Harnessing LPS Metrics for Smarter Resource Allocation and Project Control through Gamification

Capacity planning is crucial for optimizing resource allocation in cloud computing environments, ensuring that resources are efficiently utilized to meet demand while minimizing costs. Site Reliability Engineering (SRE) offers a systematic approach to capacity planning by integrating reliability, scalability, and operational efficiency into the management of cloud resources. This paper examines how SRE principles and practices can be applied to enhance capacity planning in cloud computing, focusing on optimizing resource allocation and maintaining system performance. The study explores the definition and significance of capacity planning within the context of cloud computing, highlighting its role in balancing supply and demand. By leveraging SRE methodologies, organizations can implement robust capacity planning strategies that align with business objectives and operational requirements. Key SRE practices, such as monitoring, forecasting, and automated scaling, are examined for their effectiveness in predicting and addressing capacity needs. The paper delves into how SRE approaches enhance capacity planning by utilizing metrics and data-driven insights to inform resource allocation decisions. It discusses the importance of real-time monitoring and predictive analytics in identifying potential bottlenecks and adjusting resources proactively. Additionally, the integration of automation tools is highlighted as a means to streamline capacity adjustments and improve responsiveness to changing demand patterns. Case studies of successful SRE implementations in capacity planning demonstrate the practical benefits of this approach, including improved resource utilization, cost savings, and enhanced system reliability. The findings underscore that adopting an SRE-driven capacity planning strategy enables organizations to achieve optimal resource allocation, reduce operational overhead, and maintain high levels of performance and availability.

This chapter introduces capacity planning that exploits advance reservation mechanism. Capacity planning plays a critical role in management of an infrastructure for optimized utilization of perishable resources. This applies to the Grid as well. Once the time has passed the computing power is perished. However, in the Grid, capacity planning is largely ignored due to the dynamic Grid behavior, multi-constrained contending applications, lack of support for advance reservation and its associated challenges like under utilization and agreement enforcement concerns. These issues force a resource manager to make resource allocations at runtime with reduced quality of service (QoS). To remedy these, we introduce Grid capacity planning and management with negotiation-based advance reservation and multi-constrained optimization. A 3-layer negotiation protocol is introduced along with algorithms that optimize resource allocation in order to improve the Grid utility. We model resource allocation as an on-line strip packing problem and introduce a new mechanism that optimizes resource utilization and other QoS parameters while generating contention-free solutions. We have implemented the proposed solution and experimented to demonstrate the effectiveness of our approach.

The purpose of this paper is to compare Critical Path Method (CPM) and Last Planner System (LPS) with respect to Planning and Scheduling of METRO Rail Project, Ahmedabad, Gujarat. Critical Path Method emphasises on updating the network for tracking the progress as well as to identify the delays. Last Planner System works on the weekly schedules prepared from the Master Plan and Look-ahead schedules to avoid the delays. One of the stretch from North-South Corridor was selected for the study from Vijaynagar to Usmanpura. The data such as activities, duration of activities, sequence and inter-relation of activities etc. were collected to prepare the network as well as weekly schedules. The network was updated and original network was compared with the updated one and the delays were spotted for the stretch selected. Weekly plans were also prepared for the selected stretch from the look-ahead schedule and Master Plan. PPC (Percent Plan Complete) were calculated to track the progress as per planned schedule. The data were collected by conducting interviews of various personnel and visual observations. Both the approaches (CPM and LPS) have been applied on the selected stretch by action research process. The delays were calculated and studied for both the methods and it was observed that Last Planner System is more appropriate to use for big infrastructure projects like this to avoid time-overrun and consecutive cost over-run. Resources can be well utilized with the Last Planner System, too. The type of this paper is a Case Study.

Although the growing demand for healthcare services has enhanced the occurrence of inefficiencies in care provision, healthcare systems should ensure safe, timely and equitable access to care. In particular, the long waiting lists, the lack of beds, and the shortage of healthcare workforce have highlighted the necessity to effectively manage and strategically plan the available resources. Capacity Planning (CP) contributes to determine what, when, where and how much product or service to deliver, with the goal of analysing and optimizing the use and allocation of human and material resources (e.g., physical spaces, equipment, beds, workforce) based on the demand. CP demonstrates to be extremely relevant for hospital units to schedule appointments, diagnostics, surgeries and admissions. Complex contexts and broader healthcare networks could benefit even more from this approach, helping to coordinate multiple care providers and aligning the available resources with the demand pace. The current research presents the implementation of CP in the context of Paediatric Palliative and Hospice Care (PPHC), delivered in an Italian facility. PPHC provides long-term, interdisciplinary and coordinated care between territorial, ambulatory and hospice services. It encompasses follow-up activities, parental training, end-of-life and respite care. In the Italian case analysed, the shortage of beds affects the quality of care delivered to these patients, in terms of accessibility to the service, effectiveness and timeliness of care. Hence, a CP analysis has been performed to pursue a twofold objective: a) to understand if the current capacity of the facility meets the demand for care; b) to improve productivity through an optimized allocation of resources, particularly the available beds and the healthcare workforce. The expected results involve a more effective use of resources, an increased bed occupancy rate and a reduction in rescheduled admissions due to inadequate planning. Optimizing capacity in this specific setting can serve as a pilot project for other similar contexts, aiming to improve patients’ access to healthcare services, their quality of life and supporting the workforce involved in PPHC. This research offers new insights from both the academic and managerial point of view, providing evidence about the implementation of CP in complex healthcare settings.

