Measuring operational excellence: an operational excellence profitability (OEP) approach.

The pursuit of operational excellence in the manufacturing industry is at rise, but its measurement still lacks of appropriate indicators to determine its financial benefits. The ambiguity is due to the impact arisen from manufacturing fluctuations such as price and cost, production mix, and direct and indirect parameters variations. Manufacturing fluctuations distort the cost benefit of operational excellence. This paper therefore proposes the OEP (Operational Excellence Profitability) indicators to isolate the impact of manufacturing fluctuation, and distinctly identify the payback of operational excellence strategies and initiatives through cost benefits of achieving higher efficiency and yield. The paper presents the conceptual and mathematical development of the proposed OEP indicators and the formulas used for their calculation. Hypothetical and industrial-based investigations and applications of the OEP indicators are conducted for their validation. The results obtained from the hypothetical exercise and industrial case suggest that OEP indicators can provide an effective cost benefit analysis of operational excellence. This would contribute in providing manufacturing organisations with more complete information regarding the performance of their processes, which will allow their directors and managers to take better decisions related to the management and improvement of their processes.N/

Model for Prioritization of High Variation Elements in Discrete Production Systems

The complexity of the modern manufacturing enterprise has led companies to look for techniques and methodologies for improving production performance. Lean manufacturing techniques have been applied in the US with varying degrees of success, and Theory of Constraints (TOC) has been used to emphasize the flow of production and identify performance improvement projects. One aspect of manufacturing for which there has been limited academic or industrial research till date is the impact of variation on production performance and the identification of improvement projects based on variation. This thesis develops a methodology to incorporate random and simultaneous occurrence of variability in a manufacturing facility, e.g., equipment failure, variabilities in the arrival time of raw materials and in-station processing time, to model system performance. Two measures of performance are developed corresponding to time and material. A prioritization algorithm is developed to utilize the “Coefficient of Variation” to identify a Bundle of High Variation Elements (BHVs) affecting the performance of a production system. The Bundled Variation-based Project Prioritization Model (BVPM) is a closed-loop model designed to provide decision makers with a list of projects to improve system performance while monitoring the implementation of projects

Simulation Modeling and Analysis for Productivity Improvement in the Production Line

Lean manufacturing addresses the growing need for all types of organizations that drive process change and performance improvements in their organization environment and supports the evolution toward demand-driven supply networks. Lean principles are derived from the Japanese manufacturing industry. It is the set of "tools" that give contribution in the identification and steady elimination of waste (muda). As waste is eliminated, quality improves while production time and cost are reduced. The key to lean manufacturing is to compress time by eliminating waste and this continually improving the process. Ohno (1988) defines waste as all elements of production that only increase cost without adding value that customer is willing to produce. The total productive maintenance (TPM) is mostly regarded as an integral part of Lean. TPM originated in Japan in 1971 as a method for improved machine availability through better utilization of maintenance and production resources. TPM uses an overall equipment effectiveness (OEE) index to indicate equipment and plant effectiveness. The technique works to eliminate the six big losses indicated by Nakajima, as down time (caused by equipment failure, set-up and adjustment), speed losses (owed by idling, minor stoppage and reduced speed) and defects (caused by process defects and reduced yield). The Japan Institute of Plant Maintenance promoted TPM which includes the OEE in 1971. In 1988, Nakajima introduced the TPM to the U.S. OEE has since gained a lot of attention as the ultimate performance measure of a piece of equipment. Sohal et al., (2010), from survey results, found that OEE typically advances from a base measure for efficiency (as its initial purpose), to being a tool to improve effectiveness for analyzing data to support continuous improvement objectives. It’s through the identification and elimination of six big losses, namely (i) breakdowns, (ii) setups and changeovers, (iii) running at reduced speeds, (iv) minor stops and idling, (v) quality defects, scraps, yields, reworks, and (vi) start-up losses. The first two affect Availability rate (A), the second two affect Performance efficiency (P), and the last two affect Quality rate (Q). These three OEE elements, since being introduced by Nakajima until this research was conducted, already experienced several improvements involving a weight calculation method for OEE elements. This study proposes a procedure to obtain weight settings of each OEE element and OEE estimation for productivity improvement in the production line. The first research proposal is sought to offer a procedure to cover the drawbacks of weighting OEE elements. The research motivation was initiated by several researches of OEE improvement, which met difficulty when determining the proper weight for each OEE element. The calculation results of OWEE and PEE by STP also showed better results than the original OEE for the simulation model case study. From the result analysis, it can be concluded that the outcome of this research experiment can be implemented in OEE with a weighted method, among others; for example, in PEE (Production Equipment Effectiveness) as well as OWEE (Overall Weight Equipment Effectiveness). A simulation model was chosen because it is able to mimic a real production line and therefore act as a suitable experiment tool. This study provide a lean overview followed by a description of how simulation is being used to enhance lean performance. This study offering simulation as the lean way to implement and accelerate the TPM. The STP (Simulation Taguchi method Procedure) provided characteristic mapping of OEE elements through a response table. Naturally, even though STP seems to be difficult to implement, the outcome is worthwhile. Moreover, the company will have obvious data to consider when making decisions for the improvement of priorities in their production line. The second research proposal offers OEE enhancement scheme, which provides a company with the appropriate information for decision-making on priority improvement in the production line. By using the Taguchi method and simulation as an experimental tool, this scheme can measure and estimate the contribution for each OEE element to an OEE score. This procedure can be implemented in a specific WS or in a production line if the factory is made up of more than one manufacturing line. They provide measurements for each OEE element in order to observe the extent of the influence the simulation experiment has on the OEE elements and scores. All of those research proposals are to improve the OEE as a KPI in the factory. In order to meet the objective of the TPM itself, increasing the sustainability of the company by continuous improvements