Advanced Cost-Efficient Production Scheduling in Hi-Tech Manufacturing Industry at Test Operation

2011 ◽  
Vol 110-116 ◽  
pp. 3922-3929 ◽  
Author(s):  
Tanaporn Sillapa-Archa ◽  
Phiphlu Thaninthanadech ◽  
Darin Smitasiri
Keyword(s):  
Production Scheduling ◽  
Product Life Cycle ◽  
Manufacturing Industry ◽  
Production Efficiency ◽  
Rapid Change ◽  
Shop Floor ◽  
Efficient Production ◽  
Manufacturing Companies ◽  
Test Operation ◽  
Scheduling Plan

Rapid change in customer demand and aggressive business competition in high technology industry has driven the need for greater performance with lower price product under short product life cycle. This stimulates semiconductor manufacturing companies to pay much more attention to their cost control and production built strategy. Bad performance in shop-floor planning results in huge inventory, poor equipment efficiency, and therefore it would jeopardize the company sustainability. Consequently, the main objectives of an effective production planning intelligence are to maximize company benefits, meet customer expectation and improve production efficiency. This paper presents an algorithm of production loading plan that balances cost, meet customer delivery request and optimize production efficiency by considering all important constrained factors in manufacturing environment, namely test cost per hour, throughput rate, machine capacity and utilization, hardware set up matrix and limitation, the number of change-over and customer delivery date. The analysis is done by comparing the performance outcome with the common scheduling rules. The result of this new planning logic solution is proved to increase the effectiveness of the production scheduling plan based on available resources on hand and complex of constraint. It also decreases human interfere causing error in planning procedure.

scholarly journals Managing Manufacturing Complexity Drivers on Performance – An Initial Study

2018 ◽  
Vol 2 (1) ◽  
pp. 45-50
Author(s):  
Keyword(s):  
Strong Correlation ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Initial Study ◽  
Efficient Production ◽  
Product Management ◽  
Nonlinear Behaviour ◽  
Manufacturing Companies ◽  
Manufacturing Performance ◽  
Manufacturing Productivity

Manufacturing systems, in pursuit of cost, time and flexibility optimisation are becoming more and more complex, exhibiting a dynamic and nonlinear behaviour. Unpredictability is a distinct characteristic of such behaviour and effects production planning significantly. Complexity continues to be a challenge in manufacturing systems, resulting in ever-inflating costs, operational issues and increased lead times to product realisation. This challenge must be met with appropriate decision-making by manufacturing companies to secure competitive advantage without compromising sustainability. Assessing complexity realises the reduction and management of complexity sources which contribute to lowering associated engineering costs and time, improves productivity and increases profitability. Therefore, this study was undertaken to investigate the priority level and current achievement of manufacturing performance in Malaysia’s manufacturing industry and the complexity drivers on manufacturing productivity performance. The results showed that Malaysia’s manufacturing industry prioritised product quality and they managed achieved a good on-time delivery performance. However, for other manufacturing performance, there was a difference where the current achievement of manufacturing performances in Malaysia’s manufacturing industry is slightly lower than the priority given to them. The strong correlation of significant value for priority status was observed between efficient production levelling (finished goods) and finish product management while the strong correlation of significant value for current achievement was minimised the number of workstation and factory transportation system. This indicates that complexity drivers have an impact towards manufacturing performance. Consequently, it is necessary to identify complexity drivers to achieve well manufacturing performance.

scholarly journals Implementasi Algoritma Wagner-Within pada Manajemen Inventori di PT X

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Puspandam Katias ◽  
Achmad Affandi
Keyword(s):  
Production System ◽  
Manufacturing Industry ◽  
Raw Materials ◽  
Raw Material ◽  
Efficient Production ◽  
Manufacturing Companies ◽  
Inventory Cost ◽  
Qualitative Descriptive ◽  
Total Inventory ◽  
Supporting Material

The increasing competition in the manufacturing industry caused increasing inconsumer demand of the quality and quantity of a good product. Therefore, manufacturing companies must have reliable services, policies and product qualities to satisfy its customers. So it needs to be supported by efficient production system and inventory system. To able to create an efficient production system then need a good raw material inventory planning. This research aims to compere how efficiently in planning raw materials inventory between Wagner-Within Algorithm with the actual concept that applied to PT X, Sidoarjo. The methodology of this research is qualitative descriptive research. The findings this research is engaged in packaging (woven bag and jumbo bag) with main raw materials such as plastic ore and supporting material in inner, thread, additive, and pigment. Based on the result of the analysis is known that the actual concept of the company gives the total inventory cost IDR 3.151.000.000 with the frequency of ordering 12 times while WagnerWithin Algorithm method provides a more efficient total inventory cost of Rp. 2.685.821.101 with8 times the frequency of ordering and can savings of 14.8% of total raw material inventory cost.

