Performance Improvement Through Benchmarking for Small and Medium Manufacturers (SMM)

Die wichtigsten Kostenfaktoren innerhalb einer Lieferkette lassen sich drei Kategorien zuordnen: Produktions-, Transport-und Lagerkosten. Die Strukturen dieser operativen Kosten im Hinblick auf die Gesamtkosten variieren stark je nach Industriesektor. Produktionskosten stellen dennoch die höchste Kostenart in fast allen Branchen dar, weniger bedeutend folgen danach jeweils die Transport- und Lagerkosten. Die Optimierung einer dieser Kategorien ohne Rücksicht auf die anderen kann zur Erhöhung der Gesamtkosten sowie der allgemeinen Leistungsfähigkeit führen. Diese Dissertation befasst sich mit dem „production distribution problem“ wobei synchronisierte Strategien entwickelt werden können, um die Leistung der Supply Chain zu verbessern und gleichzeitig die Gesamtkosten zu minimieren. Dazu wurde eine Fallstudie aus der Realität untersucht, nämlich das Praxisbeispiel eines Herstellers von Waschmitteln. Zwei Hauptszenarien werden bewertet. Das erste Szenario ist der konventionelle Plan, wobei die Hersteller dominieren. Dies bedeutet, dass der Hersteller findet seinen eigenen optimalen Job-Scheduling-Plan, während die Distribution versucht mit Hilfe dessen ihren optimalen Plan zu finden. Dadurch erhöhen sich die Distributionskosten. Das zweite Szenario betrifft die Synchronisation der Produktions-, Lagerhaltungs- und Transportzeitpläne. Ein zu diesem Zweck entwickeltes Java-Programm und die Job-Scheduling-Software Simal wurden für die Modellierung der konventionellen und integrierten Szenarien verwendet. Beide Szenarien wurden verglichen und validiert. Die Fallstudie betrachtet mehrere Produkte sowie ein schwer zu planendes flowshop- System. Die Ergebnisse zeigen, dass die Gesamtkosten, einschließlich der Einrichtungs-, Lager- und Transportkosten, minimiert werden können, wenn das synchronisierte System angewendet wird.The main cost factors within a supply chain can be put into the categories of production, transportation, and inventory costs. The composition of these operational costs relative to total costs varies largely by industry. However, production cost is the largest of all in almost all the industries, followed by transportation and inventory costs. Optimizing one of these categories without consideration of the others may increase the total cost and reduce the overall performance. This dissertation deals with the production distribution problem of developing synchronized strategies to improve the supply chain performance and to minimize the total cost. A real case study is investigated. This real-life case study is a powder detergent plant located in Libya. There are two main scenarios evaluated. The first scenario is the conventional plan, where the manufacturer dominates. This means the manufacturer finds his own optimum job-scheduling plan, and the distributor tries to find the optimum plan according to it. This will increase the distribution cost. The second scenario involves synchronizing the production, inventory and transportation schedules. A Java program and SimAl (job-schedulingsoftware) were constructed for modelling conventional and integrated scenarios. The two scenarios were compared and validated. The case study considered multiple products and a flowshop system which is difficult to schedule. The results show that the total costs, including setup, inventory and transportation, can be minimized when the synchronized system is applied

Using Multiple Linear Regression and Artificial Neural Network to Predict Surface Roughness in Turning Operations

Quality of surface roughness has a great impact on machine parts during their useful life. The machining process is more complex, and therefore, it is very hard to develop a comprehensive model involving all cutting parameters. In this paper, the surface roughness is measured during turning operation at different cutting parameters such as speed, feed rate, and depth of cut. Two mathematical models are developed to predict the surface roughness and to select the required surface roughness by using the Multi-regression model and Artificial Neural Networks (ANN). To test the developed models, 27 pieces of steel alloy HRC15 were operated and the roughness of their surfaces measured. The results showed that the ANN model estimates the surface roughness with high accuracy compared to the multiple regression model with the average deviation from the real values of about 1%

A Novel Approach for the Selection of Power-Generation Technology Using a Linguistic Neutrosophic CODAS Method: A Case Study in Libya

