The Relationship of Critical Success Factors of the Mineral Industry: A Study on India

Abstract

The mineral industry environment is an interlinked chain of activities. Identification of major activities of the mineral industry, termed as critical success factors (CSFs), and their relationship structure will be approachable, productive and efficiency-oriented for the industry to manage. The aim of the article is to develop a relationship structure consisting of CSFs influencing the mineral industry. Sixteen such operational influencing CSFs are identified through a broad literature review and subsequent dialogues held with mineral-field experts. The interpretive structural modelling (ISM) approach is applied for analysing CSFs and finding a relationship structure. The study is carried out in Odisha, Chhattisgarh and Jharkhand, mineral-rich states of India. Identification of CSFs and their relationship is carried out in nine phases sequentially to arrive at the latent structure. This is the only study to develop relationships of CSFs for any mineral industry in India. The findings of the study will provide insights on the relationship structure of CSFs of the mineral industry’s operation and be helpful for improvements in addressing operational difficulties for corporates, as well as academics. Among the 16 CSFs identified, ‘training and skill level’, ‘infrastructure’ and ‘political influence’ have the maximum driving force and are the least dependent. The projected model is helpful in understanding the relationship among CSFs and the business operation as a whole.

Keywords

Business Environment Mineral Industry Critical Success Factors Interpretive Structural Modelling Relationship of CSRs India

Executive Summary

The mineral industry contributed around a quarter of the Indian GDP and was the major driving force behind a decade of growth, doubling the GDP to US$2.6 trillion in 2017 from US$1.32 trillion in 2009 (Trading Economics, 2020) in 2010. After the end of 2002–2010, supercycle profitability collapsed. The mineral industry showed an intense interest in reversing all the excesses of 2000s. It was witnessing a struggle with unpredictability and market uncertainty. The industry was experiencing varieties of challenges, such as social, environmental, technological and economical, impacting sustainability and productivity. For a sustainable and productive mineral industry, some work has been done, and still there is wide scope for further improvement. The aim of this article is to develop a relationship structure consisting of critical success factors (CSFs) influencing the mineral industry using interpretive structural modelling (ISM).

43 articles were chosen out of 95 articles published between 2008 and 2020, consisting of research articles, trade journals, expert blogs, technical papers, reports, etc. CSFs were identified considering various research outputs. Political influence is a factor raised by Indian field experts on the author’s field of study, which was included in the study.

ISM is a well-established methodology for identifying relationships among specific items. This model or process helps in relating key success factors or CSFs of an outcome in a structured way. This study was conducted at selected mineral industries of Odisha, Chhattisgarh and Jharkhand with 12 experts rich in exposure and work experience. Many constructed meetings were held to derive the outcomes.

The mineral business operation environment is an interlinked chain of activities. Identification of CSFs and their relationship will be approachable, productive and efficiency-oriented. In this paper, 16 key CSFs of the Indian mineral Industry are identified with inputs from an extensive literature review and field discussions held with experts of this industry. These key factors can be considered for any massively deposited mineral industry with or without minimal change for successful implementation. The ISM methodology is projected for the development of the structural model establishing the relationships among the CSFs. The driving power and dependence level of each CSF is determined to develop the driving power and dependence diagram of relationship structure. It is found that the key factor ‘infrastructure’ has maximum driving power and minimum dependence level among the 16 key factors. Therefore, infrastructure should be designed taking the whole mineral business operation into consideration to create an amicable environment. A benchmarking model is developed on the basis of a structural model for CSFs. The projected model is an excellent tool for understanding the relationship of CSFs and the business operation as a whole. This technique can also be applied to other types of mineral industries and manufacturing industries, capturing their own unique CSFs.

Introduction

The mineral industry was contributing a quarter of the Indian GDP in 2010 and was one of the major driving forces from the perspectives of revenue, employment generation and social empowerment. A decade of growth, which saw the GDP doubling to US$2.6 trillion in 2017 from US$1.32 trillion in 2009, is remembered as the golden era of mining (Trading Economics, 2020).

Problems in the mineral industry started appearing just after the end of the supercycle 2002–2010. The profitability of mining companies almost collapsed after the supercycle got over. The mineral industry had to reverse all the excesses of the 2000s. CEOs (chief executive officers) started to notify their investors that poor productivity performances were prevailing which needed immediate attention (AusIMM Bulletin, 2015). The mineral industry became unpredictable, with an uncertain market. Low-demand clubbed with higher costs delayed in capital projects. Community expectation, government interference, corruption and tax prices all went high. Mineral companies were compelled to start withdrawing or cancelling projects, stopped their constructions and adopting other financial measures, like mergers (Deloitte, 2012). The mineral industry started experiencing varieties of challenges, such as social, environmental, technological and economical, impacting sustainability and productivity (Bearman, 2013; Prior et al., 2012).

