Optimal Product Portfolio Formulation by Merging Predictive Data Mining With Multilevel Optimization

2008 ◽  
Vol 130 (4) ◽  
Author(s):  
Conrad S. Tucker ◽  
Harrison M. Kim
Keyword(s):  
Data Mining ◽  
Product Family ◽  
Product Architecture ◽  
Product Portfolio ◽  
Market Demand ◽  
Customer Preference ◽  
Data Mining Approach ◽  
Engineering Level ◽  
Initial Stage ◽  
Predictive Data Mining

This paper addresses two important fundamental areas in product family formulation that have recently begun to receive great attention. First is the incorporation of market demand that we address through a data mining approach where realistic customer preference data are translated into performance design targets. Second is product architecture reconfiguration that we model as a dynamic design entity. The dynamic approach to product architecture optimization differs from conventional static approaches in that a product architecture is not fixed at the initial stage of product design, but rather evolves with fluctuations in customer performance preferences. The benefits of direct customer input in product family design will be realized through the cell phone product family example presented in this work. An optimal family of cell phones is created with modularity decisions made analytically at the engineering level that maximize company profit.

A Data Mining Approach to Forming Generic Bills of Materials in Support of Variant Design Activities

2004 ◽  
Vol 4 (4) ◽  
pp. 316-328 ◽  
Author(s):  
Carol J. Romanowski , ◽  
Rakesh Nagi
Keyword(s):  
Data Mining ◽  
Product Family ◽  
Research Direction ◽  
Design Constraints ◽  
Variant Design ◽  
Data Mining Approach ◽  
Design Activities ◽  
Using Data ◽  
New Research

In variant design, the proliferation of bills of materials makes it difficult for designers to find previous designs that would aid in completing a new design task. This research presents a novel, data mining approach to forming generic bills of materials (GBOMs), entities that represent the different variants in a product family and facilitate the search for similar designs and configuration of new variants. The technical difficulties include: (i) developing families or categories for products, assemblies, and component parts; (ii) generalizing purchased parts and quantifying their similarity; (iii) performing tree union; and (iv) establishing design constraints. These challenges are met through data mining methods such as text and tree mining, a new tree union procedure, and embodying the GBOM and design constraints in constrained XML. The paper concludes with a case study, using data from a manufacturer of nurse call devices, and identifies a new research direction for data mining motivated by the domains of engineering design and information.

scholarly journals Predicting COVID-19 Incidence Using Data Mining Techniques: A case study of Pakistan

2020 ◽  
Vol 11 (4) ◽  
pp. 168-184
Author(s):  
Saba NOOR ◽  
◽  
Waseem AKRAM ◽  
Touseef AHMED ◽  
Qurat-ul-Ain Qurat-ul-Ain ◽  
...  
Keyword(s):  
Data Mining ◽  
Machine Learning Algorithms ◽  
Wuhan City ◽  
Data Mining Approach ◽  
Life Threatening ◽  
Precautionary Measures ◽  
The Government ◽  
Predictive Data Mining ◽  
Using Data

The Outbreak of Coronavirus (COVID-19) came to the world in early December 2019. The early cases of coronavirus were reported in Wuhan City, Hubei Province, China. Till May 18, 2020, 198 countries have been affected by this life-threatening disease. The most common and known traits of COVID-19 are tiredness, fever, and dry cough. In this paper, we have discussed the Predictive data mining approach for COVID-19 predictions. In Predictive data mining, a model is developed and trained using supervised learning and then it predicts the behavior of provided data. Predictive data mining is a renowned technique known to many health organizations for the classification and prediction of diseases such as Heart disease and various types of cancers etc. There are several factors for comparing the model's accuracy, scalability, and interpretability. This predictive model is compared to the basics of its accuracy. In this proposed approach, we have used WEKA as it provides a vast collection of many machine learning algorithms. The main objective of this paper is to forecast the possible future incidence of corona cases in Pakistan. This study concludes that the number of corona cases will increase swiftly. If the government take proactive steps and strictly implement precautionary measures, then Pakistan may be able to overcome this pandemic.

Product Family Concept Generation and Validation Through Predictive Decision Tree Data Mining and Multi-Level Optimization

2007 ◽  
Author(s):  
Conrad S. Tucker ◽  
Harrison M. Kim
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Engineering Design ◽  
Product Family ◽  
Optimization Techniques ◽  
Computational Time ◽  
Product Portfolio ◽  
Performance Expectations ◽  
Multi Level ◽  
Product Concepts

The formulation of a product family requires extensive knowledge about the product market space and also the technical limitations of a company’s engineering design and manufacturing processes. We present a methodology to significantly reduce the computational time required to achieve an optimal product portfolio by eliminating the need for an exhaustive search of all possible product concepts. This is achieved through a data mining decision tree technique that generates a set of product concepts that are subsequently validated in the engineering design level using multi-level optimization techniques. The final optimal product portfolio evaluates products based on the following three criteria: 1) The ability to satisfy customer’s price and performance expectations (based on predictive model) defined here as the feasibility criterion. 2) The feasible set of products/variants validated at the engineering level must generate positive profit that we define as the optimality criterion. 3) The optimal set of products/variants should be a manageable size as defined by the enterprise decisions makers and should therefore not exceed the product portfolio limit. The strength of our work is to reveal the tremendous savings in time and resources that exist when data mining predictive techniques are applied to the formulation of an optimal product portfolio. Using data mining tree generation techniques, a customer response data set of 40,000 individual product preferences is narrowed down to 46 product family concepts and then validated through the multilevel engineering design response of feasible architectures. A cell phone example is presented and an optimal product portfolio solution is achieved that maximizes company profit, while concurrently satisfying customer product performance expectations.

scholarly journals Implementation of Predictive Data Mining Techniques for Identifying Risk Factors of Early AVF Failure in Hemodialysis Patients

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Mohammad Rezapour ◽  
Morteza Khavanin Zadeh ◽  
Mohammad Mehdi Sepehri
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Research Society ◽  
Hemodialysis Patients ◽  
Diabetic Patients ◽  
Prediction Methods ◽  
Early Failure ◽  
Data Mining Approach ◽  
Predictive Data Mining ◽  
Accuracy Rates

Arteriovenous fistula (AVF) is an important vascular access for hemodialysis (HD) treatment but has 20–60% rate of early failure. Detecting association between patient's parameters and early AVF failure is important for reducing its prevalence and relevant costs. Also predicting incidence of this complication in new patients is a beneficial controlling procedure. Patient safety and preservation of early AVF failure is the ultimate goal. Our research society is Hasheminejad Kidney Center (HKC) of Tehran, which is one of Iran's largest renal hospitals. We analyzed data of 193 HD patients using supervised techniques of data mining approach. There were 137 male (70.98%) and 56 female (29.02%) patients introduced into this study. The average of age for all the patients was 53.87 ± 17.47 years. Twenty eight patients had smoked and the number of diabetic patients and nondiabetics was 87 and 106, respectively. A significant relationship was found between “diabetes mellitus,” “smoking,” and “hypertension” with early AVF failure in this study. We have found that these mentioned risk factors have important roles in outcome of vascular surgery, versus other parameters such as “age.” Then we predicted this complication in future AVF surgeries and evaluated our designed prediction methods with accuracy rates of 61.66%–75.13%.

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