Category: analytics

Chris Rocke and Jane Long from Whirlpool presented on their experiences with integrating LSS tools into BPM practices to move beyond traditional process mapping. Whirlpool is a mature Six Sigma company: starting in their manufacturing areas, it has spread to all other functions, and they’ve insourced their own training certification program. Six Sigma is not tracked as separate cost/benefit within a project, but is an inherent part of the way every project is done.

They introduced BPM during to a large-scale overhaul of their systems, processes and practices; their use of BPM is includes process modeling and monitoring, but not explicit process automation with a BPMS outside of their existing financial and ERP systems. However, they are creating a process-centric culture that does manage business processes in the governance and management sense, if not the automation sense in all cases. They brought LSS tools to their BPM efforts, such as process failure mode and effects analysis (PFMEA), data sampling and structure methods, thought maps and control charts; these provide more rigorous analysis than is often done within BPM projects.

Looking at their dashboards, they had the same problem as Johnson & Johnson: lots of data but no consistent and actionable information. They developed some standard KPIs, visualized in a suite of seven dashboards, with alert when certain control points are exceeded. Their Six Sigma analytics are embedded within the dashboards, not explicit, so that the business owners view and click through the dashboards in their own terms. The items included in the dashboard are fairly dynamic: for example, in the shipping dashboard, the products that vary widely from expected and historic values are brought forward, while those that are within normal operating parameters may not even appear. Obviously, building the models underlying this was a big part of the work in creating the dashboards: for example, shipping dashboard alerts are based on year-over-year differences (because sales of most products are seasonal) with control limits that are the mean of the YOY differences +/-  two standard deviations for a yellow alert, or three standard deviations for a red alert, plus other factors such as checking to see if the previous year’s value was an anomaly, weighted by the number of units shipped and a few other things thrown in.

The analytical calculations behind a dashboard might include internal forecasts or market/industry values, include seasonal fluctuations or not, depending on the particular measurement. The dashboard visuals, however, conceal all the complications of the underlying model. Alerts aren’t necessarily bad, but indicate a data point that’s outside the expected range and warrants investigation or explanation. They’ve seen some success in reducing variability and therefore making their forecasts more accurate: preventing rather than detecting defects.

They’re also using SAP’s Xcelsius for the dashboard itself; that’s the third company that I’ve heard here that is using that, which is likely due in part to the large number of SAP users but also gives credit to the flexibility and ease of use of that tool. They’re using SAP’s Business Warehouse for housing the data, which extracts from their core ERP system nightly: considerably more up-to-date than some of the others that we’ve seen here, which rely on monthly extracts manipulated in Excel. Although IT was involved in creating and maintaining BW, the LSS team owns their own use of Xcelsius, which allows them to modify the dashboards quickly.

I missed the first morning of the IQPC Lean Six Sigma & Process Improvement conference in Toronto today, but with my usual impeccable timing, showed up just in time for lunch (where we had to explain the rules of curling to the American attendees). The first session this afternoon is with David Brown, a black belt at Motorola, where the term “Six Sigma” was first coined and is still used to make their processes more effective, efficient, productive, and transparent.

There has been a transformation for them in how they analyze their processes: ranging from just looking at transactions to high-level intelligence including complex simulations and forecasting. Since they run SAP for their ERP, they have a number of SAP business intelligence (Xcelsius and Business Objects) products, although their most complex analysis is done with Oracle Crystal Ball.

Brown’s presentation was short – less than 10 minutes – and the rest of the session was an interactive one-on-one interview with questions from Charles Spina of e-Zsigma, the conference chair. The Q&A explored much more about how Motorola uses business analytics tools, and opened it up to the (small) audience for their experience with analytics. Not surprisingly, there has been quite a bit of success through the introduction of analytics to process improvement teams: sometimes it’s the black belts themselves, sometimes it’s a separate analytics group that works closely to develop the reports, analysis, and more complex intelligence based on the large volumes of data collected as part of any process improvement project.

Reporting tools can be as simple as Excel – for simple needs – through more complex solutions that include ETL from multiple data sources and regularly scheduled reports, such as Crystal Reports and Xcelsius. Legacy systems can make that a bit of a challenge; often these end up as extracts to Excel or Access, which are then remixed with other sources. Extracts such as this can be really problematic, as I’ve seen first-hand with many of my customers, since there’s no way to keep the data completely in sync with the underlying systems, and typically any one legacy system doesn’t have all the relevant data, so there can be a real problem in matching up related data from multiple systems. Brown underlined that the key issue is to get all of your data into a central data warehouse in order to determine if your data is complete and clean, and to facilitate reporting and analytics. This is especially important for process engineers when trying to do time studies over long periods of time: if you don’t have some consistent representation of the processes over the time period in question, then your analysis will suffer.

Motorola is using their data analytics to improve operational processes, such as order shipping, but also what-if scenarios to inform salespeople on the impact of discount levels to the bottom line. In many cases, this is an issue of data integration: Sabrina Lemos from United Airlines (who will be on the panel following) shared what they were able to recover in late container fees just by integrating their container tracking system with a database (Access, alas) that generates their invoices. Interestingly, I wouldn’t have thought of this as a process improvement initiative – although it is – but rather just as an artifact of doing some clever system integration.

They also discussed the challenges with presenting the results of analytics to the less numerically inclined, which often entails rolling data up to some simpler charts that can be drilled into as required, or just presented in a PowerPoint or PDF file. The real ROI may come from more interactive tools, however, such as dashboards that show operational alerts, or real-time what-if analysis to support human and automated decisions. Since Lean and Six Sigma tools are inherently analytical, this isn’t a new problem for the people in this audience; this is a matter of building relationships early with the non-analytical business managers, getting some early successes in projects to encourage adoption, and using different presentation and learning styles to present the information.

Because of the nature of this audience, the analytics that they’re discussing are typically for human consumption; in the BPM world, this is more and more moving to using the analytics to generate events that feed back into processes, or to inform automated decisioning. Either way, it’s all about improving the business processes.