Enhanced Changeover Detection in Industry 4.0 Environments with Machine Learning

Changeover times are an important element when evaluating the Overall Equipment Effectiveness (OEE) of a production machine. The article presents a machine learning (ML) approach that is based on an external sensor setup to automatically detect changeovers in a shopfloor environment. The door statuses, coolant flow, power consumption, and operator indoor GPS data of a milling machine were used in the ML approach. As ML methods, Decision Trees, Support Vector Machines, (Balanced) Random Forest algorithms, and Neural Networks were chosen, and their performance was compared. The best results were achieved with the Random Forest ML model (97% F1 score, 99.72% AUC score). It was also carried out that model performance is optimal when only a binary classification of a changeover phase and a production phase is considered and less subphases of the changeover process are applied.

Designing optimizing procedures for task switching to ensure efficiency in the hospital laboratory

This study aims to improve the efficiency of task switching in hospital laboratories. In a laboratory, several medical technicians perform multiple tasks. Technicians are not aware of the marginal amount of time it takes to switch between tasks, and this accumulation of lost minutes can cause the technician to worry more about the remaining working time than work quality. They rush through their remaining tasks, thereby rendering their work less efficient. For time optimization, we identified work changeover times to help maintain the work quality in the laboratory while reducing the number of task switching instances. We used the turnaround time (TAT) compliance rate of emergency room samples as an indicator to evaluate laboratory performance and the number of task switching instances as an index of the task performer perspective (TPP). We experimented with a monitoring system that populates the time for sample classification according to the optimal time for task switching. Through the proposed methodology, we successfully reduced not only the instances of task switching by 10% but also the TAT non-compliance rate from 4.97 to 2.66%. Consequently, the introduction of new methodology has greatly increased work efficiency.

Collaboration, Interruptions, and Changeover Times: Workflow Model and Empirical Study of Hospitalist Charting

Problem definition: Collaboration is important in services but may lead to interruptions. Professionals exercise discretion on when to preempt individual tasks to switch to collaborative tasks. Academic/practical relevance: Discretionary task switching can introduce changeover times when resuming the preempted task and, thus, can increase total processing time. Methodology: We analyze and quantify how collaboration, through interruptions and discretionary changeovers, affects total processing time. We introduce an episodal workflow model that captures the interruption and discretionary changeover dynamics—each switch and the episode of work it preempts—present in settings in which collaboration and multitasking is paramount. A simulation study provides evidence that changeover times are properly identified and estimated without bias. We then deploy the model in a field study of hospital medicine physicians: “hospitalists.” The hospitalist workflow includes visiting patients, consulting with other caregivers to guide patient diagnosis and treatment, and documenting in the patient’s medical chart. The empirical analysis uses a data set assembled from direct observation of hospitalist activity and pager-log data. Results: We estimate that a hospitalist incurs a total changeover time during documentation of five minutes per patient per day. Managerial implications: This estimate represents a significant 20% of the total processing time per patient: caring for 14 patients per day, our model estimates that a hospitalist spends more than one hour each day on changeovers. This provides evidence that task switching can causally lead to longer documentation time.

SMED

Short changeover times have always been critical in manufacturing and are a necessity nowadays in all types of industries, due every wasted minute means inefficiency. Single Minute Exchange of Dies (SMED) is a methodology developed by Shigeo Shingo in 1985, which seeks to reduce the setup time of a machine to less than ten minutes (Shingo, 1985). It provides a rapid and efficient way of converting a manufacturing process from a current product that is been running in the production process, to the next product (Tharisheneprem, 2008), aimed always to decrease the setup time in industrial machinery, given flexibility in product and their characteristics. Through this research, we found that we can achieve some benefits through the implementation of the SMED methodology such as: the reduction of changeover time up to 90% with moderate investments (Cakmakci, 2009), reduce waste and increase quality, it makes low cost flexible operations possible.

