveness

Organize to master change

Leverage the impact of people and information

Production quantity
Enhancement of mass production
Break with mass production; emphasis on mass customization

Production flexibility
Flexible production for product variety
Greater flexibility for customized products

Emphasis
On technical, operational issues and management of human resources
On organizational and people issues

Application l
To the factory level
To the enterprise level

Area of management
Emphasis on supplier management
Formation of virtual enterprises

Area of change
Relies on smooth production schedule
Attempts to be responsive to change

Attempts to eliminate source of waste
Embrace unpredictable market change

Market life
Short
Short

Order initiation
Produce to order
Produce to order

Information content
High
High

Customer relationship
Continuing relationship
Continuing relationship

Pricing by customer value
Pricing by customer value

3. Constructed Latent of Agile Manufacturing System

The proposed agile manufacturing system (AMS) is assumed to involve eight-construct latent listed in Table 3. These are as follows: (1) manufacturing equipment and technology MET, (2) processes technology and know-how PTK, (3) quality and productivity improvement and measures QPIM, (4) production planning and control PPC, (5) shop floor management SFM, (6) product design and development PDD, (7) supplier relationship management SRM, and (8) customer relationship management CRM.

Table 3: Constructed latent of agile manufacturing system.

No.
Constructed latent
Identifier

Agile manufacturing system (AMS)
1
Manufacturing equipment and technology
MET

2
Processes technology and know-how
PTK

3
Quality and productivity improvement and measures
QPIM

4
Production planning and control
PPC

5
Shop floor management
SFM

6
Product design and development
PDD

7
Supplier relationship management
SRM

8
Customer relationship management
CRM

3.1. Agile Selection of Manufacturing Equipment and Technology (MET)

The relations between the specific equipment configurations with visual control and group technology are developed by [5, 6]. The explanations of how to design cellular layouts are given by [7, 8]. Li-Hua and Khalil [9] investigated the rapid changes in the business environment. They showed how companies can maximize business opportunities when the risks are considered. The relation between new equipment/technologies and production process reengineering is developed by [10, 11]. A discussion about how the production process reengineering increases productivity and efficiency is presented in [12, 13]. In order to have a formal investigation about the effect of MET, the following hypotheses are proposed:

: MET implementation has a significant, positive effect on the development of AMS. : MET implementation has no effect on the development of AMS. : MET implementation has a significant, positive effect on the development of PTK. : MET implementation has no effect on the development of PTK. : MET implementation has a significant, positive effect on the development of QPIM. : MET implementation has no effect on the development of QPIM. : MET implementation has a significant, positive effect on the development of SFM. : MET implementation has no effect on the development of SFM. : MET implementation has a significant, positive effect on the development of PDD. : MET implementation has no effect on the development of PDD.

3.2. Processes Technology and Know-How (PTK)

The elimination of waste can (1) simplify organizations processes [14], (2) allow business to be more agile and dynamic which offers the opportunity to meet customer demands in new products and services, and (3) allow business to be more responsive to customers' concerns [15].

Researchers on AM have established that flexibility is the foundation of AM. Flexibility is classified into machine flexibility, routing flexibility, product flexibility, manufacturing system flexibility, strategic flexibility, volume flexibility, and so forth, [16]. Yusuf et al. [14] stated that “agility is reflected in: the successful exploration of competitive bases through the integration of reconfigurable resources and best practices in a knowledge-rich environment to provide customer-driven products and services in a fast-changing market environment”.

Quinn et al. [17] defined agility as the ability to accomplish rapid changeover from the assembly of one product to the assembly of different product [18]. Gunasekaran [19] found that the rapidprototyping is one of the major enablers of agility. Prototyping describes the design and generation of an early version of product. Many strategies/techniques such as rapid-partnership formation, e-manufacturing, and rapid prototyping can be employed to improve the responsiveness of the overall system for customer requirements [19]. This eventually leads to an increase in the customers' investments. Accordingly, the following hypotheses are formulated:

: PTK implementation has a significant, positive effect on the development of AMS.: PTK implementation has no effect on the development of AMS.: PTK implementation has a significant, positive effect on the development of QPIM.: PTK implementation has no effect on the development of QPIM.

