Application of emerging technologies in ERP implementation in Indian manufacturing enterprises: an exploratory analysis of strategic benefits Shree Ranjan, Vijay K. Jha & Pralay Pal
The International Journal of Advanced Manufacturing Technology ISSN 0268-3768 Int J Adv Manuf Technol DOI 10.1007/s00170-016-8770-6
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Author's personal copy Int J Adv Manuf Technol DOI 10.1007/s00170-016-8770-6
ORIGINAL ARTICLE
Application of emerging technologies in ERP implementation in Indian manufacturing enterprises: an exploratory analysis of strategic benefits Shree Ranjan 1 & Vijay K. Jha 2 & Pralay Pal 1
Received: 23 August 2015 / Accepted: 11 April 2016 # Springer-Verlag London 2016
Abstract Manufacturing “smart connected products” and building “factories of future” are need of the hour in global manufacturing arena, which is forcing enterprise decision makers to develop deeper insight in relevance of emerging technologies in enterprise resource planning (ERP) such as mobility, cloud computing, analytics, social network computing and Internet of Things (IoT) to leverage them for strategic benefit and competitive advantage. In this paper, we explore strategic engagement of these technologies in manufacturing enterprises. We conducted exploratory factor analysis (EFA) of the benefits and studied their impact on four objective indicator areas such as employee, process, customer, and finance. We used IBM SPSS to perform EFA on the response data from questionnaire survey to identify critical benefit factors and beneficiary objective indicators. We compared our work with other research findings. This work will help practitioners develop better insight and decisiveness for investing in advanced technologies in pursuit of manufacturing excellence. For academia, the work will open new research directions.
Keywords Enterprise . ERP . Advanced manufacturing . Mobility . Cloud computing . BSC . SPSS . Decisiveness . Transformation driver
* Pralay Pal
[email protected] 1
Tata Technologies, Jamshedpur, Jharkhand, India
2
BIT Mesra, Ranchi, Jharkhand, India
1 Introduction Enterprise resource planning (ERP) facilitates seamless flow of the information through the organization that integrates, optimizes, and controls all the manufacturing processes and transactions in order to enhance efficiency and maintain a competitive position [2]. ERP systems have become vital strategic tools for advanced manufacturing enterprises to improve the performance of the supply chain and reduce the cycle times of new products and product variants launch, fostering innovative culture in the organization, creating customer loyalties and shareholder values [6]. Investment in ERP emerging technologies in manufacturing enterprises particularly, in original equipment manufacturers (OEMs) is strategic due to complexity involved, high cost of implementation, change management issues, and possibility of business disruption. Therefore, it is crucial for decision makers to understand the relevance of ERP technologies with the enterprise objectives that deliver performance to fulfill implementation objectives [2, 21, 24]. The objective of the study is to find out relevance of such technologies connecting ERP and manufacturing and their individual potential to contribute in strategic benefits earning. We choose a set of manufacturing enterprise functions based on enterprise balanced scorecards (BSCs), addressing various strategic areas as in Fig. 1. State-of-the-art technology components such as social virtual networking, mobility, big data analytics, cloud computing, and Internet of Things (IoT) have been considered, and their interface with the strategic objective indicators are explained. We deliberated on role of transformation drivers in an ecosystem between enterprise computing and emerging technologies and their impact on advanced manufacturing facilities. To conduct analysis of benefits, we listed applications of these technologies and carefully formulated survey questionnaire along with their possible benefits.
Author's personal copy Int J Adv Manuf Technol Fig. 1 BSCs derived from MVV and strategic function integration using ERP
MISSION Why the OEM exists?
VALUES In what the OEM believes?
VISION What the OEM wants to be?
STRATEGIC PLANNING What is OEM’s game plan?
OBJECTIVE INDICATORS
A/C payable, receivable Bank/Excise /MRP Cost, Profit Centre A/C Mat. Purchase to Prod, Spare
General Ledger
Marketng Mgt. Dealer, Client Collaboraon Sales Territory Management
MES & SRM
Prodn. Book, Sourcing
Sales Distribn.
Producon Planning
Quality Mgt.
Talent Management
Travel Mgmt.
Payroll, Emp. Engagement
Org. Managemt.
ERP FUNCTION
Personnel Mgmt.
BSCs
Return On Capital Expense
EBIDTA
Revenue growth
Cash Flow
Customer
Profitability
Sales Touch-point
Market Share
Customer, Dealer Loyalty
BSCs
Increasing Shareholder Values
Service Touch-point
ERP FUNCTION
Finance
Delivering specific Value to Market Customer Sat
Process
Capacity Ulize
Vendor Capact. Expansion
Inventory Turns
NPI & TTM
Deviaons/ Incidents
Arion Rate
IPRs, Employee Engagement
Performance Measure
Succession Plan
Training Hours
Employee Strategic Iniaves
Customer
Developing Strategic Capabilies, Efficiency
Learning and Growth to Innovate
Account Sales Proposal Mgt.
Process
CRM, Pipeline Management
Employee
Financial
Strategic Iniave @ ERP Implementaon
Exploratory factor analysis (EFA) was performed on the collected responses using IBM SPSS Statistics V23 to identify critical benefit factors adopting the principal component analysis (PCA) approach. Eight most important benefit factors and their corresponding technology enablers were identified and ranked. The results are compared with outcome of research conducted by other authors and implementability of the work in modern age manufacturing has been discussed. The organization of this paper is as follows. Review of research on ERP emerging technologies is presented in Sect. 2. Significance of BSCs and how ERP offerings address BSCs are discussed in Sect. 3. Section 4 deals with an ecosystem of enterprise computing and emerging technologies to illustrate importance of adoption of these technologies. Research design, methodology, and data analysis are presented in Sect. 5. Summary results and comparison of findings with other works are presented in Sect. 6. How practitioners will implement these research findings in manufacturing has been expounded in Sect. 7. Limitations of research and future directions are deliberated in Sect. 8. Section 9 presents a conclusion of the work.