After lagging behind in several factors, some strong operating performances were observed in 2019 and some pocket of progress on contemporary factors. However, compared with the progress of other industries, there was no such sign of quantum shift in priorities (PwC, 2019). The traditional risk factors of the mineral industry are remote locations, inhospitable areas and susceptibility to natural catastrophes. In addition to these, the mineral industry still faces many wider challenges, such as new technologies, climatic changes, economic uncertainties and protected delivery of essential consumables, like water, electricity, gas, etc. (Marsh & McLennan Companies, 2020). Most commodities might move back into the global market balance or even surplus, but the mineral sector’s challenges are not yet over. The mineral pits are going deeper, with more complex mineral bodies, social and geopolitical risks and rising energy costs, and mining companies are under exceptional pressure to control costs, heighten efficiency and improve their safety performances (Deloitte, 2020).

Mining companies need to broaden their strategic outlooks. When done well, strategic planning cycles consider a range of issues in addition to producing at lowest cost, including the role of individual assets in the portfolio, the path to value creation, the between risk and return, and how the company is differentiating itself in the eyes of its stakeholders. (Phil Hopwood, Deloitte Global Mining & Metals leader, 2019)

Mineral industry has a window of opportunities in coming years with strong operating fundamentals along with changing & growing expectations of stakeholders (PwC, 2019). In the past, mineral companies had a track record of making continuous improvements in safety and risk governance standards. The professionalism and expertise present within the mineral industry will ensure that any new and emerging risks/challenges are dealt with in an equally determined fashion. Mining companies can effectively identify and mitigate all forms of risks with improved technologies, new managerial approaches and exchange of productive dialogues among company executives (Marsh & McLennan Companies, 2020). In this direction, a managerial approach like interpretive structural modelling (ISM) can be implemented to establish relationship/dependency/direct or indirect link(s) among various critical element(s) to analyse the business environment in a productive way (Mishra & Mohanty, 2020).

The objectives of this article are:

To collect and present attributes from the existing literature and field studies to identify critical success factors (CSFs) of the mineral industry which influence operational matters; and

To develop a relationship structure of those CSFs that influence operational matters of the mineral industry of India using the ISM technique.

Literature Review and Search for Critical Success Factors

Ninety-five articles were collected from a number of sources, such as search engines, libraries, etc., which were closely related with the mineral operations industry. Out of these 95 articles, only 42 were chosen which were published between 2008 and 2020. These consisted of full research articles, different trade journals, many consultancy and expert blogs, technical papers, etc. The gist of all these selected articles was prepared. Some group findings of articles indicated different key factors, which were later termed as CSFs influencing the mineral business. The following CSFs were identified considering various research outputs:

Cost of input resources: The input resources of mineral industries are the variable costs (Topp et al., 2008). Any price uncertainties (Krutilla et al., 2020) of input resources can influence the financial condition of the mineral industry. Cost optimization (Ozdemir & Kumral, 2018) has become a trend to improve efficiency. To reduce high prices for energy (Bradley & Sharpe, 2009), Pollack and Bongaerts (2020) formulated a mathematical model to integrate renewable energy into the mineral industry. Fluctuations in market price (Groeneveld & Topal, 2011) and expenditures on goods and services (Lala et al., 2016) have a heavy impact on the mineral business. Mining capital expenditure (CapEx) has declined sharply since the peaks of 2012 (Deloitte, 2018). Cost curve position is a vital value differentiator for mineral-mining companies. Fluctuating cost of input resources can shift cost curves from predetermined positions (SRK Consulting, 2017).

Capacity utilization and optimal ore recovery: Internet of Things modules incorporated in the mineral industry improve performance, energy efficiency, response time, scalability, accuracy, production performance, etc., that is, they lead to improved quality and quantity (Maheswari et al., 2020). All mineral industries have a particular range of installed capacity, machine capacity, capacity of other installations, etc. Mineral recovery (Groeneveld & Topal, 2011) requires special attention to the optimal quality and quantity. Low-grade minerals are rejected at the earliest (Carrasco et al., 2016) or blended with extremely high-grade minerals for a balanced output.

Communication mode: Effective information flow within an industry is vital. Any scarcity of the same will invite panic situations (Okely, 2009). Decision support models (Thaduri et al., 2019) can greatly help address the problem of communication.

Drill and blast efficiency: Optimal detonators and autonomous drilling systems (Rio Tinto, 2014) are helpful for blast optimization (Ramulu et al., 2012). Increasing the drill and blast efficiency will be helpful in enhancing crusher plant performance (Rymarchuk & Shepel, 2020).

Energy management: For present mineral industries, 15%–20% energy consumption can be reduced, and for new industries, 50% reduction is possible. This can materialize through effective energy management practices (Deloitte, 2016) or energy efficiency (Carrasco et al., 2016; Moran et al., 2019) and optimal mine design.

Infrastructure: Haul roads and connectivity, calibration units, buildings, loading points, welfare amenities, etc. are to be optimized as infrastructure (MCA, 2012). Direct or indirect influence on productivity will be observed in the case of any infrastructure constraints (Topp et al., 2008). De Haas and Poelhekke (2019) found that access to inputs and infrastructure has become more constrained in mineral industries, which adversely affects their growth.