An agent-based architecture for production scheduling in dynamic job-shop manufacturing system

2018 ◽  
Vol 66 (6) ◽  
pp. 492-502 ◽  
Author(s):  
Om Ji Shukla ◽  
Gunjan Soni ◽  
Rajesh Kumar ◽  
Sujil A
Keyword(s):  
Production Planning ◽  
Production Scheduling ◽  
Manufacturing System ◽  
Job Shop ◽  
Shop Floor ◽  
Efficient Production ◽  
Planning And Scheduling ◽  
Agent Based ◽  
Production Planning And Scheduling ◽  
Key Issues

Abstract In a highly competitive environment, effective production is one of the key issues which can be addressed by efficient production planning and scheduling in the manufacturing system. This paper develops an agent-based architecture which enables integration of production planning and scheduling. In addition, this architecture will facilitate real time production scheduling as well as provide a multi-agent system (MAS) platform on which multiple agents will interact to each other. A case study of job-shop manufacturing system (JMS) has been considered in this paper for implementing the concept of MAS. The modeling of JMS has been created in SimEvents which integrates an agent-based architecture developed by Stateflow to transform into dynamic JMS. Finally, the agent-based architecture is evaluated using utilization of each machine in the shop floor with respect to time.

scholarly journals An Artificial Intelligence-Based Collaboration Approach in Industrial IoT Manufacturing: Key Concepts, Architectural Extensions and Potential Applications

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5480 ◽  
Author(s):  
Panagiotis Trakadas ◽  
Pieter Simoens ◽  
Panagiotis Gkonis ◽  
Lambros Sarakis ◽  
Angelos Angelopoulos ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Business Intelligence ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Manufacturing Industry ◽  
Shop Floor ◽  
Efficient Production ◽  
Human In The Loop ◽  
Industrial Iot ◽  
Potential Applications

The digitization of manufacturing industry has led to leaner and more efficient production, under the Industry 4.0 concept. Nowadays, datasets collected from shop floor assets and information technology (IT) systems are used in data-driven analytics efforts to support more informed business intelligence decisions. However, these results are currently only used in isolated and dispersed parts of the production process. At the same time, full integration of artificial intelligence (AI) in all parts of manufacturing systems is currently lacking. In this context, the goal of this manuscript is to present a more holistic integration of AI by promoting collaboration. To this end, collaboration is understood as a multi-dimensional conceptual term that covers all important enablers for AI adoption in manufacturing contexts and is promoted in terms of business intelligence optimization, human-in-the-loop and secure federation across manufacturing sites. To address these challenges, the proposed architectural approach builds on three technical pillars: (1) components that extend the functionality of the existing layers in the Reference Architectural Model for Industry 4.0; (2) definition of new layers for collaboration by means of human-in-the-loop and federation; (3) security concerns with AI-powered mechanisms. In addition, system implementation aspects are discussed and potential applications in industrial environments, as well as business impacts, are presented.

scholarly journals A Novel Collaborative Optimization Model for Job Shop Production–Delivery Considering Time Window and Carbon Emission

2019 ◽  
Vol 11 (10) ◽  
pp. 2781 ◽  
Author(s):  
Wenzhu Liao ◽  
Tong Wang
Keyword(s):  
Sustainable Development ◽  
Supply Chains ◽  
Production Scheduling ◽  
Carbon Emission ◽  
Manufacturing Industry ◽  
Production Efficiency ◽  
Job Shop ◽  
Intelligent Manufacturing ◽  
Collaborative Optimization ◽  
Delivery Model

The manufacturing industry is undergoing transformation and upgrading from traditional manufacturing to intelligent manufacturing, in which Internet of Things (IoT) technology plays a central role in promoting the development of intelligent manufacturing. In order to solve the problem that low production efficiency and machine utilization lead to serious pollution emissions in the workshop caused by untimely transmission of information in all links of the production and manufacturing process to whole supply chains, this study establishes an intelligent production scheduling and logistics delivery model with IoT technology to promote green and sustainable development of intelligent manufacturing. Firstly, an application framework of IoT technology in production–delivery supply chain systems was established to improve efficiency and achieve the integration of production and delivery. Secondly, an integrated production–delivery model was constructed, which takes into account time and low carbon constraints. Finally, a two-layer optimization algorithm was proposed to solve this integration problem. Through a case study, the results show this integration production–delivery model can reduce the cost of supply chains and improve customer satisfaction. Moreover, it proves that carbon emission cost is a major factor affecting total cost, and it could help enterprises to realize the profit and sustainable development of the environment. The production–delivery model could also support the last kilometer distribution problem and extension under E-commerce applications.