2 Review of research on ERP emerging technologies We identified emerging trends and technologies in ERP having direct relevance with strategic benefits such as cloud computing, mobility, big data analytics, insurgence of social
media, and IoT and those having indirect relevance such as open-source ERP, enterprise application integration (EAI), sustainability, and special IT adoption such as data warehousing. Our work aimed at establishing a decision support methodology for corporations for investing in technologies and help academia in finding new avenues of research. Exploring strategic benefits of ERP emerging technologies is an important prerequisite of our study. To realize value from IT investment, strategic alignment between the business and IT of an organization is essential [8]. Strategic value of IT can be derived by “digitization” of business models of organizations for competitive edge [3, 4]. To achieve this, the balanced scorecard (BSC) concept was introduced as a strategic planning and performance management system for “vision to action” across four balanced perspectives in manufacturing organizations: financial, customer, internal business processes, learning, and growth [11–13] which eventually became the fundamental building block for evaluating strategic benefits of ERP [4, 18] satisfying business objectives. Lately, the objective of investing in emerging technologies in manufacturing is to achieve futuristic factories with facilities such as “smart machine supervisory system framework” [1] and manufacture “smart, connected products” [20]. Few authors advocate leveraging emerging technology stack called “social-mobile-analytics-cloud” and IoT for enterprise efficiency and effectiveness enhancements. These technologies converged to enterprise application platforms with consumerism of IT and mobility [9]. Enhancing mobile
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applications by cloud computing will lead to maturity of cloud ERP [23]. Cloud computing, multi-tenancy, and Software-asa-Service have transformed traditional legacy ERP to cloud ERP [4]. The five futuristic emerging trends are IoT, wearable technology, big data analytics, the age of context (context aware mobile apps), and finally, opening business to innovation, results of which can be seen in the bottom line of enterprises [17]. Part explosion process in MRP using hierarchical structure and query processing are important for real-time production scheduling, purchase, and inventory recording in mass and batch manufacturing [16]. In all cases, assessing the performance of an ERP system is essential with appropriate performance indicators, indicator structure, and consistent evaluation standards [24]. These contributions helped us identify emerging technologies for our work. EAI, open-source ERP, sustainable ERP, and ERP value system influence strategic alignment for manufacturing excellence. In integration of ERP with supply chain and other applications, EAI offers exchange of data, objects, and processes through application layers resulting in better globalized competitive advantage for virtual organizations [22]. The need of creation and sustenance of competitive advantage and customer focus has compelled companies to deliver value-added products and services faster through rapid flow of information using data warehousing [26]. Small and mid-sized enterprises (SMEs) prefer adopting open-source ERP than proprietary ERP [10] irrespective of cost. Semantically described business remote function call (BRFC) and concept of heterogeneous data translator are useful for seamless integration of ERP and PDM systems [25]. Similarly, agent-based framework integrates PDM and ERP fostering manufacturing collaboration such as replacement parts requirement analysis [19]. In the globalization era, automotive industry increases competitiveness by quality program, strategic planning, monitoring performance, encouraging communication, customer and process focus, innovation, and learning to mitigate challenges of high quality and trade regulations [6]. Antecedent factors such as system quality attributes, organizational capabilities, and desired strategic benefits play crucial roles in successful adoption of ERP and emerging technologies [21]. Future trends such as web-based procurement and ERP outsourcing pose challenges such as global compatibility and flexibility in ERP value system [5]. Sustainable ERP promotes collection, integration, automation, and monitoring of information to support sustainability and integration issues [3]. Tangible ERP benefits are largely industry independent while intangible benefits vary across industry and their analyses can help a firm while investing in ERP [18]. Though ERP migration to cloud platform IaaS involves low infrastructure cost and reduced support calls, it lacks in data security, legality, and privacy [14]. Study of critical failure factors (CFFs) of ERP implementation in SMEs [2] has rendered a guideline for statistical multivariate data analysis [7, 15]. Our study was quite in-depth from
strategic point of view. No literature was found on strategic benefits of emerging technologies substantiating ERP in manufacturing enterprises which we address in our work.
3 Supporting BSCs through ERP functions—a strategic approach Enterprises invest in technology initiatives in order to achieve strategic competitive advantages. Strategic planning translates mission, values, and vision into BSCs [13] to measure performances across four balanced, mutually interacting and interdependent perspectives or objective indicators such as finance, customer, internal processes, and employee related such as learning, innovation, and growth. BSC tells the knowledge, skills, and systems that employees will need (learning and growth perspective) to innovate and build the right strategic capabilities and efficiencies (internal processes perspective) that deliver specific value to the market (customer perspective) which will eventually lead to higher shareholder value (financial perspective) as in Fig. 1. An overall process measure and few critical success factors are indicated through BSCs. We show manufacturing enterprise BSCs for each objective indicator in Fig. 1. ERP functions address these BSCs based on their relevance and appropriateness in order to integrate these functions as part of strategic alignment [8]. An enterprise bridges BSCs with strategic initiative like ERP using various functions. Decision makers and technology selectors need to identify advanced technology enablers and platforms to connect ERP and operations in meeting strategic goals and earning competitive advantages.