3.3. Quality and Productivity Improvement and Measures (QPIM)

Hormozi [20] stated that agile manufacturing produces defect free product. Misra et al. [21] stated that agile approaches result in lower defect rates through fast identification of in-process defects. Agility achieves improvements in productivity and quality through flexibility of access and utilization of resources [22]. Gunasekaran [19] showed that manufacturing performance measures such as productivity would help to design the most effective agile manufacturing system.

Agile-based manufacturing organizations have higher productivity market shares [23]. Several researchers use productivity and quality as measures for process performance [24]. Others use different measures such as the multidimensional index created by Schroeder et al. [25]. AM requires modular production facilities. Gunasekaran [26] found out that AM characterized the needs for modular production facilities in decision making.

AM involves fundamental change in an organization's approach to cycle-time reduction [27]. Naylor et al. [28] showed the necessity for production lead time reduction as a prerequisite to agility. Short production lead times were addressed in [23]. Sieger et al. [29] measured responsiveness of companies relative to the product development cycle time. Vinodh and Kuttalingam [30] suggested that the one major constituent of AM is the minimization of manufacturing lead times. Accordingly, the following hypotheses are proposed:

: QPIM implementation has a significant, positive effect on the development of AMS.: QPIM implementation has no effect on the development of AMS.: QPIM implementation has a significant, positive effect on the development of SRM.: QPIM implementation has no effect on the development of SRM.: QPIM implementation has a significant, positive effect on the development of CRM.: QPIM implementation has no effect on the development of CRM.

3.4. Production Planning and Control (PPC)

Production planning and control PPC plays an important role in the competitive environments. PPC responds immediately to achieve higher service level of performance, better resource utilization, and less material loss. Yan [31] established an approach to stochastic production planning SPP for flexible automation in agile manufacturing environment. Li et al. [32] concluded that the performance of customer service level in enterprises is highly dependent on the effectiveness of its manufacturing planning and control system. Chen [33] discussed four problems of production management in the environment of agile manufacturing. These problems are (1) organization of production, (2) production planning, (3) production control, and (4) quality control. Le et al. [34] described the production planning methodology that can be implemented in agile manufacturing. They studied two multiitem lot-sizing problems. They detailed the development of the planning problem mathematically and highlighted solutions to some of their initial problems. Tunglun and sato [35] provided a model of PPC that concretely defines the PPC and allows the possibility for immediate planning and scheduling.

Gold and Thomas [36] discussed and simulated lean, agile, and hybrid supply chain strategies. Their study demonstrated that while lean management typically calls for make-to-stock replenishment driven by short-term forecasts, agile supply chains employ make-to-order provisions. Ching et al. [37] provided a structured procedure for identifying the agile drivers in the business environment. They determined the management information system requirements that enhance manufacturing agility. Adrian et al. [38] studied the evolution of information systems in manufacturing and its importance in supporting agile manufacturing. Lenny and Mike [39] concluded that the application of enterprise resource planning (ERP) has improved agility and responsiveness.

Petri [40] showed that resource management is an important part of any production system, especially when building agility in the manufacturing of the company. David and Chong [41] presented a review of agile supply partner decision making published between 2001 and 2011. The progress made in developing new models and methods applicable to this task is assessed in the context of the previous literature. Particular attention is given to those methods that are especially relevant for the use of agile in supply chains. The review highlighted the ongoing need for developing methods that are able to meet the combination of qualitative and quantitative objectives. These objectives are typically found in partner selection. Based on previous discussion, we theorized the following hypotheses:

: PPC implementation has a significant, positive effect on the development of AMS.: PPC implementation has no effect on the development of AMS.: PPC implementation has a significant, positive effect on the development of SFM.: PPC implementation has no effect on the development of SFM.

