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Author(s):  
Velappan Kaviyarasu ◽  
Palanisamy Sivakumar

Sampling plans are extensively used in pharmaceutical industries to test drugs or other related materials to ensure that they are safe and consistent. A sampling plan can help to determine the quality of products, to monitor the goodness of materials and to validate the yields whether it is free from defects or not. If the manufacturing process is precisely aligned, the occurrence of defects will be an unusual occasion and will result in an excess number of zeros (no defects) during the sampling inspection. The Zero Inflated Poisson (ZIP) distribution is studied for the given scenario, which helps the management to take a precise decision about the lot and it can certainly reduce the error rate than the regular Poisson model. The Bayesian methodology is a more appropriate statistical procedure for reaching a good decision if the previous knowledge is available concerning the production process. This article proposed a new design of the Bayesian Repetitive Group Sampling plan based on Zero Inflated Poisson distribution for the quality assurance in pharmaceutical products and related materials. This plan is studied through the Gamma-Zero Inflated Poisson (G-ZIP) model to safeguard both the producer and consumer by minimizing the Average Sample Number. Necessary tables and figures are constructed for the selection of optimal plan parameters and suitable illustrations are provided that are applicable for pharmaceutical industries.


2022 ◽  
pp. 137-161
Author(s):  
Antonio Sánchez-Herguedas ◽  
Adolfo Crespo-Márquez ◽  
Francisco Rodrigo-Muñoz

This chapter uses a semi-Markov process and the z transform to find the optimal preventive maintenance interval when dealing with maintenance decision making for a finite time planning horizon. The result is a method that can be easily implemented to assets for which a Weibull reliability analysis exists. The suggested preventive interval formulation is simple and practical. The requirements to apply this simple formula are related to the existence of asset´s reliability data as well as cost/rewards that the assets have when remaining or transitioning to a given state. The application of this method can be very straightforward, and the tool can become a good decision support tool allowing “what if” analysis for different time horizon and maintenance policies.


2021 ◽  
Vol 14 (1) ◽  
pp. 79
Author(s):  
Abdulrazag Mohamed Etelawi

This study aims to know the time between apple production and marketing to help decision makers for apple products at Washington, in the USA. In order to do so, it needs an application of OLS for a linear and non- linear model for diameter apples and length apple over the years 2010-2013. The diameter or size apple linear model includes DAFB, FB, latitude, mean80, years, and FB. The results indicated that all independent variables are significant and Adj R-squared explains about 75 percent of diameter apple. While the length apple linear model includes DAFB, years, FB, longitude, elevation, latitude, mean120, and mean70.The resulted sate that all independent variables are significant and Adj-R-squares illuminates about 84percent of size apple. Moreover, Actual value and predicted values for linear and nonlinear models are very close. Thus, those models can help farmers make a good decision for apple industry, and achieve to get best size and length for their apple crop.


2021 ◽  
Vol 948 (1) ◽  
pp. 012070
Author(s):  
D Purnomo ◽  
A Bunyamin ◽  
W Gunawan ◽  
N A Faizah ◽  
T G Danuwidjaja ◽  
...  

Abstract Indonesia is home to the greatest diversity of social bees in all over Asia, particularly species of the genus Apis. Thus, expanding the apiculture industry for commercial development is highly considerable. Although this industry has not become a special concern, the products of this industry are very popular among the Indonesian people, both for health, lifestyle, and other benefits. Research plays an essential role for good decision making, however, there is little research related to honey marketing in Indonesia. In this study, we observed the honey consumption of 246 respondents living in West Java by using online questionnaires and Decision Tree Classification to contribute to honey marketing research. This research shows that the motivation of the respondents in buying honey was merely for health reasons and the main purpose was for personal consumption. As for purchasing frequency, 86% of respondents purchased honey more than once a month. Then, a classification model of honey purchasing frequency based on respondents’ demographics which has an accuracy of 70.3% was built. The study results should be considered by the food industry and honey producers to emphasize consumer behaviour to formulate a better marketing strategy.


2021 ◽  
Author(s):  
Temitope Olubunmi Awodiji

With large amounts of unstructured data being produced every day, organizations are trying to extract as much relevant information as possible. This massive quantity of data is collected from a variety of sources, and data analysts and data scientists use it to create a dashboard that provides a complete picture of the organization's performance. Dashboards are business intelligence (BI) reporting tools that collect and show key metrics and key performance indicators (KPIs) on a single screen, enabling users to monitor and analyse business performance at a glance. An objective assessment of the company's overall performance, as well as of each department, is provided. If each department has access to the dashboard, it may serve as a springboard for future discussion and good decision-making. The goal of this article is to explain in detail the implementation of Dashboard and how it works, which will serve as a blueprint for building an effective dashboard with respect to best practices for dashboard design.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
De-Graft Owusu-Manu ◽  
Frank Ato Ato Ghansah ◽  
Ewald Kuoribo