4 Supplementing ERP framework using emerging technologies Emerging digital technologies supplement various ERP functions which address company BSCs under objective indicators as shown in Fig. 1. Social, mobile, analytics, cloud and IoT are individual technologies and platforms which came in limelight in recent years and have shown their immense potential while leveraged for augmenting productivity of various business processes [4, 9, 17, 23]. Enterprises treat these disruptive components as an integrated new master IT architecture and a new game-changer global consumer technology platform transforming traditional business models. These technologies will reinforce ERP interface pillars such as customer interface, machine and process interface, partner and shareholder interface, and the employee interface through smart mobile devices and mobile apps, high speed communication loops (social groups of connected people), predictive insights in data ocean, cloud service models, and network connectivity for automatic data sharing using embedded electronics, software, and sensors in “smart connected products” as in Fig. 2, which will
Author's personal copy Fig. 2 Ecosystem of ERP and emerging technologies and their benefits
Employee Interface Machine, Process Interface
E R P
Customer Interface Partner & Shareholder Interface
Social
Smart devices, Apps, High processing power
Mobile
Decisive, predicve, informed insight in data ocean
Analycs
SaaS, HaaS, PaaS, IaaS cloud service models
Cloud
Electronics, H/w, soware, sensors embedded, connected
IoT
M A N U F A C T U R I N G
F A C I L I T I E S Transformaon Drivers
EMPLOYEE PROJECT CENTRICITY
LEARNING MATURITY, CULTURE
DATA & INFORMATION SECURENESS
EMPLOYEE INNOVATIVENESS
MANUFACTURING EXCELLENCE
PRODUCT PERSONALIZATION, CONFIGURABILITY
PROMOTION OF CONSUMERISM
FINANCIAL DECISIVENESS
Transformaon Drivers
A D V A N C E D
High speed Communicaon Loop for, Adverse, Feedback
EMERGING TECHNOLOGIES
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Beer insights and higher decisiveness of futurisc, transformed organisaon with greater ability of penetraon, posive disrupon
eliminate large spending on IT such as communication, collaboration, and private hosting of computing infrastructures. This new architecture will transform manufacturing enterprise with low total cost of ownership (TCO) of technologies, deployment of innovative applications supporting decision making, and roll out new business models with increased reach to customers and deliver specific value to market. Figure 2 represents an ecosystem spurred from fusion of emerging technologies with ERP interface pillars and resultant “transformation drivers” which impact advanced manufacturing setup in enterprises. The ecosystem will foster transformation to futuristic organization with greater ability of market penetration and disruptive innovations. Hence, need of the hour is to understand the strategic benefits of the ecosystem and prioritize technologies with deeper insight. In this figure, we plotted eight transformation drivers based on the results of the study as specified in Table 3, i.e., critical benefit factors.
5 Research design and methodology The primary objective of our exploratory study is to analyze benefits of emerging technologies in enterprise ecosystem in terms of their relative impacts on strategic objectives. This research attempts to identify perceived critical benefit factors such that if such knowledge are considered carefully, and are made use of, it would lead a deeper insight of corporate decision makers, help in realizing the value creation to enterprise manufacturing processes, and supplement new technology
selection decisions. This paradigm was established by rigorous discussions with specialists and subject matter experts. A set of 25 unique benefit factors was shortlisted in the context of advanced manufacturing for higher reliability in analysis. No previous literatures were found in this area of research. Our methodology includes identifying all unique benefit factors, opinion survey using online questionnaire, response collection, and data analysis. 5.1 Identifying benefit factors Among several contexts of the research study, important are domains of the industry aimed for the study, geography influencing cultural diversity of the people, and the maturity of the organization indicated by successful implementations. To identify appropriate benefit factors, we conducted literature review and brain-storming, a standard industry practice. Twenty-five benefit factors are detailed in Table 1 such as “easy talent acquisition and retention,” “productive, effective connected employees,” “higher technical maturity of employees,” “higher project centricity of employees,” “decisive insights in data-ocean,” “enhanced employee availability,” “intellectual property (IP) protection and knowledge management,” and so on. 5.2 Survey questionnaire design, hosting, and response collection The survey questionnaire was carefully framed with 25 questions, one for each benefit factor for rating their relevance in
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Faster NPI and improved product quality
1.000
0.719
options ranging “Strongly disagree,” “Disagree,” “Neither,” “Agree,” and “Strongly agree.” The questionnaire was reviewed and refined iteratively by experts. Questionnaire was hosted in Google docs with caption “Benefits of application of emerging technologies in enterprise resource planning.” Initially, the web link of the questionnaire was circulated to 200 participants and around 150 responses were collected in the first lot. There were follow-up mails and fresh 200 circulations out of which, around 100 responses were collected. Overall response was 63 %. Verification of the effectiveness of questionnaire was done through measurement of reliability and internal consistency of the data, outlined in Sect. 5.5. An additional measurement of survey effectiveness has also been presented through interrater reliability.
