Search results for: logistic costs

Authors: Sonia Butalia, Huong Luu, Alexis Guigue, Karen J. B. Martins, Khanh Vu, Scott W. Klarenbach

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Obesity is associated with significant personal impacts on health and has a substantial economic burden on payers due to increased healthcare use. A contemporary estimate of the healthcare costs associated with obesity at the population level are lacking. This evidence may provide further rationale for weight management strategies. Methods: Adults who underwent a medical procedure between 2012 and 2019 in Alberta, Canada were categorized into the investigational cohort (had body mass index [BMI]-defined class 2 or 3 obesity based on a procedure-associated code) and the control cohort (did not have the BMI procedure-associated code); those who had bariatric surgery were excluded. Characteristics were presented and healthcare costs ($CDN) determined over a 1-year observation period (2019/2020). Logistic regression and a generalized linear model with log link and gamma distribution were used to assess total healthcare costs (comprised of hospitalizations, emergency department visits, ambulatory care visits, physician visits, and outpatient prescription drugs); potential confounders included age, sex, region of residence, and whether the medical procedure was performed within 6-months before the observation period in the partial adjustment, and also the type of procedure performed, socioeconomic status, Charlson Comorbidity Index (CCI), and seven obesity-related health conditions in the full adjustment. Cost ratios and estimated cost differences with 95% confidence intervals (CI) were reported; incremental cost differences within the adjusted models represent referent cases. Results: The investigational cohort (n=220,190) was older (mean age: 53 standard deviation [SD]±17 vs 50 SD±17 years), had more females (71% vs 57%), lived in rural areas to a greater extent (20% vs 14%), experienced a higher overall burden of disease (CCI: 0.6 SD±1.3 vs 0.3 SD±0.9), and were less socioeconomically well-off (material/social deprivation was lower [14%/14%] in the most well-off quintile vs 20%/19%) compared with controls (n=1,955,548). Unadjusted total healthcare costs were estimated to be 1.77-times (95% CI: 1.76, 1.78) higher in the investigational versus control cohort; each healthcare resource contributed to the higher cost ratio. After adjusting for potential confounders, the total healthcare cost ratio decreased, but remained higher in the investigational versus control cohort (partial adjustment: 1.57 [95% CI: 1.57, 1.58]; full adjustment: 1.21 [95% CI: 1.20, 1.21]); each healthcare resource contributed to the higher cost ratio. Among urban-dwelling 50-year old females who previously had non-operative procedures, no procedures performed within 6-months before the observation period, a social deprivation index score of 3, a CCI score of 0.32, and no history of select obesity-related health conditions, the predicted cost difference between those living with and without obesity was $386 (95% CI: $376, $397). Conclusions: If these findings hold for the Canadian population, one would expect an estimated additional $3.0 billion per year in healthcare costs nationally related to BMI-defined obesity (based on an adult obesity rate of 26% and an estimated annual incremental cost of $386 [21%]); incremental costs are higher when obesity-related health conditions are not adjusted for. Results of this study provide additional rationale for investment in interventions that are effective in preventing and treating obesity and its complications.

Keywords: administrative data, body mass index-defined obesity, healthcare cost, real world evidence

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Authors: Bharatendra Rai

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Cutting tools are widely used in manufacturing processes and drilling is the most commonly used machining process. Although drill-bits used in drilling may not be expensive, their breakage can cause damage to expensive work piece being drilled and at the same time has major impact on productivity. Predicting drill-bit breakage, therefore, is important in reducing cost and improving productivity. This study uses twenty features extracted from two degradation signals viz., thrust force and torque. The methodology used involves developing and comparing decision tree, random forest, and multinomial logistic regression models for classifying and predicting drill-bit breakage using degradation signals.

