The healthcare industry, hospitals in particular, offer a number of improvements ripe for machine learning and predictive analytics. One of these areas is population health management. Hospitals can change the way they deliver care by understanding their communities through the application of machine learning and statistics. Once healthcare systems understand the drivers of utilization rates, they can begin to cluster community segments and ultimately implement preventative interventions in the communities they serve.
Mosaic Data Science has years of experience leveraging weather forecast data to improve operations across industries, and has staff meteorologists that provide subject matter expertise on this data. These assets led Mosaic to be the ML development partner of choice for a global network communications corporation involved in monitoring satellite signal quality for a number of telecommunications providers. Any interruption in communications reflected poorly on the networking firm. Mosaic’s client was well aware of the effect of weather on its business but did not have an effective way to mitigate the impact on customer satisfaction.
The public utility wanted to focus on utilizing internal data for improved business decision making, optimizing their data analytics Center of Excellence (CoE) team structure, matching analytics technology with organizational fit, and convincing business stakeholders of the value and possibilities of advanced analytics.Mosaic Data Science was contacted to perform this enterprise-wide data science center of excellence assessment. Mosaic brings over a decade of advanced analytics consulting experience.
The ability to find a set of images that are in some way similar to a given image has multiple use cases—from visual search to duplicate product detection to domain-specific image clustering. Google’s Reverse Image Search is an example of a general-purpose image similarity tool that retrieves similar images to a query image. Most modern image similarity tools apply deep learning to quantify the degree of similarity between intensity patterns in pairs of images. This standard approach may not be sufficient, however, when “similarity” must be specific to the business context.
Third party logistics (3PL) brokers operate in the trucking spot market, where agents match one-off shipments with truckers (carriers) willing to transport them. Due to national trucking shortages and increasing demand, brokers must work quickly to contact carriers likely to accept a particular shipment while maintaining profitability. A leading 3PL firm approached Mosaic Data Science, an innovative data mining company, with the goal of using analytics to prioritize carriers for brokers to call, thereby helping the agents to source low-cost carriers with the fewest possible calls.
Mosaic, a leading data science consultancy, was engaged by the hotel chain to assess the best way to predict future demand for hotel rooms across their various properties. The ultimate objective was maximizing revenue from a resource with constrained supply (i.e. limited number of rooms) and fluctuating demand over time (i.e. night(s) of stay). This is a critical analytics task for hotel chains, as unoccupied rooms on a given night earn zero revenue, while demand in excess of room capacity carries a cost in terms of lost revenue.
To maintain strategic relationships with retailers, consumer packaged goods (CPG) manufacturers must be able to reliably meet retail inventory demand. If supply chain delays result in the potential for late shipments, CPG manufacturers need advanced warning so that they can take action to avoid late deliveries. A large player in the CPG sector came to Mosaic Data Science, an innovative data science consulting firm, with a late delivery forecasting model built from historical data.
The Supply Chain department determined that a machine learning tool for automatically flagging emails requiring attention as they arrived in the PO confirmation mailbox would reduce the risk of missed exceptions and significantly reduce the workload on the Supply Chain department. The hospital system needed an analytics consulting partner who had experience implementing Natural Language Processing (NLP), text analytics, and production machine learning tools, and Mosaic Data Science was poised to assist with their PO challenges. Mosaic developed and evaluated multiple candidate machine learning models for performing email classification.
As rates of chronic disease increase in the United States and around the world, it is important for researchers and policymakers to understand how people manage long-term health conditions. Social media posts provide a rich source of first-person lifestyle information, but it is difficult to extract meaningful information about the health of a population from these posts. People frequently turn to their social media platforms to discuss symptoms from various ailments. The Centers of Disease Control (CDC) wanted to understand if they could mine social media data to understand population health. They needed a data science firm to complete this task.
