Aligning Near and Long Term Planning for LPSImplementations: A Review of Existing and NewMetrics
2016
Download PDFDOI: https://doi.org/10.60164/g4e7d9h3a
Authors: Samir Emdanat, Marcelo Azambuja
Citation:
Emdanat, S., & Azambuja, M. (2016). Aligning Near and Long Term Planning for LPS Implementations: A Review of Existing and New Metrics. In Lean Construction Journal pp. 90–101.
Abstract:
Questions: Q1: Can we develop a set of metrics to align short-term and long-term planning? Q2: Can we rapidly analyze LPS data and generate patterns/trends of planning performance? Q3: How does PPC correlate with long-term planning reliability? Q4: Is the PPC target of 75%-90% the right measure (target recommended by LCI)?
Purpose: Review existing LPS near-term planning metrics and assess their ability to predict long-term performance using technology. This paper also introduces new and more comprehensive metrics to balance near-term and long-term planning performance.
Research Method: An integrated database driven software tool that supports the LPS implementation was used to mine, analyze, and visualize large amount of data to review the existing metrics and evaluate the predictive nature of the proposed metrics designed to align near-term and long-term planning. The sample size ranged from two thousand activities to over 60,000 activities in total.
Findings: F1: Teams that focused on short-term MRP (3-6 weeks) without proper application of Phase Planning and adequate emphasis on resource planning exhibited cyclical patterns of PPC. F2: Initial analysis of the data shows no positive correlation between TA and TMR metrics and a team’s ability to reliably achieve milestone targets. F3: Teams that constantly re-plan to maintain CL, PRCO, and PPC appear to have lower overall MV (typically below 5 days) and appear to maintain better alignment between their near-term plans and their long-term plan target milestones and are thus more reliable. F4: The study suggests that the standard deviation of forecast dates of lookahead activities captured on a rolling basis at the time work plans are created may serve as a better indicator for overall planning reliability. Correlation of this metric against late dates of the same lookahead, CL, PRCO, and MV serves as a better indicator of reliability.
Limitations: Additional research is needed to monitor these metrics on a larger project sample and for longer periods of time to confirm the initial conclusions of this study.
Implications: This research demonstrated some of the advantages that integrated database driven tools can bring to improve LPS data collection and presented an overview of some of the opportunities presented by those tools to align near-term and long-term planning to improve reliability. This cannot be achieved by makeshift tools commonly used in the industry to manage LPS workflows. Those makeshift tools and associated processes result in data fragmentation, redundant entry, long cycle times, and introduce errors into the process.
Value for authors: This study advances the knowledge in understanding LPS metrics and their impact on schedule performance. New metrics have been introduced to measure reliability of long-term planning and improve the association between near- term and long-term planned activities.