Keywords: degradation signal, drill-bit breakage, random forest, multinomial logistic regression

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Authors: Hanen Khanchel, Karim Ben Kahla

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Pharmaceutical companies are facing competition. Indeed, the price differences between competing products can be such that it becomes difficult to compensate them by differences in value added. The conditions of competition are no longer homogeneous for the players involved. The price of a product is a given that puts a company and its customer face to face. However, price fixing obliges the company to consider internal factors relating to production costs and external factors such as customer attitudes, the existence of regulations and the structure of the market on which the firm evolved. In setting the selling price, the company must first take into account internal factors relating to its costs: costs of production fall into two categories, fixed costs and variable costs that depend on the quantities produced. The company cannot consider selling below what it costs the product. It, therefore, calculates the unit cost of production to which it adds the unit cost of distribution, enabling it to know the unit cost of production of the product. The company adds its margin and thus determines its selling price. The margin is used to remunerate the capital providers and to finance the activity of the company and its investments. Production costs are related to the quantities produced: large-scale production generally reduces the unit cost of production, which is an asset for companies with mass production markets. This shows that small and medium-sized companies with limited market segments need to make greater efforts to ensure their profit margins. As a result, and faced with high and low market prices for raw materials and increasing staff costs, the company must seek to optimize its production time in order to reduce loads and eliminate waste. Then, the customer pays only value added. Thus, and based on this principle we decided to create a project that deals with the problem of waste in our company, and having as objectives the reduction of production costs and improvement of performance indicators. This paper presents the implementation of the Value Stream Mapping (VSM) project in a pharmaceutical company. It is structured as follows: 1) determination of the family of products, 2) drawing of the current state, 3) drawing of the future state, 4) action plan and implementation.

Keywords: VSM, waste, production time, kaizen, cartography, improvement

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Authors: Havisha Banda

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Recent advancements in technology have facilitated commerce around the globe. The online medium of commerce has provided and will continue to provide great opportunities for consumers and businesses. Advancements in technology enable apparel stores, for instance, to improve their online services by using personalized virtual models allowing consumers to visualize the product on the model to determine correct sizing and fit. In addition to many advantages in online shopping the consumers will also have to undergo many types of switching costs in this process of buying apparel online. This study is to identify such switching costs and switching benefits from traditional shopping to online shopping and to understand what the consumers value the most. The scope of this study is to understand the types of switching costs and the factors that actually allow the consumers to shift from brick and mortar to online shopping and also to understand why a certain set of customers consider to purchase offline. Hence this study helps to understand the perceived cost and perceived benefit relation that the consumer draws in purchasing the garments online. This will help the upcoming e-commerce sites and brick and mortar store to understand the various factors and formulate new policies and implement strategies in their own ways to attract the customers and to retain them. A sample of 35 is considered for the process of laddered interviews. In the era of e-commerce there are people who feel comfortable to shop in a retail store rather than online purchase. Few respondents who shop online do not prefer to shop apparel online. Few respondents said that they shop online only for apparels. Most of the variables match in terms of switching costs and also in regard to benefits.

Keywords: e-commerce, switching costs, switching benefits, apparel shopping

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Authors: Florian Mach, Eric Hund, Malte Stonis

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The dismantling of disused industrial facilities such as nuclear power plants or refineries is an enormous challenge for the planning and control of the logistic processes. Existing control models do not meet the requirements for a proper dismantling of industrial plants. Therefore, the paper presents an approach for the control of dismantling and post-processing processes (e.g. decontamination) in plant decommissioning. In contrast to existing approaches, the dismantling sequence and depth are selected depending on the capacity utilization of required post-processing processes by also considering individual characteristics of respective dismantling tasks (e.g. decontamination success rate, uncertainties regarding the process times). The results can be used in the dismantling of industrial plants (e.g. nuclear power plants) to reduce dismantling time and costs by avoiding bottlenecks such as capacity constraints.

Keywords: dismantling management, logistics planning and control models, nuclear power plant dismantling, reverse logistics

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Authors: Michael Dimmer, Holger Flederer

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The basis of realizing a construction project is a technically flawless concept which satisfies conditions regarding environment and costs, as well as static-constructional terms. The presented software system actively supports civil engineers during the setup of optimal designs, by giving advice regarding durability, life-cycle costs, sustainability and much more. A major part of the surrounding conditions of a design process is gathered and assimilated by experienced engineers subconsciously. It is a question about eligible building techniques and their practicability by considering emerging costs. Planning engineers have acquired many of this experience during their professional life and use them for their daily work. Occasionally, the planning engineer should disassociate himself from his experience to be open for new and better solutions which meet the functional demands, as well. The developed expert system gives planning engineers recommendations for preferred design options of new constructions as well as for existing bridge constructions. It is possible to analyze construction elements and techniques regarding sustainability and life-cycle costs. This way the software provides recommendations for future constructions. Furthermore, there is an option to design existing road bridges especially for heavy duty transport. This implies a route planning tool to get quick and reliable information as to whether the bridge support structures of a transport route have been measured sufficiently for a certain heavy duty transport. The use of this expert system in bridge planning companies and building authorities will save costs massively for new and existent bridge constructions. This is achieved by consequently considering parameters like life-cycle costs and sustainability for its planning recommendations.

