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.
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.
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.
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.
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.
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 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.
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.
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.