Articles

Heavy-duty powertrain systems resource multiple sensors collecting streams of data during production. Sensor data streams, originating from various production machines, simultaneously encounter the challenging situation of limited resilience to sensor quality issues in terms of sensors being non-linear, outdated, spawning false alarms, or going offline. Alleviating this critical situation requires a reinforcement technology to assure resilience, with both verified lineage-based approaches and a observe-and-repair mechanism of limited quality. Moreover, legislation advances aimed to guarantee higher future operational safety put additional pressure on the overall manufacturing sensor monitoring frame. For heavy-duty powertrain systems harnessed for combustion engines, an integrated technology based on automatic data lineage and quality governance detection of production machine sensor data streams resource the frame using a unified provenance model and ontology supporting the explicit definition of sensor quality rules. Provenance models automatic integration form the basis that enables the monitoring of multiple machine resources. Used quality rules enable the detection of bad-quality sensor segments while, in parallel, additional quality rules based on observed data control supported by human intervention capabilities repair gaps during production. The proposed automated technology provides an additional layer of resilience that improves the reliability of sensor data stream quality and contributes to the adherence of operational safety future rules. The implementation case study focuses on sensors deployed in powertrain systems production that modern industries must follow to ensure next-generation production safety levels.

Imagine a consumer financial services company with 20 million customers. Its sales and marketing organizations collaborate across product lines, deploying hundreds of marketing campaigns each quarter that aim to increase customer product usage and/or cross-buying of products. Each campaign is based on forecasts of customer responses derived from predictive models updated every quarter. The goals of these models are to achieve large return on investment ratios and to maximize contribution to local profit centers. What’s important is that their modeling is based only on data created, curated and maintained by these marketing organizations. The difference today is that the modeling is no longer based solely on a small number of response-determined variables that are constantly assessed in terms of importance. A quarterly campaign update generates hundreds of statistical models — involving campaign responses, purchase-lag time, the relative magnitude of the direct effect, and the cross-buying effects — using thousands of variables, including customer demographics, life stage, product transactions, household composition, and customer service history. It’s a network of models, not just a table of variable-by-residual importance values. But that’s only part of the story of data products. The predictive modeling utilized by these campaign plans is based on analytics and data preparation, which are data products in their most diminutive form. These products would be even more elementary were they not crafted quarterly by highly skilled, experienced modelers using advanced software and processes. Most companies have enough data to create models that contain not simply hundreds of variables, but thousands, so that the focus can return to information instead of data reduction. These models largely replace the internal econometric models previously used to produce advanced forecasts in the absence of campaign modeling. People used these forecasts to simulate ROI and contribution forecasts for the planned campaigns. In the old days, reliance on econometrically forecast ROI-guideline contribution values reduced the reliance on the marketing campaign modelers because of a lack of trust in their predictive ability.