Many organizations need to forecast large numbers of time series that are discretely valued. These series, called count series, fall approximately between continuously valued time series, for which there are many forecasting techniques (ARIMA, UCM, ESM, and others), and intermittent time series, for which there are a few forecasting techniques (Croston's method and others). This paper proposes a technique for large-scale automatic count series forecasting and uses SAS^® Forecast Server and SAS/ETS^® software to demonstrate this technique.

As pollution and population continue to increase, new concepts of eco-friendly commuting evolve. One of the emerging concepts is the bicycle sharing system. It is a bike rental service on a short-term basis at a moderate price. It provides people the flexibility to rent a bike from one location and return it to another location. This business is quickly gaining popularity all over the globe. In May 2011, there were only 375 bike rental schemes consisting of nearly 236,000 bikes. However, this number jumped to 535 bike sharing programs with approximately 517,000 bikes in just a couple of years. It is expected that this trend will continue to grow at a similar pace in the future. Most of the businesses involved in this system of bike rental are faced with the challenge of balancing supply and inconsistent demand. The number of bikes needed on a particular day can vary on several factors such as season, time, temperature, wind speed, humidity, holiday and day of the week. In this paper, we have tried to solve this problem using SAS^® Forecast Studio. Incorporating the effects of all the above factors and analyzing the demand trends of the last two years, we have been able to precisely forecast the number of bikes needed on any day in the future. Also, we are able to do the scenario analysis to observe the effect of particular variables on the demand.

A group tasked with testing SAS^® software from the customer perspective has gathered a number of helpful hints for SAS^® 9.4 that will smooth the transition to its new features and products. These hints will help with the 'huh?' moments that crop up when you are getting oriented and will provide short, straightforward answers. We also share insights about changes in your order contents. Gleaned from extensive multi-tier deployments, SAS^® Customer Experience Testing shares insiders' practical tips to ensure that you are ready to begin your transition to SAS 9.4. The target audience for this paper is primarily system administrators who will be installing, configuring, or administering the SAS 9.4 environment. (This paper is an updated version of the paper presented at SAS Global Forum 2014 and includes new features and software changes since the original paper was delivered, plus any relevant content that still applies. This paper includes information specific to SAS 9.4 and SAS 9.4 maintenance releases.)

The need to measure slight changes in healthcare costs and utilization patterns over time is vital in predictive modeling, forecasting, and other advanced analytics. At BlueCross BlueShield of Tennessee, a method for developing member-level regression slopes creates a better way of identifying these changes across various time spans. The goal is to create multiple metrics at the member level that will indicate when an individual is seeking more or less medical or pharmacy services. Significant increases or decreases in utilization and cost are used to predict the likelihood of acquiring certain conditions, seeking services at particular facilities, and self-engaging in health and wellness. Data setup and compilation consists of calculating a member's eligibility with the health plan and then aggregating cost and utilization of particular services (for example, primary care visits, Rx costs, ER visits, and so on). A member must have at least six months of eligibility for a valid regression slope to be calculated. Linear regression is used to build single-factor models for 6, 12, 18 and 24 month time spans if the appropriate amount of data is available for the member. Models are built at the member-metric time period resulting in the possibility of over 75 regression coefficients per member per monthly run. The computing power needed to execute such a vast amount of calculations requires in-database processing of various macro processes. SAS^® Enterprise Guide^® is used to structure the data and SAS^® Forecast Studio is used to forecast trends at a member level. Algorithms are run the first of each month. Data is stored so that each metric and corresponding slope is appended on a monthly basis. Because the data is setup up for the member regression algorithm, slopes are interpreted in the following manner: a positive value for -1*slope indicates an increase in utilization/cost; a negative value for -1*slope indicates a decrease in utilization/cost. The ac tual slope value indicates the intensity of the change in cost in utilization. The insight provided by this member-level regression methodology replaces subjective methods that used arbitrary thresholds of change to measure differences in cost and utilization.

