Methodology

Table of Contents:

The HBWM proposed by (Tabatabaei et al. 2019) was used to evaluate and calculate the weights of the barriers and sub-barriers. This method was used because it needs fewer data and has more consistent pairwise comparisons needed for decision making. HBWM is very useful when the decision-making problem has different criteria

TABLE 3.1

Barriers to BDA in LSCM

Main Barriers

Sub-barriers

A Brief Description

Key

Reference

Technological barriers (BR|)

Lack of accessibility of specific BDA tools (BRn)

In various supply chains, choosing the appropriate BDA tools will improve performance.

(Moktadir et al. 2019)

Lack of infrastructural facility (BR,,)

The implementation of new technologies in the organization requires proper infrastructure.

(Trelles et al. 2011)

Lack of interest in using novel technology (BR,,)

Existing technology for BDA in supply chains is costly.

(Moktadir et al. 2019)

High cost of implementation (BRI4)

The implement of BDA tools in organizations requires high costs.

(Sun et al. 2018)

New Technology Compatibility (BR|S)

The characteristics of BD are perceived as being consistent with the existing IT architecture in an organization

(Sun et al. 2018)

Expertise and human-related barriers (BR,)

Lack of IT experts (BR2I)

Lack of IT experts may increase data processing errors, data damage, or confound data analysis and interpretation.

(Alharthi, Krotov, and Bowman 2017)

Ease of reception (BR,,)

The development of BDA tools in the organization requires special attention to the conditions of the employees.

This study

Lack of appropriate facilities to improve BDA tools (BR,,)

Lack of appropriate facilities in educational organizations to research existing difficulty and improve BDA tools.

(Moktadir et al. 2019)

Data-related barriers (BR,)

The complexity of data (BR„)

A variety of data formats from distinct sources may create complexity in data storage and integration.

(Fallik 2014)

Data quality (BR,,)

Data quality is sensitive due to a variety of data sources, storage tools, firms, etc.

(Malaka and Brown 2015

Data security and privacy (BR„)

Lack of data security and privacy are the significant barriers to the adoption of BDA, as data must be secure if they are to compete in the international market.

(Alharthi, Krotov, and Bowman 2017)

Rate of data growth (BR,4)

Increasing the rate of data generation can continually cause problems for the BDA. Many organizations simply opt to delete old data instead of trying to accommodate data growth.

(Alharthi, Krotov, and Bowman 2017)

TABLE 3.1 (Continued) Barriers to BDA in LSCM

Main Barriers

Sub-barriers

A Brief Description

Key

Reference

Organizational barriers (BR4)

Lack of training facilities (BRj,)

Adaptation of BDA inside companies may perhaps be obstructed by the absence of suitable training facilities for employees.

(Malaka and Brown 2015)

Time constraints (BR4,)

Time constraints are one of the biggest issues in handling new technologies or tools in industries.

(Zhong et al. 2016)

Organizational culture (BR43)

The system of assumptions, values, and beliefs of employees in dealing with new technology and how to accept changes and mutations.

This study

Share data between organizations (BR44)

Lack of data sharing policies betw een organizations.

(Moktadir et al. 2019)

Management support (BR^)

Managers are willing to allocate sufficient resources and encourage the initial adoption of BD.

(Sun et al. 2018)

Organizational structure (BR46)

The organization has a w'ell-organized structure that is w’ell-suited to the reception of BD.

(Sun et al. 2018)

Political barriers (BR;)

Sanctions imposed by governments (BRM)

Existence of international sanctions over disputes by governments as a barrier to access to modern information and technology and tools.

This study

The international restrictive laws (BR52)

International law’s that apply to technology transfer may affect the use of new' technology or tools.

This study

Competitive pressure (BR54)

The degree to which firms influence the decision-making of BD adoption according to the competitor’s behavior.

This study

and subcriteria, and here, it calculates the importance of barriers and sub-barriers based on pairwise comparisons made by experts. In this study, HBWM is used to calculate the importance of barriers and sub-barriers. The hierarchical structure of the decision-making problem in this study is shown in Figure 3.2.

The Steps of HBWM

The notations and their descriptions for the HBWM technique are provided in Table 3.2.

Hierarchical structure of barriers to BDA in the supply chain management

FIGURE 3.2 Hierarchical structure of barriers to BDA in the supply chain management.