Supply chain alignment, low inventory Reliable, scalable, agile computing
1.000 1.000
0.636 0.662
5.3 Organizational context
Capturing individual user experiences Scalable way to co-create, collaborate
1.000 1.000
0.742 0.754
Table 1
Communalities of factors
Factors
Initial
Extraction
Easy talent acquisition and retention
1.000
0.686
Productive, effective connected employees
1.000
0.830
Higher technical maturity of employees Higher project centricity of employees
1.000 1.000
0.718 0.768
Decisive insights in data ocean Enhance employee availability
1.000 1.000
0.728 0.582
IP protection and knowledge management
1.000
0.860
Sensing the pulse of stakeholders Low security threat to vital business data
1.000 1.000
0.788 0.748
More scope of innovative thinking Business processes refinement and dashboard
1.000 1.000
0.781 0.600
Brand loyalty of customers and dealers
1.000
0.779
Promotion of consumerism through IoT Easier life-cycle support and disposal Voice of customers and pulse of market Long term value of investing in ETs
1.000 1.000 1.000 1.000
0.716 0.602 0.564 0.767
Impact on organizational value chain Strategic financial decision making ability Low carbon footprint due to cloud ERP Resource management in global operations
1.000 1.000 1.000 1.000
0.769 0.749 0.690 0.622
Extraction method: principal component analysis
view of the central idea, i.e., benefits of application of ERP emerging technologies. These questions were segregated into four subsets (response categories) addressing all objective indicator areas such as seven questions for “employee,” seven for “process,” six for “customer,” and five questions for “finance.” Each of these subsets addressed emerging technology areas such as social networking, mobile computing, big data analytics, cloud computing, and IoT. For example, in question subset under employee, effectiveness of the talent acquisition and retention process is measured by asking “Does social networking help HR practice in talent acquisition and retention in your organization?” in a five-point scale. Similarly, employee project centricity due to adoption of smart devices can be measured by asking “Can smart devices help you pay more attention to project activities?” in a 5-point scale. In framing the questions, few important basic criteria were followed such as in each question, its relation to the objective of the study was clearly stated, and ambiguities were avoided. A 5-point standard Likert scale was used for responding each question with response
The survey was conducted in multinational OEMs of Indian origin with perspective of ERP emerging technology adoption in advanced manufacturing enterprises. The criteria for selection of an OEM were market share, employee strength, and turnover and OEM was expected to rank within the top three positions as per the selection criteria in their respective domain such as aerospace and automobile manufacturing. Another important selection criterion was that an OEM should have futuristic advanced manufacturing facilities to produce smart connected products. Both the companies selected for study qualified in their respective domains satisfying designated criteria. We browsed many website databases to find out ranking of the OEMs and almost all websites were unanimous about the ranking of OEMs based on our specified criteria; for example, http://top10companiesinindia.co.in/ and http://business.mapsofindia.com/. We selected two large OEMs, one automobile making and the other aerospace, both Indian multinational in nature with their business units spread across the Asia-Pacific (APAC) region. These companies continue to adopt ERP and emerging and advanced manufacturing technologies from time to time and do not have scientific statistical model to support decisiveness for their adoption. These companies mostly resort to TCO-net present value (NPV)-return on investment (ROI) type of financial calculations, guesswork, and rule of thumb in the absence of a statistical model to strengthen manufacturing operations and improve efficiencies and effectiveness of internal processes. The targeted survey participants were employees in the middle, senior, and top management levels spread across all functional areas, strategic management areas, IT infrastructure, and systems department. In all, 252 responses were collected. The ratio of respondents from automotive and aerospace was 60:40.
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5.4 Data analysis and results Our exploratory research worked on identified 25 significant factors in support of objective of study. Using analysis package IBM SPSS Statistics V23, we found that the correlation among these 25 factors were quite high, and Barlett’s test of sphericity was found to be significant (p < 0.05). We further conducted Shapiro-Wilk test to check absence of unwanted normal distribution of data to authenticate it for factor analysis. Since our motto was to conduct EFA, we used PCA factor extraction method on different measures and validated various dimensions of the problem in hand. Basic objective of application of FA on exploratory variables is to extract minimum number of factors that account for maximum variance in data [7, 15]. Table 1 exhibits communality measures of the exploratory variables adopting PCA extraction technique and “varimax” rotation method. In PCA dimension reduction, we use the Kaiser-Mayer-Olkin (KMO) index for measure of sampling adequacy and Barlett’s test of sphericity to check redundancy between the variables and whether we can summarize the information by initial variables in a few number of factors. Factors whose eigenvalues are greater than 1 constitute 71.44 % cumulative variance confirmed by the “total variance explained” table (not shown) in SPSS indicating appreciable factor analysis has been conducted with 25 items as per KMO criteria resulting into extraction of 8 components. PCA is a widely used first phase of EFA for factor extraction in which factor weights are computed in order to extract the maximum possible variance, with successive factoring continuing until there is no further meaningful variance left. The factor model must then be rotated for analysis. Rotation serves to make the output more understandable by allowing the factors to correlate, since a pattern of loadings where items load most strongly on one factor and much more weakly on the other factors. Varimax rotation is the most common rotation option, which is an orthogonal rotation of the factor axes to maximize the variance of the squared loadings of a factor (column) on all the variables (rows) in a factor matrix (such as rotated component matrix in SPSS), which has the effect of differentiating the original variables by the extracted factor. Each factor will tend to have either large or small loadings of any particular variable. A varimax solution yields results which make it easy to identify each variable with a single factor. Table 2 represents rotated component matrix, which summarizes the results of factors identified by varimax orthogonal rotation. The idea of applying varimax orthogonal rotation is to achieve uncorrelated factors in the form of rotated component matrix in which the variables having higher factor loading values (in Table 2) are considered as most important and relevant [7, 15], i.e., identification of most influential and relevant factors contributing to the objective. The most influential factors identified based on entries in Table 2 are (sequentially, with priority): higher project centricity of employees
(5), IP protection and knowledge management (1), low security threat to vital business data (6), more scope of innovative thinking (8), faster new product introduction (NPI) and improved product quality (3), capturing individual user experiences (2), promotion of consumerism through IoT (7), and strategic and financial decision making ability (4). 5.5 Validity of data and computing In SPSS, reliability analysis and internal consistency of input data are conducted in categorical principal component analysis (CATPCA) dimension reduction technique of factor analysis, which was found good (0.7 ≤ α ≤ 0.9) by computing Cronbach’s alpha using menu options and subjecting all 25 questions, i.e., factor equivalents for the test. Our Cronbach’s alpha consistency value was computed as 0.744, which is within acceptable range [7]. KMO measure of sampling adequacy is 0.610 (>0.50) which indicates that data is useful for factor analysis. Bartlett’s test of sphericity tests the hypothesis that the correlation matrix is an identity matrix, which would indicate that the variables are unrelated and therefore unsuitable for structure detection. Small values (less than 0.05) of the significance level indicate that a factor analysis may be useful with data. In our case, the value is well below this limit indicating factor analysis is useful. The Shapiro-Wilk’s statistic is calculated for no weights or integer weight scenario, to detect whether the variables are normally distributed using normality plots of individual variables when the weighted sample size lies between 3 and 5000. None of our variables was found normally distributed from the Shapiro-Wilk’s normality plots. Interrater reliability is the degree of agreement, concordance, or consistency between raters, i.e., survey participants. It gives a score of homogeneity, or consensus in the ratings given by survey participants. If various raters (survey participants) do not agree, either the scale is defective or the raters need to be retrained. For measuring interrater reliability, we adopted ANOVA Interclass correlation coefficient in SPSS for 95 % confidence interval, for lower and upper bounds at 0.695 and 0.789, respectively, for the 25 item-variables and 252 respondents with smaller diversity (automotive, aerospace), validated both adequate awareness level of respondents and the accepted interrater reliability of data for analysis.