A novel optimal method of robotic weld assembly line balancing problems with changeover times: a case study

PurposeThe balancing of robotic weld assembly lines has a significant influence on achievable production efficiency. This paper aims to investigate the most suitable way to assign both assembly tasks and type of robots to every workstation, and present an optimal method of robotic weld assembly line balancing (ALB) problems with the additional concern of changeover times. An industrial case of a robotic weld assembly line problem is investigated with an objective of minimizing cycle time of workstations.Design/methodology/approachThis research proposes an optimal method for balancing robotic weld assembly lines. To solve the problem, a low bound of cycle time of workstations is built, and on account of the non-deterministic polynomial-time (NP)-hard nature of ALB problem (ALBP), a genetic algorithm (GA) with the mechanism of simulated annealing (SA), as well as self-adaption procedure, was proposed to overcome the inferior capability of GA in aspect of local search.FindingsTheory analysis and simulation experiments on an industrial case of a car body welding assembly line are conducted in this paper. Satisfactory results show that the performance of GA is enhanced owing to the mechanism of SA, and the proposed method can efficiently solve the real-world size case of robotic weld ALBPs with changeover times.Research limitations/implicationsThe additional consideration of tool changing has very realistic significance in manufacturing. Furthermore, this research work could be modified and applied to other ALBPs, such as worker ALBPs considering tool-changeover times.Originality/valueFor the first time in the robotic weld ALBPs, the fixtures’ (tools’) changeover times are considered. Furthermore, a mathematical model with an objective function of minimizing cycle time of workstations was developed. To solve the proposed problem, a GA with the mechanism of SA was put forth to overcome the inferior capability of GA in the aspect of local search.

SMED

Short changeover times have always been critical in manufacturing and are a necessity nowadays in all types of industries, due every wasted minute means inefficiency. Single Minute Exchange of Dies (SMED) is a methodology developed by Shigeo Shingo in 1985, which seeks to reduce the setup time of a machine to less than ten minutes (Shingo, 1985). It provides a rapid and efficient way of converting a manufacturing process from a current product that is been running in the production process, to the next product (Tharisheneprem, 2008), aimed always to decrease the setup time in industrial machinery, given flexibility in product and their characteristics. Through this research, we found that we can achieve some benefits through the implementation of the SMED methodology such as: the reduction of changeover time up to 90% with moderate investments (Cakmakci, 2009), reduce waste and increase quality, it makes low cost flexible operations possible.

The Use of SMED in Engineering Manufacturing

To survive in such an increasingly competitive world, there is a need of continuous improvement in every type of industry. An answer to these challenges for manufacturing companies is the implementation of lean concepts. The importance of short changeover times, SMED, has always been critical for manufacturing companies. It is one of the many lean production methods for reducing waste in the manufacturing process. It's a quick and effective way adjustment process of the actual product to another product. The practical part is focused on usage of SMED in manufacturing. Project was realized in engineering company oriented on machining heavy products. Separating time to external and internal we saved about 10% of total production time. The result is proposal timetable of standard changeover process. Also timetable of two pieces in batch, because insertion two pieces in the batch reduced the total time of production. Total time was reduced of two changeovers.

Discrete-Time State Dependent Bulk Service Queue with Multiple Vacations and Changeover Times

This paper presents the analysis of a discrete-time renewal input multiple vacations queue with state dependent service and changeover times under (a,c,b) policy. The service times, vacation times, and changeover times are geometrically distributed. The server begins service if there are at least c units in the queue and the services are performed in batches of minimum size a and maximum size b (a≤c≤b). At service completion instant, if the queue size is less than c but not less than a secondary limit a, the server continues to serve and takes vacation if the queue size is less than a-1. The server is in changeover period whenever the queue size is a-1 at service completion instant and c-1 at vacation completion instant. Employing the supplementary variable and recursive techniques, we have derived the steady state queue length distributions at prearrival and arbitrary epochs. Based on the queue length distributions, some performance measures of the system have been discussed. A cost model has been formulated and optimum values of the service and vacation rates have been evaluated using genetic algorithm. Numerical results showing the effect of model parameters on the key performance measures are presented.