Does Integration of Business Processes and ERP Improves Supply Chain Performances? Evidence from Indian Capital Goods Industry

Abstract

In today’s competitive market environment coupled with post effect of globalization, managing supply chains is critical to any business. Rather than becoming function-oriented, firms are now selectively deploying valuable resources on automated processes operated through technology-enabled ‘information communication’ platforms to improve performance. However, there are visible gaps in integrating business processes with information-enabled domain like enterprise resource planning (ERP), which has the potential to bring in the radical improvement in firm’s performance. Again, integration always does not come with positive results, specifically to Indian capital goods sector. Under this consideration, an attempt has been made to explore this unknown domain with conceptualization of a hypothesized model. Data for validation of this study were collected through questionnaire from 25 different units under Indian capital goods industry and relationship proposed in the hypothesized framework is tested by using structural equation modelling. Analysis of result indicates a favourable disposition of the model framework wherein synergy between business processes and ERP, on an integrated mode, can fetch higher level of performance. Considering multiple performance-related issues deterring growth of India’s capital goods industry, it is strongly recommended to synergize business processes under information system domain in order to make business sustainable.

Keywords

Business Processes Information Communication Technology Enterprise Resource Planning Supply Chain Performance Structural Equation Modelling

Introduction

With advent of liberalization, privatization and globali-zation in the Indian economy during the early 1990s, the state-owned public sector entities with their dominant presence in the capital goods sector were given the difficult mandate to compete with the private sector enterprises, both from India and abroad, on a level playing field. Since then, India’s industrial policy has undergone cataclysmic changes from the license and control regime to that of a free market economy, where all industry players across different sectors are competing not only among themselves but also with the overseas market leaders. The com-prehensive policy reforms have certainly resulted in upward shift of growth rate and productivity for the Indian economy, and in particular that of manufacturing/capital goods sector (Ahluwalia, 1991). However, India’s capital goods sector could not keep pace with the reform process due to a host of legacy issues plaguing the industry for a long time. Of the many constraints continuing unabated even today, non-refinement of process flows, delay in adoption of new technology and integrating the same with the best of supply chain practices are considered to be the three main deterrents in the growth of this sector. Organizational level constraints and inefficiencies not-withstanding, modern management study provides new concepts and tools which can be judiciously employed together to bring transformational changes in the enter-prise’s business profile including its sustainability.

Managing business processes, relatively a new concept to the business management strategy, made its definite impact in the industry sector worldwide during the early 1990s. In a market-driven economy, this concept has added significance as it provides business edge to the key sector industries including that of capital goods. Again, enterprise resource planning (ERP), an information communication technology-driven cross-functional system, running seam-lessly on an integrated suite of software modules, gives an enterprise a real time view of its core business processes.

In any customer centric business, it is always paramount to critically analyse the business process flow in specific context of enterprises (Stadtler, 2005). As demand-supply situation in this sector is mainly dependent on dynamic requirement of core sector industries, managing business processes holds key to their long-term sustenance in terms of profitability and sustainability by properly addressing issues and problems of external stakeholders (Meixell & Luoma, 2015). Planning functionality of an ERP system provides the business enterprises the much sought after means to manage production capacity, material availability, shipment schedules and so on.

In globalized market economy, virtual enterprises are, nowadays, redefining the business activities due to which coordination of supply chains has become strategically most important (Palmieri, Parola, Dong-Wook, & Baglieri, 2019). Organizations are no more restricted to their own defined line of sources to manufacture goods within their own premises; rather they have broadened scope of their supply chains with a networked approach (Eskandarpour, Dejax, Miemczyk, & Péton, 2015). Integration, collaboration and use of information tech-nology (IT)-enabled synergization are all depicted as building blocks of house of supply chains of modern-networked business organizations (Swanson, Goel, Francisco, & Stock, 2018). Integration of refined business processes on a web-enabled virtual IT platform can therefore facilitate effective coordination among different supply chain functions, thereby improving supply chain performance (SCP) majorly contributing to firm’s performance (Bernon, Upperton, Bast, & Cullen, 2013).