Rapid increases in energy demand and international drive to reduce carbon emissions from fossil fuels have led many oil-rich countries to diversify their energy portfolio and resources. Libya is one of these countries, and it has recently become interested in utilizing its renewable-energy resources in order to reduce financial and energy dependency on oil reserves. This paper introduces an original multicriteria decision-making Pairwise-CODAS model in which the modification of the CODAS method was made using Linguistic Neutrosophic Numbers (LNN). The paper also suggests a new LNN Pairwise (LNN PW) model for determining the weight coefficients of the criteria developed by the authors. By integrating these models with linguistic neutrosophic numbers, it was shown that it is possible to a significant extent to eliminate subjective qualitative assessments and assumptions by decision makers in complex decision-making conditions. The LNN PW-CODAS model was tested and validated in a case study of the selection of optimal Power-Generation Technology (PGT) in Libya. Testing of the model showed that the proposed model based on linguistic neutrosophic numbers provides objective expert evaluation by eliminating subjective assessments when determining the numerical values of criteria. A sensitivity analysis of the LNN PW-CODAS model, carried out through 68 scenarios of changes in the weight coefficients, showed a high degree of stability of the solutions obtained in the ranking of the alternatives. The results were validated by comparison with LNN extensions of four multicriteria decision-making models

A Novel Approach for the Selection of Power-Generation Technology Using a Linguistic Neutrosophic CODAS Method: A Case Study in Libya

Rapid increases in energy demand and international drive to reduce carbon emissions from fossil fuels have led many oil-rich countries to diversify their energy portfolio and resources. Libya is one of these countries, and it has recently become interested in utilizing its renewable-energy resources in order to reduce financial and energy dependency on oil reserves

SITE SELECTION OF DESALINATION PLANT IN LIBYA BY USING COMBINATIVE DISTANCE-BASED ASSESSMENT (CODAS) METHOD

Libya is one of the arid regions of the world, and it is facing a serious water supply shortage due to the increase in both population and water consumption in various sectors. Ground water is the main source of water in Libya, but it is limited and over exploited. Desalination of sea water is one of the possibilities for Libyan government to meet the problem of water shortage. Selecting the best location of desalination plant is important and a complex process because it is related to a variety of criteria. The aim of this paper is to select the best location of desalination plant in the northwestern coast of Libya. The selection of the best location was done by two main steps. The first step based on the criterion of minimizing water transportation cost, and the second step considered the influence of the external criteria on the location selection. The results of the case study show that the best location is the capital city (Tripoli) with respect to the assessment of Combinative Distance-based Assessment (CODAS) method. The sensitivity analysis was conducted to evaluate the robustness of the selected locations and it reveals that the CODAS method is stable and efficient to deal with multi-criteria decision-making problems. This study provides a suitable and useful tool for the decision makers concerning the optimum location of desalination facilities

A new methodology for treating problems in the field of traffic safety: case study of Libyan cities

Traffic safety is an area of great importance, since there are many traffic accidents every day in which a significant number of people are killed. Defining certain strategies and identifying potentially the most dangerous towns and cities regarding this area are, on the one hand, a necessity, and, on the other hand, a challenge. In this paper, integrated Multi-Criteria Decision-Making (MCDM) model for ranking cities in Libya from the aspect of traffic safety has been proposed. The model implies a set of 8 criteria on the basis of which 5 decision-makers rated the 10 most deprived cities in Libya. The Full Consistency Model (FUCOM) in combination with the rough Dombi aggregator is used to determine the significance of the criteria. The Rough Simple Additive Weighting (R-SAW) method is used to rank the alternatives. The rough Dombi aggregator is also used for averaging in group decision-making while evaluating the alternatives. The stability of the model and the obtained results has been verified by the sensitivity analysis, which implies a 2-phase procedure. In the 1st phase, rough Additive Ratio Assessment (R-ARAS), Rough Weighted Aggregated Sum Product Assessment (R-WASPAS), Rough Complex Proportional Assessment (R-COPRAS) and Rough Multi-Attributive Border Approximation-area Comparison (R-MABAC) methods are applied. The 2nd phase implies changing the parameter ρ in the procedure of rough Dombi aggregator, while the 3rd phase includes the calculation of Spearman’s Correlation Coefficient (SCC) that shows a high correlation of ranks

Efficacy and safety of cardioprotective drugs in chemotherapy-induced cardiotoxicity: an updated systematic review & network meta-analysis