Innovation and technology: Mining has become an advanced industry with modern techniques and machinery. Over the years, this industry has adopted smarter exploration, efficient mining practices and safer working conditions and has also become more responsible. Innovation solves many problems, including productivity constraints (Aznar-Sánchez et al., 2019; MCA, 2012). Re-engineering and continuous improvement have become integral parts of the mineral industry (Deloitte & PDAC, 2015). New technology has contributed immensely to make the industry safer and productive (Volk, 2016). Many authors have indicated that new, improved mining equipment can improve the productivity of the industry (Kalra et al., 2018).

Labour productivity: Low productivity is reflected in the balance sheet, which destabilizes organizations (Mishra et al., 2018). Labour productivity is measured as output per employee over a given time period. Labour productivity in the mineral sector is on the decline around the world. With more technological advancements, industries are trying to move out of ground, engaging less manpower (Birrell & McCloskey, 2019; Garcia et al., 2000; Lala et al., 2016).

Machine reliability: The mining machinery– maintenance cost varies from 30% to 50% of the operating expenditure. To reduce the cost and to increase the availability (Lanke et al., 2016) and reliability of the machinery (Jakkula et al. 2019; Nabizadeh et al., 2019), different techniques, such as spare-part management, reliability-centred maintenance and continuous improvement, are employed.

Mine design: Mine design is an aspect of mining which focuses on a proper mining plan, appropriate mining method, the area to be mined and proper recommendations for all constraints (Epstein et al., 2012; Groeneveld et al., 2019). A good mine design addresses slope stability, in-pit and ex-pit road design and maintenance, economical dewatering, bench configuration, etc.

Optimality of equipment and use: Mining equipment are meant to handle minerals effectively. Excavators and their productivity (Drygin et al., 2017), optimized loader operations (Bodziony et al., 2016), mode of transportation and mining fleet management system (Moradi-Afrapoli & Askari-Nasab, 2017) are some of the elements coming under the factor of optimality of equipment and their use. Its importance lies in the fact that a major chunk of the expenditure of the mineral industry falls under equipment and use.

Optimality of production scheduling: Production scheduling is concerned with allocation of machinery, such as excavators, loaders and trucks. Resource allocation (Davkaev & Lyakhovets, 2016) and production scheduling (Htwe, 2019; Jamshidi & Osanloo, 2018) are two interrelated activities of optimal production scheduling.

Scale of economies: ‘Scale of economies’ in mining refers to the quantity of output mined beyond the level of break-even quantity (Budeba et al., 2016; Sun & Anwar, 2019). The greater the quantity of output produced, the lower is the per-unit fixed cost at scale of economies.

Training and skill level: Human capital reflects the driving force, with creativity operating within an industry. Scenario-based training enhances the overall productivity of the mineral industry. Usage of human capital (Groeneveld & Topal, 2011; Khayrulina et al., 2020) is an integral part in the mineral industry. Multitasking (Takahashi, 2011) workers are considered as assets to the industry.

Working Conditions and Practices: The work environment of mining (Bodziony et al., 2016) is exposed to tough conditions. The weather condition may impact work practices (Topp et al., 2008).

Gap of the Study

Politics and political actors seem a latent factor of the mineral industry for different reasons. Their influence certainly impacts mineral operations, but on many occasions, it is unrecognized. Finding faults with mining companies has become a habitual practice of these political strata. The gap was discovered through interaction with field experts.

Summary of Findings

Literatures were reviewed from 2008 to 2020 from various sources considered in this study and gap analysis was carried out during field study from experts. Combining all summary of attributes and CFS, this study presenting the summary of study in the Table 1.

Table 1.