3.5. Shop Floor Management (SFM)

In 1995, shop floor control functional diagram was developed by Technologies Enabling Agile Manufacturing “TEAM” [42]. A hybrid integration approach was developed to solve the problem of shop floor scheduling [43]. Ribeiro et al. demonstrated how the seamless integration of the shop floor with external tools is achieved [44]. A multiagent architecture of agile manufacturing system and a hybrid strategy for shop floor scheduling were adopted by Li et al. [45]. Software architecture for control of an agile manufacturing work cell is developed by Kim et al. [46]. Jacobs et al. provided a strong empirical evidence of the advantages of increasing the modularity of products in the firm's portfolio [47]. Chick et al. provided a descriptive model of the machining system selection process that is focused on capital intensive [48]. Swafford et al. found that information technology integration enables firms to utilize their flexibility [49]. Jacobs et al. studied the product and process modularity's effects on manufacturing agility and firm growth performance [47]. Forsythe summarized human factors contributions to the development of agile business practices and design of enabling technologies. The author also discussed human factors related to the communications and information infrastructure essential to organization to become agile [50]. Chunxia and Shensheng proposed a web-based agile architecture of supply chain management system [51]. Moore et al. proposed virtual manufacturing approach for designing, programming, testing, verifying, and deploying control systems for agile modular manufacturing machinery [52]. Based on previous discussion, we suggest the following hypothesis:

: SFM implementation has a significant, positive effect on the development of AMS.: SFM implementation has no effect on the development of AMS.

3.6. Product Design and Development (PDD)

Andrew [53] considered the outsourcing strategy and how it affects product design. He explained how outsourcing permits manufacturers to remain more agile and competitive by retaining local manufacture. Computer-aided design (CAD) is used to bring out new models for achieving design agility [54]. Agility is greatly influenced by the emergence and growth of new technologies such as CAD, CAM, CNC, RP, and so forth, [55]. Modular architecture for developing product platform is crucial to agile manufacturing and product variety that satisfies various customers’ needs and high agility [56]. The relations of CAPP/CAM packages, simulators, design analysis and synthesis tools, and decision support systems with agility are discussed in [57]. Based on this analysis, we postulate the following hypothesis:

: PPD implementation has a significant, positive effect on the development of AMS.: PPD implementation has no effect on the development of AMS.

3.7. Supplier Relationship Management (SRM)

SRM practices create common frame of reference to enable effective communication between enterprises. In agile environments, relationships and communication between suppliers should be flexible and responsive [58]. Relationships with suppliers in agile manufacturing are considered in [26, 59]. Accordingly, we theorize the following hypothesis:

: SRM implementation has a significant, positive effect on the development of AMS.: SRM implementation has no effect on the development of AMS.

3.8. Customer Relationship Management (CRM)

Traditional ways of communication with customers include Internet, business to customer B2C, business to business B2B. The Internet offers several advantages such as reduction of ordering process cost, revenue flow increase because of credit cards payment, global access, and pricing flexibility. In-house inventory placement, inventory pooling, forward placement, vendor-managed inventories VMI, and continuous replenishment program CRP may be used to build an effective agile customer relationship model. Hence, the following hypothesis is proposed:

: CRM implementation has a significant, positive effect on the development of AMS.: CRM implementation has no effect on the development of AMS.

4. Structural Equation Model (SME) and Research Hypotheses

The conceptual relationship model between the eight-construct latent considered in this study is shown in Figure 1. The relationships between the various-model latent are defined and summarized in Table 4. The relationship model is constructed based on authors' experience. Therefore, this model investigates the important relationships between the eight considered agile areas and the impacts of their implementations on the development of AMS.

Table 4: Summary of relationships between various-model latent.

H16₀
CRM implementation has a significant, positive effect on the development of AMS.

H16₁
CRM implementation has no effect on the development of AMS.

H15₀
SRM implementation has a significant, positive effect on the development of AMS.

H15₁
SRM implementation has no effect on the development of AMS.

H14₀
PPD implementation has a significant, positive effect on the development of AMS.

H14₁
PPD implementation has no effect on the development of AMS.

H13₀
SFM implementation has a significant, positive effect on the development of AMS.

H13₁
SFM implementation has no effect on the development of AMS.

H12₀
PPC implementation has a significant, positive effect on the development of SFM.

H12₁
PPC implementation has no effect on the development of SFM.

H11₀
PPC implementation has a significant, positive effect on the development of AMS.

H11₁
PPC implementation has no effect on the development of AMS.

H10₀
QPIM implementation has a significant, positive effect on the development of CRM.

H10₁
QPIM implementation has no effect on the development of CRM.

H9₀
QPIM implementation has a significant, positive effect on the development of SRM.

H9₁
QPIM implementation has no effect on the development of SRM.

H8₀
QPIM implementation has a significant, positive effect on the development of AMS.