BACKGROUND: Cancer patients receiving chemotherapy have an increased risk of cardiovascular complications. This limits the widespread use of lifesaving therapies, often necessitating alternate lower efficacy regimens, or precluding chemotherapy entirely. Prior studies have suggested that using common cardioprotective agents may attenuate chemotherapy-induced cardiotoxicity. However, small sample sizes and conflicting outcomes have limited the clinical significance of these results. HYPOTHESIS: A comprehensive network meta-analysis using updated and high-quality data can provide more conclusive information to assess which drug or drug class has the most significant effect in the management of chemotherapy-induced cardiotoxicity. METHODS: We performed a literature search for randomized controlled trials (RCTs) investigating the effects of cardioprotective agents in patients with chemotherapy-induced cardiotoxicity. We used established analytical tools (netmeta package in RStudio) and data extraction formats to analyze the outcome data. To obviate systematic bias in the selection and interpretation of RCTs, we employed the validated Cochrane risk-of-bias tools. Agents included were statins, aldosterone receptor antagonists (MRAs), ACEIs, ARBs, and beta-blockers. Outcomes examined were improvement in clinical and laboratory parameters of cardiac function including a decreased reduction in left ventricular ejection fraction (LVEF), clinical HF, troponin-I, and B-natriuretic peptide levels. RESULTS: Our study included 33 RCTs including a total of 3,285 patients. Compared to control groups, spironolactone therapy was associated with the greatest LVEF improvement (Mean difference (MD) = 12.80, [7.90; 17.70]), followed by enalapril (MD = 7.62, [5.31; 9.94]), nebivolol (MD = 7.30, [2.39; 12.21]), and statins (MD = 6.72, [3.58; 9.85]). Spironolactone was also associated with a significant reduction in troponin elevation (MD =  - 0.01, [- 0.02; - 0.01]). Enalapril demonstrated the greatest BNP reduction (MD =  - 49.00, [- 68.89; - 29.11]), which was followed by spironolactone (MD =  - 16.00, [- 23.9; - 8.10]). Additionally, patients on enalapril had the lowest risk of developing clinical HF compared to the control population (RR = 0.05, [0.00; 0.75]). CONCLUSION: Our analysis reaffirmed that statins, MRAs, ACEIs, and beta-blockers can significantly attenuate chemotherapy-induced cardiotoxicity, while ARBs showed no significant effects. Spironolactone showed the most robust improvement of LVEF, which best supports its use among this population. Our analysis warrants future clinical studies examining the cardioprotective effects of cardiac remodeling therapy in cancer patients treated with chemotherapeutic agents

Supplier selection using the rough BWM-MAIRCA model: A case study in pharmaceutical supplying in Libya

The quality of health system in Libya has witnessed a considerable decline since the revolution in 2011. One of the major problems this sector is facing is the loss of control over supply medicines and pharmaceutical equipments from international suppliers for both public and private sectors. In order to take the right decision and select the best medical suppliers among the available ones, many criteria have to be considered and tested. This paper presents a multiple criteria decision-making analysis using   modified BWM (Best-Worst method) and MAIRCA (Multi-Attribute Ideal-Real Comparative Analysis) methods. In the present case study five criteria and three suppliers are identified for supplier selection. The results of the study show that cost comes first, followed by quality as the second and company profile as the third relevant criterion. The model was tested and validated on a study of the optimal selection of supplier

A case study of supplier selection for a steelmaking company in libya by using the combinative distance-based assessment (CODAS) model

Multi-Criteria Decision Making (MCDM) problems have received considerable attention from various researchers over the past decades. A great variety of methods and approaches has been developed in this field. The aim of this paper is to use a new COmbinative Distance-based ASsessment (CODAS) method to handle MCDM problems for a steelmaking company in Libya. So far no literature dealing with supplier selection using the (CODAS) method in the steelmaking company in Libya has been found. The concept of this method is based on computing the Euclidean distance and the Taxicab distance in order to determine the desirability of an alternative. The Euclidean distance is used as a primary measure, while the Taxicab distance as a secondary one. The developed method was applied to a real-world case study for ranking the suppliers in the Libyan Iron and Steel Company (LISCO). An attempt in this regard could enhance a decision-making technique for selecting the best suppliers for the selected case company. The results showed that the proposed method was effectively able to select the best supplier among six alternative ones