Summary of Critical Success Factors of the Mineral Industry

Authors (year and country)	Findings	Key Factor	CSF No.
Topp et al. (2008, Australia)	Input resources	Cost of input resources	1
Krutilla et al. (2020, United States)	Price uncertainties
Ozdemir and Kumral (2018, Canada)	Cost optimization
Pollack and Bongaerts (2020, Germany)	High prices for energy
Bradley and Sharpe (2009, Canada)	High prices for energy
Lala et al. (2016, Australia)	Goods and service expenditures
Groeneveld and Topal (2011, Australia)	Market prices
Deloitte (2018, United Kingdom)	Declining capital intensity
SRK Consulting (2017, China)	Cost of input resources
Maheswari et al. (2020, India)	Optimal quality and optimal quantity	Capacity utilization and optimal ore recovery	2
Groeneveld and Topal (2011, Australia)	Ore recovery
Carrasco et al. (2016, Australia)	Low-grade ores' rejection at the earliest
Okely (2009, Australia)	Panic situations	Communication mode	3
Thaduri et al. (2019, Sweden)	Decision support models
Rio Tinto (2014, Australia)	Autonomous drilling system	Drill and blast efficiency	4
Rymarchuk and Shepel (2020, Ukraine)
Ramulu et al. (2012, India)	Blast optimization
Deloitte (2016, Australia)	Energy management	Energy management	5
Carrasco et al. (2016, Australia)	Energy efficiency
Moran et al. (2019, Chile)	Energy efficiency
MCA (2012, Australia)	Optimized infrastructure	Infrastructure	6
Topp et al. (2008, Australia)	Infrastructure constraints
De Haas and Poelhekke (2019, United Kingdom)	Infrastructure constraints
Deloitte and PDAC (2015, Canada)	Re-engineering and continuous improvement	Innovation and technology	7
MCA (2012, Australia)	Innovative activities
Volk (2016, Germany)	New technologies
Kalra et al. (2018, India)	Mining equipment
Aznar-Sánchez et al. (2019, Spain)	Innovation and technology
Lala et al. (2016, Australia)	Labour invested	Labour productivity	8
Birrell and McCloskey (2019, Australia); Mishra et al. (2018, India)	Labour productivity
Garcia et al. (2000, Chile)	Non-labour operating expenditures
Lanke et al. (2016, Sweden)	Availability of machines	Machine reliability	9
Nabizadeh et al. (2019, Iran)	Reliability and maintainability
Jakkula et al. (2019, India)	Equipment reliability
Groeneveld et al. (2019, Australia)	Long-term mine planning	Mine design	10
Epstein et al. (2012, Chile)
Drygin et al. (2017, Russia)	Excavator productivity	Optimality of equipment and use	11
Bodziony et al. (2016, Poland)	Selection of dump machines
Moradi-Afrapoli and Askari-Nasab (2017, Iran)	Mining fleet management systems
Davkaev and Lyakhovets (2016, Russia)	Resource allocations	Optimality of production scheduling	12
Htwe (2019, Australia)	Production scheduling
Jamshidi and Osanloo (2018, Iran )
Sun and Anwar (2019, China)	Economies of scale	Scale of economies	13
Budeba et al. (2017, South Africa)
Groeneveld and Topal (2011, Australia)	Human capital	Training and skill level	14
Khayrulina et al. (2020, Russia)
Takahashi (2011, Australia)	Multitasking
Bodziony et al. (2016, Poland)	Working conditions	Working conditions and practices	15
Topp et al. (2008, Australia)	Weather conditions
Topp et al. (2008, Australia)	Work practices
Indian Field Experts (2019, India)	Political influence	Political influence	16

Source: The authors.

Limitations of Studies Conducted on the Mineral Industry

The top management of mineral industries has to rethink the mineral business as a whole rather than partly focusing on technology, product and process improvement (Mishra et al., 2017). The authors could not locate/find any prevalent model for the mineral industry’s operation. This study has tried its best to establish a basic model of available critical factors influencing the mineral business in India. ISM is one of the most popular modelling techniques used in this study in formulating a basic model.

Research Methodology

This study tries to explore CSFs and establish relationships among these factors that directly or indirectly influence the operations of the mineral industry.

Geographical boundary and period of study: This study was conducted at selected mineral industries of Odisha, Chhattisgarh and Jharkhand between September 2019 and January 2020. These states of India are rich in minerals and contribute substantially to the Indian economy.

Commodities covered: The study is based on findings from non-coal surface mining mineral industries, particularly from iron ore–, limestone- and dolomite-mining industries. All these minerals are being mined from the surface of the earth.

Factor identification technique: All attributes were identified through intense an literature review from 2008 to 2020 and a gap analysis through a field study.

Data analysis: Twelve experts from the three mining states of Odisha, Chhattisgarh and Jharkhand were identified based on their exposure and years of work experience. After receipt of all possible attributes, ISM was applied with all 12 experts to find out the relational structure. One-to-one interviews and the Delphi technique were applied to trace all the CSFs and formulate the relationship structure.

Development of a Model of Mineral Industry Operations Using Interpretive Structural Modelling Methodology

ISM is a well-established methodology for identifying relationships among specific items that define a problem or an issue (Attri et al., 2013). It is used to establish relationships between drivers and dependents in a structured manner. ISM helps in developing and understanding a complex situation for individuals or groups and makes it simpler for understanding relationships among critical success factors, and ultimately makes the outcome clearer in any messy situation. The key success factors/elements of a possible outcome are explored critically to see how these factors are linked to each other by a group decision-making process. This model or process helps in relating key success factors or CSFs in a structured way. The decisions are believed to be more accurate than taking the decision by an individual, being a collective process (Sage, 1977).

‘V’, ‘A’, ‘X’ and ‘O’ mentioned in Figure 1 represent the following:

V—The first variable has the potential to influence the second variable (i influences j);

A—The second variable has the potential to influence the first variable (j influences i);

X—Both variables have the potential to influence each other (i and j influence each other);

O—There is no linkage between the two variables under consideration (i and j do not influence each other).

The flow diagram of element structure using the ISM followed in this study is presented in Figure 1.

Figure 1.

Source: Created by author after referring various studies, especially with reference to (Sage, 1977; Attri et al., 2013).

Application of Interpretive Structural Modelling for Analysing Factors of Mines

Many constructive meetings were held to arrive at the outcomes, which are noted down in tabular form below. This study was carried out on the guideline set by ISM methodology, which is in stepwise form as explained below.

Table 2 represents a structural self-interaction matrix (SSIM), which was developed by taking the help of 16 CSFs derived from previous studies and expert opinions. ‘V’, ‘A’, ‘X’ and ‘O’ are used to establish the pairwise relationships.

Table 2.

