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Organizational Efficiencies and LSOPM Business

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Operational Sustainability in the Mining Industry

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Abstract

Organizational resources need to match up with the organizational requirements and needs to be used efficiently. Organizational efficiency can be described as obtaining maximum output by using minimum resources. Productivity and cost optimization are two factors that are relevant and have been elaborately explained in this chapter. This chapter also deals with the concepts of productivity and cost optimization and specifically showcases the results of the primary research in terms of the interrelationship between costs and productivity. Further, this chapter also deals with the relevance of equipment and its efficiency in mining by representing it in the form of maintenance, equipment availability, and utilization.

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Author information

Authors and Affiliations

  1. College of Industrial Management, King Fahd University of Petroleum and Minerals, Dhahram, Saudi Arabia

    Hassan Qudrat-Ullah

  2. Academic Doctoral Consultant-Freelance, North York, ON, Canada

    Pramela Nair Panthallor

Authors
  1. Hassan Qudrat-Ullah
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  2. Pramela Nair Panthallor
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Corresponding author

Correspondence to Hassan Qudrat-Ullah .

Appendix: Additional Information on Organizational Efficiencies in LSOPM Operations

Appendix: Additional Information on Organizational Efficiencies in LSOPM Operations

See Tables 3.3, 3.4, 3.5, 3.6, 3.7, 3.8 and 3.9.

Table 3.3 List of possible definitions of production
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Table 3.4 List of possible definitions of productivity
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Table 3.5 List of possible definitions of cost optimization
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Table 3.6 List of possible factors for improving productivity
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Table 3.7 List of possible key drivers of cost optimization
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Table 3.8 List of possible outcomes for effect of change in productivity on cost optimization
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Table 3.9 List of possible outcomes for role of equipment utilization on productivity
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Qudrat-Ullah, H., Panthallor, P.N. (2021). Organizational Efficiencies and LSOPM Business. In: Operational Sustainability in the Mining Industry. Asset Analytics. Springer, Singapore. https://doi.org/10.1007/978-981-15-9027-6_3

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