Manufacturers of any product from toys to medicine to automobiles must create items that are, above all else, safe to use. Not only is this essential to long-term brand value and corporate success, but it's also required by law. Although perfection is the goal, defects are bound to occur, especially in advanced products such as automobiles. Automobiles are the largest purchase most people make, next to a house. When something that costs tens of thousands of dollars runs into problems, you tend to remember. Recalls in part reflect growing pains after decades of consolidation in the auto industry. Many believe that recalls are the culmination of years of neglect by manufacturers and the agencies that regulate them. For several reasons, automakers are acting earlier and more often in issuing recalls. In the past 20 years, the number of voluntarily recalled vehicles has steadily grown. The automotive-recall landscape changed dramatically in 2000 with the passage of the federal TREAD Act. Before that, federal law required that automakers issue a recall only when a consumer reported a problem. TREAD requires that companies identify potential problems and promptly notify the NHTSA. This is largely due to stricter laws, heavier fines, and more cautious car makers. This study helps automobile manufacturers understand customers who are talking about defects in their cars and to be proactive in recalling the product at the right time before the Government acts.

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Data that are gathered in modern data collection processes are often large and contain geographic information that enables you to examine how spatial proximity affects the outcome of interest. For example, in real estate economics, the price of a housing unit is likely to depend on the prices of housing units in the same neighborhood or nearby neighborhoods, either because of their locations or because of some unobserved characteristics that these neighborhoods share. Understanding spatial relationships and being able to represent them in a compact form are vital to extracting value from big data. This paper describes how to glean analytical insights from big data and discover their big value by using spatial econometric methods in SAS/ETS^® software.

For all business analytics projects big or small, the results are used to support business or managerial decision-making processes, and many of them eventually lead to business actions. However, executives or decision makers are often confused and feel uninformed about contents when presented with complicated analytics steps, especially when multi-processes or environments are involved. After many years of research and experiment, a web reporting framework based on SAS^® Stored Processes was developed to smooth the communication between data analysts, researches, and business decision makers. This web reporting framework uses a storytelling style to present essential analytical steps to audiences, with dynamic HTML5 content and drill-down and drill-through functions in text, graph, table, and dashboard formats. No special skills other than SAS^® programming are needed for implementing a new report. The model-view-controller (MVC) structure in this framework significantly reduced the time needed for developing high-end web reports for audiences not familiar with SAS. Additionally, the report contents can be used to feed to tablet or smartphone users. A business analytical example is demonstrated during this session. By using this web reporting framework based on SAS Stored Processes, many existing SAS results can be delivered more effectively and persuasively on a SAS^® Enterprise BI platform.

Detection and adjustment of structural breaks are an important step in modeling time series and panel data. In some cases, such as studying the impact of a new policy or an advertising campaign, structural break analysis might even be the main goal of a data analysis project. In other cases, the adjustment of structural breaks is a necessary step to achieve other analysis objectives, such as obtaining accurate forecasts and effective seasonal adjustment. Structural breaks can occur in a variety of ways during the course of a time series. For example, a series can have an abrupt change in its trend, its seasonal pattern, or its response to a regressor. The SSM procedure in SAS/ETS^® software provides a comprehensive set of tools for modeling different types of sequential data, including univariate and multivariate time series data and panel data. These tools include options for easy detection and adjustment of a wide variety of structural breaks. This paper shows how you can use the SSM procedure to detect and adjust structural breaks in many different modeling scenarios. Several real-world data sets are used in the examples. The paper also includes a brief review of the structural break detection facilities of other SAS/ETS procedures, such as the ARIMA, AUTOREG, and UCM procedures.

Creating an environment that enables and empowers self-service and agile analytic capabilities requires a tremendous amount of working together and extensive agreements between IT and the business. Business and IT users are struggling to know what version of the data is valid, where they should get the data from, and how to combine and aggregate all the data sources to apply analytics and deliver results in a timely manner. All the while, IT is struggling to supply the business with more and more data that is becoming available through many different data sources such as the Internet, sensors, the Internet of Things, and others. In addition, once they start trying to join and aggregate all the different types of data, the manual coding can be very complicated and tedious, can demand extraneous resources and processing, and can negatively impact the overhead on the system. If IT enables agile analytics in a data lab, it can alleviate many of these issues, increase productivity, and deliver an effective self-service environment for all users. This self-service environment using SAS^® analytics in Teradata has decreased the time required to prepare the data and develop the statistical data model, and delivered faster results in minutes compared to days or even weeks. This session discusses how you can enable agile analytics in a data lab, leverage SAS analytics in Teradata to increase performance, and learn how hundreds of organizations have adopted this concept to deliver self-service capabilities in a streamlined process.