Structural Self-interaction Matrix

Var.	Critical Factors	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
1	Labour productivity		A	A	X	A	V	V	A	A	A	V	A	A	A	A	A
2	Political influence			O	O	V	O	O	V	V	V	O	O	O	O	O	O
3	Training and skill level				V	V	V	V	V	A	V	V	V	V	V	V	V
4	Work practices					A	V	V	A	A	V	V	V	V	V	A	A
5	Cost of input resources						V	V	O	V	V	V	V	V	V	A	A
6	Scale of economies							A	A	A	A	V	A	A	A	A	A
7	Capacity utilization								A	A	V	V	A	A	A	A	A
8	Communication mode									A	V	V	V	V	V	O	O
9	Infrastructure										V	V	V	V	V	A	A
10	Drill and blast efficiency											V	O	A	V	A	A
11	Energy management												O	A	O	A	0
12	Machine reliability													V	V	A	O
13	Optimality of equipment and use														X	A	O
14	Optimality of production scheduling															A	A
15	Innovation and technology adoption																V
16	Mine design

Source: The authors.

Figure 2.

Source: The authors.

Figure 3.

Source: The authors.

Reachability matrix was developed by a binary method from SSIM, which is explained in Table 3 is based on their pairwise relationship, 1 or 0 is assigned based on step-3 of ISM.

Table 3.

Primary Reachability Matrix

Var.	Critical Factors	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16
1	Labour productivity	1	0	0	1	0	1	1	0	0	0	1	0	0	0	0	0
2	Political influence	1	1	0	0	1	0	0	1	1	1	0	0	0	0	0	0
3	Training and skill level	1	0	1	1	1	1	1	1	0	1	1	1	1	1	1	1
4	Work practices	1	0	0	1	0	1	1	0	0	1	1	1	1	1	0	0
5	Cost of input resources	1	0	0	1	1	1	1	0	1	1	1	1	1	1	0	0
6	Scale of economies	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0
7	Capacity utilization	0	0	0	0	0	1	1	0	0	1	1	0	0	0	0	0
8	Communication mode	1	0	0	1	0	1	1	1	0	1	1	1	1	1	0	0
9	Infrastructure	1	0	1	1	0	1	1	1	1	1	1	1	1	1	0	0
10	Drill and blast efficiency	1	0	0	0	0	1	0	0	0	1	1	0	0	1	0	0
11	Energy management	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
12	Machine reliability	1	0	0	0	0	1	1	0	0	0	0	1	1	1	0	0
13	Optimality of equipment and use	1	0	0	0	0	1	1	0	0	1	1	0	1	1	0	0
14	Optimality of production scheduling	1	0	0	0	0	1	1	0	0	0	0	0	1	1	0	0
15	Innovation and technology adoption	1	0	0	1	1	1	1	0	1	1	1	1	1	1	1	1
16	Mine design	1	0	0	1	1	1	1	0	1	1	0	0	0	1	0	1

Source: The authors.

Table 4 is developed after considering transitivity. Changed values are marked with an asterisk (*). In Table 4, the final reachability matrix is developed per step 4 of the ISM guidelines.

Table 4.

Final Reachability Matrix after Considering Transitivity

Var.	Critical Factors	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	Driving Power
1	Labour productivity	1	0	0	1	0	1	1	0	0	1*	1	1*	1*	1*	0	0	9
2	Political influence	1	1	1*	1*	1	1*	1*	1	1	1	1*	1*	1*	1*	0	0	14
3	Training and skill level	1	0	1	1	1	1	1	1	1*	1	1	1	1	1	1	1	15
4	Work practices	1	0	0	1	0	1	1	0	0	1	1	1	1	1	0	0	9
5	Cost of input resources	1	0	0	1	1	1	1	0	1	1	1	1	1	1	0	0	11
6	Scale of economies	0	0	0	0	0	1	0	0	0	0	1	0	0	0	0	0	2
7	Capacity utilization	1*	0	0	0	0	1	1	0	0	1	1	0	0	1*	0	0	6
8	Communication mode	1	0	0	1	0	1	1	1	0	1	1	1	1	1	0	0	10
9	Infrastructure	1	0	1	1	0	1	1	1	1	1	1	1	1	1	1*	1*	14
10	Drill and blast efficiency	1	0	0	1*	0	1	1*	0	0	1	1	0	1*	1	0	0	8
11	Energy management	0	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0	1
12	Machine reliability	1	0	0	1*	0	1	1	0	0	1*	1*	1	1	1	0	0	9
13	Optimality of equipment and use	1	0	0	1*	0	1	1	0	0	1	1	0	1	1	0	0	8
14	Optimality of production scheduling	1	0	0	1*	0	1	1	0	0	1*	1*	0	1	1	0	0	8
15	Innovation and technology adoption	1	0	0	1	1	1	1	0	1	1	1	1	1	1	1	1	13
16	Mine design	1	0	0	1	1	1	1	1*	1	1	1*	1*	1*	1	0	1	13
	Dependency	14	1	3	13	5	15	14	5	6	14	16	10	13	14	3	4	150

Source: The authors.

Note: *Changed values.