H8₁
QPIM implementation has no effect on the development of AMS.

H7₀
PTK implementation has a significant, positive effect on the development of QPIM.

H7₁
PTK implementation has no effect on the development of QPIM.

H6₀
PTK implementation has a significant, positive effect on the development of AMS.

H6₁
PTK implementation has no effect on the development of AMS.

H5₀
MET implementation has a significant, positive effect on the development of PDD.

H5₁
MET implementation has no effect on the development of PDD.

H4₀
MET implementation has a significant, positive effect on the development of SFM.

H4₁
MET implementation has no effect on the development of SFM.

H3₀
MET implementation has a significant, positive effect on the development of QPIM.

H3₁
MET implementation has no effect on the development of QPIM.

H2₀
MET implementation has a significant, positive effect on the development of PTK.

H2₁
MET implementation has no effect on the development of PTK.

H1₀
MET implementation has a significant, positive effect on the development of AMS.

H1₁
MET implementation has no effect on the development of AMS.

Figure 1: The proposed conceptual model and research hypotheses.

Eight different questionnaire drafts were developed. The preliminary questionnaires were pilot tested and reviewed by managers of several industrial companies, extensive literature review, and group of graduate students. This process continues until all questions in the eight questionnaires are unambiguous, appropriate, and acceptable to respondents. Every questionnaire is concerned with the implementation of one impact area. It consists of five-point Likert scale anchored at (1) “Poor”, (2) “Fair”, (3) “Good”, (4) “Very good”, and (5) “Excellent”.

5. Data Collection and Analysis

Jordanian companies listed in Jordan chamber of commerce were screened according to whether they have a potential of implementing lean tools or not. Consequently, questionnaire packets were distributed to 500 services and manufacturing companies. 456 companies have responded to the questionnaire packets. Data were collected through production managers, quality engineers, consultants, and owners. Cronbach's alpha is a tool that measures and tests consistency validity and scale reliability. As shown in Table 5, Cronbach's alpha value of the whole AMS equals to 0.830 and the AGility index is 60.1%. AG is used to measure the overall implementation level of agile tactics in the studied sample. The results of reliability test indicate that both internal consistency and overall model reliability are high. Mean, variance , area-tactic correlations, model-tactic correlations, tactic agility index-area correlations, and area agility index are evaluated and summarized in Table 6. Agile tactics with no significant correlations at the 0.05 or less level (2 tailed) are identified. The results of tactics-tactics correlation test are summarized in Table 7.

Table 5: Consistency and reliability of the model.

Model mean
Model variance
Cronbach’s alpha
Internal correlations
Agility index (AG%)

Model
3.003
0.008
0.830
0.011
60.1

Table 6: Some statistics of model and correlation coefficients.

Area
Agile tactic
Tactic mean
Tactic variance
Area-tactic correlations
Model-tactic correlations
Tactic (AG%)
Model-area correlations
Area (AG%)