Would you like to be more confident in producing graphs and figures? Do you understand the differences between the OVERLAY, GRIDDED, LATTICE, DATAPANEL, and DATALATTICE layouts? Finally, would you like to learn the fundamental Graph Template Language methods in a relaxed environment that fosters questions? Great this topic is for you! In this hands-on workshop, you are guided through the fundamental aspects of the GTL procedure, and you can try fun and challenging SAS^® graphics exercises to enable you to more easily retain what you have learned.

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Do you need to add annotations to your graphs? Do you need to specify your own colors on the graph? Would you like to add Unicode characters to your graph, or would you like to create templates that can also be used by non-programmers to produce the required figures? Great, then this topic is for you! In this hands-on workshop, you are guided through the more advanced features of the GTL procedure. There are also fun and challenging SAS^® graphics exercises to enable you to more easily retain what you have learned.

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A leading global information and communications technology solution company provides a broad range of telecom products across the world. Their finished products share commonality in key components, and, in most cases, are assembled after the customer orders are realized. Each finished product typically consists of a large number of key components, and the stockout of any components causes a delay of customer orders. For these reasons, the optimal inventory policy of one component should be determined in conjunction with those of other components. Currently the company uses business experience to manage inventory across their supply chain network for all of the components and finished products. However, the increasing variety of products and business expansion raise difficulties in inventory management. The company wants to explore a systematic approach to optimizing inventory policies, assuring customer service level and minimizing total inventory cost. This paper describes using SAS/OR^® software and SAS^® inventory optimization technologies to model such a multi-echelon assembly system and optimize inventory policies for key components and finished products.

When analyzing data with SAS^®, we often use the SAS DATA step and the SQL procedure to explore and manipulate data. Though they both are useful tools in SAS, many SAS users do not fully understand their differences, advantages, and disadvantages and thus have numerous unnecessary biased debates on them. Therefore, this paper illustrates and discusses these aspects with real work examples, which give SAS users deep insights into using them. Using the right tool for a given circumstance not only provides an easier and more convenient solution, it also saves time and work in programming, thus improving work efficiency. Furthermore, the illustrated methods and advanced programming skills can be used in a wide variety of data analysis and business analytics fields.

Every organization, from the most mature to a day-one start-up, needs to grow organically. A deep understanding of internal customer and operational data is the single biggest catalyst to develop and sustain the data. Advanced analytics and big data directly feed into this, and there are best practices that any organization (across the entire growth curve) can adopt to drive success. Analytics teams can be drivers of growth. But to be truly effective, key best practices need to be implemented. These practices include in-the-weeds details, like the approach to data hygiene, as well as strategic practices, like team structure and model governance. When executed poorly, business leadership and the analytics team are unable to communicate with each other they talk past each other and do not work together toward a common goal. When executed well, the analytics team is part of the business solution, aligned with the needs of business decision-makers, and drives the organization forward. Through our engagements, we have discovered best practices in three key areas. All three are critical to analytics team effectiveness. 1) Data Hygiene 2) Complex Statistical Modeling 3) Team Collaboration

Optimizing delivery routes and efficiently using delivery drivers are examples of classic problems in Operations Research, such as the Traveling Salesman Problem. In this paper, Oberweis and Zencos collaborate to describe how to leverage SAS/OR^® procedures to solve these problems and optimize delivery routes for a retail delivery service. Oberweis Dairy specializes in home delivery service that delivers premium dairy products directly to customers homes. Because freshness is critical to delivering an excellent customer experience, Oberweis is especially motivated to optimize their delivery logistics. As Oberweis works to develop an expanding footprint and a growing business, Zencos is helping to ensure that delivery routes are optimized and delivery drivers are used efficiently.

