Development of a justification tool for advanced manufacturing technologies: system-wide benefits value analysis

Ordoobadi, Sharon M.; Mulvaney, Nancy J

doi:10.1016/s0923-4748(01)00033-9

Cited by 86 publications

(41 citation statements)

References 25 publications

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“…The power of AHP has been validated by empirical application in diverse areas such as healthcare [16], planning [19,25], mining [14], project management [1], missile systems [7], new product development [1] and manufacturing [13]. In addition AHP has been used in making decisions that involve ranking, selection, evaluation, and selection of machines and IT based systems [4,10,20,22].To construct the hierarchy of objectives and attributes that allow establishing a prioritization scale an extended review of the related literature was conducted. This review focused on spare parts criticality aspects and the relationships between maintenance and logistics points of view.…”

Section: Multicriteria Decision Modelmentioning

confidence: 99%

Spare parts criticality analysis using a fuzzy AHP approach

Durán

2015

Teh. vjesn.

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Original scientific paper A distinctive characteristic of the spare parts inventory system is the need to evaluate or define the criticality of items in the inventory. Therefore, there is a need to consider factors such as cost, failure rate and availability, among others. There are few papers in literature that discuss relevant methods to define a reliable hierarchy of a set of spare parts. Up to now, no article has considered ambiguity and uncertainty factors when performing criticality analysis of spare parts. In addition, criticality analysis involves the simultaneous consideration of multiple criteria, including tangible and intangible factors; prioritizing these factors can be a great challenge and a complex task. Therefore, no attempt has been made to incorporate fuzziness into multicriteria decision-making in the criticality analysis in spare parts management. This work proposes a fuzzy-based methodology for prioritization of spare parts. The proposal is based on a multicriteria decision method called Analytical Hierarchy Process (AHP) with triangular numbers. An example is given to illustrate the proposed methodology. Keywords: Analytic Hierarchy Process (AHP); criticality; fuzzy AHP; spare parts Analiza kritičnosti rezervnih dijelova primjenom pristupa neizrazitog analitičkog hijerarhijskog procesIzvorni znanstveni članak Specifično obilježje sustava zalihe rezervnih dijelova je potreba da se procijeni ili odredi kritičnost stavki u zalihi. Stoga je, između ostalih, potrebno razmotriri čimbenike poput cijene, intenziteta kvarenja i raspoloživosti. Postoji mali broj radova u literaturi koji obrađuju relevantne metode za određivanje pouzdane hijerarhije u skupini rezervnih dijelova. Do sada, nijedan rad nije obradio čimbenike nesigurnosti i dvosmislenosti prilikom analize kritičnosti rezervnih dijelova. Uz to, analiza kritičnosti uključuje istovremeno razmatranje višestrukih kriterija, uključujući stvarne i nestvarne čimbenike; davanje prednosti tim čimbenicima može predstavljati veliki izazov i složen zadatak. Zbog toga se nije pokušalo uključiti neizrazitost (fuzziness) pri donošenju odluka o višestrukim kriterijima u analizi kritičnosti kod upravljanja rezervnim dijelovima. U ovom se radu predlaže fuzzy-temeljena metodologija kod određivanja prednosti (prioritizacije) rezervnih dijelova. Prijedlog se zasniva na metodi donošenja odluke na temelju višestrukih kriterija nazvanoj Analitički Hijerarhijski Proces (AHP) s trokutastim (triangular) brojevima. Daje se primjer za ilustraciju ove metodologije.

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Section: Multicriteria Decision Modelmentioning

confidence: 99%

Spare parts criticality analysis using a fuzzy AHP approach

Durán

2015

Teh. vjesn.

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“…The representative ones are: Abdel-Kader, et al, 2001;Agarwal, 1997;Boyer, et al, 1997;Canada, et al, 1989;Canada, et al, 1990;Choobineh, 1986;Datta, et al, 1992;Hynek, et al, 2007;Kaplan, 1986;Karsak, et al, 2001;Kumar, et al, 1996;Marri, et al, 2006;Meredith, et al, 1990;Meredith, et al, 1986;Naik, et al, 1992;Ordoobadi, et al, 2001;Primrose, 1991;Sambasivarao, et al, 1997;Slagmulder, et al, 1992;Small, 2006;Stainer, et al, 1996;Zammuto, et al, 1992.…”

Section: Effectiveness Of Amsmentioning

confidence: 99%

A comprehensive bibliography on effectiveness measurement of manufacturing systems

Goyal¹,

Grover²

2012

ijiec

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Globalization has put fierce competition for manufacturing managers in terms of flexibility, smaller lead times, competitive costs, etc. To attain these capabilities, manufacturing managers are taking heedless decisions for investing in advanced manufacturing technologies, without measuring their actual effectiveness for their organisations. There is a need to measure the effectiveness of manufacturing systems to make better future policies and investment planning. This paper provides a comprehensive bibliography on the techniques and their rationale in the effectiveness measurement of advanced manufacturing systems. The paper cites 265 articles from a variety of published sources. The list contains published research mainly from 1990 to 2012 and a selected published work prior to 1990.

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“…(Ross, 1995). Five main steps are involved in modeling a fuzzy expert system which is shown in Figure 1 (Ordoobadi & Mulvaney, 2001):…”

Section: Modeling Of Production Systemmentioning

confidence: 99%

Selection of the optimal number of shifts in fuzzy environment: manufacturing company’s facility application

Azeem

Paul

2010

JIEM

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This paper addresses the selection of optimal shift numbers considering inventory information, customer requirements and machine reliability using fuzzy logic.Number of shift is one of the most important criteria for the production planners to minimize the production costs and is essential for appropriate production planning. The main task involves optimizing the shift periods considering constraints of raw material, due date, demand, finished goods inventory and machine breakdown. A model is developed for any kind of manufacturing company where shift periods affect company's profit and cost. Fuzzy control is used to optimize the number of shifts under the constraints of raw material, due date, demand, finished goods inventory and machine breakdown. MATLAB Fuzzy Logic Tool Box is used to develop the model.

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Development of a justification tool for advanced manufacturing technologies: system-wide benefits value analysis

Cited by 86 publications

References 25 publications

Spare parts criticality analysis using a fuzzy AHP approach

Spare parts criticality analysis using a fuzzy AHP approach

A comprehensive bibliography on effectiveness measurement of manufacturing systems

Selection of the optimal number of shifts in fuzzy environment: manufacturing company’s facility application

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