(1) MET
GTC
2.999
0.115
0.516*
0.517*
59.9

PPR
2.986
0.120
0.518*
0.337*
59.7

FMS
3.001
0.117
0.446*
0.300*
60.1
0.517*
60.1

CNC
3.000
0.106
0.497*
0.381*
60.0

PLC
3.028
0.126
0.524*
0.366*
60.0

CIM
3.020
0.123
0.502*
0.380*
60.0

(2) PTK
RWM
3.002
0.120
0.561*
0.470*
60.0

RCS
3.009
0.114
0.354*
0.493*
60.1

RSC
2.997
0.114
0.413*
0.048
59.9
0.542*
60.1

SOP
3.004
0.109
0.486*
0.357*
60.0

E-M
3.005
0.113
0.546*
0.463*
60.1

(3) QPIM
FID
3.014
0.114
0.533*
0.333*
60.3

LPP
3.002
0.106
0.386*
0.394*
60.0
0.520*
59.9

MPF
3.004
0.114
0.577*
0.428*
60.0

FPT
2.969
0.135
0.579*
0.398*
59.3

(4) PPC
EIS
3.015
0.123
0.232*
0.155*
60.3

MTO
3.014
0.131
0.580*
0.407*
60.2
0.496*
60.0

DMK
2.996
0.132
0.548*
0.351*
59.9

MRP
3.016
0.120
0.526*
0.497*
60.3

(5) SFM
HFS
2.984
0.108
0.542*
0.352*
59.7

GPE
2.982
0.101
0.513*
0.366*
59.6

ECT
3.011
0.124
0.558*
0.589*
60.2
0.627*
59.9

RWM
3.002
0.120
0.470*
0.561*
60.0

MLT
2.998
0.107
0.556*
0.371*
60.0

(6) PDD
QIN
2.984
0.121
0.578*
0.384*
59.7

RCC
3.007
0.125
0.569*
0.377*
60.2
0.543*
60.1

E-M
3.005
0.113
0.560*
0.463*
60.1

CIM
3.020
0.123
0.575*
0.380*
60.0

(7) SRM
ECT
3.011
0.124
0.689*
0.589*
60.2

LTR
3.000
0.111
0.613*
0.430*
60.0
0.571*
60.1

SES
3.000
0.117
0.632*
0.396*
60.0

(8) CRM
IQD
3.016
0.122
0.556*
0.351*
60.3

ECT
3.011
0.124
0.485*
0.589*
60.2

MTO
3.014
0.131
0.505*
0.407*
60.2
0.633*
60.2

CUC
2.997
0.119
0.515*
0.355*
59.9

PCU
3.006
0.114
0.492*
0.337*
60.1

Significant at two-tailed 0.01 significance level.

Table 7: Tactic-tactic correlations at two-tailed significance level less than or equal to 0.05.

Independent tactic
Dependent tactic

MLT
MTO
EIS
MBF
FID
RSC
RCS
RWM
CIM
PLC
CNC
FMS
PPR
GTC

PPR

No

CNC

No

PLC

No

CIM

No

RSC

No
No
No
No
No
No
No
No

SOP

No

No

EM

No

FID

No

LPP

No

No

MBF

No

FPT

No
No
No

No

EIS

No

No

No

MTO

No

No
No
No

DMK

No

MRP

No
No

HFS

No

GPE

No

No

ECT

No

No
No

MLT

No

No

QIN

No

No

No

RCC
No

No
No

LTR

No

SES

No
No

No

IQD
No

No

No

CUC

No
No

No

PCU

No

No

No: Signifies no tactic-tactic correlation; otherwise there is a tactic-tactic correlation.

Interrelations between production areas are computed and investigated using correlation coefficients (see Table 8). It is observed that the correlation is significant at the 0.01 level (2 tailed) between some areas like MET-CRM, CRM-SRM. On the other hand, SRM-PDD correlation and CRM-SRM correlation is significant at the 0.05 level (2 tailed), where there is no significant correlation between SRM and PPC. AMOS software version 19 is used to test the model fit for each area. The results of the area-area correlation test and fit indices are shown in Table 8. A good model fit is found. All items loading on their corresponding production area are high and significant at the 0.05 or less level (2 tailed). Significance level at 0.05 is recommended.

Table 8: Correlation matrix and fit indices for each production area.

Area-area correlation coefficients
Production area
Fit indices

CRM
SRM
PDD
SFM
PPC
QPIM
PTK
MET

GFI
RMSEA
value
Chi ratio
DF

0.150**
0.135**
0.355**
0.209**
0.192**
0.173**
0.195**
1
MET
0.91
0.023
0.00
2.69
90
242.1

0.213**
0.161**
0.328**
0.348**
0.106*
0.128**
1

PTK
0.70
0.024
0.00
2.72
65
177.1

0.262**
0.179**
0.131**
0.215**
0.202**
1

QPIM
0.96
0.026
0.00
2.95
189
558.9

0.320**
0.054
0.153**
0.192**
1

PPC
0.87
0.072
0.00
3.21
189
606.3

0.350**
0.386**
0.130**
1

SFM
0.93
0.032
0.00
3.75
324
1215.6

0.200**
0.119*
1

PDD
0.99
0.071
0.00
3.66
299
1095.4

0.352**
1

SRM
0.97
0.010
0.00
2.97
90
267.1

1

CRM
0.94
0.017
0.00
1.76
90
158.6

Correlation is significant at the 0.01 level (2 tailed).

Correlation is significant at the 0.05 level (2 tailed).