Keywords: expert system, planning process, road bridges, software system

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Authors: Sreejith Gopinath, Aspen Olmsted

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This paper is based on our previous work that proposed a risk-transference-based architecture for healthcare systems to store sensitive data outside the system boundary, rendering the system unattractive to would-be bad actors. This architecture also allows a compromised system to be abandoned and a new system instance spun up in place to ensure business continuity without paying a ransom or engaging with a bad actor. This paper delves into the details of various attacks we simulated against the prototype system. In the paper, we discuss at length the time and computational costs associated with storing and retrieving data in the prototype system, abandoning a compromised system, and setting up a new instance with existing data. Lastly, we simulate some analytical workloads over the data stored in our specialized data storage system and discuss the time and computational costs associated with running analytics over data in a specialized storage system outside the system boundary. In summary, this paper discusses the total costs of data storage, access, and analytics incurred with the proposed architecture.

Keywords: cybersecurity, healthcare, ransomware, resilience, risk transference

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Authors: Ons Sassi, Wahiba Ramdane Cherif-Khettaf, Ammar Oulamara

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In this paper, we consider a new real-life Heterogenous Electric Vehicle Routing Problem with Time Dependant Charging Costs and a Mixed Fleet (HEVRP-TDMF), in which a set of geographically scattered customers have to be served by a mixed fleet of vehicles composed of a heterogenous fleet of Electric Vehicles (EVs), having different battery capacities and operating costs, and Conventional Vehicles (CVs). We include the possibility of charging EVs in the available charging stations during the routes in order to serve all customers. Each charging station offers charging service with a known technology of chargers and time-dependent charging costs. Charging stations are also subject to operating time windows constraints. EVs are not necessarily compatible with all available charging technologies and a partial charging is allowed. Intermittent charging at the depot is also allowed provided that constraints related to the electricity grid are satisfied. The objective is to minimize the number of employed vehicles and then minimize the total travel and charging costs. In this study, we present a Mixed Integer Programming Model and develop a Charging Routing Heuristic and a Local Search Heuristic based on the Inject-Eject routine with three different insertion strategies. All heuristics are tested on real data instances.

Keywords: charging problem, electric vehicle, heuristics, local search, optimization, routing problem

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Authors: C. M. Mat Isa, H. Mohd Saman, S. R. Mohd Nasir, A. Jaapar

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International market expansion involves a strategic process of market entry decision through which a firm expands its operation from domestic to the international domain. Hence, entry timing choices require the needs to balance the early entry risks and the problems in losing opportunities as a result of late entry into a new market. Questionnaire surveys administered to 115 Malaysian construction firms operating in 51 countries worldwide have resulted in 39.1 percent response rate. Factor analysis was used to determine the most significant factors affecting entry timing choices of the firms to penetrate the international market. A logistic regression analysis used to examine the firms’ entry timing choices, indicates that the model has correctly classified 89.5 per cent of cases as late movers. The findings reveal that the most significant factor influencing the construction firms’ choices as late movers was the firm factor related to the firm’s international experience, resources, competencies and financing capacity. The study also offers valuable information to construction firms with intention to internationalize their businesses.

Keywords: factors, early movers, entry timing choices, late movers, logistic regression model, principal axis factorial analysis, Malaysian construction firms

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Authors: Nabilah Filzah Mohd Radzuan, Andi Putra, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

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Precipitation forecast is important to avoid natural disaster incident which can cause losses in the involved area. This paper reviews three techniques logistic regression, decision tree, and random forest which are used in making precipitation forecast. These combination techniques through the vector auto-regression (VAR) model help in finding the advantages and strengths of each technique in the forecast process. The data-set contains variables of the rain’s domain. Adaptation of artificial intelligence techniques involved in rain domain enables the forecast process to be easier and systematic for precipitation forecast.

