1. Introduction
In recent years, the consciousness in regard to sustainability has been growing tremendously in terms of government regulations, consumer awareness, market competition, etc. Sustainability aims to create and maintain a harmonious balance between human activities and natural resources, without sacrificing and compromising the needs of future generations [
1,
2]. The concept of sustainability in the manufacturing and service industry has been materialized, as an increasing number of people have been paying attention to environmental issues, such as global warming, environment deterioration, and resource and energy conservation [
3,
4]. Also, the public has realized the side effects of the manufacturing and service industry and the adverse impact of disposing of nonbiodegradable products [
5]. The manufacturing and service industry holds the potential to lessen the adverse impact of environmental pollution in terms of all levels of material flow, from raw materials acquisition, to product manufacturing, to product packaging, to product distribution, to product consumption and, finally, to product disposal [
6]. Moreover, the demand for environmentally hygienic services, green products, and substitution of nonrenewable resources is increasing significantly, and such a trend in the manufacturing and service industry drives the core of industrial economies from cost-effective strategies to sustainable strategies in production scheduling, network design, implementation of reverse logistics, and other aspects [
7,
8,
9]. In other words, various green activities and green operations can contribute to the improvement of the environmental, economic, and social development by means of preserving natural resources, introducing competitive innovations and providing higher standards of living [
10]. The necessity of deploying green operations arouses the awareness of academic researchers and industrial practitioners in seeking green solutions in the manufacturing and service industry [
11,
12].
The catering service industry is a special category in the manufacturing and service industry, which emphasizes the provision of food, beverages and other services. The requirement for catering services increases significantly in metropolitan cities along with the development of the economy, and various catering service providers have arisen, such as large catering corporations, small restaurants, and even individuals. Apart from providing high quality food, catering service providers face some other challenging problems, such as food waste, unhygienic tableware, and overuse of disposable tableware. In order to reduce the overuse of disposable tableware, catering service providers prefer to use recyclable tableware. However, the dishwashing of dirty tableware is a time-consuming and labor-intensive job for catering service providers. Therefore, an increasing number of catering service providers tend to outsource their dishwashing activities by choosing a professional dishwashing service provider, which provides a centralized dishwashing service for the large amount of dirty tableware from restaurants, using their professional machines and human operations. However, facing an increasing amount of dirty tableware, the increasing labor force cost, and fluctuating labor supply, dishwashing service providers need to improve their operational performance by enhancing the labor force utilization rate and balancing the labor force cost and labor workload [
13]. Given this consideration, this research explores the major challenges faced by dishwashing service providers towards the environmental development of this industry.
In order to explore the challenges facing the dishwashing service industry, the simulation approach was employed in this research, which is a practical approach to anticipate possible effects and to measure operational efficiency. The simulation approach acts as a comparable counterpart to explicitly demonstrate the functions and operations of the system [
14]. A simulation model with distinct goals and reliable processes can imitate the practical scenarios and generate reasonable suggestions for decision makers, and can be iteratively implemented with necessary adjustments for continuous improvement [
15]. To be more specific, discrete event simulation (DES), in contrast to continuous simulation, modelling the activities of a specific system as a discrete sequence of events is employed in this research. DES has the capability of shaping up virtual computational experiments with guided processing sequences [
16], which is a useful tool for decision makers to measure the efficiency of a production system and identify key components for productivity improvement.
In this research, an in-plant logistics DES model was implemented to measure the efficiency of a dishwashing factory in terms of identifying the main bottlenecks and key operations in the dishwashing process. The contribution of this research is threefold. First, it is a pilot application of DES into the dishwashing service process, and this successful implementation can provide useful insights for DES application in other scenarios. Second, the comparison between simulation results and the actual situation provides valuable suggestions for decision-making concerning labor force optimization and productivity improvement in this specific dishwashing factory. Third, such a labor optimization approach in a dishwashing factory can inspire similar exploration in other labor-intensive manufacturing and service industries towards both performance improvement and sustainable development.
The remainder of this paper is organized as follows. In
Section 2, a literature review is conducted in view of the application of simulation modelling.
Section 3 describes the operations and processes of the targeted factory. In
Section 4, a simulation model is built and executed, in which the result analysis is provided and analyzed. Finally,
Section 5 concludes this research.