In 2012, US Customs scanned nearly 4% and physically inspected less than 1% of the 11.5 million cargo containers that entered the United States. Laundering money through trade is one of the three primary methods used by criminals and terrorists. The other two methods used to launder money are using financial institutions and physically moving money via cash couriers. The Financial Action Task Force (FATF) roughly defines trade-based money laundering (TBML) as disguising proceeds from criminal activity by moving value through the use of trade transactions in an attempt to legitimize their illicit origins. As compared to other methods, this method of money laundering receives far less attention than those that use financial institutions and couriers. As countries have budget shortfalls and realize the potential loss of revenue through fraudulent trade, they are becoming more interested in TBML. Like many problems, applying detection methods against relevant data can result in meaningful insights, and can result in the ability to investigate and bring to justice those perpetuating fraud. In this paper, we apply TBML red flag indicators, as defined by John A. Cassara, against shipping and trade data to detect and explore potentially suspicious transactions. (John A. Cassara is an expert in anti-money laundering and counter-terrorism, and author of the book Trade-Based Money Laundering. ) We use the latest detection tool in SAS^® Viya , along with SAS^® Visual Investigator.

Every sourcing and procurement department has limited resources to use for realizing productivity (cost savings). In practice, many organizations simply schedule yearly pricing negotiations with their main suppliers. They do not deviate from that approach unless there is a very large swing in the underlying commodity. Using cost data gleaned from previous quotes and SAS^® Enterprise Guide^®, we can put in place a program and methodology that move the practice from gut instinct to quantifiable and justifiable models that can easily be updated on a monthly basis. From these updated models, we can print a report of suppliers or categories that we should consider for cost downs, and suppliers or categories that we should work on to hold current pricing. By having all cost models, commodity data, and reporting functions within SAS Enterprise Guide, we are able to not only increase the precision and effectiveness of our negotiations, but also to vastly decrease the load of repetitive work that has been traditionally placed on supporting analysts. Now the analyst can execute the program, send the initial reports to the management team, and be leveraged for other projects and tasks. Moreover, the management team can have confidence in the analysis and the recommended plan of action.

Have you ever used a control chart to assess the variation in a process? Did you wonder how you could modify the chart to tell a more complete story about the process? This paper explains how you can use the SHEWHART procedure in SAS/QC^® software to make the following enhancements: display multiple sets of control limits that visualize the evolution of the process, visualize stratified variation, explore within-subgroup variation with box-and-whisker plots, and add information that improves the interpretability of the chart. The paper begins by reviewing the basics of control charts and then illustrates the enhancements with examples drawn from real-world quality improvement efforts.

In industrial systems, vibration signals are the most important measurements for indicating asset health. Based on these measurements, an engineer with expert knowledge about the assets, industrial process, and vibration monitoring can perform spectral analysis to identify failure modes. However, this is still a manual process that heavily depends on the experience and knowledge of the engineer analyzing the vibration data. Moreover, when measurements are performed continuously, it becomes impossible to act in real time on this data. The objective of this paper is to examine using analytics to perform vibration spectral analysis in real time to predict asset failures. The first step in this approach is to translate engineering knowledge and features into analytic features in order to perform predictive modeling. This process involves converting the time signal into the frequency domain by applying a fast Fourier transform (FFT). Based on the specific design characteristics of the asset, it is possible to derive the relevant features of the vibration signal to predict asset failures. This approach is illustrated using a bearing data set available from the Prognostics Data Repository of the National Aeronautics and Space Administration (NASA). Modeling is done using R and is integrated within SAS^® Asset Performance Analytics. In essence, this approach helps the engineers to make better data-driven decisions. The approach described in this paper shows the strength of combining ex

Increasingly, customers are using social media and other Internet-based applications such as review sites and discussion boards to voice their opinions and express their sentiments about brands. Such spontaneous and unsolicited customer feedback can provide brand managers with valuable insights about competing brands. There is a general consensus that listening to and reacting to the voice of the customer is a vital component of brand management. However, the unstructured, qualitative, and textual nature of customer data that is obtained from customers poses significant challenges for data scientists and business analysts. In this paper, we propose a methodology that can help brand managers visualize the competitive structure of a market based on an analysis of customer perceptions and sentiments that are obtained from blogs, discussion boards, review sites, and other similar sources. The brand map is designed to graphically represent the association of product features with brands, thus helping brand managers assess a brand's true strengths and weaknesses based on the voice of customers. Our multi-stage methodology uses the principles of topic modeling and sentiment analysis in text mining. The results of text mining are analyzed using correspondence analysis to graphically represent the differentiating attributes of each brand. We empirically demonstrate the utility of our methodology by using data collected from Edmunds.com, a popular review site for car buyers.