6 Summary of results According to our analysis of factor loading, the most influencing strategic benefit factors are listed with computed priority in Table 3 for the engagement of emerging technologies in ERP. The table displays the corresponding objective indicator of each benefit factor, their corresponding enabler technologies and transformation drivers. The Scree plot presents eigenvalue plot for 25 component factors in Fig. 3.
Author's personal copy Int J Adv Manuf Technol Table 2
SPSS result of rotated component matrix
Factors
Component 1
2
3
4
5
Easy talent acquisition and retention Productive, effective Connected employees
0.111 0.210
0.382 0.792
−0.113
0.599 0.173
0.263 0.117
−0.321
Higher technical maturity of employees
0.578
0.188
−0.118
0.140
0.500
−0.160
0.831 0.783
0.121 −0.149
0.100 0.169
0.127
Higher project centricity of employees Decisive insights in data ocean
−0.220
Enhance employee availability IP protection and knowledge management
−0.118
0.158
0.274
Sensing the pulse of stakeholders
0.691
0.536 −0.222
−0.373 0.150
0.256 0.832
0.532
Supply chain alignment, low inventory Reliable, scalable, agile, computing
0.130 0.542
0.125 0.516
0.528
Capturing individual user experiences Scalable way to co-create, collaborate
0.841 −0.170
0.115
0.129
Brand loyalty of customers, dealers Promotion of consumerism IoT based
0.466
0.602
0.152
Easier lifecycle support and disposal Voice of customers and pulse of market Long term value of investing in ETs Impact on organizational value chain Strategic financial decision making ability
0.684 −0.146 0.541 0.430
0.257 −0.192 0.476 0.283 −0.164 −0.198
Low carbon footprint due to cloud ERP Resource management of global operations
0.120
−0.189 0.140
0.489 −0.155 0.831
8
−0.247
−0.152
−0.187 0.244
−0.122
0.135 −0.236 0.374 0.538 −0.146
0.102 0.775
0.380
0.272
−0.132
0.148
0.394
0.244
0.785
0.105
0.793
0.273 −0.269
0.271 0.260 −0.102
0.277
−0.267
−0.162
−0.201 0.411
0.178
−0.203 −0.462 −0.137 0.103
0.311 0.759
0.882 0.310
0.800 0.126
Business processes refinement, dashboard Faster NPI and improved product quality
7
−0.470
0.416 −0.189
Low security threat to vital business data More scope of innovative thinking
6
−0.183
0.703
0.185 −0.115
Extraction method: principal component analysis; rotation method: varimax with Kaiser Normalization; rotation converged in 10 iterations
Table 3
Critical benefit factors
Benefit factor/strategic gain
Rank/influence Objective indicator Emerging Technology/ Transformation driver in ecosystem platform enabler
Higher project centricity of employees IP protection and knowledge management Low security threat to vital business data More scope of innovative thinking
5 1 6
Employee Employee Process
Mobility, cloud hosting Employee project centricity Cloud hosting Learning maturity and learning culture Cloud hosting Data secureness
8
Process
Faster NPI and improved product quality Capturing individual user experiences
3
Process
Mobility, cloud hosting, IoT Big data analytics
2
Customer
Virtual social N/w, mobile apps, IoT
7
Customer
4
Finance
Virtual social N/w, mobile apps Big data analytics
Promotion of consumerism through IoT Strategic financial decision making ability
Innovativeness of employee Manufacturing excellence through quality and process innovation Product personalization and configurability, collaboration, co-creation, smart connected products Promotion of consumerism Financial decisiveness for low TCO and high ROI
Author's personal copy Int J Adv Manuf Technol Fig. 3 Scree plot from SPSS
Extracon Method: Principal Component Analysis
Eight key benefit factors emanating from the analysis addresses all four objective indicators. The prioritized benefits belonging to specific objective indicator are as follows: &
&
&
&
Two factors belonging to “employee” with overall highest priority (1) attributed to “IP protection and knowledge management,” with transformation driver “learning maturity.” Two factors belonging to objective indicator “customer” with highest priority (2) attributed to “capturing individual user experiences”; transformation driver is “product personalization and configurability.” Three factors belonging to objective indicator “process” with highest priority (3) attributed to “faster NPI and improved product quality”; transformation driver is “manufacturing excellence.” One factor in objective indicator “finance” with priority (4) attributed to “strategic financial decision making ability”; transformation driver is “financial decisiveness.”
