Engineering Case Studies: Analyzing the Quality of Steel and Glass Products via Statistical Process Control Charts

Statistical Process Control (SPC) is the collection of various problem detection and solving techniques which can be applied to any process. Statistical process control is a crucial technique in quality improvement. The major objective of SPC is to detect the root cause and frequency of issues in the processes so that corrective actions may be taken before non-conforming (defective) units are generated. The ultimate goal of SPC is to eliminate the variability in the process. The basic seven tools of SPC, i.e., Magnificent Seven are listed below: 1. Process Flowchart 2. Check Sheets / Tally Charts 3. Cause and Effect Diagram (Ishikawa) 4. Scatter Diagram 5. Histogram and Graphs 6. Pareto Chart 7. Control Chart

Big Data Analytics in Supply Chain Management: A Literature Review on Supply Chain Analytics

The amount of the data produced by the government, the private sector and the general public has been rising especially over the past decade. With this growing trend, utilizing big data to add value to organizations became a popular topic for the industry and academic research. Converting unorganized and unstructured big data to useful information is investigated under Big Data Analytics (BDA). BDA, when employed appropriately, offers great potential to organizations helping in creating well-defined and meaningful strategic planning process. Supply Chain Analytics (SCA) is a member of BDA with a narrower spectrum, concerned exclusively with supply chain and logistics operations. SCA utilizes various Big Data Analytics techniques such as future trend analysis and prediction and/or operational optimization to increase the overall performance of related activities. This study presents a comprehensive literature survey in the area of Supply Chain Analytics and defines the literature gap in the related area

A Decision Support System for Performance Evaluation: A Combined D-ANP & ANN Approach

In this study, a novel performance evaluation approach combining DEMATEL, ANP and ANN methods is proposed. The influenced weights for the evaluation criteria are calculated by D-ANP and following this the final ranking of the stores is obtained by utilizing historical data in artificial neural networks

A Two-Stage Multi-Criteria Decision Making Approach for Green Supplier Selection

Awareness of environmental protection and sustainability in the manufacturing industry has grown making the green image a more critical factor in the supplier selection process. Supplier evaluation and selection are well studied in the literature. However, studies with a green focus are relatively limited. In order to fill this gap, this paper proposes a green supplier evaluation and selection (GSES) method that evaluates suppliers according to their green competencies and environmental performances. In this regard, a combined multi-criteria decision making (MCDM) approach capable of handling imprecise quantitative and qualitative data is proposed. To demonstrate the functionality of the approach, a case study is conducted on a U.S. based company that manufactures and distributes plastic closures and dispensing systems, internationally. The results of the approach along with the discussion for future research are also provided

Forecasting E-Waste in Presence of Limited Data

Electronic waste (E-waste) has emerged as one of the fastest growing municipal solid waste streams in the United States due to rapid changes in technology and increasing consumer demand. Accurate estimations on the amount of e-waste might help in increasing the efficiency of waste collection, recycling and disposal operations. The literature offers various methodologies focusing on prediction of e-waste generation. Among these, Grey Modeling (GM) approach has drawn attention due to its ability to provide meaningful results with utilizing relatively small-sized data. In order to improve the overall success rate of the approach, several GM-based models have been developed over the years. The performance of these models, however, heavily rely on the parameters used with no established consensus regarding the suitable criteria for better accuracy. This study presents a novel GM approach improved by Particle Swarm Optimization (PSO). A case study utilizing Washington State e-waste data is provided to demonstrate the comparative analysis proposed in the study

Intelligent Multi-Attribute Decision Making Applications: Decision Support Systems for Performance Measurement, Evaluation and Benchmarking

