Big data analytics application for sustainable manufacturing operations: analysis of strategic factors

Abstract

In the present era of Industry 4.0, organizations are transforming from traditional production systems to digital production systems. This transformation is in terms of additional deployment of technologies that lead to digitization and integration of products and services, business processes and customers, etc. A high volume of unstructured data is being created across different processes due to digitization. The digitization captures the data that includes text, images, multimedia, etc., due to multiplicity of platforms, e.g., machine-to-machine communications, sensors networks, cyber-physical systems, and Internet of Things. Managing this huge data generated from different sources has become a challenging task. Big data analytics (BDA) may be helpful in managing this unstructured data for effective decision making and sustainable operations. Many organizations are struggling to integrate BDA with their manufacturing processes for sustainable operations. The application of BDA from a sustainability perspective is not extensively researched in the current literature. Therefore, firstly this study explores the contribution of BDA in sustainable manufacturing operations. It further identifies strategic factors for the successful application of BDA in manufacturing for sustainable operations. For a detailed analysis of strategic factors in manufacturing, a hybrid approach comprising the analytic hierarchy process, fuzzy TOPSIS and DEMATEL is used. Results revealed that development of contract agreement among all stakeholders, engagement of top management, capability to handle big data, availability of quality and reliable data, developing team of knowledgeable, and capable decision-makers have emerged as major strategic factors for the application of BDA in the manufacturing sector for sustainable operations. Major contribution of this study is in analyzing BDA benefits for manufacturing sector, identifying major strategic factors in implementation and categorization of these factors into cause and effect group. These findings may be used by managers as guidelines for successful implementation of BDA across different functions in their respective organization to achieve sustainable operations goal. The results of this study will also motivate industry professionals to integrate BDA with their manufacturing functions for effective decision making and sustainable operations.

Graphic abstract

Appendices

Appendix 1

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Table 12 Thomas Saaty’s scale for pairwise comparison of criteria (Saaty 1994)

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Appendix 2

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Table 13 Average random index value (Saaty 2000)

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Appendix 3

Let P₁ be the pairwise comparison matrix and P₂ principal vector matrix.

$$ {\text{P}}_{1} \; = \;\left[ {\begin{array}{*{20}c} 1 & {0.143} & {0.25} & {0.2} & {0.2} & {0.33} & {0.125} \\ 7 & 1 & 5 & 3 & 2 & 6 & {0.33} \\ 4 & {0.2} & 1 & {0.33} & {0.2} & 5 & {0.25} \\ 5 & {0.33} & 3 & 1 & {0.33} & 4 & {0.2} \\ 5 & {0.5} & 5 & 3 & 1 & 7 & {0.5} \\ 3 & {0.167} & {0.2} & {0.25} & {0.143} & 1 & {0.167} \\ 8 & 3 & 4 & 5 & 2 & 6 & 1 \\ \end{array} } \right] $$

$$ {\text{P}}_{2} \; = \;\left[ {\begin{array}{*{20}c} {0.025} \\ {0.2260} \\ {0.0765} \\ {0.1030} \\ {0.1940} \\ {0.0395} \\ {0.3360} \\ \end{array} } \right]\;{\text{P}}_{3} \; = \;{\text{P}}_{1} *{\text{P}}_{3} \; = \;\left[ {\begin{array}{*{20}c} {0.19087} \\ {1.8283} \\ {0.5750} \\ {0.8213} \\ {1.568} \\ {0.2421} \\ {2.660} \\ \end{array} } \right]\;{\text{and}}\;{\text{P}}_{4} \; = \;{\text{P}}_{3} /{\text{P}}_{2} \; = \;\left[ {\begin{array}{*{20}c} {0.19087} \\ {1.8283} \\ {0.5750} \\ {0.8213} \\ {1.568} \\ {0.2421} \\ {2.660} \\ \end{array} } \right] $$

$\lambda \max ,$ Average of the element of P₄= 7.7481.

Now, consistency Index (CI) = $\frac{\lambda \max - n}{{n - 1}}$ = (7.7481–7)/(7–1) = 0.12468.

And, consistency ratio (CR) = CI/RCI = (n Appendix 2).

CR = 0.12468/1.35 0.0923, i.e., CR < 0.1. So, result is consistent.

Appendix 4

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Table 14 Fuzzy decision matrix D

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Appendix 5

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Table 15 Un-weighted fuzzy matrix R

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Appendix 6

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Table 16 Distance of the ratings of each factor from A⁺ with respect to each criterion

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Appendix 7

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Table 17 Distance of the ratings of each factor from a with respect to each criterion

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Appendix 8

See Table

Table 18 Influence matrix

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Appendix 9

Variable used in the article.

AHP Methodology

P1 = Pairwise comparison matrix

P2 = Principal matrix

λ_max = Average of the elements of P₄.

CI = Consistency index.

CR = Consistency Ratio.

RCI = random consistency Index.

n = Number of elements.

PV = priority Vector.

Fuzzy TOPSIS Methodology

A⁺  = Fuzzy ideal solution.

A⁻ = Fuzzy negative ideal solution.

Y_ij = (d_ij, e_ij, and f_ij) = Triangular fuzzy number for the linguistic term.

$c_{{j^{*} }}^{*}$ and $d_{j}^{ - }$ = Benefit criteria and cost criteria respectively.

W = Weight of criteria.

V^* and V⁻ = Values considered for the ideal and negative ideal solution.

g, h, and i = aThe real numbers.

D⁺  = Distance between to fuzzy numbers.

D⁻ = Distance of rating.

C = Closeness the ideal solution.

DEMATEL Methodology

Z = Average matrix.

x_ij = Elements of average matrix.

N = Normalized initial direct-relation matrix.

n_ij = Elements of normalized initial direct-relation matrix.

Y = Total relation matrix.

I = Identity matrix.