Strategic objective indicators, in order of relevance to the objective of study are (1) employee, (2) customer, (3) process, and (4) finance. Employees will be benefitted in terms of IP protection, IP management, and knowledge management and being highly project centric leading to creating long-term value and competitive advantages of the enterprise, which is learning maturity. The most important drivers for transforming the organization were found out to be (1) learning maturity, (2) product personalization and configurability, (3) manufacturing excellence, and (4) financial decisiveness. All emerging
technologies are indispensable such as mobility and cloud hosting, virtual social networking and mobile applications and IoT, and big data analytics. The survey study and analysis of results will help advanced manufacturing and OEM enterprises in myriad ways. Investment for imbibing new technology in the organization can be supplemented by perceived decisiveness and statistically guaranteed alignment with the strategic objectives. Corporations can have deeper insight into the relevance of the benefit factors besides NPV, ROI, and TCO calculations leading to sustainable and profitable decisions. With the insight of benefits, decision makers can focus on fewer emerging technologies leading to maximum competitive advantage gain for the company and long-term value creation for the customer. The approach focuses more on technology relevance rather than on mere technology adoption and aligns with the mission, vision, and values and strategic planning of the enterprises. The results are supported and validated by the global trends of large OEMs increasingly considering IP protection as mandate rather than as choice in terms of patents and copyright filing, publication of innovative works in scholarly international journals, reaching out more and more to individual customers and valuing individual feedbacks resulting in co-creation, enhancing personalization, configurability, and customizability of the product, i.e., creating personalized products and solutions, inculcating habit of faster highquality new product introduction, and so on. All these are direct evidences of validity of the research findings.
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6.1 Comparing results with other research findings We have compared our findings with the results presented by contemporary researchers. While most researchers presented benefits of ERP and emerging technologies, some of them listed risk factors also since, Gartner, the world’s leading IT research firm, has estimated that 55 to 75 % of all ERP projects fail to meet their objectives [4]. While listing the benefit factors, it is essential that objective indicators from the organizational perspective [11–13] and the strategic alignment of IT for organizational transformation [8] be emphasized. While very few researchers distinctly consider BSCs [6] for ERP implementation, others overlook the relevance of BSCs and strategic objectives such as employee, process, customer, and finance. But interestingly, the stated benefits address these four indicators automatically in almost all cases. To compare the results, we rationalize and segregate the benefits presented in various literature explicitly advocating benefits of ERP and emerging technologies based on their relevance with four strategic objective indicators and presented them in Table 4. The comparison demonstrates good correlation between our findings and the findings reported by other researchers. The critical benefit factors are italicised in Table 4 and it has been investigated whether other research outcomes are corroborating them. It has been found that at least four key benefits have been confirmed by Hurbean et al [9], O’Leary [18], and Ularu et al. [23]. All other listed authors in Table 4 mentioned at least two common benefits [4, 14, 22, 24–26]. While the same benefits have been mentioned in different forms in different literature, all of them advocate benefits of ERP and emerging technologies.
7 Implementation of research findings in manufacturing ERP emerging technologies and analysis of their strategic benefits touch virtually every aspect of the strategy pillars in a manufacturing enterprise irrespective of the type of industry. The research findings can be used mainly in three distinct ways: first, building early decisiveness for new advanced manufacturing technology adoption through confirmatory analysis; second, preempt manufacturing strategies to stay ahead in competition, i.e., applying a panoramic view to further the limit beyond technical benchmark; and third, foster seamless integration between business and engineering and also within advanced technologies for organizational benefit and competitive advantage.
competitive advantages beyond mere benchmark exercises particularly in an era of smart factories and smart connected products. For example, while selecting a metal cutting machine, instead of only assessing its productivity, some more factors such as its ability to network with other product systems and management systems [20], smart features, data security, lower operator engagement time for allowing innovative thinking, scope of artificial intelligence (AI), etc. should also be considered. To achieve such decisiveness, we propose multivariate analysis with variables such as employee project centricity, learning maturity, data and information secureness, manufacturing excellence, employee innovativeness, product personalization and configurability, and consumerism and financial decisiveness using structural equation modeling in a confirmatory analysis approach. Final decisiveness will be the resultant effect of all such variables in an appropriate structural relationship model. 7.2 Preempt manufacturing strategies Obsolescence, upgrade, or outsource decisions are part of a manufacturing strategy which necessitates evaluation of alignment of existing manufacturing setup with corporate strategies. With the help of both exploratory and confirmatory approach, it is possible to develop decisiveness to preempt “make-or-buy,” phasing out or upgrade a technology to stay ahead in competition. 7.3 Foster seamless integration Information technology is revolutionizing the products and production facilities which mandate seamless integrations in the context of manufacturing smart connected products [20] and promoting futuristic manufacturing concepts [1]. Besides mechanical and electrical parts, these products also combine hardware, sensors, data storage, microprocessors, software, and connectivity in myriad ways to become complex systems. Such products are made possible by improvements in processing power, device miniaturization, and availability of ubiquitous network connectivity to promote new functionality, greater reliability, higher product utilization, and capabilities [20]. Our research will pave the way for manufacturers to seamlessly integrate manufacturing facilities with ERP emerging technologies to foster design, development, and manufacture of smart connected products.
7.1 Acquire decisiveness for technology adoption
8 Limitations and further scope of work
Acquisition and adoption of new advanced technologies requires assessment in a holistic perspective for achieving better
We identified the following limitations of our work, each of which warrants a future scope of work:
–
Machine learning for ERP configure
Using implement objectives for ERP perf. Eval.
