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A manufacturer increases Productivity through Resource Optimization


A manufacturer wants to institute mechanisms by which it can boost productivity levels of its operations in a sustainable fashion. A range of linear & non-linear optimization techniques are deployed to model various complexities in the firm’s production cycles. Such models enable the manufacturer to accommodate the 3-way constraints related to time, costs and quality. This allows the firm to put in place business strategies that ensure optimal usage of machinery, materials and people, in order to enhance productivity levels in a sustainable fashion, leading to better capacity utilization and better profitability.

Business Backdrop

A manufacturer wants to enhance the overall productivity of its plant operations. The company wants to optimize its usage of machinery, people and time in order to boost capacity utilization and in turn the firm’s profitability.

Operational efficiency of manufacturers hinges critically on the degree of optimality in usage of factor inputs, such as capital goods (plant & machinery, etc), consumables (raw materials & sub-assemblies, etc), energy inputs (fuel & electric power, etc), and human capital (workers & supervisors, etc).

Firms that are able to optimize their usage of machinery, materials and people enjoys the benefits of better capacity utilization. This in turn leads to better profitability. To achieve such optimal levels of resource utilization, manufacturers need to attain a fine balance between supply and demand of the different resource types at their disposal.

While over-capacity is undesirable, firms also need to refrain from over-utilization, as the latter often leads to disproportionate fatigue as well as wear & tear, both for equipment & machinery as well as for the human workforce. Leaving capacity idle entails costs, but so does indiscriminate exploitation of machinery and people.

Analytical Approach

A realistic modelling of manufacturing processes needs to consider constraints that are essentially 3-way in nature, viz, those related to time, costs and quality. Business decisions taken with the sole objective of reducing production cycle times usually lead to higher costs, sometimes with detrimental effects on quality as well. Similarly, sub-optimal cost reduction initiatives often compromise product quality, leading to hidden cost leakages that surface later. Likewise, quality control & assurance mechanisms take time and entail additional costs.

In order to circumvent the problems associated with lop-sided tactical decisions that focus on just one (or two) of the three inherent constraints, manufacturers need a strategic and holistic framework that accommodates the constraints related to all the three dimensions of time, costs and quality.

Subsequent to a thorough analysis of the many business processes that comprise typical manufacturing operations, a range of optimization models are developed. To reflect the complex nature of real-world manufacturing, a mix of linear and non-linear optimization techniques are deployed.

Goal programming, Markov Chain modelling, and Monte Carlo simulation techniques are used to test & validate the various models under a range of likely scenarios.

Solution Framework

  • Exploratory Analysis
  • Goal Programming
  • Lean & Six-Sigma methodologies
  • Markov Chain modelling
  • Monte Carlo simulation
  • Scenario Analysis

Business Benefits

Leveraging on a mix of linear & non-linear optimization techniques, the manufacturer is able to model the various complexities inherent in its production processes as well as accommodate the 3-way constraints related to time, costs and quality. Such optimization models allow the manufacturer to achieve optimal levels of utilization of its different resource types, viz, machinery, materials, and people. This leads to better (and, more importantly, sustainable) productivity enhancement and in turn to better profitability.

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