2. Literature Review
Simulation modelling has been widely used in the manufacturing and service industry in order to acquire managerial insights from existing or proposed systems, among which DES is a popular method for modelling complex dynamic systems by abstracting discrete events, and has been widely used in various industrial settings. For example, Chen et al. [
17] used DES modelling to classify the inventory level for a chemical plant. Iannoni and Morabito [
18] employed DES to evaluate the cross-docking operation within a supply chain. Bergero and Kofman [
19] introduced power discrete event system specification (PowerDEVS) for hybrid modelling. Bonaventura et al. [
20] introduced a CD++Builder, which aimed at providing user-friendly graphical modelling tools to simplify the model construction and simulation execution. Wang et al. [
21] designed a DES system for multiple resources and materials allocation to resolve job-shop scheduling problems. Seebacher, Winkler, and Oberegger [
16] studied the in-plant logistics efficiency considering the downtime of each machine and the duration of an empty run using DES. Leonardo and Emre [
22] evaluated alternative resource allocation in a hospital emergency department using DES. Zupan et al. [
23] conducted a layout optimization study of a production cell using DES. Yentl and Hans [
24] provided an overview of different DES tools by identifying their supported formalisms, compliance, features, and performance.
In this research, simulation modelling approach is employed to measure the operational performance, identify the key components, and further improve the productivity. To be more specific, ProModel, which is a powerful windows-based DES tool, is used to develop the in-plant logistics simulation model, considering its capability to streamline and alter existing processes for continuous improvement. The ProModel simulation tool has also been employed in a number of research studies. For example, Yu et al. [
25] employed ProModel to simulate the production operations for an automotive assembly workstation. Tearwattanarattikal et al. [
26] conducted a case study regarding the plant layout design and planning in a plastic packaging factory using ProModel. Sandanayake et al. [
27] used ProModel to simulate different experimental scenarios for just-in-time (JIT) manufacturing environment.
3. Case Study
In order to better understand the practical situation of dishwashing services in the catering service industry, a case study was conducted in a one-stop environmental hygiene catering service provider. This service provider has been facing an ever-increasing demand in dishwashing services. The obligation of the service provider is to provide a hygienic dishwashing service to its customers within the required time periods. To be more specific, customers usually request a one-day cycle of dishwashing service, which includes the time to deliver dirty dishes to factory, to execute dishwashing processes, and to return cleaned dishes to corresponding customers. Due to the high customer demand, the factory has to operate 16 h per day on normal working days and may need extra overtime work on holidays. The service provider faces severe challenges in maintaining a high service level considering the labour workload cost and customer demand.
The current operational scenario is described in
Figure 1, which consists of four major processes, i.e., (1) solid waste removal, (2) hygienic dishwashing, (3) output enumeration, and (4) packing. The operational flow of this case company can be described as follows. Firstly, soiled dishes are collected by truck from restaurants, and then delivered to this factory. Then, the soiled dishes are stored temporarily in Zone A until the availability of the next process. After that, workers transfer the soiled dishes to Zone B for the process of solid waste removal. This process, which is highly labour intensive, removes observable wastage before feeding the dishes into the automated hygiene dishwashing machine. The automated hygiene dishwashing machines, which are essential for dish sterilisation, subsequently provide deep-cleaning to ensure the cleaning quality. Following the self-propelling cleaning system in Zone C, an enumeration process is conducted in Zone D by workers to calculate the number and weight of each box of tableware for data recording and service-fee charging. The cleaned dishes are eventually packed on pallets in Zone E, and ready for distribution.
5. Conclusions
This study analyzes a challenging problem arising from environmental issues in the catering service industry. Due to the fluctuating labor supply, it is difficult for enterprises to sustain green operations and high-quality services without a dynamic and efficient human resource management approach. For this purpose, this study provides an approach to construct an in-plant logistics simulation model for green service providers to solve the bottleneck of production and optimize human resources utilization. Apart from the analysis and optimization of current processes, the proposed model is capable of visualizing the operational processes through realistic animation. Useful information can be provided for decision makers to streamline and alter the existing processes for improvement. A case study using simulation is conducted in a one-stop environmentally hygienic catering service provider. The simulation results highlight that the pre-washing process is a main bottleneck in tableware washing work. Moreover, the simulation results show that productivity can increase 7% and no overtime work is required if two additional workers are present. The outcome of this study can not only improve the productivity of the tableware washing factory, but also can allow green service providers to optimize their labor forces and resources in a more dynamic, efficient, and systematic way.
Even though the proposed model is capable of providing useful suggestions for decision makers, there are still some limitations and future work, which can improve the quality of this study. One of the limitations is related to human working performance. Individual workers have their own capability and working experience. They have different working performance in completing the tasks. The long working hours also greatly affect working performance and lessen productivity. However, in the simulation model, the workers are assumed to work in a constant way. In some cases, the simulation model may vary with the real situation. Another limitation concerns data collection. In this study, two months were spent on data collection without considering the increasing demand before or after holidays. To improve the situation, data regarding particular periods should be collected and investigated. Further research can be considered in adopting emerging technologies, e.g., Internet of Things, Cloud Computing and Cyber Physical System, to upgrade the traditional manufacturing or industrial processes to smart factories and enable Industry 4.0.