Guoxian Xiao

Maintenance Opportunity Planning System

Timely performance of preventive maintenance (PM) tasks is a critical element of manufacturing systems. Since the majority of PM tasks requires that equipment be stopped, these tasks can generally only be performed during nonproduction shifts, breaks, or other scheduled downtime. Thus, there is a trade-off between time dedicated to production and time available for preventive maintenance. One approach to mitigate this trade-off is to perform maintenance during scheduled production time by strategically shutting down equipment for short time periods. This research developed a systematic method on when to shut down equipment to do maintenance in an automotive assembly environment. It is called maintenance opportunity. The method incorporated real-time information about production and machine failure conditions. A simulation-based algorithm is developed by utilizing the buffer contents as well as machine starvation and congestion to obtain maintenance opportunities during production time.

Energy Saving Opportunity Analysis of Automotive Serial Production Systems (March 2012)

Conventionally, improving production efficiency, flexibility and responsiveness has been the primary research focus of production management, while energy consumption has received relatively little attention. Energy consumption plays a more and more important role in the manufacturing environment. This is mainly driven by energy cost and environmental concerns. When the energy system becomes complicated and coupled with ongoing production, it is very difficult to hunt the “hidden treasure” which affects the overall benefit of a manufacturing system. This paper provides a systematic method to search for energy saving opportunities and strategies. We start from dynamic production transient analysis and provide quantitative analysis for identifying energy saving opportunity in a system. Furthermore, energy saving strategy is justified through cost analysis for tradeoffs between energy savings and throughput loss. A case study is conducted to demonstrate its potential on energy savings in a multistage manufacturing system.

Transient Analysis of Downtimes and Bottleneck Dynamics in Serial Manufacturing Systems

In manufacturing industry, downtimes have been considered as major impact factors of production performance. However, the real impacts of downtime events and relationships between downtimes and system performance and bottlenecks are not as trivial as it appears. To improve the system performance in real-time and to properly allocate limited resources/efforts to different stations, it is necessary to quantify the impact of each station downtime event on the production throughput of the whole transfer line. A complete characterization of the impact requires a careful investigation of the transients of the line dynamics disturbed by the downtime event. We study in this paper the impact of downtime events on the performance of inhomogeneous serial transfer lines. Our mathematical analysis suggests that the impact of any isolated downtime event is only apparent in the relatively long run when the duration exceeds a certain threshold called opportunity window. We also study the bottleneck phenomenon and its relationship with downtimes and opportunity window. The results are applicable to real-time production control, opportunistic maintenance scheduling, personnel staffing, and downtime cost estimation.

Supervisory Factory Control Based on Real-Time Production Feedback

One key characteristic of any process performance is variability; that is, a process rarely performs consistently over time. The bottleneck is one of the main reasons causing the system variability and fluctuation in production. Short-term production analysis and short-term bottleneck identification are imperative to enable manufacturing operations to optimally respond to dynamic changes in system behavior. However, conventional throughput and bottleneck analysis focus on long-term statistic bottleneck identification, which is usually not applicable to a short-term period. An on-line supervisory control method is introduced to search for short-term production constraints with unknown machine reliability distribution and mitigate those constraints to improve system throughput. The control mechanism uses playback simulation of the real production data to identify the bottleneck station, and control parameters of that station to reach a near balanced production line operation by understanding the bottleneck inertia phenomenon. The results ensure the smooth flow of products on the production line and increase the line’s performance.

Assembly Strategies for Remanufacturing Systems With Variable Quality Returns

This paper studies optimal policy for modular product reassembly within a remanufacturing setting where a firm receives product returns with variable quality and reassembles products of multiple classes to customer orders. High-quality modules are allowed to substitute for low-quality modules during reassembly to provide the remanufacturing system with flexibility such that shortage in lower quality modules can be smoothed out by higher quality module inventories. We formulate the problem as a Markov decision process and characterize the structure of the optimal control policy. In particular, we show that the optimal reassembly and substitution follow a state-dependent threshold-based control policy. We also establish the structural properties of the thresholds. Using numerical experimentation, we study how system performance is influenced by key cost parameters including unit holding cost, unit assembly cost and shortage penalty cost. Finally, we compare the optimal policy with an exhaustive reassembly policy and show that there is great benefit in module substitution and threshold-based assembly control.