scholarly journals Implementing total productive maintenance in a manufacturing small or medium-sized enterprise

2021 ◽  
Vol 14 (2) ◽  
pp. 152
Author(s):  
Zhang Tian Xiang ◽  
Chin Jeng Feng
Keyword(s):  
Action Research ◽  
Capital Investment ◽  
Manufacturing Industry ◽  
Production Efficiency ◽  
Model Development ◽  
Steering Committee ◽  
Shop Floor ◽  
Total Productive Maintenance ◽  
Active Involvement

Purpose: This paper develops a ‘light’ total productive maintenance (TPM) model suitable for small and medium-sized enterprises (SMEs). By design, the system is rudimentary, using a relatively small sum of capital investment and resources. The model recommends TPM implementation in three stages, namely plan, improve, and sustain.Design/methodology/approach: The literature review provides the inputs to the model development. Action research is used to demonstrate and verify the effectiveness and practicability of the framework, in an SME manufacturing hydraulic parts in China. Overall Equipment Effectiveness (OEE) and awareness of employees were studied before and after the implementation. Findings: The case study shows a significantly improved production efficiency of the equipment. The framework structuralizes TPM deployment and binding different levels of the organization into the program, from planning, implementation to sustaining the practices. To break the barrier of shop-floor resistance, the leader must drive many activities unassisted, it, therefore, necessitates an open endorsement of authority by the steering committee composed of top management. The Prudent pilot run of TPM helped to accelerate the implementation at critical equipment, in addition to cultivating experience and hence confidence among staff.Research limitations/implications: This study provides a pragmatic reference to other researchers and practitioners to promote a light TPM model in SMEs, without losing the essence of TPM. Being action research with the case study in a specific manufacturing industry, the resultant evidence, therefore, is anecdotal.Originality/value: The model adopts a phased method to implement TPM, without aggravating the financial and human resource burden of the enterprise. It promotes the cultivation of employees’ TPM awareness and active involvement, which can lay a solid foundation for the wide implementation of TPM in SMEs.

scholarly journals Human Resource Scorecard (HRSC) and Objective Matrix as Performance Measurement and Performance Booster Method to Optimize Production Performance

2019 ◽  
Vol 6 (6) ◽  
pp. 173 ◽  
Author(s):  
Jesen Hardi ◽  
Deppy Supardi ◽  
Christopher Angelo ◽  
Nicko Farhan ◽  
Farrell CND ◽  
...  
Keyword(s):  
Performance Measurement ◽  
Human Resource ◽  
Manufacturing Industry ◽  
Production Efficiency ◽  
Production Performance ◽  
Manufacturing Companies ◽  
Business Units ◽  
Financial Perspective ◽  
Development Technology ◽  
And Performance

The development technology is something that happens quickly and surely this is marked by the number of new manufacturing companies that are emerging and spreading throughout the world. In the manufacturing industry the most important thing that must always be maintained is production efficiency, but the majority of industries only pay attention to the efficiency aspects of their production activities without regard to the aspects of humans who work in the industry. Human resource scorecard (HRSC) is an appropriate measurement method in identifying and measuring the relationship between workers, strategy, and performance to produce a good company. This human resource performance measurement is looking at business units from four perspectives: financial perspective (F), the customer (C), internal business process (I), and learning and growth (L). Each perspective in HRSC weighted by Analytical Hierarchy process method and then calculated by Objective Matrix (OMAX) to identify which category needed an extra attention and need to be repaired. The end result of OMAX will be reviewed by management theory so the solution made in this analysis will produce the best end result that needed by the company. This measurement is very critical to the company because the competition between company is getting tougher nowadays. HRSC measurement was carried in PT OCM by describing the company’s vision and mission into human resource action whose contribution can be measured using AHP analysis in 4 perspectives: financial perspective with a weight of 0.396, customer perspective (employee) with a weight of 0.118, internal business perspective with a weight of 0.240, and growth and development perspective with a weight of 0.247 after that PT OCM use that measurement in OMAX method to determine what KPI needed extra attention and repairment, management based theory will be applied in that solution to give the best result.

