Results and analysis using the Lean Six-Sigma define, measure, analyze, improve, and control (DMAIC) Framework

This section presents a discussion of the research data. The data was received as secondary data however, it was originally collected using the time study techniques. Data validation is a crucial step in the data analysis process to ensure that the data is accurate, complete, and reliable. Descriptive statistics was used to validate the data. The mean, mode, standard deviation, variance and range determined provides a summary of the data distribution and assists in identifying outliers or unusual patterns. The data presented in the dataset show the measures of central tendency which includes the mean, median and the mode. The mean signifies the average value of each of the factors presented in the tables. This is the balance point of the dataset, the typical value and behaviour of the dataset. The median is the middle value of the dataset for each of the factors presented. This is the point where the dataset is divided into two parts, half of the values lie below this value and the other half lie above this value. This is important for skewed distributions. The mode shows the most common value in the dataset. It was used to describe the most typical observation. These values are important as they describe the central value around which the data is distributed. The mean, mode and median give an indication of a skewed distribution as they are not similar nor are they close to one another. In the dataset, the results and discussion of the results is also presented.
This section focuses on the customisation of the DMAIC (Define, Measure, Analyse, Improve, Control) framework to address the specific concerns outlined in the problem statement. To gain a comprehensive understanding of the current process, value stream mapping was employed, which is further enhanced by measuring the factors that contribute to inefficiencies. These factors are then analysed and ranked based on their impact, utilising factor analysis. To mitigate the impact of the most influential factor on project inefficiencies, a solution is proposed using the EOQ (Economic Order Quantity) model. The implementation of the 'CiteOps' software facilitates improved scheduling, monitoring, and task delegation in the construction project through digitalisation. Furthermore, project progress and efficiency are monitored remotely and in real time. In summary, the DMAIC framework was tailored to suit the requirements of the specific project, incorporating techniques from inventory management, project management, and statistics to effectively minimise inefficiencies within the construction project.

本节对研究数据进行深入探讨。该数据虽为次级数据所获，但其原始收集系采用时间研究技术。在数据分析过程中，数据验证是至关重要的步骤，以确保数据的准确性、完整性与可靠性。描述性统计被用于验证数据。均值、众数、标准差、方差及范围的分析，为数据分布提供概述，并有助于识别异常值或非典型模式。数据集中展示的指标包括集中趋势的度量，如均值、中位数和众数。均值代表表中每个因素的平均值，这是数据集的平衡点，是数据集的典型值及其行为。中位数是每个因素的中间值，即数据集被划分为两部分，一半的值低于此值，另一半的值高于此值。对于偏态分布，这一点尤为重要。众数展示数据集中最常见的值，被用于描述最典型的观察结果。这些值的重要性在于它们描述了数据分布的中心值。均值、众数和中位数给出了偏态分布的指示，因为它们彼此不同，也都不接近。在数据集中，还对结果及其讨论进行了呈现。

本节聚焦于DMAIC（界定、测量、分析、改进、控制）框架的定制化，以解决问题陈述中概述的特定关切。为了全面理解当前流程，采用了价值流图绘制方法，并通过测量导致低效率的因素进一步优化。这些因素随后被分析并按其影响进行排序，运用因子分析方法。为减轻最具有影响力的因素对项目低效率的影响，提出了基于经济订货量（EOQ）模型的解决方案。'CiteOps'软件的实施通过数字化促进了建设项目中的优化调度、监控和任务委派。此外，项目进度和效率得以远程实时监控。总之，DMAIC框架被量身定制以适应特定项目的需求，融入了库存管理、项目管理及统计学技术，以有效降低建设项目中的低效率。