Critical success factors and risk mitigation strategy for new product development

”Success in new product development (NPD) offers a competitive and comparative advantage in the marketplace. A primary objective in an NPD project is to launch world class products with minimal risk. To deliver the superior quality and performance customers require, a company must develop the right NPD structure and framework for seamless execution by the NPD project teams throughout the product lifecycle. Companies must understand how to identify and mitigate risk to enable the success of their NPD projects. The costs to develop new products are often a considerable portion of an organization’s budget; however, studies have shown only 60 percent of new products making it to the market are commercially successful. Therefore, NPD project teams need to have a risk mitigation strategy, methodology, or framework to help with the identification and mitigation of risks in the product development process. This research conducted a systematic literature review to document the current research in the development of a risk mitigation framework tied to critical success factors (CSFs) that can be applied in the NPD process. The purpose of this research was to 1) determine the top CSFs that enable successful NPD through a worldwide multi-industry survey and 2) develop an NPD framework to mitigate risk. The survey responses were analyzed using the Kruskal-Wallis non-parametric statistical analysis to determine statistical differences in the CSFs based on rank. The top CSFs were then grouped to provide a conceptual highlevel view for managers to consider when developing or continuously improving their NPD execution structure, methods, and processes. An NPD framework was proposed based on the CSFs in order to mitigate risk”--Abstract, page iv

Using data mining techniques for improving customer relationship management

Customer relationship management (CRM) refers to the managerial efforts to technologies and processes that helped to understand firms’ customers. For this reason data mining techniques have an important role to extract the hidden knowledge and information which is inherited in the data used by researchers. This investigation focuses on the current automotive maintenance industry in Iran and applies various data mining technologies to partitioning customers. Its purpose is to determine the group of potential customers who are more likely to purchase optional services. Whereas the dataset used in this study is the real data of company, many steps of preprocess were applied and dataset records have been divided into two categories by attributing labels to the records. After preprocess steps, CAID and C5.0 methods of decision tree have been applied to classify customers and help the desired organization to make decision. By the results of two decision tree methods, there are some more important features for the firm to making decision

Using data mining techniques for improving customer relationship management

Customer relationship management (CRM) refers to the managerial efforts to technologies and processes that helped to understand firms’ customers. For this reason data mining techniques have an important role to extract the hidden knowledge and information which is inherited in the data used by researchers. This investigation focuses on the current automotive maintenance industry in Iran and applies various data mining technologies to partitioning customers. Its purpose is to determine the group of potential customers who are more likely to purchase optional services. Whereas the dataset used in this study is the real data of company, many steps of preprocess were applied and dataset records have been divided into two categories by attributing labels to the records. After preprocess steps, CAID and C5.0 methods of decision tree have been applied to classify customers and help the desired organization to make decision. By the results of two decision tree methods, there are some more important features for the firm to making decision

Using data mining techniques for improving customer relationship management

Customer relationship management (CRM) refers to the managerial efforts to technologies and processes that helped to understand firms’ customers. For this reason data mining techniques have an important role to extract the hidden knowledge and information which is inherited in the data used by researchers. This investigation focuses on the current automotive maintenance industry in Iran and applies various data mining technologies to partitioning customers. Its purpose is to determine the group of potential customers who are more likely to purchase optional services. Whereas the dataset used in this study is the real data of company, many steps of preprocess were applied and dataset records have been divided into two categories by attributing labels to the records. After preprocess steps, CAID and C5.0 methods of decision tree have been applied to classify customers and help the desired organization to make decision. By the results of two decision tree methods, there are some more important features for the firm to making decision

The strategic potential of community-based hybrid models: the case of global business services in Africa

Firms in latecomer economies, such as Sub-Saharan Africa, often have limited success with traditional business models. We explain why it can be more feasible to adopt a hybrid model in such a region that combines profitability with serving local communities, e.g., by promoting inclusive employment or by targeting underserved markets. Sub-Saharan Africa supports hybrid models and social enterprises mainly through local community resources (e.g., labor, market ideas), community organizations giving access to such resources, and experience with business-community alliances. Hybrid models can benefit from such conditions when business clients and governments support the social mission and when firms are ready to target niche rather than mainstream markets. We illustrate the business potential of hybrid enterprises based on the case of ‘impact sourcing’ in global business services in Kenya and South Africa

Improving the Economic Decision-Making Capability and Viability of Chinese Wool Textile Mills

The successful restructuring of Chinese industries is of immense importance not only for the continued development of China but also to the stability of the world economy. The transformation of the Chinese wool textile industry illustrates well the many problems and pressures currently facing most Chinese industries. The Chinese wool textile industry has undergone major upheaval and restructuring in its drive to modernize and take advantage of developments in world textile markets. Macro level ownership and administrative reforms are well advanced as is the uptake of new technology and equipment. However, the changing market and institutional environment also demands an increasing level of sophistication in mill management decisions including product selection, input procurement, product pricing, investment appraisal, cost analysis and proactive identification of new market and growth opportunities. This paper outlines a series of analyses that have been integrated into a decision-making model designed to assist mill managers with these decisions. Features of the model include a whole-of-mill approach, a design based on existing mill structures and information systems, and the capacity for the model to be tailored to individual mills. All of these features facilitate the adoption of the model by time and resource constrained managers seeking to maintain the viability of their enterprises in the face of extremely dynamic market conditions.China, wool textile mills, industry transition, decision-making models, Agribusiness, Livestock Production/Industries,

Collaborative-demographic hybrid for financial: product recommendation

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM processes, several financial institutions are striving to leverage customer data and integrate insights regarding customer behaviour, needs, and preferences into their marketing approach. As decision support systems assisting marketing and commercial efforts, Recommender Systems applied to the financial domain have been gaining increased attention. This thesis studies a Collaborative- Demographic Hybrid Recommendation System, applied to the financial services sector, based on real data provided by a Portuguese private commercial bank. This work establishes a framework to support account managers’ advice on which financial product is most suitable for each of the bank’s corporate clients. The recommendation problem is further developed by conducting a performance comparison for both multi-output regression and multiclass classification prediction approaches. Experimental results indicate that multiclass architectures are better suited for the prediction task, outperforming alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study provides important contributions for positioning the bank’s commercial efforts around customers’ future requirements. By allowing for a better understanding of customers’ needs and preferences, the proposed Recommender allows for more personalized and targeted marketing contacts, leading to higher conversion rates, corporate profitability, and customer satisfaction and loyalty