Research shows that competition influences the value of IT and, therefore, firm’s incentive to invest in it (Melenovsky, Sinur, Hill, & McCoy, 2005). However, how a firm’s IT investment decision changes with compe-titive environ-ment is still less clear (Xin & Choudhary, 2018). Prior research suggests two conflicting predictions. On one hand, firms in highly competitive sectors may be more motivated to invest in IT sector since successful use of IT improves firm’s performance and competitive position (Szozda, 2017). On the other hand, firms in highly competitive industries may be less motivated in investing IT sometimes. Prior studies show that firm’s incentive to invest in technological innovation depends on the ability to app-ropriate the return to their investment (Keller, Daronco, & Cortimiglia, 2017), and the value of IT investment is more likely to be competed away in competitive industries and captured by customers in the form of lower prices and higher quantity (Xin & Choudhary, 2018).

The legacy-related issues notwithstanding India’s capital goods industries, thus, need the much-desired impetus to get over the performance constraints, mostly in terms of technology management and their integration within business processes (Pattanayak & Roy, 2019). This warrants a definite window for both academic researchers and business practitioners for an integrated study on managing business processes and ERP that may have significant influence on firm performance.

Purpose of this study is to highlight the issues of integration primarily related to business processes. The study also unravels how business processes and information systems work/join hand in hand to achieve better performance within supply chain domain. A hypothesized model is framed accordingly and the same is validated by using primary information that is collected from real field of business to testify the model by using statistical analyses. Present study is empirical in nature; domain of research is restricted to India’s capital goods industry.

Literature Support

Over the last few years, organizations have shifted their supply chain management (SCM) focus from micro-factory level to a broad enterprise-level management (Gunasekaran, Williams, & McGaughey, 2005). Ever-increasing border-less businesses have added several new challenges to globalization by increasing volume of outsourcing, vendor-managed inventory and advanced planning systems, and integration of supply chain functions (Ellram & Cooper, 2014). New challenges have broadened the definition of supply chain and clearly differentiated ‘traditional’ organi-zations from ‘networked’ organizations of today that emphasize on importance of strategic alliances, global outsourcing, shorter product life cycles, supply chain collaboration, agility, responsiveness, flexibility, reverse logistics and extended enterprise integration (Bendoly & Kaefer, 2004; Gunasekaran & Kobu, 2002).

Research on the subject of managing supply chain has focused on increasing importance of IT in supporting integration and performance (Gunasekaran & Ngai, 2004; Gunasekaran & Ngai, 2005; Kelle & Akbulut, 2005; Pant, Sethi, & Bhandari, 2003). Further, as digital technology disrupts the way business is conducted, supply chain is not left unaffected. Tech-savvy organizations have already taken the digital route to supply chain, thereby giving birth to the concept of e-SCM, which is an effective use of internet-enabled technology in supply chain (Akyüz & Rehan, 2009; Gunasekaran, Patel, & Mcgaughey, 2004). Modern organizations today are using a collaborative approach in their supply chain. Incorporating best IT practices, these organizations streamline their supply chain for more integration and collaboration across disparate supply chain partners, thus bringing in more visibility across the extended value chain (Gunasekaran & Ngai, 2004). This helps them to reduce cost and ensure on-time delivery of the products and, thus, better serve their consumers (Gimenez & Lourenco, 2004).

Reengineering, management and innovation in busi-ness process have been the primary strategies adopted by several organizations to manage their business successfully (Martin, Matias, Gerardo, Daniel, & Josue, 2018). Driven by technology, conversations today revolve around the concepts of business process reengineering (BPR), busi-ness process management (BPM) and business process innovation (BPI). This becomes evident from the fact that literature, over the last few years, has extensively focused on BPM, BPR and BPI (Autry, Rose, & Bell, 2014). BPR was adopted by many industries worldwide to bring radical changes in their business processes, the targeted benefits of the gruelling exercise remained far short of expectations. So, it gradually lost its sheen and stranglehold on the industry paving way for a better variant, that is, BPM. In contrast to BPR, BPM is a new emerging field in business and is gaining increased recognition in the management literature (Smith & Finger, 2003). Like BPR, origin of BPM also dates back to the 1990s when it was considered to be the next big thing after the work-flow wave (Autry et al., 2014). In recent years, BPM has gained popularity on the back of digital disruptions that are forcing businesses across industries to redefine their processes in a way that encourages monitoring, optimization and traceability across the entire value chain (Tanco, Escuder, Heckmann, Jurburg, & Velazquez, 2018). In real sense, BPM requires an approach with a fundamental shift in thinking from function-centric to the process-centric by which it provides visibility into and control over business processes (Melenovsky et al., 2005).

