Lean manufacturing and Toyota Production System terminology applied to the procurement of vascular stents in interventional radiology

Bucourt, Maximilian de; Busse, Reinhard; Güttler, Felix; Wintzer, Christian; Collettini, Federico; Kloeters, Christian; Hamm, Bernd; Teichgräber, Ulf

doi:10.1007/s13244-011-0097-0

Cited by 17 publications

(13 citation statements)

References 19 publications

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“…Elimination of waste (Muda) includes both wastes, i.e. waste created through overburden ("Muri") and waste created through unevenness (de Bucourt et al, 2011). "Value" is any action or process that a customer would be willing to pay for; that is, the value of a product is defined solely by the customer.…”

Section: Benefits Of Lean Manufacturingmentioning

confidence: 99%

“…SMED (Kumar and Kumar, 2012) Following sequenced and structured work instructions to perform and finish the job within minimum possible time 3. Kanban (M de Bucourt, 2011;Wong and Wong, 2011) Communicating customer requirement from downstream to upstream worker so as to replenish the moved quantity by the new one 4. Total Productive Maintenance/TPM/ (Kumar and Kumar, 2012) It involves: cleaning, lubricating, inspection, tightening activity of machines to reduce break down time and enhances operational efficiency of machines 5.…”

Section: Ijlss 102mentioning

confidence: 99%

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Lean manufacturing as a vehicle for improving productivity and customer satisfaction

Goshime

Kitaw

Jilcha

2019

IJLSS

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Purpose This study aims to improve productivity and customer satisfaction through lean manufacturing for metals and engineering industries. Its aims also to understand the concept of lean manufacturing, various tools and techniques of lean, lean implementation benefits and barrier toward its implementation. Then, on the basis of the result, a conceptual frame work was developed to reduce the existing gaps. Design/methodology/approach Lean thinking is one of the methods that can bring productivity and customer’s demand improvement for manufacturing and service giving industries. To arrive at the lean thinking productivity improvement and customer satisfaction of the sector (MEIs), intensive literature review and secondary data investigation were conducted. Findings Articles and secondary data related to the case were reviewed and found the existing gaps. The gaps identified such as missing energy waste, space waste and material waste, waste of knowledge or talents. In addition to the 5 S of kaizen, this study added safety as the sixth on the existing Kaizen’s strategies. In lean practice, managers give priority to waste reduction and ignore the product quality aspect, which lead to dissatisfaction among customers. Fragmented implementation of lean manufacturing and the conflict between human resource waste and unemployment were reconciled in this study. A model that can improve productivity and increase customer satisfaction was developed. Solutions to alleviate the problems and speed up development were forwarded. Research limitations/implications This study focused solely on the manufacturing industries of developing countries, specifically deals with basic metals and engineering industries. In addition to this, the research didn’t take a case study on a specific firm as it is a literature review. Practical implications The findings of this study emphasized that lean manufacturing is the key for wise resource utilization, which enables a firm for cost, lead time and waste reductions on one hand and productivity and flexibility improvements on the other. To the end, lean can bring sustainable development and bright images to firms, and wellbeing life to workers together with customer satisfaction. Originality/value The gaps that have not been identified by other researchers were clearly discussed, and on the basis of the gaps, a new conceptual model was developed. This is useful to basic metals and engineering industries in overcoming resource-limitation problems by eliminating wastes.

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Section: Benefits Of Lean Manufacturingmentioning

confidence: 99%

Section: Ijlss 102mentioning

confidence: 99%

Lean manufacturing as a vehicle for improving productivity and customer satisfaction

Goshime

Kitaw

Jilcha

2019

IJLSS

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“…If ( , ) = 1, it means that station can process operation . In addition, ( , ) subjects to the constraints represented by Equations ( 7) and (8). If the chromosome is infeasible, it will be corrected by altering one gene randomly from 0 to 1 to meet the minimum requirement.…”

Section: Encodementioning

confidence: 99%

“…Seven potential sources of wastes including over-production, waiting, transportation, over-processing, inventory, motion, and defects, are often mentioned that need due consideration during lean implementation [6,7]. Some researchers also add excess human resources as the eighth waste [8].…”

Section: Introductionmentioning

confidence: 99%

Planning semi-automated precast production using GA

Chen

Phang

Tiong

2020

Int. J. Industrialized Constr.

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Although fully automated production systems have been developed and used in some industry leaders, most of the precast factories have yet to be developed to that stage. Semi-automated production lines are still popularly used. As production productivity can be maximally improved within the physical constraints by applying a sound production plan, this paper tends to propose a production planning method for the semi-automated precast production line using genetic algorithm (GA). The production planning problem is formulated into a flexible job shop scheduling problem (FJSSP) model and solved using an integrated approach. Thanks to the development of new technologies such as building information modeling (BIM) platform and radio frequency identification (RFID), implementation of a just-in-time (JIT) schedule in the semi-automated precast production line becomes practicable on the grounds of risk mitigation and enhanced demand forecast capability. In this regard, the optimization objectives are minimum makespan, station idle time, and earliness and tardiness penalty. An example was applied to validate the integrated GA approach. The experimental results show that the developed GA approach is a useful and effective method for solving the problem that it can return high-quality solutions. This paper thus contributes to the body of knowledge new precast production planning method for practical usage.

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“…This key and decisive factor influences the final cost of the product however, among the activities carried out in the supply chain, it is considered as a necessary activity that does not generate more value to the product. This is the reason why, its cost seeks to be minimized [17,26,40]. Transport models as mentioned in [21]; can be solved by the known VRP (Vehicle Routing Problem) which its objective is to find the optimal set of routes for a fleet of vehicles that must meet the demands of a given set of customers.…”

Section: Introductionmentioning

confidence: 99%

The Vehicle Routing Problem Complex Network Analysis

Chípuli¹,

Mota²,

Reyna³

et al. 2018

JAMC

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One of the main characteristics of transportation problems is not only the hard design model process; but also, in the characterization of the elements that make up the supply chain. A more detailed analysis will grant undoubtedly a greater knowledge of the system. However, the use of regular networks for the development of models can eliminate key characteristics for correct decision making. Faced with this fact, the characterization of transport models is proposed through the conceptualization of complex networks. This new proposal will allow to apply metrics and perform more detailed analysis to the distribution network; such as vulnerability analysis, fault propagation, element grouping identification, among other metrics. The objective of this research is to show a methodology to build the Vehicle Routing Problem models and analyze the distribution network using the corresponding metrics of complex networks.

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Lean manufacturing and Toyota Production System terminology applied to the procurement of vascular stents in interventional radiology

Cited by 17 publications

References 19 publications

Lean manufacturing as a vehicle for improving productivity and customer satisfaction

Lean manufacturing as a vehicle for improving productivity and customer satisfaction

Planning semi-automated precast production using GA

The Vehicle Routing Problem Complex Network Analysis

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