Keywords: logistic regression, decisions tree, random forest, VAR model

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Authors: Dana Al-Qattan, Faiza Goodarzi, Heba Al-Resheedan, Kawther Shehab, Shoug Al-Ansari

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This project involves working with different types of forecasting methods and facility planning tools to help the company we have chosen to improve and reduce its inventory, increase its sales, and decrease its wastes and losses. The methods that have been used by the company have shown no improvement in decreasing the annual losses. The research made in the company has shown that no interest has been made in exploring different techniques to help the company. In this report, we introduce several methods and techniques that will help the company make more accurate forecasts and use of the available space efficiently. We expect our approach to reduce costs without affecting the quality of the product, and hence making production more viable.

Keywords: production planning, inventory management, inventory control, simulation, facility planning and design, engineering economy and costs

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Authors: Birome Holo Ba, Christian Prins, Caroline Prodhon

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Optimization is an important aspect of logistics management. It can reduce significantly logistics costs and also be a good tool for decision support. In this paper, we address a planning problem specific to biomass supply chain. We propose a new mixed integer linear programming (MILP) model dealing with different feed stock production operations such as harvesting, packing, storage, pre-processing and transportation, with the objective of minimizing the total logistic cost of the system on a regional basis. It determines the optimal number of harvesting machine, the fleet size of trucks for transportation and the amount of each type of biomass harvested, stored and pre-processed in each period to satisfy demands of refineries in each period. We illustrate the effectiveness of the proposal model with a numerical example, a case study in Aube (France department), which gives preliminary and interesting, results on a small test case.

Keywords: biomass logistics, supply chain, modelling, optimization, bioenergy, biofuels

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Authors: Hatim Laalej, Jon Stammers

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In machining, monitoring of tool wear is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Currently, the task of monitoring the wear on the cutting tool is carried out by the operator who performs manual inspections of the cutting tool, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from loss of productivity. The cutting tool consumable costs may also be higher than necessary when tools are changed before the end of their useful life. Furthermore, damage can be caused to the workpiece when tools are not changed soon enough leading to a significant increase in the costs of manufacturing. The present study is concerned with the development of break sensor printed on the flank surface of poly-crystalline diamond (PCD) cutting to perform on-line condition monitoring of the cutting tool used to machine Titanium Ti-6al-4v bar. The results clearly show that there is a strong correlation between the break sensor measurements and the amount of wear in the cutting tool. These findings are significant in that they help the user/operator of the machine tool to determine the condition of the cutting tool without the need of performing manual inspection, thereby reducing the manufacturing costs such as the machine down time.

Keywords: machining, manufacturing, tool wear, signal processing

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Authors: En-Tzu Wang, Ting-Ann Wang, Yi-Hui Shen, Yu-Min Chou, Chi-Tai Fang, Chin-Hui Yang

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Background and purpose: A mass varicella immunization program was established to provide free 1-dose vaccination for all 1-year-old children throughout Taiwan since 2004. The epidemiological characteristics and disease burden of varicella from 2000 to 2012 was investigated and the results will be essential to refine the national immunization policy. Method: We included patients (n = 17,838–164,245) with ICD-9-CM codes 052 (chickenpox) from the 2000 to 2012 National Health Insurance Database. The age, period, and cohort-specific incidence of varicella were calculated. The hospital admission rate, medical costs and indirect costs from the societal perspective of varicella including travel costs to the medical facility, registration fee, productivity losses of the patients and caregivers were also estimated. Result: There were 979,252 patients for medical treatment due to varicella from 2000 to 2012 in Taiwan. The implementation of a routine childhood varicella vaccination program has resulted in 87% decline in morbidity (881.49 to 115.17 per 100,000). The average age of patients increased from 7.9 years to 16.3 years. The overall varicella-related hospital admission rate was 15.5 per 1000 patients, and peaked in the groups of infants younger than 1 year, adults aged from 20 to 39 years and elders over 70 years. Among patients admitted to hospital, 33.5% of them had one or more complications. Patients with underlying diseases had higher admission rate (241.6 per 1,000) and longer duration of hospital stay (6.61 days vs. 4.76 days). The annual varicella-related medical expense declined after 2002 and the proportion of medical costs for admission has increased to 42%. The annual indirect costs from the societal perspective of varicella were 5.29 to 9.63 times higher than varicella-related medical costs. Every one dollar invested in the varicella immunization program, 2.97 dollars of medical and social costs were saved on average. Conclusion: The dramatic decline in morbidity, hospitalization, medical and social costs of varicella can be directly attributed to the implementation of the mass immunization program. Two-dose vaccination is recommended for both children with underlying diseases and susceptible adults to prevent serious complications and hospitalizations.