Using BFRC integrate ERP-PDM
ERP and data warehousing integration
Social, mobile, analytics, cloud, IoT
Ularu et al. [23]
Wei, Chun-chin [24]
Wei, Zhe et al. [25]
Zeng, Yun et al. [26]
Ranjan, Jha, Pal (Our work)
Productive, effective, connected, aware, Innovative employees, higher project centricity, Decisive insights
Support organizational information need
–
Manageable, maintainable system, more understanding and control of B-processes Flexibility with mobile app, faster employee connectivity
ERP technologies in general
Process
Finance Multi-tenancy benefits— business cost reduction –
Manage income and outgoing
Customer Global outreach
Customer collaboration, Responsiveness Offer new products, services, improved status
Low fin close cycle, transport/ On-time delivery, sales logistics, IT cost, better cash automation, customer mgmt, B-performance, responsiveness, globalization revenue/profit, fin control, acquisition – Reduce operational cost, Reduce data, application employees population redundancy, B-cycle shortened, enhance data reliability Automated ERP configuration, Faster connectivity Cost-effective connectivity to data portability to customer employee, customer Responsiveness Quality-cost flexibility, Customized, tailored, turnover, ROI reengineered B-processes, performance enhancement Integrating heterogeneous – – data systems, stability, reliability, reusability, security, privacy – Support organizational Responsiveness, closeness, information need faster value-added product, services Easy talent acquisition, Long term value of Individual user experience, retention, IP protection, investment, Strategic and co-create, collaborate, KM, data security, reliable, financial decision making, brand loyalty, customer’s scalable, agile processes, low carbon footprint, voice, consumerism Faster NPI, better global operations product quality
Multi-tenancy Data reliability, redundancy benefits—scalability, avoidance, cloud benefits— upgradability, low accessibility, mobility, usability implementation time Better operational efficiency Mobile devices to employees, by B-process transformation, worker collaboration, Worker productivity life work balance – Less tedious work, improve work satisfaction, skill development, growth Low inventory, maintenance, Information/visibility, improve process, speed, redundancy avoid, less productivity, integration, training, transfer, analysis, standardization, flexibility decisiveness, growth
Themistocleous et al. [22] EAI, ERP and supply chain
O’Leary [18]
Mobile, social, cloud, enterprise app platform
Hurbean et al. (Yankee group data) [9] Khajeh-Hosseini, [14]
Cloud migration— enterprise IT to IaaS
Cloud computing, SaaS, multi-tenancy
Employee
ERP emerging technology area Strategic objective indicators
Strategic benefits advocated by various authors
Engebrethson [4]
Author
Table 4
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a. Proposed exploratory analysis of strategic benefits of ERP technologies deals with building decisive and predictive insights of decision makers based on opinion survey of experts and statistical analysis of survey data. The model, however, cannot serve as an expert system since it is not empirically supported by large number of project data from applications in industries. b. Our opinion survey participants are distributed in the APAC region. Survey in other important geographies such as Europe, USA, and South America has not been considered. Opinion data collection from across all geographies could be a relevant task and geography-based results can be compared to understand variation in perceived decisiveness about imbibing emerging technologies across geographies due to geospatial behavioral diversities of people. c. TCO and NPV calculation of emerging technologies could be an intriguing area of research dealing with both tangible and intangible benefits and values and so is ROI. These calculations will help corporate decision makers in budget provisioning for imbibing emerging technologies besides exploratory analysis of benefits. These two approaches can be integrated. d. We anticipate a strong possibility of confirmatory analysis based on hypothesized causal covariant structures as a part of multivariate data analysis and thereby finding interrelationships between the factor variables. For example, “IP protection and knowledge management” (learning maturity) could be an antecedent and cause for “more scope of innovative thinking” (innovativeness). Similarly, “higher project centricity of employees” could be antecedent for “faster NPI and improved product quality” (manufacturing excellence). Such confirmatory analysis could be of immense help to corporate policy makers and senior leadership for strategic alignment with the help of statistically supported perceived decisiveness to achieve business objectives. e. SMEs could be benefitted besides OEMs. For this, exploratory analysis should be extended to technologies having indirect impacts on strategic objectives and benefits; for example, open-source ERP [10], sustainable ERP [3], and EAI [22]. Survey and analyses of these technologies could be relevant for small and medium manufacturing industry sectors.
for better ability to meet strategic objectives and create higher competitive advantages. We conducted opinion survey on benefit factors in four strategic objective indicator areas, i.e., employee, process, customer, and finance and analyzed the data in IBM SPSS adopting the PCA approach to find out the most significant benefits. We found “IP protection and knowledge management” of highest priority followed by “capturing individual user experience,” “faster NPI and improved product quality,” and “strategic financial decision making ability.” These four top benefits hailed from all objective indicator domains. The addressing emerging technologies were also identified and prioritized. In today’s dynamic and changing environment, manufacturing companies have strong need to create and sustain competitive advantages. This is possible by embracing right emerging technologies rapidly in which, our research will play an advisor role. By understanding the potential of individual technologies to contribute to strategic objectives, it will be easier for industries to make important investment and obsolescence decisions. For academia, our research will open new research avenues. We listed limitations in our work, each of which can potentially further the research. The work detailed in this paper is exploratory, analytical, and versatile and it aligns with the technology trends in vogue in global advanced manufacturing and OEM enterprises and promises a deeper sustenance of corporate decisions culminating in higher ROI, competitive advantage gain for the enterprise, and higher value creation for the customer to stay ahead in competition.
References 1.