Faced with diminishing forecast returns from the forecast engine within the existing replenishment application, Tractor Supply Company (TSC) engaged SAS^® Institute to deliver a fully integrated forecasting solution that promised a significant improvement of chain-wide forecast accuracy. The end-to-end forecast implementation including problems faced, solutions delivered, and results realized will be explored.

This workshop provides hands-on experience using SAS^® Forecast Server. Workshop participants will learn to: create a project with a hierarchy, generate multiple forecast automatically, evaluate the forecasts accuracy, and build a custom model.

The two primary objectives of multi-tiered causal analysis (MTCA) are to support and evaluate business strategies based on the effectiveness of marketing actions in both a competitive and holistic environment. By tying the performance of a brand, product, or SKU at retail to internal replenishment shipments at a point in time, the outcome of making a change to the marketing mix (demand) can be simulated and evaluated to determine the full impact on supply (shipments). The key benefit of MTCA is that it captures the entire supply chain by focusing on marketing strategies to shape future demand and to link them, using a holistic framework, to shipments (supply). These relationships are what truly define the marketplace and all marketing elements within the supply chain.

In today's omni-channel world, consumers expect retailers to deliver the product they want, where they want it, when they want it, at a price they accept. A major challenge many retailers face in delighting their customers is successfully predicting consumer demand. Business decisions across the enterprise are affected by these demand estimates. Forecasts used to inform high-level strategic planning, merchandising decisions (planning assortments, buying products, pricing, and allocating and replenishing inventory) and operational execution (labor planning) are similar in many respects. However, each business process requires careful consideration of specific input data, modeling strategies, output requirements, and success metrics. In this session, learn how leading retailers are increasing sales and profitability by operationalizing forecasts that improve decisions across their enterprise.

How does historical production data relate a story about subsurface oil and gas reservoirs? Business and domain experts must perform accurate analysis of reservoir behavior using only rate and pressure data as a function of time. This paper introduces innovative data-driven methodologies to forecast oil and gas production in unconventional reservoirs that, owing to the nature of the tightness of the rocks, render the empirical functions less effective and accurate. You learn how implementations of the SAS^® MODEL procedure provide functional algorithms that generate data-driven type curves on historical production data. Reservoir engineers can now gain more insight to the future performance of the wells across their assets. SAS enables a more robust forecast of the hydrocarbons in both an ad hoc individual well interaction and in an automated batch mode across the entire portfolio of wells. Examples of the MODEL procedure arising in subsurface production data analysis are discussed, including the Duong data model and the stretched exponential data model. In addressing these examples, techniques for pattern recognition and for implementing TREE, CLUSTER, and DISTANCE procedures in SAS/STAT^® are highlighted to explicate the importance of oil and gas well profiling to characterize the reservoir. The MODEL procedure analyzes models in which the relationships among the variables comprise a system of one or more nonlinear equations. Primary uses of the MODEL procedure are estimation, simulation, and forecasting of nonlinear simultaneous equation models, and generating type curves that fit the historical rate production data. You will walk through several advanced analytical methodologies that implement the SEMMA process to enable hypotheses testing as well as directed and undirected data mining techniques. SAS^® Visual Analytics Explorer drives the exploratory data analysis to surface trends and relationships, and the data QC workflows ensure a robust input space for the performance forecasting methodologies that are visualized in a web-based thin client for interactive interpretation by reservoir engineers.

Managing and organizing external files and directories play an important part in our data analysis and business analytics work. A good file management system can streamline project management and file organizations and significantly improve work efficiency . Therefore, under many circumstances, it is necessary to automate and standardize the file management processes through SAS^® programming. Compared with managing SAS files via PROC DATASETS, managing external files is a much more challenging task, which requires advanced programming skills. This paper presents and discusses various methods and approaches to managing external files with SAS programming. The illustrated methods and skills can have important applications in a wide variety of analytic work fields.