Keywords: disease burden, epidemiology, medical and social costs, varicella, varicella vaccine

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Authors: Fabiha Tanzeem

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BACKGROUND: Osteoporosis is a skeletal disease that is associated with a reduction in bone mass and microstructures of the bone and deterioration of bone tissue. Fragility fracture due to osteoporosis is the most significant complication of osteoporosis. The increasing prevalence of fragility fractures presents a growing burden on the global economy. There is a rapidly evolving need to improve awareness of the costs associated with these types of fractures and to review current policies and practices for the prevention and management of the disease. This systematic review will identify and describe the direct and indirect costs associated with osteoporotic fragility fractures from a global perspective from the included studies. The review will also find out whether the costs required for the treatment of fragility fractures due to osteoporosis impose an economic burden on the global healthcare system. METHODS: Four major databases were systematically searched for direct and indirect costs of osteoporotic fragility fracture studies in the English Language. PubMed, Cochrane Library, Embase and Google Scholar were searched for suitable articles published between 1990 and July 2020. RESULTS: The original search yielded 1166 papers; from these, 27 articles were selected for this review according to the inclusion and exclusion criteria. In the 27 studies, the highest direct costs were associated with the treatment of pelvic fractures, with the majority of the expenditure due to hospitalization and surgical treatments. It is also observed that most of the articles are from developed countries. CONCLUSION: This review indicates the significance of the economic burden of osteoporosis globally, although more research needs to be done in developing countries. In the treatment of fragility fractures, direct costs were the main reported expenditure in this review. The healthcare costs incurred globally can be significantly reduced by implementing measures to effectively prevent the disease. Raising awareness in children and adults by improving the quality of the information available and standardising policies and planning of services requires further research.

Keywords: systematic review, osteoporosis, cost of illness

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Authors: Mayada Attia Ibrahim

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Evaluating and developing the electroplating production process is a key challenge in this type of process. The process is influenced by several factors such as process parameters, process costs, and production environments. Analyzing and optimizing all these factors together requires extensive analytical techniques that are not available in real-case industrial entities. This paper presents a practice-based framework for the evaluation and optimization of some of the crucial factors that affect the costs and production times associated with this type of process, energy costs, material costs, and product flow times. The proposed approach uses Design of Experiments, Discrete-Event Simulation, and Theory of Constraints were respectively used to identify the most significant factors affecting the production process and simulate a real production line to recognize the effect of these factors and assign possible bottlenecks. Several scenarios are generated as corrective strategies for improving the production line. Following that, data envelopment analysis CCR input-oriented DEA model is used to evaluate and optimize the suggested scenarios.

Keywords: electroplating process, simulation, design of experiment, performance optimization, theory of constraints, data envelopment analysis

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Authors: Jalal Soleimannejad, Mohammad Asadizeidabadi, Mahmoud Koorki, Mojtaba Azarpira

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A significant percentage of production costs is the maintenance costs, and analysis of maintenance costs could to achieve greater productivity and competitiveness. With this is mind, the maintenance of machines and installations is considered as an essential part of organizational functions and applying effective strategies causes significant added value in manufacturing activities. Organizations are trying to achieve performance levels on a global scale with emphasis on creating competitive advantage by different methods consist of RCM (Reliability-Center-Maintenance), TPM (Total Productivity Maintenance) etc. In this study, increasing the capacity of Concentration Plant of Golgohar Iron Ore Mining & Industrial Company (GEG) was examined by using of reliability and maintainability analyses. The results of this research showed that instead of increasing the number of machines (in order to solve the bottleneck problems), the improving of reliability and maintainability would solve bottleneck problems in the best way. It should be mention that in the abovementioned study, the data set of Concentration Plant of GEG as a case study, was applied and analyzed.

