The efficiency and operational excellence are the key factors in order for a manufacturing company to stay competitive. Optimization of manpower and forecasting manpower needs in modern conglomerates are an essential part of the future strategic planning and a very important different nature of business imperatives. As firm’s business diversification has entered different business platform through firm supply chain operation increases more complexity for forecasting & planning manning level. The key driver analysis method was used in manpower modeling for five different nature types of business; sales based, volume based, trading based, project based, and back office based management. The distribution of manpower requirement in each specialty varied as different business context. This empirical study on manpower distribution and best fit modeling to predict the human resource planning in operation. Firstly, longitudinal study of manpower analysis was evolved to conduct the as-is manpower tiering; front process, core process, and support process for each business group. Later, all key financial & non-financial metrics were conducted for analysis. Finally, regression analysis was conducted to determine the development of manpower modeling or forecast model of demand/ supply in current energy context companies. Considering the market constraints & fluctuation with the governmental demand, policies, or advance in process technology impacted different demand & supply manning level for each of five business types. Finally, the study is to design a HR operating expenses (HROPEX) as the way to determine the resource headcount forecast for human resource planning regardless of employee competency constraint. The research findings showed significant relationship for each key specific metrics or model for each business group. Therefore, five manpower models were developed on each specialty for the strategic planning of human resource to serve the development of the industry.

Keywords: Development, Energy Conglomerates, Manpower Modeling & Optimization: Asia

Article Review Status: Published

Pages: 34-57 (Download PDF)

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