Information-oriented ERP system is defined as a set of packaged application software modules with an integrated architecture. These modules serve as the tool for integrating data, processes and IT best practices, in real time, across internal and external value chains (Shang & Seddon, 2002). Primary role of ERP is to improve productivity and this aim is the main motivation for companies to adopt ERP in their businesses (Davenport & Books, 2004; Pant et. al., 2003). ERPs provide deep insights into business proces-ses thus equipping management with multidimensional information to make informed decisions. Therefore, it is critical that organizations employ ERP in an intelligent manner (Gefen & Ragowsky, 2005). There are mainly two dimensions in accrual of benefits due to the adoption of an ERP system, namely operational efficiency and strategic effectiveness (Jenson & Johnson, 1999). Therefore, ERP systems have become widely applied in all types of organizations today for being connected to other enterprises in the value chain for achieving competitiveness (Wu & Wang, 2006; Zabjek, Kovacic, & Stemberger, 2009). In several instances, firms have changed their information management strategies in the past decade by investing heavily in ERP application software rather than developing in-house software to meet business challenges (Trierweiller et al., 2016).

Managing supply chain is generally seen as a systematic and strategic coordination of disparate supply chain part-ners with the view to improve long-term performance (Christopher, 1998; Kauffman, 2002; Mentzer et al., 2001). Organizations adopt various business improvement tech-nologies to improve business performance with supply chain being one among them (Kathawala & Abodou, 2003). With global competition and outsourcing entering the arena, the days of fragmented supply chain are over; the e-SCM or the IT-enabled SCM that supports integration and collaboration is now the way forward (Chang, Ellinger, Kim, & Franke, 2015).

Performance measurement in supply chain is meant to help businesses achieve their objectives and also fulfil their mission and vision in terms of manpower and cost in a time framed manner (Pattanayak & Roy, 2015). Most of the literature reviews find a coherent relationship between SCM and SCP, wherein the latter is an outcome to the actions made through the previous (Ellram & Cooper, 2014). But the extent of the relationship between the two and the relative degree of success in the final output as a measure of SCP gets influenced with a lot many factors during the execution of the supply chain activities (Leuschner, Rogers, & Charvet, 2013). Today’s business enterprises rely a lot on the SCP as it majorly contributes to the organizational performance, which is nothing but a multidimensional concept illustrating how well an org-anization fulfils its ﬁnancial and market-oriented goals (Marin-Garcia, Alfalla-Luque, & Machuca, 2018).

Empirical testing of the relationship between managing business processes and SCP is important for leveraged performance (Cazeri, Anholon, Ordoñez, & Novaski, 2017; Xia, Zu, & Shi, 2015). Research shows positive relationship between BPM, supply chain collaboration, collaborative advantage and organizational performance (Pradabwong, Braziotis, Pawar, & Tannock, 2015). Some studies have also showcased the positive relationship between internal SCM resources and multiparty SCM resources (Nyaga, Whipple, & Lynch, 2010). Again, research highlighting any firm that is ERP-enabled can also be well positioned to collaborate with its supply chain partners (Simatupang & Sridharan, 2008).