Level Partition

Different levels are derived by number of iterations. Final reachability matrix and antecedent sets are the sources from which levels are decided. Reachability set is a set of elements which content the element itself and those elements which it influences. Similarly, an antecedent set is a set containing the element itself and those elements that influence it. The common elements of a reachability set and an antecedent set form an intersection set. Wherever we find a factor common to the reachability set and the intersection set, we mark that factor as a level. Once a level is identified, that same factor is taken out from the next level of iteration. This process continues till all levels are derived.

From the 12 iterations from Table 5 to Table 16 below, all the levels are derived. In level I, factor 11 is identified. Similarly, factor 6 is identified in level II, factor 7, factor 10 and factor 14 are identified in level III, factor 4 and factor 13 are identified in level IV, factor 1 and factor 12 are identified in level V, factor 8 is identified in level VI, factor 9 is identified in level VII, factor 5 is identified in level VIII, factor 16 is identified in level IX, factor 15 is identified in level X, factor 3 is identified in level XI, and factor 2 is identified in level XII.

Table 5.

Iteration 1

Element	Critical Factors	Reachability Set	Antecedent Set	Intersection Set	Level
1	Labour productivity	1,4,6,7,10,11,12,13,14	1,2,3,4,5,7,8,9,10,12,13,14,15,16	1,4,7,10,12,13,14
2	Political influence	1,2,3,4,5,6,7,8,9,10,11,12,13,14	2	2
3	Training and skill level	1,3,4,5,6,7,8,9,10,11,12,13, 14,15,16	2,3,9	3,9
4	Work practices	1,4,6,7,10,11,12,13,14	1,2,3,4,5,8,9,10,12,13,14,15,16	1,4,10,12,13,14
5	Cost of input resources	1,4,5,6,7,9,10,11,12,13,14	2,3,5,15,16	5
6	Scale of economies	6,11	1,2,3,4,5,6,7,8,9,10,12,13,14, 15,16	6
7	Capacity utilization	1,6,7,10,11,14	1,2,3,4,5,7,8,9,10,12,13,14,15,16	1,7,10,14
8	Communication mode	1,4,6,7,8,10,11,12,13,14	2,3,8,9,16	8
9	Infrastructure	1,3,4,6,7,8,9,10,11,12,13,14, 15,16	2,3,5,9,15,16	3,9,15,16
10	Drill and blast efficiency	1,4,6,7,10,11,13,14	1,2,3,4,5,7,8,9,10,12,13,14,15,16	1,4,7,10,13,14
11	Energy management	11	1,2,3,4,5,6,7,8,9,10,11, 12,13,14,15,16	11	I
12	Machine reliability	1,4,6,7,10,11,12,13,14	1,2,3,4,5,8,9,12,15,16	1,4,12
13	Optimality of equipment and use	1,4,6,7,10,11,13,14	1,2,3,4,5,8,9,10,12,13,14,15,16	1,4,10,13,14
14	Optimality of production scheduling	1,4,6,7,10,11,13,14	1,2,3,4,5,7,8,9,10,12,13,14,15,16	1,4,7,10,13,14
15	Innovation and technology adoption	1,4,5,6,7,9,10,11,12,13,14,15,16	3,9,15	9,15
16	Mine design	1,4,5,6,7,8,9,10,11,12,13,14,16	3,9,15,16	9,16

Source: The authors.

Table 6.

Iteration 2

Element	Critical Factors	Reachability Set	Antecedent Set	Intersection Set	Level
1	Labour productivity	1,4,6,7,10,12,13,14	1,2,3,4,5,7,8,9,10,12,13,14, 15,16	1,4,7,10,12,13,14
2	Political influence	1,2,3,4,5,6,7,8,9,10,12,13,14	2	2
3	Training and skill level	1,3,4,5,6,7,8,9,10,12,13,14, 15,16	2,3,9	3,9
4	Work practices	1,4,6,7,10,12,13,14	1,2,3,4,5,8,9,10,12,13,14,15,16	1,4,10,12,13,14
5	Cost of input resources	1,4,5,6,7,9,10,12,13,14	2,3,5,15,16	5
6	Scale of economies	6	1,2,3,4,5,6,7,8,9,10,12,13,14,15,16	6	II
7	Capacity utilization	1,6,7,10,14	1,2,3,4,5,7,8,9,10,12,13,14, 15,16	1,7,10,14
8	Communication mode	1,4,6,7,8,10,12,13,14	2,3,8,9,16	8
9	Infrastructure	1,3,4,6,7,8,9,10,12,13,14,15,16	2,3,5,9,15,16	3,9,15,16
10	Drill and blast efficiency	1,4,6,7,10,13,14	1,2,3,4,5,7,8,9,10,12,13,14, 15,16	1,4,7,10,13,14
12	Machine reliability	1,4,6,7,10,12,13,14	1,2,3,4,5,8,9,12,15,16	1,4,12
13	Optimality of equipment and use	1,4,6,7,10,13,14	1,2,3,4,5,8,9,10,12,13,14,15,16	1,4,10,13,14
14	Optimality of production scheduling	1,4,6,7,10,13,14	1,2,3,4,5,7,8,9,10,12,13,14, 15,16	1,4,7,10,13,14
15	Innovation and technology adoption	1,4,5,6,7,9,10,12,13,14,15,16	3,9,15	9,15
16	Mine design	1,4,5,6,7,8,9,10,12,13,14,16	3,9,15,16	9,16

Source: The authors.