The Last PlannerSystem (LPS) has been implemented on construction projects to increase work flow reliability, a precondition for project performance againstproductivity and progress targets. The LPS encompasses four tiers of planning processes:master scheduling, phase scheduling, lookahead planning, and commitment / weeklywork planning. This research highlights deficiencies in the current implementation of LPS including poor lookahead planning which results in poor linkage between weeklywork plans and the master schedule. This poor linkage undetermines the ability of theweekly work planning process to select for execution tasks that are critical to projectsuccess. As a result, percent plan complete (PPC) becomes a weak indicator of project progress. The purpose of this research is to improve lookahead planning (the bridgebetween weekly work planning and master scheduling), improve PPC, and improve theselection of tasks that are critical to project success by increasing the link betweenShould, Can, Will, and Did (components of the LPS), thereby rendering PPC a betterindicator of project progress. The research employs the case study research method to describe deficiencies inthe current implementation of the LPS and suggest guidelines for a better application ofLPS in general and lookahead planning in particular. It then introduces an analyticalsimulation model to analyze the lookahead planning process. This is done by examining the impact on PPC of increasing two lookahead planning performance metrics: tasksanticipated (TA) and tasks made ready (TMR). Finally, the research investigates theimportance of the lookahead planning functions: identification and removal ofconstraints, task breakdown, and operations design.The research findings confirm the positive impact of improving lookaheadplanning (i.e., TA and TMR) on PPC. It also recognizes the need to perform lookaheadplanning differently for three types of work involving different levels of uncertainty:stable work, medium uncertainty work, and highly emergent work.The research confirms the LPS rules for practice and specifically the need to planin greater detail as time gets closer to performing the work. It highlights the role of LPSas a production system that incorporates deliberate planning (predetermined andoptimized) and situated planning (flexible and adaptive). Finally, the research presents recommendations for production planningimprovements in three areas: process related, (suggesting guidelines for practice),technical, (highlighting issues with current software programs and advocating theinclusion of collaborative planning capability), and organizational improvements(suggesting transitional steps when applying the LPS).

This paper introduces a set of integrated software tools that improve a shipyard’s ability to conduct effective capacity planning and analyses as well improve their spatial scheduling throughout all their facilities. The Long-Range Capacity Planning Tool replaces a manually intensive planning process through a user-friendly interface. The Spatial Scheduling Tool is a vastly improved version of previously developed software (Finke et al., 2007; 2008) to determine which areas of the shipyard are in use throughout the build plan. Finally, a Shop-Level Capacity Planning Tool is introduced that provides planners a view of work order schedules and their impact on work center capacity. These tools can be used to generate and save different build plan scenarios to perform what-if analyses and to perform more accurate capacity analyses to explore how adding a new hull to the build plan will affect current production plans. These tools interface with a Central Data System, which extracts information from legacy data systems, and acts as the central repository for all capacity planning data, ensuring that the tools have identical and up to date information. The tools also provide reporting/export functions that enable efficient integration back to the shipyard’s legacy execution system.

This paper introduces a systematic management procedure of Make-to-order (MTO) production system including Make-to-stock (MTS) products. The newly developed MTO management system consists of capacity planning and production planning based on the optimized resource allocation and individual production orders or estimated orders. For semiconductor manufacturing, the optimal resource allocation during capacity and production planning could reduce the cost and the risk relating to time as the result of performance evaluation of actual factory. In addition, the mixture of MTO and MTS is discussed, and several scenarios were compared to coordinate the purpose conflict between MTO and MTS.