The Costs of Downtime Incidents in Serial Multistage Manufacturing Systems

Downtime is arguably the single most significant contributor to system inefficiency in a multistage manufacturing system. Achieving near-zero downtime has been the ultimate goal of production operation management at the plant floor. Accurate estimation of the impact of each downtime incident is of great importance for deciding where to allocate limited resources among various manufacturing stages. In this paper, we focus on quantitative analysis of the impact of each individual downtime event in terms of permanent production loss and financial cost. We start from the transient analysis of a single downtime event and later extend to more generic scenarios where downtime incidents occur concurrently at different stages. Apart from the analytical study, a practical computation procedure using real-time production records can be readily derived and implemented at the plant floor. Case studies are conducted to demonstrate its potential in facilitating the decision making at plant floor on project identification, prioritization, and budget allocation in a multistage manufacturing system.

Production system design to achieve energy savings in an automotive paint shop

Vehicle painting typically consumes the largest amount of energy in an automotive assembly plant. Effective reduction of energy usage in paint shops will lead to significant savings. Substantial effort has been devoted to reducing energy usage in paint shops through renovating the painting process and equipment. In this paper, we introduce a case study at an automotive paint shop to show that the energy consumption can be reduced significantly through production system design. Specifically, by selecting the appropriate repair capacity, the number of repainted jobs can be reduced, and less material and energy will be consumed. In addition, less atmospheric emissions would be generated during the painting process. Such a technique does not need to invent new chemicals, new painting processes or new control systems in painting booths and ovens. It provides an alternative way for energy and emission reduction to achieve energy-efficient and environmentally friendly manufacturing.

Energy Efficiency Management of an Integrated Serial Production Line and HVAC System

Modern manufacturing facilities waste many energy savings opportunities (ESO) due to the lack of integration between the facility and the production system. To explore the energy savings opportunities, this paper combines the two largest energy consumers in a manufacturing plant: the production line and the heating, ventilation, and air conditioning (HVAC) system. The concept of the energy opportunity window (OW) is utilized, which allows each machine to be turned off at set periods of time without any throughput loss. The recovery time of each machine is the minimum amount of time a machine must be operational between opportunity windows to guarantee zero production loss and it is explored both analytically and numerically. The opportunity window for the production line is synced with the peak periods of energy demand for the HVAC system to create a heuristic rule to optimize the energy cost savings. This integrated system is modeled and tested using simulation studies.

Simulation study of a bottleneck-based dispatching policy for a maintenance workforce

Maintenance is important for production operations and for continuous improvement. Appropriate dispatching of the maintenance workforce to quickly respond to equipment failures and carry out preventive services can improve system productivity. The first-come-first-served policy is typically used in many manufacturing industries. In this paper, we present a priority-based dispatching policy, a dynamic bottleneck policy, based on the analysis of real-time data. In such a policy, priority is assigned to the bottleneck machine after a fixed time period, and the maintenance worker will service the high-priority machine (i.e. bottleneck machine) first when multiple service requests are received. It is shown by extensive simulation experiments that this policy can lead to a greater improvement in system throughput compared with the first-come-first-served policy. To implement such a policy, the appropriate time period for data collection and the frequency for carrying out bottleneck analysis are investigated. In addition, a sensitivity study suggests that the results obtained are insensitive to machine downtime, efficiency, and reliability models.

Bottleneck Detection of Manufacturing Systems Using Data Driven Method

Bottlenecks in a production line have been shown to be one of the main reasons that impede productivity. Correctly and efficiently identifying botdeneck locations can improve the utilisation of finite manufacturing resources, increase the system throughput, and minimize the total cost of production. Current bottleneck detection schemes can be separated into two categories: analytical and simulation-based. For the analytical method, the system performance is assumed to be described by a statistical distribution. Although an analytical model is good at long term prediction, this type of model is not adequate for solving the bottleneck detection problem in the short term. On the other hand, the simulation-based method has disadvantages, such as long development time and decreased flexibility for different production scenarios, which greatly impede its wide implementation. Because of all these problems, a data driven bottleneck detection method has been constructed based on the real-time data from manufacturing systems. Using this new method, bottleneck locations can be identified in both the short term and long term. Furthermore, the proposed data driven bottleneck detection method has been verified using the results from both the analytical and simulation methods.