TABLE 3.2

Notations and Their Descriptions

Notation

Definition

Sets

Barriers

Sub-barriers

Parameters

Preference of the most important barrier overy-th barrier

Preference ofy-th barrier over the least important barrier

Preference of the most important sub-barrier over k-th sub-barrier for y-th barrier

Preference of k-th sub-barrier over the least important sub-barrier for y-th barrier

Variables

Weight of the most important barrier

Weight of y-th barrier

Weight of the least important barrier

Weight of the most important sub-barrier for y-th barrier

Weight of k-th sub-barrier for y-th barrier

Weight of least important sub-barrier for y-th barrier

Global weight of k- th sub-barrier for y-th barrier

According to Figure 3.2, the steps of this technique are shown as follows:

Step 1. Determining the set of problem barriers and sub-barriers: At this stage, the problem barriers and sub-barriers are determined as {ci,c2,...,c„} and {ci*.,c2*,...,c„t}, respectively.

Step 2. Determining the most important (best) and the least important (worst) barrier and sub-barriers: the most important and the least important barrier and sub-barriers are identified.

Step 3. At this stage, the preference of the most important barrier over each of other barriers is defined as a number between 1 and 9, which is shown as AB = (am,aB2,where ав, is the preference of the most important barrier over ;'th barrier and aBB = 1.

Step 4. At this stage, the preference of each barrier over the least important barrier is defined as a number between 1 and 9, which is shown as Aw =(aw,a2W,...,a„w), where ajW is the preference of jth barrier over the least important barrier and aWw = 1.

Step 5. At this stage, the preference of the most important sub-barrier over each of other sub-barriers is defined as a number between 1 and 9, which is shown as AB = (aJBl,aJB2,...,aJBkj, where aJBk is the preference of the most important sub-barrier over jth sub-barrier and aJBB= 1.

Step 6. At this stage, the preference of each sub-barrier over the least important sub-barrier is defined for each barrier as a number between 1 and 9, which is shown as AB =(a{w,a{w,...,ajkw where a{w is the preference of ftth sub-barrier over the least important sub-barrier for jth barrier and a(vw =1.

Step 7. Find the weights of the barriers (vv, ,u’2,...,vc*) and sub-barriers

Given that the non-negativity of the weights, we can formulate the programming model of HBWM as follows (Equations 3.1-3.8):

represents the framework of the research methodology

Figure 3.3 represents the framework of the research methodology.

Framework of the research methodology

FIGURE 3.3 Framework of the research methodology.

Determining the Consistency Rate

According to the proposed approach, of reference comparisons determined for the barriers and of reference comparisons determined for the sub-barriers of each barrier are calculated separately by the HBWM technique, and the results are solved in a similar way to the original best-worst method (BWM), the consistency index (Cl) presented for the original BWM can be used to calculate the consistency rate (CR) of the reference comparisons performed for the barriers and sub-barriers.

The CR in the BWM is determined concerning the value of the preference of the most important barrier over the least important barrier and the preference of the most important sub-barrier over the least important sub-barrier of jth barrier. The Cl value is specified in Table 3.3 (Rezaei 2015).

TABLE 3.3 Cl in BWM

1

2

3

4

5

6

7

8

9

0.00

0.44

1.00

1.63

2.30

3.00

3.73

4.47

5.23

Considering the minimum deviations of the reference comparisons performed for the barriers к*) and the sub-barriers of each barrier (|‘) which are obtained through the BWM, and the Cl specified in Table 3.3, we can obtain the CR for the barriers and sub-barriers of each barrier using Equations (3.9) and (3.10).

Results and Discussion

Developing countries are looking to use BDA in their industries and manufacturing companies, but there are many obstacles to accepting BDA in LSCM of these countries. Industry managers in developing countries seek to identify and address these barriers. Iran is one of the countries facing many challenges in implementing BDA. In this study, barriers to BDA in LSCM are identified and ranked according to the existing production environment in Iran. The identified barriers include the five main barriers and their sub-barriers. HBWM is used to weight and rank the barriers and sub-barriers. The HBWM reference comparison questionnaires were prepared by consulting multiple experts in the Iranian manufacturing industry. The most significant and least significant barriers were selected for reference comparisons and then the rest of the barriers w'ere compared and a number ranging from 1 to 9 was assigned to each of them. Table 3.4 shows reference comparisons of the main and sub-barriers.

Using Table 3.4 and the HBWM model presented in Equations (3.1)—(3.8), the weights and ranks of the main and sub-barriers were calculated using LINGO software. Finally, the weights of the main barriers and local and global weights of sub-barriers were calculated. The weights and ranks of the barriers are shown in Table 3.5.

The results show that the data-related barriers have the highest weight in the LSCM, and also, the most important sub-barrier in this area is the degree of data complexity that will undoubtedly have a significant impact on BD analysis. This shows that managers should pay more attention to the barriers that are directly related to data w'hen using BDA in LSCM. Technological barriers were identified as the second major barriers. Technological factors play an important role in the acceptance and implementation of BD, and appropriate infrastructure for technological tools must be prepared. Expertise and human-related barriers and organizational barriers jointly ranked third, and political barriers ranked fifth.

