A simulation analysis of the impact of production lot size and its interaction with operator competence on manufacturing system performance
Introduction
In the process of production planning, one of the functions is to define the production quantity to be loaded to the production line. In the current manufacturing world which stresses short lead time and Quick Response supply, planners usually split the customer order into lots of different sizes before loading them to the production lines in order to enhance flexibility and responsiveness. The decision is very important as production quantity has been convincingly recognized as an influential factor affecting the operator performance curves [1], [23], [29]. However, the decision regarding the production quantity to be loaded is made mainly based on the primary concerns of avoiding late completion of contracts and minimizing costs [9]; minimal concern has been placed on the impact of the defined production quantity and its interaction with operator performance curves on the manufacturing system performance. In the related literature, researchers also neglect the close relationship between the production quantity and operator performance curves. They mostly emphasize on the modeling of different extensions in attempt to imitate a realistic manufacturing situation and generate line loading plan with minimum costs for different situations [3], [5]. To provide more insights to the production planning process, this paper aims to explore the impact of the production quantity to be loaded to the production line and its interaction with operator competence levels on the manufacturing system performance in terms of average work-in-progress (WIP) level, flow time, machine and operator utilization rates.
A simulation-based factorial design is proposed in the research. The experimental design is carried out by simulating a manufacturing system using the ProModel package and output is analyzed using the SPSS statistical package. In the simulation process, two inadequacies are identified in the existing simulation literature which would affect the investigation on the above issue. They concern the failure in simulating realistic operator learning curves [6], [12], [16], [18], [28] and the incompetent approach of simulation experimental analysis [16], [28]. Therefore, this research also aims to propose a practical simulation modeling approach by refining these inadequacies to aid accurate assessment of the manufacturing system performance.
The remainder of the paper is organized as follows. Section 2 is the background and motivation for the research. In Section 3, we will explain the proposed simulation modeling approach; the experimental factors considered and present an analysis of the experimental output including the main effect and interactions between factors. Finally, a conclusion is drawn in Section 4.
Section snippets
Background and motivation
In common practices when planners split the customer order into lots of different sizes, they decide mainly based on two primary concerns: to avoid late completion of contracts and to minimize costs [11]. Little emphasis has been placed on how the decision regarding lot sizes affects manufacturing system performance on operative level in terms of WIP level, flow time, machine and operator utilization rates. In academic prospective, researchers tend to focus on factors such as batch size,
Framework of the proposed simulation model
A new simulation model is proposed to fill the above inadequacies before it is used for the mentioned research issue regarding the quantity loaded to the production line. The proposed simulation model consists of three phases (Fig. 1).
In Phase 1, real production data collected from the factory is used to configure a specific manufacturing system. The data covers operator sequences, the Standard Allowed Minutes (SAM) of each operation and operator competence records. Based on the data,
Conclusion
This paper attempts to draw the attention to the impact of quantity loaded to a production line on the manufacturing system performance in the hope of improving the production loading process. A simulation-based factorial design is conducted for the study. During the simulation process, two inadequacies regarding simulation of operator learning curves and simulation experimental analysis are identified in the existing literature which hinders accurate analysis. The application of a
References (30)
- et al.
Towards a theoretical framework for human performance modelling within manufacturing systems design
Simul. Model. Pract. Theory
(2005) - et al.
A linear programming embedded genetic algorithm for an integrated cell formation and lot sizing considering product quality
Eur. J. Oper. Res.
(2008) - et al.
A simulation based experimental design to analyze factors affecting production flow time
Simul. Model. Pract. Theory
(2008) - et al.
A note on single-machine group scheduling problems with position-based learning effect
Appl. Math. Model.
(2009) - et al.
Labor and machine sizing through a simulation-expert-system-based approach
Simul. Model. Pract. Theory
(2007) Scheduling problems with a learning effect
Eur. J. Oper. Res.
(2001)- et al.
Experiential learning and forgetting for manual and cognitive tasks
Int. J. Ind. Ergon.
(2000) - et al.
A population of learners: a new way to measure organizational learning
J. Oper. Manage.
(1998) - et al.
Learning curve modelling of work assignment in mass customized assembly lines
Int. J. Prod. Res.
(2007) - et al.
Modeling practical lot-sizing problems as mixed-integer programs
Manage. Sci.
(2001)
Productivity improvement with lean production in glove manufacturing industry
Key Eng. Mater.
Garment assembly line simulation based on ProModel
J China Univ. Metrol.
The design of flexible manufacturing systems using simulations
Intell. Manuf. Syst.
Apparel Manufacturing: Sewn Product Analysis
Modeling lean, agile and leagile supply chain strategies
J. Bus. Logist.
Cited by (9)
Simulation-based Optimization of Material Requirements Planning Parameters
2022, Procedia Computer ScienceKey factors for operational performance in manufacturing systems: Conceptual model, systematic literature review and implications
2021, Journal of Manufacturing SystemsCitation Excerpt :As a result of this narrow focus, the generalizability of the research on this topic is still limited. For example, studies have analyzed experimental simulations to determine how lead time is influenced by delay, routing, and sequencing [13], and how utilization rate and work in process (WIP) are affected by the batch size and the operators' skills [14]. Furthermore, mathematical models have been used to study the relationship between quality and settling time in serial MS [15] and to examine how maintenance strategies affect the production rate and WIP in automated serial MS [16].
Operator competency model for mechanical engineering expertise
2023, AIP Conference ProceedingsReal-time data-driven discrete-event simulation for garment production lines
2022, Production Planning and ControlAnalysis of Control Room Operators' Competence using Cognitive Engineering Approaches to Improve Process Safety
2021, 2021 International Conference on Maintenance and Intelligent Asset Management, ICMIAM 2021