The science of predictive analyticsYour data gets smarter
The science of predictive analytics
Putting your data to work
See how Infor’s data science team uncovers high-impact solutions for tough industry problemsWatch video ›
Infor Dynamic Science Labs
Shaping the next generation of enterprise software
The next generation of enterprise software won't just collect, report, and distribute information. It will anticipate problems and respond with solutions. It will uncover opportunities and recommend next steps. It will drive strategies in all areas of business, from recruiting and staffing to asset management and pricing.
How we’re building it
We work with Infor customers and product managers to identify industry-wide challenges and then solve them using elegant but understandable scientific methods. Right now, we're improving inventory availability and staffing allocations for hospitals and manufacturers, enhancing assortment for retailers, reinventing price optimization for distributors—and that's just the beginning. If you're interested in learning more about predictive analytics in your Infor solution or collaborating with us on a future project, contact your Infor representative or email us at firstname.lastname@example.org.
Use case: Healthcare
Can a charge nurse more effectively distribute workload, matching patient-specific needs with nurse skill sets?
Nurses are chronically overworked and understaffed. According to a 2014 study conducted by Nursing Times and ITV, eight out of ten nurses reported they don't have enough time to give patients adequate care, and a quarter believed they had put a patient's life at risk because they were too busy.
Every day, it is the charge nurse's job to create an optimal staffing plan for the next 24-hour period. He or she must balance each nurse's workload while also considering factors such as continuity of care, language, room proximity, and most importantly, matching the expertise of a nurse based on the needs of a patient.
How we're solving it
We are currently working with several healthcare providers to better understand their specific variables, objectives, and constraints. With this information, we can build an optimization model that obeys multiple constraints. One important step is to ensure that every patient is assigned to a nurse:
Further, depending on the type of nurse and availability, each nurse may have different workload capacities. This would constrain the number of patients they can see between F and G:
After we test, review, and refine the solutions with our healthcare partners, we will deliver the solutions through Infor software, empowering charge nurses to easily create daily staffing assignments optimized for every nurse and every patient.
Use case: B2B Pricing
How can distributors optimize prices?
Working closely with Infor customers, we are creating models that will help sales managers set prices that maximize both customer loyalty and their businesses' margins.
Our price models allow What-If analysis to provide insight into the impact of future price changes. Instead of manually managing prices for each individual customer, our solutions will help sales managers maintain dynamic customer groups using customer characteristics such as industry type as well as real-time product-specific financial performance, seasonality, and price response data. The dynamic nature of these customer clusters ensures that they stay up to date over time as new customers are acquired and current customer purchasing patterns shift.
Through beautifully crafted software, Infor simplifies the way people work. And now, with Dynamic Science Labs and the power of predictive analytics, Infor is simplifying the way organizations make decisions.
Ziad Nejmeldeen Chief Scientist
Infor Dynamic Science Labs
The team behind the science
We are a growing group of scientists, mathematicians, economists, and engineers based in the heart of innovation: Kendall Square in Cambridge, Massachusetts. We’re currently recruiting top talent from the best science programs in the country, starting with our neighbors at MIT and Harvard.
Interested in joining us?
Imagine you could make your data work harder.
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