Buffer Sizing in CCPM Portfolios with Different Resource Capacities

Buffer Sizing in CCPM Portfolios with Different Resource Capacities

Mahdi Ghaffari (University of Manchester, Manchester, United Kingdom) and Margaret W. Emsley (University of Manchester, Manchester, United Kingdom)
DOI: 10.4018/IJITPM.2017070103
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Abstract

Although buffer sizing is one of the most investigated subjects in Critical Chain Project Management (CCPM), almost all the focus has been on its implications for single-project environments. Among the entire invented buffer sizing methods, the cut and paste method (C&PM) is the most accepted by practitioners and long-established CCPM software packages. This study concentrates on the use of C&PM in CCPM portfolios with different resource capacities with the aim of examining the performance of this method and identifying the most appropriate sizes of project and feeding buffers. This aim is pursued through considering ten different levels of resource capacity, conducting Monte Carlo simulations of randomly generated project data and comparing the results with deterministic duration values of the same portfolios with 30%, 40% and 50% buffer sizes. As a significant contribution, this study recommends what buffer sizes should be allotted in CCPM portfolios with different resource capacities.
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Introduction

Goldratt (1997) extended application of his Theory of Constraints (TOC) to project management in a business novel named “Critical Chain” and advocated it as a new method for scheduling and managing single and multiple projects. The method was later called Critical Chain Project Management (CCPM) (Leach, 2000) and since then, many authors have studied and critically reviewed its fundamentals, principles and literature: e.g. Leach (1999, 2003, 2014), Lechler, Ronen & Stohr, (2005), Herroelen & Leus (2001), Ghaffari & Emsley (2015), Trietsch (2005), Raz, Barnes & Dvir, (2003), Steyn (2001, 2002), Anantatmula & Webb (2014). Other than the concept of buffer sizing and management that is explained below, main properties of CCPM are: being against multitasking, not considering fixed activity due dates and scheduling non-critical chains to their latest start. It is not within the scope of this study to elaborate on CCPM basics. Thus, readers are kindly referred to the noted studies for further information on CCPM fundamentals, principles and literature.

One prominent feature of CCPM is replacement of task-embedded safety times, as in the critical path method, with various time buffers including project buffer, feeding buffer, capacity constrained buffer, resource buffer and drum buffer (the latter two buffers were later realised to be redundant and were replaced with prioritised task lists (Newbold, 2008; Leach, 2014)). The goal has been to reduce the required safety times by aggregating them in the end of activity chains (benefiting from the central limit theorem) and also provide means for a new project monitoring and control system, called buffer management, that is built upon levels of buffer penetration and their demonstration on fever charts.

Appropriate sizing of these buffers is one of the most investigated subjects in CCPM, having led to numerous studies developing a variety of buffer sizing methods. The first author to write on this was Goldratt himself who assumed the embedded safety times consist of about half of duration of each chain and recommended that it is a “good-enough” solution to take out these safety times, cut them by 50% and place them in the end of each chain to protect them against uncertainty (Goldratt, 1997), what was later called the cut and paste method (C&PM). Other authors have attempted to create more scientific and effective rules. The Product Development Institute (1999) introduced the Root Square Error Method (RSEM) (Leach (2005) identifies this as Square Root of the Sum of the Squares (SSQ)). Newbold (1998) also provides a formula that encompasses the level of safety times considered for different tasks and therefore the uncertainty associated with them, using a lognormal distribution. Furthermore, Leach (2003) considers possible biases included in buffer estimation that might lead to its underestimation. He defines bias as “anything that might invalidate pooling of variances of the individual tasks to size schedule or cost buffers” and introduces the Bias Plus Root Square Error Method (BPRSEM).

In addition to the above, there are some other buffer sizing methods, namely High Confidence RSEM by Ashtiani, Jalali, Aryanezhad & Makuti (2007), Adaptive Procedure With Resource Tightness and Adaptive Procedure With Density by Tukel, Rom & Egsioglo (2006), Improved RSEM (IRSE) by Xue-mei, Yang & Lin, (2010), Forecasting Error Approach by Caron & Mancini (2008), RSEM Based on Lognormal Distribution and Dependence Assumption Between Activities by Bie, Cui & Zhang (2012) and some other highly sophisticated approaches using computerised simulations (Tenera & Cruz Machado, 2007) and Fuzzy Logic (Shi & Gong, 2009; Min & Rongqui, 2008; Long & Ohsato, 2008).

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