Keywords: bottleneck, golgohar iron ore mining & industrial company, maintainability, maintenance costs, reliability

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Authors: Ozan Kahraman, Hao Feng

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Several models such as Weibull, Modified Gompertz, Biphasic linear, and Log-logistic models have been proposed in order to describe non-linear inactivation kinetics and used to fit non-linear inactivation data of several microorganisms for inactivation by heat, high pressure processing or pulsed electric field. First-order kinetic parameters (D-values and z-values) have often been used in order to identify microbial inactivation by non-thermal processing methods such as ultrasound. Most ultrasonic inactivation studies employed first-order kinetic parameters (D-values and z-values) in order to describe the reduction on microbial survival count. This study was conducted to analyze the E. coli O157:H7 inactivation data by using five microbial survival models (First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic). First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic kinetic models were used for fitting inactivation curves of Escherichia coli O157:H7. The residual sum of squares and the total sum of squares criteria were used to evaluate the models. The statistical indices of the kinetic models were used to fit inactivation data for E. coli O157:H7 by MTS at three temperatures (40, 50, and 60 0C) and three pressures (100, 200, and 300 kPa). Based on the statistical indices and visual observations, the Weibull and Biphasic models were best fitting of the data for MTS treatment as shown by high R2 values. The non-linear kinetic models, including the Modified Gompertz, First-order, and Log-logistic models did not provide any better fit to data from MTS compared the Weibull and Biphasic models. It was observed that the data found in this study did not follow the first-order kinetics. It is possibly because of the cells which are sensitive to ultrasound treatment were inactivated first, resulting in a fast inactivation period, while those resistant to ultrasound were killed slowly. The Weibull and biphasic models were found as more flexible in order to determine the survival curves of E. coli O157:H7 treated by MTS on apple-carrot juice.

Keywords: Weibull, Biphasic, MTS, kinetic models, E.coli O157:H7

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Authors: Erika Ruíz, Luis Amaya, Diego Carreño

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The main objective of this study is to design a mathematical model for the logistics of mining collection centers in the northern region of the department of Boyacá (Colombia), determining the structure that facilitates the flow of products along the supply chain. In order to achieve this, it is necessary to define a suitable design of the distribution network, taking into account the products, customer’s characteristics and the availability of information. Likewise, some other aspects must be defined, such as number and capacity of collection centers to establish, routes that must be taken to deliver products to the customers, among others. This research will use one of the operation research problems, which is used in the design of distribution networks known as Location Routing Problem (LRP).

Keywords: location routing problem, logistic, mining collection, model

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Seher Arslankaya, Bahar Uludağ

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Meeting the needs and demands of customers and pleasing the customers are important requirements for companies in food sectors where the growth of competition is significantly unpredictable. Customer satisfaction is also one of the key concepts which is mainly driven by wide range of customer preference and expectation upon products and services introduced and delivered to them. In order to meet the customer demands, the companies that engage in food sectors are expected to have a well-managed set of Total Quality Management (TQM), which sets out to improve quality of products and services; to reduce costs and to increase customer satisfaction by restructuring traditional management practices. It aims to increase customer satisfaction by meeting (their) customer expectations and requirements. The achievement would be determined with the help of customer satisfaction surveys, which is done to obtain immediate feedback and to provide quick responses. In addition, the surveys would also assist the making of strategic planning which helps to anticipate customer future needs and expectations. Meanwhile, periodic measurement of customer satisfaction would be a must because with the better understanding of customers perceptions from the surveys (done by questioners), the companies would have a clear idea to identify their own strengths and weaknesses that help the companies keep their loyal customers; to stand in comparison toward their competitors and map out their future progress and improvement. In this study, we propose a survey based on customer satisfaction measurement method and its statistical analysis for sales and logistic activities of food firms. Customer satisfaction would be discussed in details. Furthermore, after analysing the data derived from the questionnaire that applied to customers by using the SPSS software, various results obtained from the application would be presented. By also applying ANOVA test, the study would analysis the existence of meaningful differences between customer demographic proportion and their perceptions. The purpose of this study is also to find out requirements which help to remove the effects that decrease customer satisfaction and produce loyal customers in food industry. For this purpose, the customer complaints are collected. Additionally, comments and suggestions are done according to the obtained results of surveys, which would be useful for the making-process of strategic planning in food industry.