Table 7.

Iteration 3

Element	Critical Factors	Reachability Set	Antecedent Set	Intersection Set	Level
1	Labour productivity	1,4,7,10,12,13,14	1,2,3,4,5,7,8,9,10,12,13,14, 15,16	1,4,7,10,12,13,14
2	Political influence	1,2,3,4,5,7,8,9,10,12,13,14	2	2
3	Training and skill level	1,3,4,5,7,8,9,10,12,13,14,15,16	2,3,9	3,9
4	Work practices	1,4,7,10,12,13,14	1,2,3,4,5,8,9,10,12,13,14,15,16	1,4,10,12,13,14
5	Cost of input resources	1,4,5,7,9,10,12,13,14	2,3,5,15,1	5
7	Capacity utilization	1,7,10,14	1,2,3,4,5,7,8,9,10,12,13,14, 15,16	1,7,10,14	III
8	Communication mode	1,4,7,8,10,12,13,14	2,3,8,9,16	8
9	Infrastructure	1,3,4,7,8,9,10,12,13,14,15,16	2,3,5,9,15,16	3,9,15,16
10	Drill and blast efficiency	1,4,7,10,13,14	1,2,3,4,5,7,8,9,10,12,13,14, 15,16	1,4,7,10,13,14	III
12	Machine reliability	1,4,7,10,12,13,14	1,2,3,4,5,8,9,12,15,16	1,4,12
13	Optimality of equipment and use	1,4,7,10,13,14	1,2,3,4,5,8,9,10,12,13,14,15,16	1,4,10,13,14
14	Optimality of production scheduling	1,4,7,10,13,14	1,2,3,4,5,7,8,9,10,12,13,14, 15,16	1,4,7,10,13,14	III
15	Innovation and technology adoption	1,4,5,7,9,10,12,13,14,15,16	3,9,15	9,15
16	Mine design	1,4,5,7,8,9,10,12,13,14,16	3,9,15,16	9,16

Source: The authors.

Table 8.

Iteration 4

Element	Critical Factors	Reachability Set	Antecedent Set	Intersection Set	Level
1	Labour productivity	1,4,12,13,	1,2,3,4,5,8,9,12,13,15,16	1,4,12,13
2	Political influence	1,2,3,4,5,8,9,12,13,	2	2
3	Training and skill level	1,3,4,5,8,9,12,13,15,16	2,3,9	3,9
4	Work practices	1,4,12,13,	1,2,3,4,5,8,9,12,13,15,16	1,4,12,13	IV
5	Cost of input resources	1,4,5,9,12,13,	2,3,5,15,1	5
8	Communication mode	1,4,8,12,13,	2,3,8,9,16	8
9	Infrastructure	1,3,4,8,9,12,13,15,16	2,3,5,9,15,16	3,9,15,16
12	Machine reliability	1,4,12,13,	1,2,3,4,5,8,9,12,15,16	1,4,12
13	Optimality of equipment and use	1,4,13,	1,2,3,4,5,8,9,12,13,15,16	1,4,13	IV
15	Innovation and technology adoption	1,4,5,9,12,13,15,16	3,9,15	9,15
16	Mine design	1,4,5,8,9,12,13,16	3,9,15,16	9,16

Source: The authors.

Table 9.

Iteration 5

Element	Critical Factors	Reachability Set	Antecedent Set	Intersection Set	Level
1	Labour productivity	1,12,	1,2,3,5,8,9,12,15,16	1,12	V
2	Political influence	1,2,3,5,8,9,12,	2	2
3	Training and skill level	1,3,5,8,9,12,15,16	2,3,9	3,9
5	Cost of input resources	1,5,9,12,	2,3,5,15,1	5
8	Communication mode	1,8,12,	2,3,8,9,16	8
9	Infrastructure	1,3,8,9,12,15,16	2,3,5,9,15,16	3,9,15,16
12	Machine reliability	1,12,	1,2,3,5,8,9,12,15,16	1,12	V
15	Innovation and technology adoption	1,5,9,12,15,16	3,9,15	9,15
16	Mine design	1,5,8,9,12,16	3,9,15,16	9,16

Source: The authors.

Table 10.

Iteration 6

Element	Critical Factors	Reachability Set	Antecedent Set	Intersection Set	Level
2	Political influence	2,3,5,8,9,	2	2
3	Training and skill level	3,5,8,9,15,16	2,3,9	3,9
5	Cost of input resources	5,9,	2,3,5,15,16	5
8	Communication mode	8,	2,3,8,9,16	8	VI
9	Infrastructure	3,8,9,15,16	2,3,5,9,15,16	3,9,15,16
15	Innovation and technology adoption	5,9,15,16	3,9,15	9,15
16	Mine design	5,8,9,16	3,9,15,16	9,16

Source: The authors.

Table 11.