In the beginning of this millennium, many researchers were of the view that in times to come, ERP would inte-grate SCM for providing more accurate information to management (Keller et al., 2017; Nah, Lau, & Kuang, 2001), and today, IT has enabled the emergence of modern SCM by being a critical factor to enhance the SCP (Fawcett, Wallin, Allred, Fawcett, & Magnan, 2011). ERP is widely acclaimed to help business organizations in realizing their benefits through proper planning and investment from long past (Froehlich, Hoover, Liew, & Sorenson, 1999). Most of the researchers are of the view that integrated ERP systems affect all aspects of busines-ses and fetch accrued benefits to the supply chain process of an organization in terms of faster response time, close interaction with supply chain partners, better order management system, more customer interaction, on or before time delivery, minimum inventory holdings, and better cash and receivables management (Wu & Wang, 2006; Zabjek et al., 2009). Improvement in supply chain functions and processes eventually reduces the direct operating costs and influences the profitability of the organizations. Akkermans, Bogerd, Yücesan, and Van Wassenhove (2003) have studied the co-relationship between ERP and SCM wherein they analysed the test results of 23 separate firms, and then modelled the future expectations of ERP systems implementations from an SCM perspective. The relationship between ERP and SCM notwithstanding, an important challenge for businesses is to understand the factors that are critical to successfully reap benefits from ERP systems and also their implications on SCM with the aim to achieve competitive advantage (Adaileh & Abu-alganam, 2010).

Finally, while analysing various aspects underlined by the peers in their path breaking studies, researchers have highlighted the fact that SCM should go beyond the enterprise integration and spread its tentacles to include strategic alliances, responsiveness, global outsourcing, partnership formation, shorter product life cycles, and collaboration, agility, flexibility, reverse logistics and extended enterprise integration (Gunasekaran et al., 2005; Yamin, Gunasekaruan, & Mavonda, 1999). As digital technology makes inroads into daily lives, the idea that ‘ERP provides the digital backbone in supply chain integration’ is gaining ground (Bendoly & Kaefer, 2004; Gunasekaran & Ngai 2004; Gunasekaran et al., 2004; Kelle & Akbulut, 2005; Pant et al., 2003).

Basing on certain key findings from the detailed literature review as above, an attempt has been made in this study to delve into the synergy related issues between key business processes and information systems within the ambit of a hypothesized framework and validate the same with survey results from Indian capital goods industry.

Research Gap and Objective of the Study

There are several studies to show that managing business processes positively impacts organizational performance (Akyüz & Rehan, 2009; Wu, Melnyk, & Flynn, 2010). However, not much focus has been given to study on the link between managing business processes with ERP and how that can impact business performance in a sustainable manner. Research studies adequately support the one-to-one symbiotic relationship between BPM, ERP and SCP, but the integrated relationship among the three is yet to be conclusively established through empirical studies. Again, most of the existing studies on the relationship between BPM, ERP and SCM are conducted mainly in context of industrially developed countries in Europe and North America (Sean, Goffnett, & Zachary, 2019). As the emerging economies around the globe are experiencing fast-growing manufacturing and economic development in recent decades, a study on the impact of BPM and ERP on SCM performances specific to Indian firms would be quite significant (Su, 2010).

Present research work is therefore aimed at unravelling the synergized effect of BPM and ERP in the SCP (Kelle & Akbulut, 2005) specific to Indian capital goods industries in order to help the decision-makers to make more informed decisions that in turn would improve firms’ performance and make it sustainable through a better management of resources. A three-fold objective underlies the proposed research study:

To identify the critical influencing factors related to BPM to improve productivity, efficiency and competitiveness of an enterprise under capital goods sector of industries

To establish the relationship among the identified critical factors of BPM and ERP and their synergized effect on the SCP of an enterprise

To test the efficacy of ERP system alongside BPM while integrating the two to measure the SCP in the said enterprise.

Hypothesized Framework

Theoretical construct to build hypothesis for this study is extracted from the key points highlighted during extensive literature review, which is further supplemented with the findings from industry-specific case study pertaining to capital goods sector. As such, all the three business pro-cesses (BPM), resource planning (ERP) and performance (SCP) find favour to be part of the triangular construct as latent variables with supposedly distinct interrelationship between BPM and SCP (H₁), BPM and ERP (H₂), and ERP and SCP (H₃). Idea behind the study is to develop a con-ceptual framework with the three important management tools as shown in the basic diagram, Figure 1, and build up hypotheses to get them validated before its acceptability as a working model for the industry.

Figure 1.

Source: Authors’ 2018.