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9 Conclusion The purpose of the study was to conduct an exploratory analysis on strategic benefits of application of ERP-based emerging technologies in large manufacturing enterprises and OEMs for strategic alignment and develop insight for better decisiveness. An ecosystem model of social computing, mobility, analytics, cloud computing, IoT, and ERP was conceived with few resultant drivers to transform the organization
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Atluru S, Huang SH, Snyder JP (2012) A smart machine supervisory system framework. Int J Adv Manuf Technol 58(5):563–572. doi:10.1007/s00170-011-3405-4 Basu R, Biswas D (2013) An approach to identify failure factors of enterprise application implementation in Indian micro enterprises. Int J Managing Value Supply Chains (IJMVSC) 4(1):37–47. doi: 10.5121/ijmvsc.2013.4104 Chofreh AG, Goni FA, Shaharoun AM, Ismail S, Kleme JJ (2014) Sustainable enterprise resource planning: imperatives and research directions. J Clean Prod 71:1–9. doi:10.1016/j.jclepro.2014.01.010 Engebrethson R (2012) Comparative analysis of ERP emerging technologies. MS Thesis California Polytechnic State University, San Luis Obispo http://digitalcommons.calpoly.edu/cgi/ viewcontent.cgi?article=1779&context=theses Gupta A (2000) Enterprise resource planning: the emerging organizational value systems. Ind Manag Data Syst 100(3):114–118. doi:10.1108/02635570010286131 Habidin NF, Yusof SM, Omar B, Mohamad SIS, Janudin SE (2012) A proposed strategic balanced scorecard model: strategic control system and organizational performance in Malaysian automotive industry. IOSR J Bus Manag (IOSRJBM) 1(6):39–44, ISSN: 2278-487X Hair JF, Black WC, Babin BJ, Anderson RE (2009) Multivariate data analysis. 7th ed. Prentice Hall ISBN-13: 978–0138132637
Author's personal copy Int J Adv Manuf Technol 8.
9.
10.
11.
12. 13. 14.
15.
16.
17.
Henderson JC, Venkatraman N (1993) Strategic alignment: leveraging information technology for transforming organizations. IBM Syst J 32(1):4–16. doi:10.1147/sj.382.0472 Hurbean L, Fotache D (2013) Mobile technology: binding social and cloud into a new enterprise application platform. Inf Econ 17(2):73–83. doi:10.12948/issn14531305/17.2.2013.06 Johansson B, Sudzina F (2008) ERP systems and open source: an initial review and some implications for SMEs. J Enterp Inf Manag 21(6):649–658. doi:10.1108/17410390810911230 Kaplan RS (2008) Conceptual foundations of the balanced scorecard. Handb Manag Account Res 3:1253–1269. doi:10.1016/ S1751-3243(07)03003-9 Kaplan RS, Norton DP (1992) The balanced scorecard—measures that drive performance. Harv Bus Rev. doi:10.1225/92105 Kaplan RS, Norton DP (1996) Linking the balanced scorecard to strategy. Calif Manag Rev 39(1):53–79. doi:10.2307/41165876 Khajeh-Hosseini A, Greenwood D, Sommerville I (2010) Cloud migration: a case study of migrating an enterprise IT system to IaaS. In IEEE 3rd Int Conf on Cloud Comput (CLOUD “10) pp 450–457 doi:10.1109/CLOUD.2010.37 Kim JO, Mueller CW (1978) Factor analysis: statistical methods and practical issues. SAGE Publications ISBN-13: 978– 0803911666 Lee HG, Na HB, Shin K, Jeong H, Park J (2007) Performance improvement study for MRP part explosion in ERP environment. Int J Adv Manuf Technol 35(3):309–324. doi:10.1007/s00170-0060718-9 Matthews D (2014) Five trends that will shape the future of ERP. Industry-Week White paper http://www.industryweek.com/ technology/five-trends-will-shape-future-erp
18.
O’Leary DE (2004) Enterprise resource planning (ERP) systems: an empirical analysis of benefits. J Emerg Technol Account 1(1): 63–72. doi:10.2308/jeta.2004.1.1.63 19. Ou-Yang C, Chang MJ (2006) Developing an agent-based PDM/ ERP collaboration system. Int J Adv Manuf Technol 30(3):369– 384. doi:10.1007/s00170-005-0076-z 20. Porter ME, Heppelmann JE (2014) How smart, connected products are transforming competition. Harvard Business Review, November Issue 21. Ram J, Corkindale D, Wu ML (2014) ERP adoption and the value creation: examining the contributions of antecedents. J Eng Technol Manag 33:113–133. doi:10.1016/j.jengtecman.2014.04.001 22. Themistocleous M, Irani Z, Love PED (2002) Enterprise application integration: an emerging technology for integrating ERP and supply chains. In European Conf of Infor on Systems (ECIS 2002): 1087–1096 http://aisel.aisnet.org/cgi/viewcontent.cgi?article= 1089&context=ecis2002 23. Ularu EG, Puican FC, Suciu G, Vulpe A, Todoran G (2013) Mobile computing and cloud maturity—introducing machine learning for ERP configuration automation. Inf Econ 17(1):40–52. doi:10. 12948/issn14531305/17.1.2013.04 24. Wei CC (2008) Evaluating the performance of an ERP system based on the knowledge of ERP implementation objectives. Int J Adv Manuf Technol 39(1):168–181. doi:10.1007/s00170-0071189-3 25. Wei Z, Tan J, Feng Y (2009) Integration technology of ERP and PDM based on business remote function call. Int J Adv Manuf Technol 40(9):1044–1052. doi:10.1007/s00170-008-1419-3 26. Zeng Y, Chiang RHL, Yen DC (2003) Enterprise integration with advanced information technologies: ERP and data warehousing. Inf M a n a g C o m p u t S e c u r 11 ( 3 ) : 11 5 – 1 2 2 . d o i : 1 0 . 11 0 8 / 09685220310480390