Table 9 summarizes the results of the hypothesis testing. The P values of the alterative hypotheses (, , , , , , , , , , , , , , , and ) are calculated. The calculated P-values are less than 0.05, which indicates that the proposed null hypotheses are true. The t-values fall within the 95% of the t-distribution (). These results provide evidence that the alternative hypotheses are rejected. Influential dependencies (see Table 10) between production areas are found, and hence multicollinearity is achieved. For the two-tailed one-way ANOVA test at the 0.05 level, the -value as shown in Table 10 exceeds (). This proves that all the considered agile tactics have a positive effect on AMS. The -value of the F-test obtained from the two-tailed one-way ANOVA analysis is less than 0.001.

Table 9: Hypothesis testing results.

Alternative hypothesis
Internal correlation
Paired samples test
value
Decision

Area-area
Pearson
t-value
DF
value

H16₁
CRM-AMS
0.633
1.061
458
0.289
0.000
Reject H16₁

H15₁
SRM-AMS
0.571
0.382
458
0.702
0.000
Reject H15₁

H14₁
PPD-AMS
0.543
0.216
458
0.829
0.000
Reject H14₁

H13₁
SFM-AMS
0.627
−1.413
458
0.158
0.000
Reject H13₁

H12₁
PPC-SFM
0.192
0.795
458
0.427
0.000
Reject H12₁

H11₁
PPC-AMS
0.496
−0.034
458
0.973
0.000
Reject H11₁

H10₁
QPIM-CRM
0.262
−1.296
458
0.196
0.000
Reject H10₁

H9₁
QPIM-SRM
0.179
−0.809
458
0.419
0.000
Reject H9₁

H8₁
QPIM-AMS
0.520
−0.892
458
0.373
0.000
Reject H8₁

H7₁
PTK-QPIM
0.128
0.661
458
0.509
0.006
Reject H7₁

H6₁
PTK-AMS
0.542
0.093
458
0.926
0.000
Reject H6₁

H5₁
MET-PDD
0.355
0.226
458
0.821
0.000
Reject H5₁

H4₁
MET-SFM
0.209
1.297
458
0.195
0.000
Reject H4₁

H3₁
MET-QPIM
0.173
1.001
458
0.317
0.000
Reject H3₁

H2₁
MET-PTK
0.195
0.322
458
0.748
0.000
Reject H2₁

H1₁
MET-AMS
0.517
0.590
458
0.556
0.000
Reject H1₁

Table 10: One-way ANOVA analysis.

Area
Factor
Sum of squares
DF

Mean square

value
Conclusion

Between groups
27.295
155

0.176

GTC
Within groups
25.452
303
155
303

2.096
0.000
GTC has an effect on MET.

Total
52.746
458

0.084

Between groups
25.946
155

0.167

PPR
Within groups
29.188
303
155
303

1.738
0.000
PPR has an effect on MET.

Total
55.134
458

0.096

Between groups
25.500
155

0.165

FMS
Within groups
28.107
303
155
303

1.774
0.000
FMS has an effect on MET.

MET

Total
53.607
458

0.093

Between groups
24.156
155

0.156

CNC
Within groups
24.279
303
155
303

1.945
0.000
CNC has an effect on MET.

Total
48.435
458

0.080

Between groups
30.639
155

4.801

PLC
Within groups
27.174
303
155
303

2.204
0.000
PLC has an effect on MET.

Total
57.813
458

0.008

Between groups
30.141
155

0.194

CIM
Within groups
26.075
303
155
303

2.260
0.000
CIM has an effect on MET.

Total
56.215
458

0.086

Between groups
30.796
142

0.217

RWM
Within groups
24.046
316
142
316

2.850
0.000
RWM has an effect on PTK.

Total
54.842
458

0.076

Between groups
27.640
142

0.195

RCS
Within groups
24.490
316
142
316

2.512
0.000
RCS has an effect on PTK.

Total
52.130
458

0.077

PTK

Between groups
28.902
142

0.205

RSC
Within groups
23.400
316
142
316

2.768
0.000
RSC has an effect on PTK.

Total
52.302
457

0.074

Between groups
25.482
142

0.179

SOP
Within groups
24.349
316
142
316

2.329
0.000
SOP has an effect on PTK.

Total
49.831
458

0.077

Between groups
28.788
142

0.203

E-M
Within groups
22.766
316
142
316

2.814
0.000
E-M has an effect on PTK.