Synergizing BPM with ERP, both at the conceptual and operational level, is expected to fetch radical changes in the business environment of Indian manufacturing sector, particularly in capital goods sector, which is otherwise suffering from a host of firm level inefficiencies built into their operational processes. Most of existing studies as performed on this type of inter-relationship are seen mainly in context of industrially developed countries in Europe and North America (Jin, Fawcett, & Fawcett, 2013; Kathawala & Abdou, 2003; McCormack et al., 2009). As emerging economies around the globe are experiencing a fast growth, a study on impact of factors like managing business processes and ERP on performances, both individually and collectively would be significant (Fawcett et al., 2011).

Research Methodology

The conceptualized model constituted with three key elements viz. BPM,ERP and SCP is framed with a three tier research design to establish interrelationships via hypothesis between BPM and SCP (H₁), BPM and ERP (H₂), and BPM and ERP with SCP (H₃). Tier 1 structure in research design (as shown in Appendix A) is identified with three latent constructs. In the Tier 2 structure, each of three latent constructs is linked with a set of three second order constructs on the basis of detailed literature review. At Tier 3 level, however, manifest variables related to each of the second order constructs is identified after careful review of the literature in specific context. Appendix A thus represents the integrated model that has been proposed for testing and validation with the application of structural equation modelling (SEM). Appendix B captures statement details for each of the indexed manifest variables along with source of literature survey. Appendix C depicts sources of survey responses and Appendix D shows the result output subsequent to the running of the survey data on the software program.

The design of the questionnaire has been done with due reference to three latent constructs as primary inputs to hypothesized model (Cai, Liu, Xiao, & Liu, 2009). Secondary constructs are however identified with extensive literature review. Experts have been duly consulted before deciding on the final attributes to be made part of the survey questionnaire. Questions are framed in such a way that there would be no ambiguity on part of the respondents to answer the questions steadfast (Flynn, Sakakibara, & Schroeder, 1994). Five point Likert scale ranging from ‘strongly agree’ to ‘strongly disagree’ has been adopted to collect responses from the target population. Norms and standards fixed as per Flynn et al. (1994) are followed to complete a pilot test on the survey questionnaire with 49 responses from 11 different industry units. Some of the characteristics such as significance of the questions (Flynn, Sakakibara, Schroeder, Bates, & Flynn, 1990), clearness in expression (Flynn et al., 1994), flow of questions and the average time needed to reply to the questionnaire (Bozarth, Warsing, Flynn, & Flynn, 2009) were examined in details before finalising the questionnaire.

Sampling framework is built around India’s capital goods industries covering four stratifications/regions, namely east, west, north and south. Target respondents are mostly represented by junior, middle or higher management. Samples have been drawn from 28 different manufacturing units under the domain of both public and private sector. Out of a target population of 450 plus, 318 survey questionnaires have been received in the filled-in form. Finally, 238 out of 318 collected from 25 different units are found relevant as input data for statistical analysis and validation thereof.

Results and Analysis

Instrument development includes four stages: item generation, pre-pilot study, pilot study and large-scale data analysis. In first stage of item generation, items have been generated after detailed literature review, and the constructs defined after due consultation and interviews with supply chain experts and practitioners working under ERP platform. In pre-pilot study, items generated are first reviewed by academicians and practicing managers. Here, items are duly evaluated through structured interviews with these practitioners who are asked to comment on appropriateness of research constructs. Based on their feedback, some items are deleted while some new items are added. Specific changes are suggested in the second stage of pre-testing, which are approved by the academicians and practicing managers.

In pilot study, Q-sort method is applied to pre-assess convergent and discriminant validity of scales. Reliability of sorting conducted by the judges is assessed by using any of or all three different measures: inter-judge raw agreement scores, Cohen’s kappa and item placement ratios (hit ratio’s). Here, it is checked whether the kappa value is high or not; kappa value has been used to eliminate any chance agreements, thereby evaluating true agreement score between two judges. In first round, Cohen’s Kappa score averages 0.78. It is thus considered as a good degree of agreement beyond chance and between the judges. To improve the method, an examination of off-diagonal entries in placement matrix has been conducted. Reworded items are then entered into second round. Value of Cohen’s kappa measure of agreement thereafter increases to 0.94 indicating an excellent level of agreement for the judges in this round. In third round of sorting, results are almost similar to the second round, showing consistency of results between second and third. Accordingly, next stage of large-scale data analysis is performed.