A leading not-for-profit children’s hospital system had seen an increase in the last two years in the number and duration of children’s Intensive Care Unit (ICU) saturation events. A saturation event occurs when the bed capacity and/or staffing levels in the ICU are not sufficient to cover patient volumes. During these events, patient levels must be controlled by, for example, raising thresholds for ICU admission or redirecting incoming Emergency Department (ED) patients to other hospitals in the network that may not be as well-equipped to provide best possible treatment.
Mosaic Data Science, an innovative analytics consulting firm with proven experience in the energy industry, was brought on to develop an improved fuel usage prediction tool. The data-driven predictions would enable business analysts and auditors to closely track daily fuel consumption for the customer’s fleet of ships and to isolate potential cases of fraud where predicted fuel burn did not match the fuel consumption reported in the ship’s logs.
A large multinational enterprise software provider noticed a severe drop in maintenance and services renewals specifically in the North American market. The software firm, which supplies a digital business product line across industries to thousands of businesses customers, turned to Mosaic Data Science, a leading analytics consulting provider, for help to combat customer churn on services contracts. To tackle this problem effectively, Mosaic’s data science consultants needed to understand the business processes governing the sale of services contracts.
The manufacturer wanted to segment their forecasts by the different markets that they serve, e.g., aerospace, consumer electronics, and life sciences. The company had been collecting transactional data by line of business, region and industry. Now that the company had collected all of this data, they needed to perform predictive analysis on it to extract value. With no internal data scientists available for this work, Mosaic was tapped. Mosaic, a premier analytics consulting firm, was asked to initially focus on a number of North American business segments. The primary data mining task was to uncover hidden relationships in the data, provide these insights back to the manufacturer’s management team, and build more robust and accurate revenue forecast models for each business segment.
Our client, one of the largest Oil & Gas companies in the world, wanted to improve the process used by industry advisers to forecast fuel price spreads and crack spreads for a variety of products. These forecasts support decisions related to production planning, refinery planning, and open market crude oil trading. The goal was to build automated prediction models to support the forecasting process, allowing advisers to make better informed decisions. Manual, time-consuming processes were predominant. Advisers collected data manually and used spreadsheets to review and obtain insights from the data. This process was repeated several times a month by multiple advisers.
A leading management consulting firm recently initiated an enterprise-wide upgrade of existing and new laptops to the Windows 10 operating system (OS). The firm deployed a fleet of 3,000 laptops to employees around the world. An alarming number of consultants began reporting the infamous ‘Blue Screen of Death’ (BSOD). This message appears right after a system wide failure and provides a cryptic description of the error. BSOD’s may indicate significant problems with a computer that could affect the computer’s performance and long-term reliability.
The company’s business intelligence and analytics team needed an order optimization capability that would support their account managers both in providing better service to customer accounts and in driving increased revenue from existing accounts. This desired capability would provide recommendations for upsell, cross-sell, and promotional orders to increase total product sales for each account. It would also automate the order process for mainline SKUs , enabling account managers to focus on value added activities during store visits.
One of North America’s leading trucking companies created a revenue management program with a strong emphasis on analytics. The program and underlying projects were identified as a key priority. The trucking organization believed they could significantly improve their business through predictive analysis and data science. The trucking company had many different projects going on at once and needed a premier analytics consulting company to provide support over a number of different areas.
One of the world’s leading healthcare information technology and innovation companies needed advanced analytics assistance in developing ‘flight path’ models to characterize the progression of patient health during the long-term course of diabetes disease management and treatment. The healthcare innovation company planned to use these models to identify effective disease management and treatment strategies in order to improve patient outcomes while controlling lifetime cost of treatment. The company had plenty of analytical talent, but lacked the advanced analytical talent Mosaic possessed.
Mosaic’s data science consultants, in collaboration with the customer data science team, determined that an automated tool that generated a recommended size profile for any product within the customer’s catalog would be the best way to implement the predictive model. The tool determines a level of aggregation based on each product’s color, brand, and product category. Based on the store assortment plan for the product, the tool then aggregates historical demand from the appropriate time period across the assorted stores for each size of a product and returns a relative (percentage) size distribution for the style-color.