Efficiency has been and continues to be an important attribute of competitive business environments where limited resources exist. Owing to growing complexity of organizations and more broadly, to global economic growth, efficiency considerations are expected to remain a top priority for organizations. Continuous performance evaluations play a significant role in sustaining efficient and effective business processes. Consequently, the literature offers a wide range of performance evaluation methodologies to assess the operational efficiency of various industries. Majority of these models focus solely on quantitative criteria omitting qualitative data. However, a thorough performance measurement and benchmarking require consideration of all available information since accurately describing and defining complex systems require utilization of both data types. Most evaluation models also function under the unrealistic assumption of evaluation criteria being dependent on one another. Furthermore, majority of these methodologies tend to utilize discrete and contemporary information eliminating historical performance data from the model environment. These shortcomings hinder the reliability of evaluation outcomes leading to inadequate performance evaluations for many businesses. This problem gains more significance for business where performance evaluations are tied in to important decisions relating to business expansion, investment, promotion and compensation. The primary purpose of this research is to present a thorough, equitable and accurate evaluation framework for operations management while filling the existing gaps in the literature. Service industry offers a more suitable platform for this study since the industry tend to accommodate both qualitative and quantitative performance evaluation factors relatively with more ease compared to manufacturing due to the intensity of customer (consumer) interaction. Accordingly, a U.S. based food franchise company is utilized for data acquisition and as a case study to demonstrate the applications of the proposed models. Compatible with their multiple criteria nature, performance measurement, evaluation and benchmarking systems require heavy utilization of Multi-Attribute Decision Making (MADM) approaches which constitute the core of this research. In order to be able to accommodate the vagueness in decision making, fuzzy values are also utilized in all proposed models. In the first phase of the study, the main and sub-criteria in the evaluation are considered independently in a hierarchical order and contemporary data is utilized in a holistic approach combining three different multi-criteria decision making methods. The cross-efficiency approach is also introduced in this phase. Building on this approach, the second phase considered the influence of the main and sub-criteria over one another. That is, in the proposed models, the main and sub-criteria form a network with dependencies rather than having a hierarchical relationship. The decision making model is built to extract the influential weights for the evaluation criteria. Furthermore, Group Decision Making (GDM) is introduced to integrate different perspectives and preferences of multiple decision makers who are responsible for different functions in the organization with varying levels of impact on decisions. Finally, an artificial intelligence method is applied to utilize the historical data and to obtain the final performance ranking. Owing to large volumes of data emanating from digital sources, current literature offers a variety of artificial intelligence and machine learning methods for big data analytics applications. Comparing the results generated by the ANNs, three additional well-established methods, viz., Adaptive Neuro Fuzzy Inference System (ANFIS), Least Squares Support Vector Machine (LSSVM) and Extreme Learning Machine (ELM), are also employed for the same problem. In order to test the prediction capability of these methods, the most influencing criteria are obtained from the data set via Pearson Correlation Analysis and grey relational analysis. Subsequently, the corresponding parameters in each method are optimized via Particle Swarm Optimization to improve the prediction accuracy. The accuracy of artificial intelligence and machine learning methods are heavily reliant on large volumes of data. Despite the fact that several businesses, especially business that utilize social media data or on-line real-time operational data, there are organizations which lack adequate amount of data required for their performance evaluations simply due to the nature of their business. Grey Modeling (GM) technique addresses this issue and provides higher forecasting accuracy in presence of uncertain and limited data. With this motivation, a traditional multi-variate grey model is applied to predict the performance scores. Improved grey models are also applied to compare the results. Finally, the integration of the fractional order accumulation along with the background value coefficient optimization are proposed to improve accuracy

COMPARATIVE EVALUATION OF TOTAL PHENOLIC/CAROTENOID CONTENTS, CHLOROGENIC ACID/RUTIN PROFILES, AND ANTIOXIDANT PROPERTIES OF TWO PRANGOS SPECIES (P. UECHTRITZII AND P. PABULARIA)

Objective: The aim of the study was to investigate the antioxidant activities, chlorogenic acid/rutin profiles, and bioactive compounds' contents of the various extracts from Prangos uechtritzii Boiss. & Hausskn. and Prangos pabularia Lindl.Methods: The antioxidant capacities of the extracts were evaluated by various methods, including the plasma lipid peroxidation inhibitory, β-carotene/linoleic acid bleaching, free radical scavenging activity, and metal chelating activity assays. Chlorogenic acid and rutin contents of the extracts were determined qualitatively and quantitatively by high-performance liquid chromatography (HPLC). Total phenolic, β-carotene, and lycopene contents of the extracts were also determined.Results: In the assays, the methanol and the water extracts showed higher antioxidant activities than the acetone and ethyl acetate extracts. According to HPLC analysis, the richest extracts in terms of rutin and chlorogenic acid were determined as P. pabularia methanol extract (12.61±0.11 µg/mg) and P. uechtritzii methanol extract (4.76±0.12 µg/mg), respectively.Conclusion: It could be suggested that these Prangos species, especially the water extract of P. uechtritzii may be used a potential source of natural antioxidants for food and pharmacy industries.Â

Optik atrofi, maküler incelme ve arkuat skotom gibi atipik klinik bulgular gösteren çoklu geçici beyaz nokta sendromu olgusu

A 57-year-old man presented with a sudden loss of vision, central scotoma, and photopsia in the left eye. Grayish-white spots localized in the deep retina around the macula and optic disc were observed in the left eye on funduscopic examination. We observed a hyperfluorescence in a wreath-like pattern with late staining in retinal lesions during the early stage of fundus fluorescein angiography. Disruption was observed in an ellipsoid zone in optical coherence tomography. Multiple evanescent white dot syndrome was diagnosed based on findings and followed up without medication. The visual acuity of the left eye improved from 1/20 to 6/10 after a 7-week follow-up. Dilated fundus examination showed optic atrophy, and visual field examination revealed an arcuate scotoma.Elli yedi yaşında erkek hasta, sol gözde ani görme kaybı, santral skotom ve fotopsi şikâyetleri ile kliniğimize başvurdu. Funduskopik muayenesinde, sol gözde makula ve optik disk etrafında, retinanın derin katlarında lokalize olmuş grimsi beyaz lezyonlar gözlendi. Fundus floresan anjiyografi erken dönemi boyunca retinal lezyonlarda gözlenen çelenk benzeri hiperfloresans, geç boyanma ile devam etti. Optik koherens tomografide, fotoreseptör iç ve dış segment tabakasında harabiyet görüldü. Mevcut bulgular eşliğinde hastaya, çoklu geçici beyaz nokta sendromu teşhisi kondu ve ilaçsız takip edildi. Yedi haftalık takip sonrası hastanın görme keskinliği 1/20’den 6/10’a yükseldi. Dilate fundus muayenesinde, optik atrofi ve görme alanında arkuat skotom oluştuğu gözlendi