TABLE 3.4

Reference Comparisons of the Main Barriers and Sub-barriers

Main Barriers

B-O

O-W

Sub-barriers

B-O

O-W

Sub-barriers

B-O

O-W

BR,

2

3

BR|i

4

3

BR,,

1

4

BR:

3

2

BR,,

1

7

BR,,

4

1

BR,

1

4

BR„

7

1

BR,,

3

2

br4

3

2

BRH

3

4

BR,

4

1

BR,5

5

2

Sub-barriers

B-O

O-W

Sub-barriers

B-O

O-W

Sub-barriers

B-O

O-W

BR,,

1

5

br41

1

5

BR,,

2

2

BR,,

4

2

br4.

2

4

BR,,

1

3

BR,,

5

1

br4,

2

4

BR„

3

1

BR,4

2

3

br44

5

1

br4,

3

3

BR.„,

4

2

TABLE 3.5

Weights and Ranks of the Main and Sub-barriers

Main

Barriers

Weights

Sub

barriers

Local

Weights

Local

Rank

Global

Weights

Global

Rank

BR,

0.221

BR„

0.140

3

0.031

12

BR,,

0.498

1

0.110

2

BR,,

0.062

5

0.014

20

BR,4

0.187

2

0.041

9

BR|;

0.112

4

0.025

16

CO

70

0.147

BR,,

0.628

1

0.092

4

BR,,

0.143

3

0.021

17

BR,,

0.228

2

0.034

11

BR,

0.397

BR„

0.500

1

0.198

1

BR,,

0.136

3

0.054

6

BR„

0.091

4

0.036

10

BR-.,

0.272

2

0.108

3

BR,

0.147

BR,i

0.336

1

0.049

7

BR,,

0.191

2

0.028

13

BR„

0.191

2

0.028

13

BR+i

0.058

6

0.085

5

BR„

0.127

4

0.019

18

BR,6

0.096

5

0.014

20

CO

TO

0.088

br„

0.292

2

0.026

15

BR,,

0.542

1

0.048

8

BR,,

0.167

3

0.015

19

Political barriers are strongly linked to international relations and existing international mechanisms and may have different outcomes for each different country. In general, data complexity was identified as the most significant sub-barrier. Lack of infrastructural facilities was identified as the second significant sub-barrier. This indicates the lack of suitable infrastructure for the BDA in Iranian manufacturing industries. The third significant sub-barrier is the rate of data growth in industries that managers have often overlooked. The ranks of the other sub-barriers are provided in Table 3.5.

Figure 3.4 shows the weights obtained using the proposed method for the main and sub-barriers. The results of this study lead to identifying the significance of the factors influencing BDA for the Iranian industrial environment. The results of this study are more applicable and helpful for industries looking to abandon traditional management practices and move toward new management approaches. It is clear that removing all barriers to BDA is very difficult at the same time, so in the first place, removing the more important barriers should be the focus of industry managers.

Conclusion

The purpose of this study was to evaluate the barriers to BDA in LSCM. A new MCDM approach, called HBWM, has been used to evaluate these barriers that can calculate the weights of the criteria and alternatives simultaneously. It is difficult to remove the barriers to BDA without a proper strategy. Therefore, identifying and prioritizing these barriers can help decision-makers. Determining the importance of each barrier helps industry managers understand the significance of each barrier in their supply chains. Also, they can formulate their future strategies and policies based on the significance of these barriers. The method can also lead to plans to overcome these barriers. In this study, HBWM was used to determine the significance of the main and sub-barriers. The benefits of this approach include the calculation of the weights of the main and sub-barriers in a single integrated model. Five categories of main barriers and 21 additional barriers were selected based on the literature

Final prioritization of the barriers to BDA usage and acceptance

FIGURE 3.4 Final prioritization of the barriers to BDA usage and acceptance.

review and experts’ opinions. Then, the HBWM reference comparisons’ questionnaires were completed by several experts in the Iranian manufacturing industry, and its data were used as inputs to the HBWM model. The results of this study showed that data-based barriers are the most important barriers. Technological barriers were identified as the second important category of barriers. Expertise and human-related barriers and organizational barriers jointly ranked third, and political barriers were identified as the least important category of the main barriers. This prioritization was also done for all sub-barriers.

The results of this study revealed the problems and challenges that the Iranian manufacturing industries face when using BDA. Organizations can use the proposed approach to develop appropriate solutions to remove barriers based on the significance and priority of each barrier and achieve a competitive advantage. This information can be helpful for managers’ future decisions on issues such as demand forecasting, production planning, and so on.