Total
51.554
458

0.072

Between groups
27.024
123

0.220

FID
Within groups
25.256
335
123
335

2.914
0.000
FID has an effect on QPIM.

Total
52.280
458

0.075

Between groups
25.765
123

0.209

LPP
Within groups
22.905
335
123
335

3.064
0.000
LPP has an effect on QPIM.

QPIM

Total
48.670
458

0.068

Between groups
27.214
123

0.221

MBF
Within Groups
25.076
335
123
335

2.956
0.000
MBF has an effect on QPIM.

Total
52.290
458

0.075

Between groups
30.022
123

0.244

FPT
Within groups
31.739
335
123
335

2.576
0.000
FPT has an effect on QPIM.

Total
61.761
458

0.095

Between groups
14.408
63

0.229

EIS
Within groups
42.077
395
63
395

2.147
0.000
EIS has an effect on PPC.

Total
56.484
458

0.107

Between groups
27.184
63

0.431

MTO
Within groups
33.016
395
63
395

5.162
0.000
MTO has an effect on PPC.

PPC

Total
60.200
458

0.084

Between groups
25.489
63

0.405

DMK
Within groups
34.816
395
63
395

4.590
0.000
DMK has an effect on PPC.

Total
60.305
458

0.088

Between groups
22.479
63

0.357

MRP
Within groups
32.326
395
63
395

4.360
0.000
MRP has an effect on PPC.

Total
54.805
458

0.082

Between groups
28.842
157

0.184

HFS
Within groups
20.503
301
157
301

2.697
0.000
HFS has an effect on SFM.

Total
49.345
458

0.068

Between groups
25.126
157

0.160

GPE
Within groups
21.336
301
157
301

2.258
0.000
GPE has an effect on SFM.

Total
46.462
458

0.071

SFM

Between groups
30.429
157

0.194

ECT
Within groups
26.146
301
157
301

2.231
0.000
ECT has an effect on SFM.

Total
56.575
458

0.087

Between groups
32.023
157

0.204

RWM
Within Groups
22.819
301
157
301

2.690
0.000
RWM has an effect on SFM.

Total
54.842
458

0.076

Between groups
28.198
157

0.180

MLT
Within groups
20.592
301
157
301

2.625
0.000
MLT has an effect on SFM.

Total
48.790
458

0.068

Between groups
32.806
198

0.166

QIN
Within groups
22.685
260
198
260

1.899
0.000
QIN has an effect on PDD.

Total
55.491
458

0.087

Between groups
34.958
198

0.177

RCC
Within groups
22.088
260
198
260

2.078
0.000
RCC has an effect on PDD.

PDD

Total
57.046
458

0.085

Between groups
31.232
198

0.158

E-M
Within groups
20.322
260
198
260

2.018
0.000
E-M has an effect on PDD.

Total
51.554
458

0.078

Between groups
35.112
198

0.177

CIM
Within groups
21.103
260
198
260

2.185
0.000
CIM has an effect on PDD.

Total
56.215
458

0.081

Between groups
32.171
57

0.564

ECT
Within groups
24.404
401
57
401

9.274
0.000
ECT has an effect on SRM.

Total
56.575
458

0.061

Between groups
25.975
57

0.456

SRM
LTR
Within groups
24.947
401
57
401

7.325
0.000
LTR has an effect on SRM.

Total
50.922
458

0.062

Between groups
27.583
57

0.484

SES
Within groups
25.817
401
57
401

7.516
0.000
SES has an effect on SRM.

Total
53.400
458

0.064

Between groups
23.490
67

0.351

IQD
Within groups
32.175
391
67
391

4.260
0.000
IQD has an effect on CRM.

Total
55.665
458

.0082

Between groups
19.922
67

0.297

ECT
Within groups
36.653
391
67
391

3.172
0.000
ECT has an effect on CRM.

Total
56.575
458

0.094

Between groups
23.125
67

0.345

MTO
Within groups
37.075
391
67
391

3.640
0.000
MTO has an effect on CRM.

CRM

Total
60.200
458

0.095

Between groups
21.242
67

0.317

CUC
Within groups
33.350
391
67
391

3.717
0.000
CUC has an effect on CRM.