Mosaic was brought in to help develop, maintain, and refresh a machine learning model which forecasts demand and factors in this predicted demand, scheduled deliveries, and information about terminal dynamics and uncertainty to more accurately predict terminal imbalances. The predictive model, which alerts schedulers when there is a high risk of a near-term inventory imbalance at a terminal, allows the client’s schedulers to get a real-time and forward-looking picture of what is happening with their assets, improves productivity, and facilitates quicker and better decision making that avoids costly inventory imbalances before they occur.
Mosaic’s efforts for NASA started with a detailed study of the potential benefits of using Cloud Computing for multiple use cases within NASA’s Aeronautics Research Mission Directorate (ARMD). In this assessment, Mosaic used a detailed model to compare capital and operational expenditures for an on-premises architecture solution versus a Cloud-based solution. Additional benefits of Cloud Computing were identified for each use case, such as the ability to scale to meet the demands for computational resources based on the complexity of the weather and traffic situation in the US airspace.
One of the world’s leading and top-rated management consulting firms was preparing to move their video conferencing and audio conferencing capabilities from an in-office hosted solution to Cisco WebEx. The firm’s internal IT team was tasked with facilitating a smooth transition, requiring that adequate network connectivity be established and available from each office to support the WebEx sessions which the firm conducts on top of other network traffic. Mosaic was contracted as a strategic data science consultant to provide quantitative and predictive analyses of these systems to give the client confidence that after the transition the bandwidth would be sufficient to support the firm’s daily operational needs.
One of the largest brick-and-mortar North American retailers needed to understand the demand of their seasonal apparel items so they could improve inventory and pricing decisions. The retailer manages over 150 million stock keeping units (SKU), and knew it needed to take a data-driven approach for pricing and promotional decisions. Mosaic answered some critical questions for the retailer; how elastic is demand? What seasonality patterns drive demand for each type of item?
One of the largest healthcare insurance companies in North America wanted to optimize their marketing efforts specifically around retention. This organization serves over 28 million members, with one in eight people in the United States relying on them for eye-care health coverage. The company had made an investment in a consumer insights team. This team believed if they could start segmenting their members into different groups, they could begin to improve their marketing efforts, ultimately identifying high revenue generating and segments at risk for leaving.
The customer wanted to develop a social networking site which connects users to like-minded business and activities. Understanding the current options in the marketplace, the customer believed the addition of a recommender engine would give their application significant competitive advantage.
Mosaic was asked to deliver training which would bring everyone on the analytic solution team to the same knowledge level. Once the team had a base level of knowledge, they could begin to apply predictive analytics and optimization techniques to the business challenges…
Cycle times are critical. Compressing the time between an aircraft’s arrival and departure would let the company offer better service and capture more revenue. Avoiding mistakes that cause delays is extremely valuable, as a late aircraft can result in tens or hundreds of thousands of dollars. Common mistakes causing delays included; ramp congestion and sending an aircraft to the wrong gate. The customer had three goals: reduce mistakes resulting in delays, make sure the nightly sort goes smoothly, and reduce jet-fuel consumption.
One of the decisions air traffic controllers make is when and how much to reduce arrival traffic at an airport in response to capacity reductions due to adverse weather conditions. These decisions are termed ground delay programs (GDPs). Traditionally controllers have issued GDPs by applying heuristics that only account for known (deterministic) weather and traffic conditions. NASA asked Mosaic to build a decision-support system (DSS) that accounts for weather uncertainty in recommending optimal GDP decisions to controllers at the San Francisco International Airport (SFO).
A primary goal of air-traffic management (ATM) is to balance airspace capacity and demand. Traditionally ATM has approached this problem assuming constant airspace configuration. A new ATM paradigm known as dynamic airspace configuration (DAC) analyzes how to reconfigure airspace…