Jeng Feng Chin

Effects of inventory classifications on CONWIP system: a case study

This paper studies the effects of integrating four inventory classification methods into constant work-in-process (CONWIP) systems in a multistage batch production. The inventory classification methods are multiple closed-loop CONWIP (MCC), parallel CONWIP (PC), analytical hierarchy procedure, and weighted linear optimization (WLO). Discrete-event simulation models were built. A full-factorial experimental design was employed. Response surface methodology generated suitable regression models for performance comparison of different ABC analysis methods. In the second stage of experiment, the ABC analysis methods from the first stage were fine-tuned and compared. PC and WLO are the most preferred methods because of higher total outputs, lower work-in-process levels, and shorter flow times compared with the other two methods. However, the revenue accrued through WLO is higher than that accrued via PC. This study complements existing literature on price setting in manufacturing organizations by delineating cl...

Feature selection in multimedia: The state-of-the-art review

Abstract Multimedia data mining, particularly feature selection (FS), has been successfully applied in recent classification and recognition works. However, only a few studies in the contemporary literature have reviewed FS (e.g., analyses of data pre-processing prior to classification and clustering). This study aimed to fill this research gap by presenting an extensive survey on the current development of FS in multimedia. A total of 70 related papers published from 2001 to 2017 were collected from multiple databases. Breakdowns and analyses were performed on data types, methods, search strategies, performance measures, and challenges. The development trend of FS presages the increased prominence of heuristic search strategies and hybrid FS in the latest multimedia data mining.

Implementation of hybrid parallel kanban-CONWIP system: A case study

Abstract The most common form of production control strategy in lean management is the pull system. One emerging form of pull system uses kanban and CONWIP systems to handle products with different demand patterns. Case studies have protractedly depicted the actual implementation of pull systems; however, the use of hybrid systems is rare. This paper examines the procedures involved in implementing a hybrid system in a low variety/low volume shop floor. This paper presents discussions on shop floor constraints in the proposed system and how the simplicity of a pull system is able to reduce work-in-process inventory by 23%. Guidelines for the replication of the system for similar production environments are also provided. The case study proves that pull systems can be successfully implemented in production environments that do not conform to the typical prerequisites of the kanban system.

Parallel kanban-CONWIP for production system with rework entrance

Parallel kanban-constant work in process (WIP) (CONWIP) (PKC) integrates kanban and CONWIP systems to regulate separately production of high- and low-demand items. This paper studies the performance of PKC variants under a production environment that requires the entrance of rework parts. A case study was performed at an electronics assembly plant with a three-stage asynchronous processing. Discrete-event simulations were conducted using an analysis enhanced by multi-factor ANOVA and response surface methodology. The studied performance measures were the total output, average WIP, flow times, and average bottleneck utilisation. A harmonising effect of the high- and low-demand items was observed when item composition was maintained at a certain ratio. The merge rework policy was consistently superior to the original rework policy. The categorisation in loading rule effectively reduced variations in PKC performance.

Labour-intensive line-cell reconfiguration with cycle time adjustment attributed to changes in situational awareness

In line-cell reconfiguration, cycle time is adjusted whenever operators involve multiple tasks. One crucial factor frequently overlooked is changes in situational awareness, which relates to the comprehension on the task importance of operators. This paper presents a line-cell reconfiguration that considers changes in situational awareness. A mathematical model is developed and tested via simulation of a case study facility. The simulation model seeks to find the best operator assignment and obtain the number of cells needed to achieve the objective. The model also determines whether it performs better than the existing assembly line conveyor system. The cells marginally perform better than the original system but are subjected to constraints in the operator assignment method. Despite the close proximity of operators, the cycle time expansion of individual tasks contributes to the poor performance of the cells. This paper is the first to examine changes in situational awareness in the context of manufacturing.

Automating computer simulation and statistical analysis in production planning and control research

Abstract Computer simulation is commonly used to study production planning and control prior to actual shop floor implementation. The majority of simulations are discrete events and involve modeling of elements and interactions. A complete simulation analysis requires multiple runs to infer stochastic behaviors of the system under different combination of factors. The analysis takes in results obtained from all the runs and confirms a hypothesis statistically. The resources required greatly rely upon the number of simulation models, simulation run length, technical knowledge, and computer resource available. Although latest commercial production simulation software allows some forms of automation, the analysis functions included are considered rudimentary. Integrating computer simulation and advanced statistical methods can result in substantial time and resource savings. In this paper, computer simulation and statistical analysis have been integrated and automated to cater for a large combination of simulation runs. With the system named as ProSA (production simulation and analysis), the work has been completed in 2011 and was demonstrated in a recent case study. The evidence provides concrete proof of such a possibility and provides an invitation to others to explore application research into technical knowledge and tasks transfer to computer.

Preprocessing compressed 3D kinect skeletal joints in enhancing human motion classification

Human motion classification has been commonly analyzed from 2D and 3D temporal body postures. Previous analyses focused on exergaming, sport science, surveillance and rehabilitation for the betterment of living. A number of recent works had also applied Microsoft Kinect captured motions for its portability and capability convenience to detect RGB images, depth images and 3D skeleton joint coordinates. Nevertheless, Kinect captured data contains inaccuracies which necessitates preprocessing efforts to improve the motion recognition accuracy. To the best of our knowledge, works had rarely detailed the data preprocessing techniques in order to improve the qualities of motion recognition. Few had demonstrated the feasibilities of 2D motion data to represent 3D motions. The importance of data preprocessing role in enhancing the classification accuracy, thus remains a major research concern to date. Therefore, this paper presents the comparisons of human motion data preprocessing analyses on 2D and 3D skeletal joints. Uncertainties observed in raw 3D motion data are identified and filtered followed by the data compression tasks into 2D data. The preprocessing performances at each level are judged using four major classifiers: Bayes, Function, Lazy and Tree aided by the WEKA tool. The approaches are employed on the skeletal joints coordinates of data retrieved from UTKinect-Action Dataset. Our findings demonstrated that preprocessing efforts on the compressed 3D into 2D skeletal joints is comparable to the actual human motion by classification accuracy and at the same time reduces the execution time.

Self-reinforcing mechanisms for cellularisation: a longitudinal case study

Firms that adopt cellular manufacturing (CM) often need to restructure their products, personnel, equipment, factory management and organisation culture. Despite voluminous literature on CM, only a few case studies related to cellularisation exist. Therefore, to enhance understanding in this area, this paper presents a longitudinal case study on cellularisation in an electronic assembly plant. Six years of data were collected from the observations and discussions of an industrialist and an academician. Events instrumental to the persistence of CM were highlighted, with reference to path dependence analysis. Cellularisation is not entirely evolutionary or teleological but is an interplay of the two; it is significantly influenced by the positive feedback of self-reinforcing mechanisms after departing from critical junctures. The observed self-reinforcing mechanisms were expounded and divided into six categories.