Iteration 7

Element	Critical Factors	Reachability Set	Antecedent Set	Intersection Set	Level
2	Political influence	2,3,5,9,	2	2
3	Training and skill level	3,5,9,15,16	2,3,9	3,9
5	Cost of input resources	5,9,	2,3,5,15,16	5
9	Infrastructure	3,9,15,16	2,3,5,9,15,16	3,9,15,16	VII
15	Innovation and technology adoption	5,9,15,16	3,9,15	9,15
16	Mine design	5,9,16	3,9,15,16	9,16

Source: The authors.

Table 12.

Iteration 8

Element	Critical Factors	Reachability Set	Antecedent Set	Intersection Set	Level
2	Political influence	2,3,5,	2	2
3	Training and skill level	3,5,15,16	2,3,	3
5	Cost of input resources	5	2,3,5,15,16	5	VIII
15	Innovation and technology adoption	5,15,16	3,15	15
16	Mine design	5,16	3,15,16	16

Source: The authors.

Table 13.

Iteration 9

Element	Critical Factors	Reachability Set	Antecedent Set	Intersection Set	Level
2	Political influence	2,3,	2	2
3	Training and skill level	3,15,16	2,3	3
15	Innovation and technology adoption	15,16	3,15	15
16	Mine design	16	3,15,16	16	IX

Source: The authors.

Table 14.

Iteration 10

Element	Critical Factors	Reachability Set	Antecedent Set	Intersection Set	Level
2	Political influence	2,3,	2	2
3	Training and skill level	3,15	2,3	3
15	Innovation and technology adoption	15	3,15	15	X

Source: The authors.

Table 15.

Iteration 11

Element	Critical Factors	Reachability Set	Antecedent Set	Intersection Set	Level
2	Political influence	2,3	2	2
3	Training and skill level	3	2,3	3	XI

Source: The authors.

Table 16.

Iteration 12

Element	Critical Factors	Reachability Set	Antecedent Set	Intersection Set	Level
2	Political influence	2	2	2	XII

Source: The authors.

MICMAC is derived from its full form: ‘cross impact matrix multiplication applied to classification’. From this analysis, the driving power and dependency of each factor is assessed or derived. This graph is prepared from the final reachability matrix. These influencing critical factors are divided into four parts: autonomous (first quadrant), dependent (second quadrant), linkage (third quadrant) and independent (fourth quadrant).

Factors falling under the autonomous segment are weak in dependency and weak in driving power too. These variables are more or less disconnected from the system or output. ‘Machine reliability’ is found to be disconnected from the research zone. Dependent factors have strong dependency but are weak in driving power. ‘Scale of economies’, ‘capacity utilization’, ‘drill and blast efficiency’, ‘optimality of equipment and use’, ‘energy management’ and ‘optimality of production scheduling’ are found to be dependent variables. Linkage variables are very strong in dependency and very strong in driving power too. Normally, these variables are unstable. Any influence on these variables can affect the outcome. Special care is to be taken for these variables. In this study, ‘machine reliability’, ‘work practices’ and ‘labour productivity’ are found to fall in this category. The last category is the driving sector. The factors in this segment have high driving power and low dependency. Each variable can influence the outcome but is independent in nature. Those in this category are ‘political influence’, ‘training and skill level’, ‘cost of input resources’, ‘infrastructure’, ‘communication mode’, ‘innovation and technology adoption’ and ‘mine design’.

Discussion

In this article, 16 CSFs of the Indian mineral industry were identified with inputs from an extensive literature review and field discussions held with experts of this industry. These key factors can be considered for any ‘massively deposited’ mineral industry with minimal change (i.e., one or two key factors may be added or discarded) for successful implementation. The ISM methodology was projected for the development of a structural model establishing the relationships among the CSFs. This projected ISM methodology was simply explained, and enough evidence was provided by literatures to validate use of this methodology. To facilitate application of the ISM methodology, Indian massively deposited surface mineral industries were considered here, such as the iron ore-, dolomite- and limestone-mining industries. The dependence level and driving power of each CSF were determined to develop a relationship structure or model. It was established that the key factors ‘training and skill level’, ‘infrastructure’ and ‘political influence’ have the maximum driving power and minimum dependence level among the 16 key factors. Hence, these three key factors should be taken great care of while running mineral-business operations. The operational model so developed on the basis of CSFs is an excellent tool for understanding the relationship of CSFs and the business operation as a whole.

Conclusion and Future Scope

The operational environment of the mineral business is an interlinked chain of activities. This article has tried to establish a relationship among various CSFs of the Indian mineral industry, particularly the iron ore-, dolomite- and limestone-mining industries. Identification of CSFs and their relationships is approachable, productive and also efficiency-oriented. This technique can also be applied on other types of mineral industries and manufacturing industries, capturing their own unique CSFs. Several studies on the mineral industry that have been conducted in the past or are ongoing are based on engineering, geological, environmental and economical points of view but not on any business/managerial approach. There are many popular and effective business management approaches that can be suitably applied in these mineral and industrial fields to analyse relationships among various activities or factors of importance and the impact of an activity on others. Similar studies can also be carried out in other mineral industries, such as the coal, bauxite, chromite, manganese, gold and silver industries. Studies can also be carried out in different deposit types of mines, such as ‘surface-deposit’, ‘underground-deposit’, ‘placer-deposit’ or other types of mines.

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.

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