Applications of lean thinking: a briefing document

This report has been put together by the Health and Care Infrastructure Research and Innovation Centre (HaCIRIC) at the University of Salford for the Department of Health. The need for the report grew out of two main simple questions, o Is Lean applicable in sectors other than manufacturing? o Can the service delivery sector learn from the success of lean in manufacturing and realise the benefits of its implementation?The aim of the report is to list together examples of lean thinking as it is evidenced in the public and private service sector. Following a review of various sources a catalogue of evidence is put together in an organised manner which demonstrates that Lean principles and techniques, when applied rigorously and throughout an entire organization/unit, they can have a positive impact on productivity, cost, quality, and timely delivery of services

Customer churn prediction in telecom using machine learning and social network analysis in big data platform

Customer churn is a major problem and one of the most important concerns for large companies. Due to the direct effect on the revenues of the companies, especially in the telecom field, companies are seeking to develop means to predict potential customer to churn. Therefore, finding factors that increase customer churn is important to take necessary actions to reduce this churn. The main contribution of our work is to develop a churn prediction model which assists telecom operators to predict customers who are most likely subject to churn. The model developed in this work uses machine learning techniques on big data platform and builds a new way of features' engineering and selection. In order to measure the performance of the model, the Area Under Curve (AUC) standard measure is adopted, and the AUC value obtained is 93.3%. Another main contribution is to use customer social network in the prediction model by extracting Social Network Analysis (SNA) features. The use of SNA enhanced the performance of the model from 84 to 93.3% against AUC standard. The model was prepared and tested through Spark environment by working on a large dataset created by transforming big raw data provided by SyriaTel telecom company. The dataset contained all customers' information over 9 months, and was used to train, test, and evaluate the system at SyriaTel. The model experimented four algorithms: Decision Tree, Random Forest, Gradient Boosted Machine Tree "GBM" and Extreme Gradient Boosting "XGBOOST". However, the best results were obtained by applying XGBOOST algorithm. This algorithm was used for classification in this churn predictive model.Comment: 24 pages, 14 figures. PDF https://rdcu.be/budK

Predicting customer's gender and age depending on mobile phone data

In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM (global system for mobile communications), not the line owner. Where sometimes they may not be the same. This work proposes a method that predicts users' gender and age based on their behavior, services and contract information. We used call detail records (CDRs), customer relationship management (CRM) and billing information as a data source to analyze telecom customer behavior, and applied different types of machine learning algorithms to provide marketing campaigns with more accurate information about customer demographic attributes. This model is built using reliable data set of 18,000 users provided by SyriaTel Telecom Company, for training and testing. The model applied by using big data technology and achieved 85.6% accuracy in terms of user gender prediction and 65.5% of user age prediction. The main contribution of this work is the improvement in the accuracy in terms of user gender prediction and user age prediction based on mobile phone data and end-to-end solution that approaches customer data from multiple aspects in the telecom domain

Privacy and Accountability in Black-Box Medicine

Black-box medicine—the use of big data and sophisticated machine learning techniques for health-care applications—could be the future of personalized medicine. Black-box medicine promises to make it easier to diagnose rare diseases and conditions, identify the most promising treatments, and allocate scarce resources among different patients. But to succeed, it must overcome two separate, but related, problems: patient privacy and algorithmic accountability. Privacy is a problem because researchers need access to huge amounts of patient health information to generate useful medical predictions. And accountability is a problem because black-box algorithms must be verified by outsiders to ensure they are accurate and unbiased, but this means giving outsiders access to this health information. This article examines the tension between the twin goals of privacy and accountability and develops a framework for balancing that tension. It proposes three pillars for an effective system of privacy-preserving accountability: substantive limitations on the collection, use, and disclosure of patient information; independent gatekeepers regulating information sharing between those developing and verifying black-box algorithms; and information-security requirements to prevent unintentional disclosures of patient information. The article examines and draws on a similar debate in the field of clinical trials, where disclosing information from past trials can lead to new treatments but also threatens patient privacy

Adoption of New Technology

The contribution of new technology to economic growth can only be realized when and if the new technology is widely diffused and used. Diffusion itself results from a series of individual decisions to begin using the new technology, decisions which are often the result of a comparison of the uncertain benefits of the new invention with the uncertain costs of adopting it. An understanding of the factors affecting this choice is essential both for economists studying the determinants of growth and for the creators and producers of such technologies. Section II of this article discusses the modeling of diffusion and Sections III to V explore the determinants of diffusion and the evidence for their importance.

Adoption of New Technology

The contribution of new technology to economic growth can only be realized when and if the new technology is widely diffused and used. Diffusion itself results from a series of individual decisions to begin using the new technology, decisions which are often the result of a comparison of the uncertain benefits of the new invention with the uncertain costs of adopting it. An understanding of the factors affecting this choice is essential both for economists studying the determinants of growth and for the creators and producers of such technologies. Section II of this article discusses the modeling of diffusion and Sections III to V explore the determinants of diffusion and the evidence for their importance.

Raising sector skills levels : how responsive is local training supply?

This UK study examines the extent and nature of any mismatches between the training requirements of employers and the local provision of vocational education and training (VET). Companies in selected sectors (maintenance and repair of motor vehicles; telecommunications services; mechanical engineering, vehicles and other engineering; and textiles, clothing and footwear manufacture) and regions were surveyed on their training requirements. Staff in colleges and training providers in the same regions were interviewed to discuss the survey findings and to investigate the extent to which these providers are already satisfying those requirements and the nature of any constraints which may be present

Knowledge Management As an Economic Development Strategy

The United States is shifting to an information economy. Productive capability is no longer completely dependent on capital and equipment; information and knowledge assets are increasingly important. The result is a new challenge to the practice of local economic development. In this information economy, success comes from harnessing the information and knowledge assets of a community and from helping local businesses succeed in the new environment. Knowledge Management (KM) can provide the tools to help economic development practitioners accomplish that task. KM is a set of techniques and tools to uncover and utilize information and knowledge assets -- especially tacit knowledge. Economic development organizations can use KM tools to enhance external communications of local companies including marketing and to promote internal communications within local businesses and help companies capture tacit knowledge. More importantly, they can use those tools to uncover and develop local intellectual assets, including helping develop information products, and helping identify entrepreneurial and business opportunities. KM tools are also useful in developing local economic clusters. Finally, these tools can be used to enhance external knowledge sharing among the economic development community and to capture and share tacit knowledge within an economic development organization