Total
54.592
458

.0085

Between groups
19.211
67

0.287

PCU
Within groups
33.062
391
67
391

3.391
0.000
PCU has an effect on CRM.

Total
52.273
458

0.085

Between groups
9.056
456

0.020

MET
Within groups
0.043
2
456
2

0.928
0.659
MET and AG have equal agility index.

Total
9.099
458

0.021

Between groups
10.625
456

0.023

PTK
Within groups
0.021
2
456
2

2.249
0.359
PTK and AG have equal agility index.

Total
10.646
458

0.010

Between groups
12.952
456

0.028

QPIM
Within groups
0.113
2
456
2

0.501
0.863
QPIM and AG have equal agility index.

AG

Total
13.066
458

0.057

Between groups
12.912
456

0.028

PPC
Within groups
0.037
2
456
2

1.539
0.477
PPC and AG have equal agility index.

Total
12.949
458

0.018

Between groups
10.510
456

0.023

SFM
Within groups
0.033
2
456
2

1.411
0.507
SFM and AG have equal agility index.

Total
10.543
458

0.016

Between groups
13.612
456

0.030

PDD
Within groups
0.006
2
456
2

10.509
0.091
PDD and AG have equal agility index.

Total
13.618
458

0.003

Between groups
18.103
456

0.040

SRM
Within groups
0.036
2
456
2

2.233
0.361
SRM and AG have equal agility index.

Total
18.139
458

0.018

Between groups
10.186
456

0.022

CRM
Within groups
0.002
2
456
2

29.566
0.033
CRM and AG have equal agility index.

Total
10.188
458

0.001

6. Discussion of Results

This paper investigates the causal relationship model among implementation of thirty-six different agile tactics. These tactics are categorized into eight impact areas (manufacturing equipment and technology MET, processes technology and know-how PTK, quality and productivity improvement and measures QPIM, production planning and control PPC, Shop Floor Management SFM, product design and development PDD, supplier relationship management SRM, and customer relationship management CRM). Analysis of data is carried out using AMOS 19 and IBM SPSS 20 for Windows. The obtained results show strongly that the model is valid. The AMOS 19 software is used to test the model fit for each impact area. The results show that the model fit is good. All items loaded significantly on their corresponding constructs at the 0.05 level. This demonstrates a good model fit. The fit statistics indicate that the hypothesized structural model achieves an acceptable fit such that no further interpretation is required. The testing of the entire hypotheses shows that all impact areas have positive effect on AMS.

It was found out that the overall assumed agility index is about 60%, the average agility index of impact areas is about 60%, and the average agility index of agile tactics is about 60%. The correlation analyses show that all model constructs have a positive correlation with overall AMS model.

Estimates of the relations in the AMS are investigated and summarized as shown in Figure 2. The results of this research may be influenced by the person who fills the questionnaires. This may lead to errors due to the personal reliability and trustworthiness.

Figure 2: Estimates of the relations between models constructs.

7. Conclusion

The implementation of agile manufacturing principles and tools in Jordanian firms is investigated. Different agile practices that are adopted by the considered firms to manage their AMS systems are identified based on empirical basis. This paper concludes that the existence of 36 different agile approaches can be adopted by the different firms to enhance their competitiveness. These approaches categorized into eight impact areas, namely, MET, PTK, QPIM, PPC, SFM, PDD, SRM, and CRM. The primary contribution of this paper is successfully analyzing the causal relationship of implementation level of agile production areas and their effect on the AMS using SME methodology. The results ensure that SEM is the correct method for investigating the relationship model between the eight-constructs considered in this study. IBM SPSS 20 and AMOS 19.0.0 software enable SEM to provide a clear and complete specification of the AMS and its constructs. The results of this study show that the studied agile tactics have significant relationship and are affected positively by the AMS. The implementation of each agile tactic contributes significantly to the performance of AMS. The approach presented in this study can be used to facilitate the implementation of agile practices in industries and measure correlation between them. It may be worthwhile to focus future research on modeling the implementation of lean production practices, such as kanban, just in time (JIT), pull production control strategy, and so forth, [60] and to compare and link the expected results with those concluded here.

Acknowledgments

The authors are thankful to the anonymous referees for their valuable comments and suggestions which improved the presentation of the paper.

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