Keywords: customer satisfaction measurement and analysis, food industry, SPSS, TQM

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Authors: Cromwell F. Gopo, Joy L. Picar

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This study aimed to predict the employability of senior high school graduates for S.Y. 2018- 2019 in the Davao del Norte Division through quantitative research design using the descriptive status and predictive approaches among the indicated parameters, namely gender, school type, academics, academic award recipient, skills, values, and strand. The respondents of the study were the 33 secondary schools offering senior high school programs identified through simple random sampling, which resulted in 1,530 cases of graduates’ secondary data, which were analyzed using frequency, percentage, mean, standard deviation, and binary logistic regression. Results showed that the majority of the senior high school graduates who come from large schools were females. Further, less than half of these graduates received any academic award in any semester. In general, the graduates’ performance in academics, skills, and values were proficient. Moreover, less than half of the graduates were not employed. Then, those who were employed were either contractual, casual, or part-time workers dominated by GAS graduates. Further, the predictors of employability were gender and the Information and Communications Technology (ICT) strand, while the remaining variables did not add significantly to the model. The null hypothesis had been rejected as the coefficients of the predictors in the binary logistic regression equation did not take the value of 0. After utilizing the model, it was concluded that Technical-Vocational-Livelihood (TVL) graduates except ICT had greater estimates of employability.

Keywords: employability, senior high school graduates, Davao del Norte, Philippines

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Authors: Ekele Alih, Enejo Jalija

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Female Genital Mutilation (FGM) is made up of three types namely: Clitoridectomy, Excision and Infibulation. In this study, we examine the factors responsible for FGM in order to identify the causality variables in a logistic regression approach. From the result of the survey conducted by the Public Health Division, Nigeria Institute of Medical Research, Yaba, Lagos State, the tau statistic, τ was used to screen 9 factors that causes FGM in order to select few of the predictors before multiple regression equation is obtained. The need for this may be that the sample size may not be able to sustain having a regression with all the predictors or to avoid multi-collinearity. A total of 300 respondents, comprising 150 adult males and 150 adult females were selected for the household survey based on the multi-stage sampling procedure. The tau statistic,

Keywords: female genital mutilation, logistic regression, tau statistic, African society

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Authors: S. Dhib, S. A. Addouche, T. Loukil, A. Elmhamedi

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In this paper, a reverse supply chain network is investigated for a decision making. This decision is surrounded by complex flows of returned products, due to the increasing quantity, the type of returned products and the variety of recovery option products (reuse, recycling, and refurbishment). The most important problem in the reverse logistic network (RLN) is to orient returned products to the suitable type of recovery option. However, returned products orientations from collect sources to the recovery disposition have not well considered in performance model. In this study, we propose a performance model for designing a network configuration on reverse logistics. Conceptual and analytical models are developed with taking into account operational, economic and environmental factors on designing network.

Keywords: reverse logistics, network design, performance model, open loop configuration

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Authors: Zunaidah Ab Hasan, Azhana Othman, Abd Halim Mohd Noor, Nor Shahrina Mohd Rafien

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Zakat is a portion of wealth contributed from financially able Muslims to be distributed to predetermine recipients; main among them are the poor and the needy. Distribution of the zakat fund is given with the objective to lift the recipients from poverty. Due to the multidimensional and multifaceted nature of poverty, it is imperative that the causes of poverty are properly identified for assistance given by zakat authorities reached the intended target. Despite, various studies undertaken to identify the poor correctly, there are reports of the poor not receiving the adequate assistance required from zakat. Thus, this study examines the determinants of poverty among applicants for zakat assistance distributed by the State Islamic Religious Council in Malacca (SIRCM). Malacca is a state in Malaysia. The respondents were based on the list of names of new zakat applicants for the month of April and May 2014 provided by SIRCM. A binary logistic regression was estimated based on this data with either zakat applications is rejected or accepted as the dependent variable and set of demographic variables and health as the explanatory variables. Overall, the logistic model successfully predicted factors of acceptance of zakat applications. Three independent variables namely gender, age; size of households and health significantly explain the likelihood of a successful zakat application. Among others, the finding suggests the importance of focusing on providing education opportunity in helping the poor.