Question: Can the Last Planner System® enhance the Commissioning and Qualification phase of project delivery? Purpose: Evaluation of Last Planner System in the Commissioning and Qualification phase of project execution; identifies challenges; proposes mitigations and opportunities for enhancing future implementations. Research Method: Mixed-methods approach encompassing critical literature review, site documentation data-analysis, focus groups, and semi-structured purposeful interviews. Findings: Last Planner System offers distinct value-add to Commissioning and Qualification. However, there is need for increased involvement of upstream and downstream players in planning construction production processes. Limitations: Conducted on a single project with limited sample size. Implications: Paper offers value for clients and practitioners as Last Planner System in the Commissioning and Qualification phase of project execution facilitates greater collaboration, increased visibility of workflow, and resulting productivity, schedule alignment, safety, cost, and client value-add benefits. Keywords: Lean Construction; Last Planner System®; collaboration; workflow; lookahead planning; handoff. Paper type: Case Study

In service oriented systems, Quality of Service (QoS) is a service selection driver. Users evaluate QoS at run time to address their service invocation to the most suitable provider. Thus, QoS has a direct impact on providers' revenues. However, QoS requirements are difficult to satisfy because of the high variability of Internet workloads. Workload variability cannot be accommodated with traditional capacity planning and allocation practices, but requires autonomic computing techniques. Autonomic computing involves two tightly inter-related problems, namely, a short-term resource allocation problem and a long-term capacity planning problem. Capacity planning requires an investment that should be balanced by the revenues obtained through resource allocation. In this paper, we provide a comprehensive framework modelling both problems. The short-term resource allocation problem is analyzed in depth. The paper proposes an optimization model that identifies the optimal resource allocation by maximizing a provider's revenues while satisfying customers QoS constraints and minimizing resource usage cost. Preliminary computational experiments are presented to support the effectiveness of our approach.

Cooperative capacity planning and resource allocation by mutual outsourcing using ant algorithm in a decentralized supply chain

Cloud computing is an Internet-based service which provides shared virtual resource and data to accomplish certain computation. In order for the servers to have sufficient resources when the request arrives, as well as save server resources as much as possible, we propose a prediction-based server capacity planning and dynamic scheduling algorithm. There are mainly three steps in our capacity planning algorithm. The first step characterizes the given data on several indices and then present an effective model in order to predict the oncoming demands in the near future. The second step generates the workload of servers combined with the predicted demands and then make capacity planning based on this workload. Thus it's obvious that the effectiveness of capacity planning depends on the accuracy of prediction to a great extent. Finally, a demand prediction based strategy on workload allocation is brought out. A dynamic resource allocation strategy is given to ensure the quality of service at any moment in future meanwhile taking energy consumption into consideration. The results of the experiment show that the required server number decreases by 33% after the prediction-based capacity planning applying on server scheduling.

Capacity planning is the method and planning of how efficiently the company can utilize its resources to meet the market demand. A well-executed capacity planning helps the organization to avoid the stock out conditions and serve the customers without any failure. The pharmaceutical capacity planning is an important aspect as this sector directly deals with mankind. The field of study is significant in order to meet the future demands, improve resource allocation, quality of the product and service level. This research work includes the secondary data collection through industrial reports, financial reports, case studies and various research papers. The main factors included for the research are the R&D investments, plant & equipment costs and inventory cost as these factors directly influence the capacity planning in manufacturing. The key findings of this study concludes that the pharma industries are continuously focusing on improving the efficiency in manufacturing by adopting the technology, reducing cost and expanding market size.

In the 4.0 industry revolution, smart factories have emerged as advanced production systems which utilize real-time information, data analysis, and automation to refine the manufacturing value chain. One of the main issues is efficient capacity planning and control due to resource allocation and production scheduling deadlines that, if not optimally met, can lead to delays or bottlenecks. In this paper, we propose a real-time control system for capacity planning and management optimal for smart factory settings. With this approach, IIoT (Industrial Internet of Things) data sensors, cloud computing, and predictive resource allocation analytics are employed to facilitate rapid responsiveness to demand changes. A hybrid approach is used where a model with deterministic planning heuristic algorithms and adaptive control strategies provides the system. Experimental results obtained from simulated data of smart factories indicate that the proposed system exceeds the expected value in traditional factories in terms of throughput, machine utilization, and responsiveness. Feedback and real-time decisions greatly boost operational efficiency, which were core in the research outcomes. This intelligent manufacturing system is resilient and easily scalable, making it a valuable addition to the collaborative ecosystem of contemporary production facilities.

Financing capacity planning with environmental considerations: A non-parametric analysis

This paper presents an approach to capacity planning and coordination for the regeneration of complex investment goods. In order to capture the specifics of the regeneration environment, Bayesian networks are utilised to improve the accuracy of the workload forecast and mathematical models are proposed for the long-, medium- and short-term capacity planning and coordination. The long- and medium-term models maximise the total profit through the optimum determination of the quantity of goods to be regenerated in-house or at the sites of external vendors, the number of regenerated goods to be stored and the extent to which penalties should be tolerated for delayed regeneration orders. The short-term model determines the optimal allocation of human and machine resources to different orders. The models are validated through the use of real-world data supplied by an industrial partner. Sensitivity analyses are conducted in order to gain insights into the model. The application of the models leads to significant improvements for the industry partner due to the reduction in regeneration costs as a result of implementing varying plant capacities over the course of a year.