The Coronavirus disease 2019 (COVID-19) global pandemic has transformed almost every facet of human society throughout the world. Against an emerging, highly transmissible disease, governments worldwide have implemented non-pharmaceutical interventions (NPIs) to slow the spread of the virus. Examples of such interventions include community actions, such as school closures or restrictions on mass gatherings, individual actions including mask wearing and self-quarantine, and environmental actions such as cleaning public facilities. We present the Worldwide Non-pharmaceutical Interventions Tracker for COVID-19 (WNTRAC), a comprehensive dataset consisting of over 6,000 NPIs implemented worldwide since the start of the pandemic. WNTRAC covers NPIs implemented across 261 countries and territories, and classifies NPIs into a taxonomy of 16 NPI types. NPIs are automatically extracted daily from Wikipedia articles using natural language processing techniques and then manually validated to ensure accuracy and veracity. We hope that the dataset will prove valuable for policymakers, public health leaders, and researchers in modeling and analysis efforts to control the spread of COVID-19.
Sales professionals need to identify new sales prospects, and sales executives need to deploy the sales force against the sales accounts with the best potential for future revenue. We describe two analytics-based solutions developed within IBM to address these related issues. The Web-based tool OnTARGET provides a set of analytical models to identify new sales opportunities at existing client accounts and noncustomer companies. The models estimate the probability of purchase at the product-brand level. They use training examples drawn from historical transactions and extract explanatory features from transactional data joined with company firmographic data (e.g., revenue and number of employees). The second initiative, the Market Alignment Program, supports sales-force allocation based on field-validated analytical estimates of future revenue opportunity in each operational market segment. Revenue opportunity estimates are generated by defining the opportunity as a high percentile of a conditional distribution of the customer's spending, that is, what we could realistically hope to sell to this customer. We describe the development of both sets of analytical models, the underlying data models, and the Web sites used to deliver the overall solution. We conclude with a discussion of the business impact of both initiatives.
In 2004, IBM introduced a set of broad operations research-based initiatives designed to improve the efficiency and productivity of its global sales force. The first solution, OnTARGET, provides a set of analytical models designed to identify new sales opportunities at existing IBM accounts and at noncustomer companies. The second solution, the Market Alignment Program (MAP), optimally allocates sales resources based on field-validated analytical estimates of future revenue opportunities in operational market segments. IBM Research developed the operations research models and initial internal websites for both solutions. The IBM Software Group initially implemented OnTARGET, which was subsequently made available to over 13,000 sales representatives across IBM sales organizations worldwide. The IBM Sales and Distribution organization deployed MAP as an integral part of its sales model to better align sales resources with the best market opportunities. We describe the development of both analytical models, and the underlying data models and websites used to deliver the solutions. We conclude with a discussion of the business impact, which we estimate as hundreds of millions of dollars annually for the combined initiatives.
In this paper, we present a suite of innovative operations research models and methods called OnTheMark (OTM). This suite supports the effective management of human capital supply chains by addressing distinct features of human talent that cannot be handled via traditional supply chain management. OTM consists of novel solutions for (1) statistical forecasting of demand and human capital requirements, (2) risk-based stochastic human-talent capacity planning, (3) stochastic modeling and optimization (control) of human capital supply evolutionary dynamics over time, (4) optimal multiskill supply-demand matching, and (5) stochastic optimization of business decisions and investments to manage human capital shortages and overages. The OTM suite was developed and deployed as an important part of the human capital management and planning process within IBM, providing support for decision making to drive better business performance. This is achieved through important contributions in the areas of stochastic models and optimization (control), and the innovative application and integration of these models and methods in human capital management applications.
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