Keywords: logistic regression, zakat distribution, status of zakat applications, poverty, education

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Authors: Pooja Rani, G. S. Mahapatra, S. K. Pandey

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In this paper, we propose a novel approach of Neural Network and Particle Swarm Optimization methods for software reliability prediction. We first explain how to apply compound function in neural network so that we can derive a Flexible Logistic (S-shaped) Growth Curve (FLGC) model. This model mathematically represents software failure as a random process and can be used to evaluate software development status during testing. To avoid trapping in local minima, we have applied Particle Swarm Optimization method to train proposed model using failure test data sets. We drive our proposed model using computational based intelligence modeling. Thus, proposed model becomes Neuro-Particle Swarm Optimization (NPSO) model. We do test result with different inertia weight to update particle and update velocity. We obtain result based on best inertia weight compare along with Personal based oriented PSO (pPSO) help to choose local best in network neighborhood. The applicability of proposed model is demonstrated through real time test data failure set. The results obtained from experiments show that the proposed model has a fairly accurate prediction capability in software reliability.

Keywords: software reliability, flexible logistic growth curve model, software cumulative failure prediction, neural network, particle swarm optimization

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Authors: Sharon Hovav, Ilya Levner, Oren Nahum, Istvan Szabo

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In modern large-scale logistic centers (e.g., big automated warehouses), complex logistic operations performed by human staff (pickers) need to be coordinated with the operations of automated facilities (robots, conveyors, cranes, lifts, flow racks, etc.). The efficiency of advanced logistic centers strongly depends on optimizing picking technologies in synch with the facility/product layout, as well as on optimal distribution of material flows (products) in the system. The challenge is to develop a mathematical operations research (OR) tool that will optimize system cost-effectiveness. In this work, we propose a model that describes an automatic logistic center consisting of a set of workstations located at several galleries (floors), with each station containing a known number of flow racks. The requirements of each product and the working capacity of stations served by a given set of workers (pickers) are assumed as predetermined. The goal of the model is to maximize system efficiency. The proposed model includes two echelons. The first is the setting of the (optimal) number of workstations needed to create the total processing/logistic system, subject to picker capacities. The second echelon deals with the assignment of the products to the workstations and flow racks, aimed to achieve maximal throughputs of picked products over the entire system given picker capacities and budget constraints. The solutions to the problems at the two echelons interact to balance the overall load in the flow racks and maximize overall efficiency. We have developed an operations research model within each echelon. In the first echelon, the problem of calculating the optimal number of workstations is formulated as a non-standard bin-packing problem with capacity constraints for each bin. The problem arising in the second echelon is presented as a constrained product-workstation-flow rack assignment problem with non-standard mini-max criteria in which the workload maximum is calculated across all workstations in the center and the exterior minimum is calculated across all possible product-workstation-flow rack assignments. The OR problems arising in each echelon are proved to be NP-hard. Consequently, we find and develop heuristic and approximation solution algorithms based on exploiting and improving local optimums. The LC model considered in this work is highly dynamic and is recalculated periodically based on updated demand forecasts that reflect market trends, technological changes, seasonality, and the introduction of new items. The suggested two-echelon approach and the min-max balancing scheme are shown to work effectively on illustrative examples and real-life logistic data.

Keywords: logistics center, product-workstation, assignment, maximum performance, load balancing, fast algorithm

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Authors: C. Hamamura, V. Gialluca

Abstract:

Much of the Eclectricity Distributors has about 70% of its electricity interruptions arising from cause "trees", alone or associated with wind and rain and with or without falling branch and / or trees. This contributes inexorably and significantly to outages, resulting in high costs as compensation in addition to the operation and maintenance costs. On the other hand, there is little data structure and solutions to better organize the trees pruning plan effectively, minimizing costs and environmentally friendly. This work describes the development of an algorithm to provide data of trees associated to power grid. The method is accomplished on several steps using satellite imagery and geographically vectorized grid. A sliding window like approach is performed to seek the area around the grid. The proposed method counted 764 trees on a patch of the grid, which was very close to the 738 trees counted manually. The trees data was used as a part of a larger project that implements a system to optimize tree pruning plan.

Keywords: image pattern recognition, trees pruning, trees recognition, neural network

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Authors: Neha Ahirwar

Abstract:

In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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Authors: Ghada A. Alfattni

Abstract:

Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

Procedia PDF Downloads 314