Purpose Efficient decision-making must be reinvigorated to make a good decision towards retirement by construction workers. In developing countries such as Ghana, researchers conducted investigations into the effects of investing in retirement decision-making and planning, but none has considered to examine and identify the factors/determinants influencing efficient decision-making by construction workers towards retirement. This study aims to examine and identify the determinants/factors that affect the retirement decision-making of construction workers in developing countries such as Ghana. Design/methodology/approach This study used primary data collected from workers of four construction companies in Ghana. The sampling technique adopted for the study was a purposive sample approach, with a survey questionnaire as a collection instrument. Means score was adopted to reveal the major determinant/factor prioritized by the respondents while binary logistic regression was used to examine and identify the effect of the retirement determinants on the retirement decision of construction workers. Findings The findings established the main significant determinants impacting retirement decision, namely, “financial condition,” “homeownership,” “age” and “family issues.” Among the determinants, “financial condition” was revealed as the major determinant of retirement decision-making in the construction industry of developing countries, which is an economic condition by which the workers can easily secure credit. Practical implications Practically, the outcome of this study serves as a base for policymakers and practitioners in making decisions concerning the retirement of workers, especially construction workers. This study also serves to provide lesson for other classifications of workers aside from the construction workers in Ghana and other developing countries. Originality/value This study contributes to knowledge by filling in the lacuna in research by examining and identifying the determinants/factors that impact the efficient decision-making by construction workers in developing countries towards retirement.


2021 ◽  
Vol 68 (3) ◽  
pp. 338-343
Author(s):  
Andreea Grosu-Bularda ◽  
◽  
Florin-Vlad Hodea ◽  
Liviu-Petre Cojocaru ◽  
Alexandru Stoian ◽  
...  

Upper limb trauma cases vary from simple to high energy impactful injuries, with different etiologies; situations which frequently require unique, demanding and challenging endeavors in order to obtain the most favorable outcome. Experience, good decision-making and knowledge of functional goals are mandatory in order to elaborate a therapeutic plan and execute it accordingly. Although cases differ in nature and prognosis, respecting a set of therapeutic principles whilst dealing with either simple or complex cases, will enhance patient outcome and give the surgeon the confidence to tackle any kind of upper limb trauma. After clearing out vital threat, the emergency surgery represents the first threshold in achieving and restoring normal function and biomechanics, mostly in young and labor active patients, with the mindset to salvage as much tissue as possible, with a thorough debridement and step-by-step approach to different types of tissues. Secondary surgery and reconstructive surgery can be planned timely, with prior discussion with both the therapist and the patient in order to enhance patient’s upper limb function and aesthetic and ensure social reintegration.


Mathematics ◽  
2021 ◽  
Vol 9 (18) ◽  
pp. 2275
Author(s):  
Radwan Abu-Gdairi ◽  
Mostafa A. El-Gayar ◽  
Mostafa K. El-Bably ◽  
Kamel K. Fleifel

Rough set philosophy is a significant methodology in the knowledge discovery of databases. In the present paper, we suggest new sorts of rough set approximations using a multi-knowledge base; that is, a family of the finite number of general binary relations via different methods. The proposed methods depend basically on a new neighborhood (called basic-neighborhood). Generalized rough approximations (so-called, basic-approximations) represent a generalization to Pawlak’s rough sets and some of their extensions as confirming in the present paper. We prove that the accuracy of the suggested approximations is the best. Many comparisons between these approaches and the previous methods are introduced. The main goal of the suggested techniques was to study the multi-information systems in order to extend the application field of rough set models. Thus, two important real-life applications are discussed to illustrate the importance of these methods. We applied the introduced approximations in a set-valued ordered information system in order to be accurate tools for decision-making. To illustrate our methods, we applied them to find the key foods that are healthy in nutrition modeling, as well as in the medical field to make a good decision regarding the heart attacks problem.


2021 ◽  
Author(s):  
Jon Gustav Vabø ◽  
Evan Thomas Delaney ◽  
Tom Savel ◽  
Norbert Dolle

Abstract This paper describes the transformational application of Artificial Intelligence (AI) in Equinor's annual well planning and maturation process. Well planning is a complex decision-making process, like many other processes in the industry. There are thousands of choices, conflicting business drivers, lots of uncertainty, and hidden bias. These complexities all add up, which makes good decision making very hard. In this application, AI has been used for automated and unbiased evaluation of the full solution space, with the objective to optimize the selection of drilling campaigns while taking into account complex issues such as anti-collision with existing wells, drilling hazards and trade-offs between cost, value and risk. Designing drillable well trajectories involves a sequence of decisions, which makes the process very suitable for AI algorithms. Different solver architectures, or algorithms, can be used to play this game. This is similar to how companies such as Google-owned DeepMind develop customized solvers for games such as Go and StarCraft. The chosen method is a Tree Search algorithm with an evolutionary layer on top, providing a good balance in terms of performance (i.e., speed) vs. exploration capability (i.e., it looks "wide" in the option space). The algorithm has been deployed in a full stack web-based application that allows users to follow an end-2-end workflow: from defining well trajectory design rules and constraints to running the AI engine and evaluating results to the optimization of multi-well drilling campaigns based on risk, value and cost objectives. The full-size paper describes different Norwegian Continental Shelf (NCS) use cases of this AI assisted well trajectory planning. Results to-date indicate significant CAPEX savings potential and step-change improvements in decision speed (months to days) compared to routine manual workflows. There are very limited real transformative examples of Artificial Intelligence in multi- disciplinary workflows. This paper therefore gives a unique insight how a combination of data science, domain expertise and end user feedback can lead to powerful and transformative AI solutions – implemented at scale within an existing organization.


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