In large-scale data analysis, full and final questionnaire is sent to respondents. Number of respondents were targeted to around 260–280. For selecting firms, stratified random sampling scheme has been performed. In next stage, capital goods firms are selected randomly in the survey from each stratified area. For measurement scaling, Likert scaling technique has been used with five response categories ranging from ‘strongly disagree’ to ‘strongly agree’.

Statistical analysis has been used to determine validity and reliability of BPM practices in terms of ERP and FP. The hypotheses as developed have also been tested by using SEM. BPM has a direct and positive influence on both ERP and FP. These two, in turn, determine intention to patronize BPM. Thus, BPM and ERP can act both dependent as well as independent variable. A hypothesized dependent variable can become an independent variable in a sub-sequent dependence relationship. Since, SEM is used to test causality; internal validity can be done informally through a discussion as to why causality exists or why alternate explanations are unlikely (Bagozzi & Yi, 1988). Since these methods often test concurrent relationships among multiple variables, confidence in internal validity of the proposed model is enhanced. The survey responses captured as per the Likert scale coding are then subjected to a stepwise process to undertake statistical analysis for testing of accuracy and missing data. Software packages viz. IBM SPSS and AMOS have been used in this empirical study. Details of the constructs and number of variables for each construct are summarized in Table 1.

Table 1.

Summary of Constructs and Variables

Framework Parameters	Construct-I/Variables	Construct–II/Variables	Construct–III/Variables
BPM	Business strategy (BS)	Enterprise capabilities (EC)	Smart Enterprise Investment (SEI)
	4	8	10
ERP	Technological factors (TF)	People factors (PF)	Organizational factors (OF)
	8	6	10
SCP	Marketing performance (MF)	Financial performance (FP)	Operational performance (OF)
	4	4	5

Source: Literatures under reference.

Descriptive Analysis

Descriptive statistics for all the three latent constructs, namely BPM, ERP and SCP, based on the questionnaire responses are collected and tabulated after preliminary run of the available data and using software and is depicted in Table 2.

Table 2.

Descriptive Statistics of BPM, ERP and SCP

Business Process Management (BPM)			Enterprise Resource Planning (ERP)			Supply Chain Performance (SCP)
Vars	Mean	SD	Vars	Mean	SD	Vars	Mean	SD
BS.1	4.18	0.86	TF.1	4.19	0.71	MP.1	3.86	0.87
BS.2	4.00	0.84	TF.2	4.07	0.68	MP.2	3.94	0.87
BS.3	4.17	0.84	TF.3	4.27	0.80	MP.3	4.13	0.82
BS.4	4.16	0.84	TF.4	4.21	0.79	MP.4	4.29	0.80
EC.1	4.11	0.81	TF.5	4.15	0.77	FP.1	4.12	0.84
EC.2	4.12	0.76	TF.6	4.27	0.68	FP.2	4.14	0.82
EC.3	4.18	0.79	TF.7	4.15	0.78	FP.3	4.11	0.81
EC.4	4.11	0.75	TF.8	4.19	0.80	FP.4	4.08	0.87
EC.5	4.08	0.78	TF.9	4.38	0.70	OP.1	4.14	0.80
EC.6	4.12	0.78	PF.1	4.20	0.73	OP.2	4.20	0.87
EC.7	3.98	0.80	PF.2	4.14	0.78	OP.3	4.31	0.85
EC.8	4.04	0.83	PF.3	4.19	0.83	OP.4	4.14	0.82
SEI.1	4.18	0.76	PF.4	4.43	0.73	OP.5	4.12	0.89
SEI.2	4.20	0.81	PF.5	4.32	0.78
SEI.3	4.23	2.47	PF.6	4.17	0.73
SEI.4	4.06	0.72	OF.1	4.17	0.75
SEI.5	4.19	0.75	OF.2	4.16	0.75
SEI.6	4.16	0.75	OF.3	4.25	0.71
SEI.7	3.79	0.91	OF.4	4.11	0.69
SEI.8	3.76	0.79	OF.5	4.32	0.76
SEI.9	4.14	0.84	OF.6	4.27	0.78
SEI.10	4.27	0.81	OF.7	3.94	0.78
			OF.8	3.87	0.79
			OF.9	4.27	0.72
			OF.10	4.43	0.71

Source: Survey data output in AMOS.

