Allocation is key. If the allocation isn t right, it can lead to out-of-stocks, lost sales, and customer dissatisfaction. Automating your most complicated and time-consuming tasks, and ensuring you are feeding the right data at the right time, is critical. This session will review how Beall s Outlet and Beall s Department Stores are managing and executing allocations at optimal levels. How using attributes and group definitions allow them to be responsive to trends, history, and plans.

Traditional merchandise planning processes have been primarily product and location focused, with decisions about assortment selection, breadth and depth, and distribution based on the historical performance of merchandise in stores. However, retailers are recognizing that in order to compete and succeed in an increasingly complex marketplace, assortments must become customer-centric. Advanced analytics can be leveraged to generate actionable insights into the relevance of merchandise to a retailer's various customer segments and purchase channel preferences. These insights enrich the merchandise and assortment planning process. This paper describes techniques for using advanced analytics to impact customer-centric assortments. Topics covered include approaches for scoring merchandise based on customer relevance and preferences, techniques for gaining insight into customer relevance without customer data, and an overall approach to a customer-driven merchandise planning process.

In healthcare, we often express our analytics results as being adjusted . For example, you might have read a study in which the authors reported the data as age-adjusted or risk-adjusted. The concept of adjustment is widely used in program evaluation, comparing quality indicators across providers and systems, forecasting incidence rates, and in cost-effectiveness research. In order to make reasonable comparisons across time, place, or population, we need to account for small sample sizes and case-mix variation in other words, we need to level the playing field and account for differences in health status and for uniqueness in a given population. If you are new to healthcare. What it really means to adjust the data in order to make comparisons might not be obvious. In this paper, we explore the methods by which we control for potentially confounding variables in our data. We do so through a series of examples from the healthcare literature in both primary care and health insurance. In this survey of methods, we discuss the concepts of rates and how they can be adjusted for demographic strata (such as age, gender, and race), as well as health risk factors such as case mix.

This presentation provides users with an update on retail solution releases that have occurred in the past year and a roadmap for moving forward.

57 Category teams. 8,500 stores. 10,000 SKUs. 1 integrated Planning Solution. Deploying a stand-alone Assortment Planning system creates an isolated planning structure that adds complexity to your ability to deliver results in a dynamic retail environment. Today s challenging competitive and economic conditions reward retailers who take the opportunity to integrate their strategic systems into their downstream execution. This presentation describes the approach Family Dollar followed to integrate SAS^® Assortment Planning with existing operational systems. The result? Reduced complexity, improved efficiency, and better on-time execution in our stores.