Abstract
Due to high fuel consumption, we face the problem of not only the increased cost, but it also affects greenhouse gas emission. This paper presents an assorted approach for monitoring fuel consumption in trucks with the objective to minimize fuel consumption. We propose a control charting structure for joint monitoring of mean and dispersion parameters based on the well-known max approach. The proposed joint assorted chart is evaluated through various performance measures such as average run length, extra quadratic loss, performance comparison index, and relative average run length. The comparison of the proposed chart is carried out with existing control charts, including a combination of X and S, the maximum exponentially weighted moving average (Max-EWMA), combined mixed exponentially weighted moving average-cumulative sum (CMEC), maximum double exponentially weighted average (MDEWMA), and combined mixed double EWMA-CUSUM (CMDEC) charts. The implementation of the proposed chart is presented using real data regarding the monitoring of fuel consumption in trucks. The outcomes revealed that the joint assorted chart is very efficient to detect different kinds of shifts in process behaviors and has superior performance than its competitor charts.
Highlights
One third of the total operating cost in logistics is mostly used as fuel and maintenance expense
A Max-CUSUM chart was proposed by Thaga [8] for joint monitoring, whereas Costa and Rahim [9] proposed a chart for joint monitoring of mean and dispersion parameters based on EWMA
The CC is a special case of combined mixed exponentially weighted moving average-cumulative sum (CMEC) and combined mixed double EWMA-CUSUM (CMDEC) control charts, CMEC and CMDEC charts become classical CUSUM chart for smoothing constant (λ = 1)
Summary
One third of the total operating cost in logistics is mostly used as fuel and maintenance expense. Control charts are one of the prime tools in SPC that are used for controlling the unnatural variations in a process These unnatural variations (known as shifts) in the process parameter(s) can be categorized as small, moderate and large. A Max-CUSUM chart was proposed by Thaga [8] for joint monitoring, whereas Costa and Rahim [9] proposed a chart for joint monitoring of mean and dispersion parameters based on EWMA. This study proposes a generalized chart based on the max approach to detect small, moderate and large amounts of shifts in the process mean and variation simultaneously. The proposal combines all the three basic structures (Shewhart, EWMA, CUSUM) both for mean and variance and targets all types of shifts in process parameter. The proposal is named as joint assorted chart for simultaneous monitoring of location and dispersion parameters.
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