Confirmatory Factor Analysis

Data as extracted from descriptive analyses are then fed into the system/software for the next stage of analysis. Confirmatory factor analysis (CFA) is used to ascertain construct validity of all the constructs as framed in the model. Three statistical parameters, namely significance of regression weight, model fit, convergent and discriminant validity, are tested by using software and results are shown in a pictorial mode of representation in Figures D1 and D2. Regression coefficients of all the observed variables are more than 0.6. Model fit indices values for measure-ment model are thus well within the norms of statistical significance level and are shown in Table 3.

Table 3.

Model Fit Indices for Measurement Model

	Actual Value	Recommended Value
Cmin	3,892.19
Cmin/df	2.304	< 3.00 (Hair, Black, Babin, Anderson, & Tatham, 2007)
Goodness of fit index (GFI)	0.912	> 0.90 (Hair et al., 2007)
Adjusted goodness of fit index (AGFI)	0.861	> 0.80
Normed fit index (NFI)	0.902	> 0.90 (Bagozzi & Yi, 1988)
Comparative fit index (CFI)	0.885	> 0.90 (Hair et al., 2007)
Root Mean Square Error of Approximation (RMSEA)	0.058	< 0.08 (Bagozzi & Yi, 1988)

Source: Literatures under reference.

As the above results for convergent validity and discriminant validity are conforming to the set statistical guidelines (Hair, Black, Babin, Anderson, & Tatham, 2007), therefore, there are no validity issues for given constructs. Validity of the structural model is basically the constructed one for examining its fit characteristics, comparing with the proposed model and testing structural relationships in the hypothesized model. Calculation of CR, AVE, MSV and ASV for the three constructs derived on the survey data as collected from across Indian capital goods industries is depicted in Table 4.

Table 4.

Calculation of CR, AVE, MSV and ASV for the Construct

	CR	AVE	MSV	ASV	SCP	BPM	ERP
SCP	0.941	0.869	0.677	0.673	0.923	–	–
BPM	0.932	0.837	0.759	0.719	0.823	0.913	–
ERP	0.931	0.823	0.758	0.717	0.819	0.874	0.913

Source: Survey data output in AMOS.

Through CFA, it has been the first determined contri-bution of each dimension to represent latent constructs, namely BPM, ERP and SCP. It also evaluates how well a set of observed variables measuring these dimensions represents these constructs.

Table 5 shows the parameters used for ascertaining the model fit of the structural model. Actual value of Fit indices, namely Cmin/df, goodness of fit index (GFI), adjusted goodness of fit index (AGFI), comparative fit index (CFI), normed fit index (NFI) and root mean square error of approximation (RMSEA) are calculated and checked. The results shown are well within the range of recom-mended value. Accordingly, it is concluded that the model as proposed fits well with the current set of data.

Table 5.

Fit Indices for Structural Model

Statistics	Actual Value	Recommended Value
Cmin	3,831.251
Cmin/df	2.354	< 3.00 (Hair et al., 2007)
GFI	0.904	> 0.90 (Hair et al., 2007)
AGFI	0.852	> 0.80
NFI	0.941	> 0.90 (Bagozzi & Yi, 1988)
CFI	0.929	> 0.90 (Hair et al., 2007)
RMSEA	0.067	> 0.08 (Bagozzi & Yi, 1988)

Source: Literatures under reference.

Next relationship as proposed is estimated in terms of values with collected set of data. Values as obtained by SEM are evaluated and are shown in Table 6.

Table 6.

Structural Equation Modelling Results

Hypothesis	Relationship	Estimate	Standard Error	CR	P-value	Relationship	Conclusion
H ₁	BPM~SCP	0.549	0.127	4.106	0.000	Significant	Supported
H ₂	BPM~ERP	0.874	0.097	10.722	0.000	Significant	Supported
H ₃	ERP~SCP	0.537	0.153	3.898	0.000	Significant	Supported

Source: Survey data output in AMOS.

