Agenda

Additive manufacturing aka 3D Printing is fast becoming a viable option for final part manufacturing as material choices grow along with advancements in core printing technology. One of the key challenges is the ability to produce high quality parts with repeatability and to address this challenge there is a need for an automated part quality monitoring and prediction system.

In this session we present a system we developed that is designed to aid process engineers from designing a process, conducting Design Of Experiments(DOE), verifying the process, to deploying and monitoring the process in real/near real time.

Real/near real time monitoring of a process is based on the concept of a process digital twin. The process digital twin is a collection of machine learning models (supervised and unsupervised) working in unison to detect anomalies and generate alerts during the manufacturing process. Each of these models that make up the digital twin are developed based on the data generated (process parameters, telemetry and metrology) during the DOE and process verification phases and may be targeted at a subsystem. The models are continuously updated based on new data collected during the production phases. The system has been deployed internally for testing purposes.

Sales forecasting is a key process in defining a realistic revenue target for public and private companies and it’s a backbone of short term and long-term strategic planning. Despite the rapid growth of AI application across many business processes, Sales forecasting to a large extent has still been driven by human intelligence, a time-consuming effort with high likelihood of human error and significant inaccuracy. Many prior efforts in developing large-scale sales forecasting engines have not been successful mainly due to the lack of clear definition on what machines can tackle vs human. This presentation demonstrates the strategy and process that led to the development of a large scale AI driven sales forecasting engine in practice that impacted many business processes including Revenue Recognition, Commercial Planning, Product Marketing, Supply Chain, and Strategic Planning. 

It takes 10-13 years to design, manufacture and deliver a new aerospace product, which can inhibit growth for the industry as a whole. With a strained global supply chain, it’s more important than ever to make design and manufacturing processes more efficient in order to keep pace with forecasted demand in commercial travel. In this session, Joakim Soederberg, Head of Data at Acubed, the Silicon Valley Innovation Center of Airbus, will discuss how to apply model-based engineering and digital technologies to manufacturing processes in order to  reduce lead times, production costs and improve workflows dynamically.

Long short term memory neural networks (LSTMs) originally rose to prominence in natural language processing, but have also shown value in time series. One drawback of LSTMs, and neural networks more generally, is that it can be difficult to derive effective confidence intervals. Two primary methods for calculating confidence intervals in LSTMs have been proposed: dropout-based simulation and estimating probability distributions.

In this case study we look at dropout-based simulation, which we found to be effective and more flexible than distributional estimation. However, added flexibility came at the cost of elevated computational burden.

Our latest research shows most models fail to deploy. Machine learning's operationalization -- the model's change to existing processes in order to improve them -- takes a lot more planning, socialization, and change-management efforts than most data scientists ever begin to realize. The problem is more in leadership than in technology; no technical solution such as MLops addresses the fundamental root of the problem. Without deployment, ML does not achieve value.

This industrywide crisis stems from a lack of proper ML leadership. The great potential of ML is intact -- the value proposition is solid and the core tech, research, and analytical results are legit. And it isn't a flop -- many ML projects succeed, even if only a minority. In this talk, Machine Learning Week founder and bestselling author of "Predictive Analytics" Eric Siegel will outline the required ML leadership practice. It ain't rocket science, but it's rarely well understood and thoroughly executed.

Value chains have been evaluated for decades. In this era of digital transformation, understanding the Data Science Value Chain is critical, but seldom is it examined as a system, nor re its component parts subject to systematic study. In this session, it will be shown how ML exists as a component within the value chain along with data acquisition, cleansing, formatting and accessibility. A conceptual case study aggregated from several non-specific sources shows the path from good data to the benefits of ML over traditional methods such as Designed Experiments. An overview of the 4.0 culture is integrated for a broader view of the benefit of ML within the value chain. Digital transformation's effects on the value chain are also integrated within the 4.0 culture. This presentation will highlight the myth that "everything data belongs to IT" by showing management and non-IT professionals the need for more knowledge about the data science value chain and where they fit within its constructs. Proposing a collaborative activity among the non-IT parts of the organization and an analytics maturity model expands where and how ML benefits decision making throughout the organization. The collaborative process also enhances communication of ML results to assist management in seeing beyond IT as a sole resource.

Water is fundamental to an effective society.  Individuals and organizations throughout society, including drinking water and wastewater systems, possess multifaceted relationships to water. Broadly, society seeks safe and abundant supplies of water while avoiding modern challenges of aging infrastructure, emerging contaminants, cybersecurity threats, legal compliance, customer satisfaction, climate change, ESG commitments, among other matters.  Machine learning (ML) applications continue to offer meaningful solutions for drinking water and wastewater systems and other organizations with a relationship to water.  The panel articulates the state of ML and its future implications in meaningfully addressing the challenges facing the water sector.

This case study presents how the biggest building material company worldwide increased sales and reduced inventory by predicting 80% of its stockouts a week ahead using ML. The Company faced high stockout levels in Brazil due to limited warehouse space against high safety stock requirements affected by COVID. No historic data would explain the new pandemic-influenced demand pattern. The Company gained operational flexibility by creating a reaction process to mitigate out-of-stock risks using stockout’s forecasts in a pragmatic, simple to run and highly comprehensible approach.A scalable data structure and pipeline was set up to give full operational visibility of the previous two years, such as daily stock levels, transit stocks, production plans, schedules, demand forecasts/actuals, committed order books, stockouts history and more. The team trained a gradient-boosting algorithm that uses tree-based learning for the supervised problem of classifying whether there will be a following week stockout for a given warehouse-product combination. To have consistent results across regions and products the algorithm was trained with different time horizons, comparing accuracy and identifying new variables to explain deviations. After two months, the initiative released an 80% accuracy algorithm and F1 over 0.7 for top 5 warehouses in volume and 87 products.

The rate of adoption for AutoML and MLOps solutions is incredible. Despite all of the great work being done to operationalize ML across industry there are two areas which still require custom work: feature engineering and product integration. The AutoML we run is only as good as the data it has to learn from. We'll be discussing a Spark based approach to automating the feature engineering portion of any MLOps solution. The result is an abstracted, extensible solution for the feature engineering portion of your AutoML or MLOps solution.

Ask our “Rockstars” anything about predictive analytics! Curious about machine learning tips, tricks, best practices and more? This is your opportunity to hear advice directly from the experts. The hardest part of machine learning can be getting models deployed into production. Our panel has done it, and is willing to spill the tea on how to make it happen. You’ll want to stick around for this ultimate session of the conference.