References

Accenture Global Operations Megatrends Study. 2014. “Big data analytics in supply chain: Hype or here to stay?”

Alharthi, Abdulkhaliq, Vlad Krotov, and Michael Bowman. 2017. “Addressing barriers to big data.” Business Horizons 60 (3): 285-92.

Barnaghi, Payarn, Amit Sheth, and Cory Henson. 2013. “From data to actionable knowledge: Big data challenges in the web of things.” IEEE Intelligent Systems 6: 6-11.

Brohi, Sarfraz Nawaz, Mervat Adib Bamiah, and Muhammad Nawaz Brohi. 2016. “Identifying and analyzing the transient and permanent barriers for big data.” Journal of Engineering Science and Technology II (12): 1793-1807.

Chen, C L Philip, and Chun-Yang Zhang. 2014. “Data-intensive applications, challenges, techniques and technologies: A survey on big data.” Information Sciences 275: 314-47. Chen, Hsinchun, Roger H L Chiang, and Veda C Storey. 2012. “Business intelligence and analytics: From big data to big impact.” MIS Quarterly 36 (4): 1165-88.

Fallik, Dawn. 2014. “For big data, big questions remain.” Health Affairs 33: 111 1-4.

Hilbert, Martin. 2016. “Big data for development: A review of promises and challenges.” Development Policy Review 34 (1): 135-74.

Malaka, Iman, and Irwin Brown. 2015. “Challenges to the organisational adoption of big data analytics: A case study in the South African telecommunications industry.” In Proceedings of the 2015 Annual Research Conference on South African Institute of Computer Scientists and Information Technologists, Stellenbosch, South Africa, 27. ACM.

Manyika, James. 2011. “Big data: The next frontier for innovation, competition, and productivity.” htttp://Www.Mckinsey.Com/Insights/MGI/Research/Technology_and_ Innovation/Big_data_The_next_frontier_for_innovation.

McAfee, Andrew, and Erik Brynjolfsson. 2012. “Big data: The management revolution.” Harvard Buiness Review, 90 (10): 61-68.

Moktadir, Md Abdul, Syed Mithun Ali, Sanjoy Kumar Paul, and Nagesh Shukla. 2019. “Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh.” Computers & Industrial Engineering 128: 1063-75.

Park, Jong-Hyun, Moon-Koo Kim, and Jong-Hyun Paik. 2015. “The factors of technology, organization and environment influencing the adoption and usage of big data in Korean firms.”

Rezaei, Jafar. 2015. “Best-worst multi-criteria decision-making method.” Omega 53: 49-57.

Sun, Shiwei, Casey G Cegielski, Lin Jia, and Dianne J Hall. 2018. “Understanding the Factors Affecting the Organizational Adoption of Big Data.” Journal of Computer Information Systems 58 (3): 193-203.

Tabatabaei, Mohammad Hashemi, Maghsoud Amiri, Mohammad Ghahremanloo, Mehdi Keshavarz-Ghorabaee, Edmundas Kazimieras Zavadskas, and Jurgita Antucheviciene. 2019. “Hierarchical decision-making using a new mathematical model based on the best-worst method.” International Journal of Computers Communications & Control 14 (6): 710-25.

Trelles, Osw'aldo, Pjotr Prins, Marc Snir, and Ritsert C Jansen. 2011. “Big data, but are we ready?” Nature Reviews Genetics 12 (3): 224.

Wang, Gang, Angappa Gunasekaran, Eric W T Ngai, and Thanos Papadopoulos. 2016. “Big data analytics in logistics and supply chain management: Certain investigations for research and applications.” International Journal of Production Economics 176: 98-110.

Wang, Yichuan, and Nick Hajli. 2017. “Exploring the path to big data analytics success in healthcare.” Journal of Business Research 70: 287-99.

Yadegaridehkordi, Elaheh, Mehdi Hourmand, Mehrbakhsh Nilashi, Liyana Shuib, Ali Ahani, and Othman Ibrahim. 2018. “Influence of big data adoption on manufacturing companies’ performance: An integrated DEMATEL-ANFIS approach.” Technological Forecasting and Social Change 137: 199-210.

Zakir, Jasmine, Tom Seymour, and Kristi Berg. 2015. “Big data analytics.” Issues in Information Systems 16 (2): 81-90.

Zhong, Ray Y, Stephen T New'man, George Q Huang, and Shulin Lan. 2016. “Big data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives.” Computers & Industrial Engineering 101: 572-91.

A Big Data in Procurement 4.0
 
Source
< Prev   CONTENTS   Source   Next >