SEM results as shown in Table 6 confirm to the validation of the hypotheses from the management perspective. Hypothesis 1 (H₁) refers to the relationship between BPM and SCP, which is found to be significant from both estimate value and p-value. It can be also confirmed that the improvement in managing business processes has a positive impact on SCP. Hypotheses 2 and 3 (H₂ and H₃) are also confirmed from SEM results accordingly. Both the relationships, that is, BPM~ERP and ERP~SCP are statistically significant with p-value in either of the cases. Hence, validation implies that ERP has positive impact on both BPM and SCP. In totality, it may be inferred that, SEM results successfully confirm/validate hypothesized model as proposed.

Results support H₁, which inter alia states that efficient use of BPM by Indian capital firms will invariably yield high level of the firm performance. Standardized coefficient as estimated at 0.549 is statistically significant. From the value itself, it can be concluded that, BPM practices have moderate influence on firm’s performance directly. Strategically, it may have a better impact (direct influence) on firm’s marketing and operational performance. H₂ supports direct impact of BPM on ERP with a standardized coefficient as estimated at 0.874 which is statistically significant. This value lends credence to the underlying fact that BPM has greater impact in framing ERP than on firm’s performance. Standardized coefficient estimated as 0.537 is significant in case of H₃ also, which confirms the positive impact between ERP and firm’s performance. Finally, result supports proposition of higher level of integration between BPM and ERP that would ultimately lead to higher level of firm’s performance.

As evident from the present research findings, there is an ample of scope to improve firm level performance by synergizing on supply chain issues. Unlike the old management philosophy to strengthen organizational activities to maximize firm’s output both in terms of quantity and quality, present management theory empha-sizes more on integration of supply issues with technology for business sustainability. Another important issue that a critique would like to point out is the use of SEM application for ‘causal interpretation’. Major applications of SEM are based on non-experimental data, yet it interprets final model as causal model. Most significantly, SEM transforms correlation data into causal conclusion. However, mere corroboration of data in SEM does not mean that the hypothesized model is proven beyond doubt. In this study, strength may be signified in synchronizing real-time critical issues as seen in business firms under the parlance of supply chain. Multiple hypotheses has been framed based on the current business scenario for the Indian capital goods industry which is yet to come out of the legacy issues and improve on its performance by integrating business processes with ERP. To testify parallel and multiple hypotheses in a single analysis, strength of using SEM is envisaged here.

Further, it is worthwhile to highlight that result as obtained in empirical analysis is very logical and true with current scenario of Indian Capital Goods Industry wherein efficient practices of business processes under information systems platform are expected to show better integration which would further lead to improved firm performance. Although, inference as outcome of this study may not match with peer review on the available literatures, the key findings of this study are nevertheless convincing enough to infer that BPM alongside supply chain integration on ERP platform can influence firm’s performance to a considerable extent.

Conclusion

Managing supply chain plays a very critical role in performance of manufacturing as well as capital goods Industry. In today’s market scenario coupled with fourth generation industrial revolution (in era of Industry 4.0), capital goods of Indian firms are facing more complex environment than even before. Success, here, is considered as no longer a matter of analysing any particular firm but a chain of firms involved in delivering and supplying, wherein individual firm operates as a single entity in the entire chain of supply.

One of the critical objectives of this study is to establish causal relationship among ERP, BPM and firm perfor-mance while ignoring possible recursive relationships. However, it may be possible that ‘more integrated supply chain integration and increased firm performance’ could have improved levels of ERP practice. This part is beyond the present scope of study; further study is envisaged in this direction. Future studies of the present work may also be directed to examine proposed relationship by incorporating other contextual factors such as firm size, structure and complexity of supply chain, and a firm’s position in supply chain. Again, it will be intriguing to examine how ERP differs across firm size within an integrated supply chain.

Finally, it is concluded that in the ever-changing competitive business scenario, continuous improvement encompassing all possible areas of business activities are essentially required to keep pace with the development goals, albeit with enough space for sustained leadership. Any firm with a superior information system capability, as a matter of fact, might lose its leadership status if it fails to upgrade that capability at a rate faster than its competitors towards supply chain’s integrate and win-to-sustain phenomena.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Appendices

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