Improving Production Performance Through Multi-Plant Cross Learning

2021 ◽  
pp. 1-17
Author(s):  
Jing Huang ◽  
Qing Chang ◽  
Yu Qian ◽  
Jorge Arinez ◽  
Guoxian Xiao
Keyword(s):  
Manufacturing Sector ◽  
Production Performance ◽  
System Model ◽  
Production Data ◽  
Production Lines ◽  
Product Lines ◽  
Work Production ◽  
Critical Machines ◽  
Feasible Action

Abstract The advancement in Web-/Internet-based technologies and applications in manufacturing sector have increased utilization of cyber workspace to enable more efficient and effective ways of doing manufacturing from distributed locations. This work introduces a novel continuous improvement framework to improve the performance of production lines through multi-plant comparison and learning among identical or similar production lines in different locations by leveraging the information stored on factory cloud. In this work, production data from multiple identical production lines are collected and analyzed to learn the “best” feasible action on critical machines, which offers a new way to optimize the management of product lines. Machine learning and system model are used to find the relationships between the performance index and the available data. A real case study based on multiple similar automotive plants is provided to demonstrate the method and the increase of throughput is predicted.

scholarly journals The Performance of Adaptive Approach in Lean and Green Operations

2021 ◽  
Vol 333 ◽  
pp. 06006
Author(s):  
Wei Dong Leong ◽  
Hon Loong Lam ◽  
Chee Pin Tan ◽  
Sg Ponnambalam
Keyword(s):  
Manufacturing Sector ◽  
Dynamic Performance ◽  
Resource Scarcity ◽  
Production Performance ◽  
Literature Study ◽  
Green Approach ◽  
Lean And Green ◽  
Main Components ◽  
Green Model

In recent years, the manufacturing sector has been pressured with global warming and resource scarcity. This issue has triggered the industry to seek for solutions to improve the sustainability of their production. Based on literature study, the main components of an organisation consists of manpower, machine, money, material and environment. Thus, the fundamentals need to be addressed to improve the production performance. In this study, an adaptive lean and green approach is presented to identify the priority areas that can improve the organisation performance. Backpropagation algorithm is incorporated into the adaptive model to analyse the dynamic performance of the organisation. However, the input of industry expert is required to prioritise the initial input of the main components. This is relatively important as prioritisation of main components defer from sectors. A case study will be illustrated with the adaptive lean and green model. Operation improvements shall be observed through the implementation of the proposed method.

Application of Dynamic Instance Queuing to Activity Sequences in Cooperative Business Process Scenarios

2016 ◽  
Vol 25 (01) ◽  
pp. 1650002 ◽  
Author(s):  
Johannes Pflug ◽  
Stefanie Rinderle-Ma
Keyword(s):  
Information Systems ◽  
Business Process ◽  
Real World ◽  
Process Model ◽  
Business Processes ◽  
Production Lines ◽  
Computing Services ◽  
Collaborative Processes ◽  
Work Production

The optimization of their business processes is a crucial challenge for many enterprises. This applies especially for organizations using complex cooperative information systems to support human work, production lines, or computing services. Optimizations can touch different aspects such as costs, throughput times, and quality. Nowadays, improvements in workflows are mostly achieved by restructuring the process model. However, in many applications there is a huge potential for optimizations during runtime as well. This holds particularly true for collaborative processes with critical activities, i.e. activities that require a high setup or changeover time, typically leading to waiting queues in instance processing. What is usually suggested in this situation is to bundle several instances in order to execute them as a batch. How the batching is achieved, however, has been only decided on static rules so far. In this paper, we feature dynamic instance queuing (DIQ) as an approach towards clustering and batching instances based on the current conditions in the process, e.g. attribute values of the instances. Specifically, we extend our previous work on applying DIQ at single activities towards a queuing approach that spans activity sequences (DIQS). The approach is evaluated based on a real-world case study from the manufacturing domain. We discuss limitations and further applications of the DIQ idea, e.g. with respect to collaborative human tasks.

A Case Study on the Un-Used Capacity Assessment Using Time Driven Activity Based Costing for Magnetic Components

2020 ◽  
Vol 6 (1) ◽  
pp. 18-39
Author(s):  
Areena Zaini ◽  
Haryantie Kamil ◽  
Mohd Yazid Abu
Keyword(s):  
Historical Data ◽  
Manufacturing Sector ◽  
Type I ◽  
Activity Based Costing ◽  
Cost Rate ◽  
Continuous Innovation ◽  
Magnetic Components ◽  
Alignment Process ◽  
Capacity Cost

The Electrical & Electronic (E&E) company is one of Malaysia’s leading industries that has 24.5% in manufacturing sector production. With a continuous innovation of E&E company, the current costing being used is hardly to access the complete activities with variations required for each workstation to measure the un-used capacity in term of resources and cost. The objective of this work is to develop a new costing structure using time-driven activity-based costing (TDABC) at . This data collection was obtained at E&E company located at Kuantan, Pahang that focusing on magnetic component. The historical data was considered in 2018. TDABC is used to measure the un-used capacity by constructing the time equation and capacity cost rate. This work found three conditions of un-used capacity. Type I is pessimistic situation whereby according to winding toroid core, the un-used capacity of time and cost are -14820 hours and -MYR2.60 respectively. It means the system must sacrifice the time and cost more than actual apportionment. Type II is most likely situation whereby according to assembly process, the un-used capacity of time and cost are 7400 hours and MYR201575.45 respectively. It means the system minimize the time and cost which close to fully utilize from the actual apportionment. Type III is optimistic situation whereby according to alignment process, the un-used capacity of time and cost are 4120 hours and MYR289217.15 respectively. It means the system used small amount of cost and time from the actual apportionment.

scholarly journals Defining and Quantifying Intermittency in the Power Sector

Energies ◽  
2020 ◽  
Vol 13 (13) ◽  
pp. 3366
Author(s):  
Daniel Suchet ◽  
Adrien Jeantet ◽  
Thomas Elghozi ◽  
Zacharie Jehl
Keyword(s):  
Power Source ◽  
Production Data ◽  
Power Sector ◽  
Predictive Capacity ◽  
The Public ◽  
Current State ◽  
Definition Of ◽  
Quantitative Definition ◽  
The Impact

The lack of a systematic definition of intermittency in the power sector blurs the use of this term in the public debate: the same power source can be described as stable or intermittent, depending on the standpoint of the authors. This work tackles a quantitative definition of intermittency adapted to the power sector, linked to the nature of the source, and not to the current state of the energy mix or the production predictive capacity. A quantitative indicator is devised, discussed and graphically depicted. A case study is illustrated by the analysis of the 2018 production data in France and then developed further to evaluate the impact of two methods often considered to reduce intermittency: aggregation and complementarity between wind and solar productions.

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