Selecting the best warehouse data collecting system by using AHP and FAHP methods

U zadnje su se vrijeme kompanije počele koristiti načinom skladištenja kako bi stekle prednost na tržištu, uporabom sustava linijskog koda i RFID (Radio Frekvencijska Identifikacija) u vođenju skladišta. U ovom se radu koriste AHP (Analytic Hierarchy Process) i FAHP (Fuzzy Analytic Hierarchy Process) pri izboru sustava linijskog koda ili RFID sustava za sustav koji će kompanija odabrati kao način skladištenja. Na donošenje odluke djeluju četiri kriterija: cijena, funkcionalnost, održivost i performansa. U odnosu na AHP, sustav linijskog koda preferira 68 %, a RFID 32 %. Kad se radi o FAHPu linijski kod preferira 72 %, a RFID 28 %. Prema tome, AHP vrijednosti se slažu s FAHP vrijednostima. Konačno, sustav linijskog koda je odabran kao sustav za vođenje podataka o skladištu, a smatra se da je FAHP relativno uspješniji kad se radi o opisu tog postupka donošenja odluke zbog njegove nejasnoće (fuzziness) i neodređenosti u odnosu na AHP metodu.Recently companies have begun to use their storage effectively to attain leadership in the market environment, utilizing Barcode and RFID (Radio Frequency Identification) systems for warehouse management. In this study AHP (Analytic Hierarchy Process) and FAHP (Fuzzy Analytic Hierarchy Process) are used to choose between Barcode and RFID systems for the company warehouse data collection system. This decision is affected by four criteria which are: cost, functionality, sustainability and performance. The barcode system was preferred by 68 % and RFID was preferred by 32 % according to AHP. For FAHP, barcode system was preferred by 72 % and RFID was preferred by 28 %. Consequently AHP values are consistent with FAHP values. Finally barcode system is selected for the company’s warehouse data collection system and FAHP is found to be relatively more sufficient in terms of description of this decision-making process because of its fuzziness and vagueness compared to AHP method

Image processing based rapid upper limb assessment method

Occupational Musculoskeletal System Disorders (OMSDs) are disorders that inflict a great deal of economical burden on enterprises and nations, decrease quality, productivity and cause inability to sustain livings of employees. One of the most important factor that cause OMSDs is working posture. In literature, there are various methods for determining risk levels of working postures. In this study because of its common usage, Rapid Upper Limb Assessment Method (RULA) that identfies hazard level created by working postures on employees' upper limb musculoskeletal health is selected for improving with image processing systems. It is necassary to improve RULA's performance due to complications stemming from its implementation method based on observation, lack of information on the best duration of observation, subjectivity on results and extensive analysis time etc. For compansate these requirements a modified method is proposed in this study named as Advanced RULA (ARULA). Reliability and validity analysis are implemented statistically for ARULA. As a result, ARULA is recommended as a practical tool for analyzing risk levels of working postures for tasks that contain intensive usage of upper extremity

A stochastic approach for failure mode and effect analysis

This study presents a novel approach combining Failure Mode and Effect Analysis (FMEA) and Multi-Attributive Border Approximation Area Comparison (MABAC) method based on a stochastic evaluation process to prioritize potential failure modes (FMs) in an assembly line. The aim of the proposed approach is to improve the performance of FMEA by eliminating its shortcomings addressed in the study. In this context, firstly the risk factor (RF) importance weights and the performance values of the FMs for the RFs are determined by generating random numbers having uniform distribution in a range of minimum and maximum value of a limited number of expert evaluations. In this wise, the number of experts are increased to improve effectiveness of the risk evaluation process. Diverse opinions of experts are also assessed more precisely. Secondly, the priorities of the FMs are identified by implementing MABAC method. MABAC is a practical and reliable tool which provides stability for solutions. Finally, a comparative analysis is implemented to confirm the effectiveness of Stochastic FMEA-MABAC approach

A stochastic approach for failure mode and effect analysis

This study presents a novel approach combining Failure Mode and Effect Analysis (FMEA) and Multi-Attributive Border Approximation Area Comparison (MABAC) method based on a stochastic evaluation process to prioritize potential failure modes (FMs) in an assembly line. The aim of the proposed approach is to improve the performance of FMEA by eliminating its shortcomings addressed in the study. In this context, firstly the risk factor (RF) importance weights and the performance values of the FMs for the RFs are determined by generating random numbers having uniform distribution in a range of minimum and maximum value of a limited number of expert evaluations. In this wise, the number of experts are increased to improve effectiveness of the risk evaluation process. Diverse opinions of experts are also assessed more precisely. Secondly, the priorities of the FMs are identified by implementing MABAC method. MABAC is a practical and reliable tool which provides stability for solutions. Finally, a comparative analysis is implemented to confirm the effectiveness of Stochastic FMEA-MABAC approach