Mission reliability-oriented integrated scheduling optimization model by combining production planning and maintenance activities

2020 ◽  
pp. 095440542097108
Author(s):  
Changchao Gu ◽  
Yihai He ◽  
Zhaoxiang Chen ◽  
Xiao Han ◽  
Di Zhou ◽  
...  
Keyword(s):  
Production Planning ◽  
Production Scheduling ◽  
Manufacturing Systems ◽  
Production Efficiency ◽  
Manufacturing System ◽  
Integrated Scheduling ◽  
Scheduling Model ◽  
Production Practice ◽  
Maintenance Activities ◽  
Scheduling Plan

Machine utilization and production efficiency of manufacturing systems can be effectively improved through reasonable production scheduling. Traditionally, production scheduling and maintenance planning are considered as two independent issues, but it may lead to a suboptimal solution that is unable to maximize the productivity of the manufacturing system. Therefore, a mission reliability-oriented integrated scheduling model that considers production planning and maintenance activities is proposed. Firstly, the mission reliability that takes into account product type and equipment performance is defined to characterize production rhythm. Secondly, the maintenance strategy based on machine degradation cumulative failure and stochastic failure is proposed to guarantee the mission reliability of the machine effectively. Thirdly, an integrated scheduling model is established with the goal of minimizing total operational time, and Genetic algorithm is tailored to find the best production scheduling plan. Finally, a case study and comparative study of the cylinder head manufacturing system are presented to demonstrate the effectiveness of the proposed method. Results show that the proposed method is more suitable for production practice than the previous production scheduling strategy.

Challenges of handling assembly information in global manufacturing companies

2019 ◽  
Vol 31 (5) ◽  
pp. 955-976
Author(s):  
Pierre Eric Christian Johansson ◽  
Lennart Malmsköld ◽  
Åsa Fast-Berglund ◽  
Lena Moestam
Keyword(s):  
Manufacturing Industry ◽  
Shop Floor ◽  
Manufacturing Companies ◽  
Structured Interviews ◽  
Content Type ◽  
Global Manufacturing ◽  
Current State ◽  
Multiple Case ◽  
Process Disturbances ◽  
Industry Needs

Purpose The purpose of this paper is to describe challenges the manufacturing industry is currently facing when developing future assembly information systems. More specific, this paper focuses on the handling of assembly information from manufacturing engineering to the shop floor operators. Design/methodology/approach Multiple case studies have been conducted within one case company between 2014 and 2017. To broaden the perspective, interviews with additionally 17 large and global manufacturing companies and 3 industry experts have been held. Semi-structured interviews have been the main data collection method alongside observations and web questionnaires. Findings Six focus areas have been defined which address important challenges in the manufacturing industry. For manual assembly intense manufacturing company, challenges such as IT challenges, process challenges, assembly process disturbances, information availability, technology and process control, and assembly work instructions have been identified and hinder implementation of Industry 4.0 (I4.0). Originality/value This longitudinal study provides a current state analysis of the challenges the manufacturing industry is facing when handling assembly information. Despite the vast amount of initiatives within I4.0 and digitalization, this paper argues that the manufacturing industry needs to address the six defined focus areas to become more flexible and prepared for the transition toward a digitalized manufacturing industry.

A Highly Efficient and Optimised Simulation Based Multi objective Decision-Making for FMS Control

2018 ◽  
Vol 6 (6) ◽  
pp. 98
Author(s):  
Shreyanshu Parhi ◽  
S. C. Srivastava
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Manufacturing Industry ◽  
Simulation Software ◽  
Shop Floor ◽  
Simulation Tool ◽  
Routing Flexibility ◽  
Decision System ◽  
Multi Objective ◽  
Arena Simulation

Optimized and efficient decision-making systems is the burning topic of research in modern manufacturing industry. The aforesaid statement is validated by the fact that the limitations of traditional decision-making system compresses the length and breadth of multi-objective decision-system application in FMS.  The bright area of FMS with more complexity in control and reduced simpler configuration plays a vital role in decision-making domain. The decision-making process consists of various activities such as collection of data from shop floor; appealing the decision-making activity; evaluation of alternatives and finally execution of best decisions. While studying and identifying a suitable decision-making approach the key critical factors such as decision automation levels, routing flexibility levels and control strategies are also considered. This paper investigates the cordial relation between the system ideality and process response time with various prospective of decision-making approaches responsible for shop-floor control of FMS. These cases are implemented to a real-time FMS problem and it is solved using ARENA simulation tool. ARENA is a simulation software that is used to calculate the industrial problems by creating a virtual shop floor environment. This proposed topology is being validated in real time solution of FMS problems with and without implementation of decision system in ARENA simulation tool. The real-time FMS problem is considered under the case of full routing flexibility. Finally, the comparative analysis of the results is done graphically and conclusion is drawn.

Sign in / Sign up

Export Citation Format

Share Document