With increasingly volatile market conditions and rapid product innovations, operational decision-making for large-scale systems entails solving thousands of problems with limited data. Data aggregation is proposed to combine the data across problems to improve the decisions obtained by solving those problems individually. We propose a novel cluster-based Shrunken-SAA approach that can exploit the cluster structure among problems when implementing the data aggregation approaches. We prove that, as the number of problems grows, leveraging the given cluster structure among problems yields additional benefits over the data aggregation approaches that neglect such structure. When the cluster structure is unknown, we show that unveiling the cluster structure, even at the cost of a few data points, can be beneficial, especially when the distance between clusters of problems is substantial. Our proposed approach can be extended to general cost functions under mild conditions. When the number of problems gets large, the optimality gap of our proposed approach decreases exponentially in the distance between the clusters. We explore the performance of the proposed approach through the application of managing newsvendor systems via numerical experiments. We investigate the impacts of distance metrics between problem instances on the performance of the cluster-based Shrunken-SAA approach with synthetic data. We further validate our proposed approach with real data and highlight the advantages of cluster-based data aggregation, especially in the small-data large-scale regime, compared to the existing approaches.

This paper presents a data-driven approach to mitigate the effects of air pollution from industrial plants on nearby cities by linking operational decisions with weather conditions. Our method combines predictive and prescriptive machine learning models to forecast short-term wind speed and direction and recommend operational decisions to reduce or pause the industrial plant's production. We exhibit several trade-offs between reducing environmental impact and maintaining production activities. The predictive component of our framework employs various machine learning models, such as gradient-boosted tree-based models and ensemble methods, for time series forecasting. The prescriptive component utilizes interpretable optimal policy trees to propose multiple trade-offs, such as reducing dangerous emissions by 33-47% and unnecessary costs by 40-63%. Our deployed models significantly reduced forecasting errors, with a range of 38-52% for less than 12-hour lead time and 14-46% for 12 to 48-hour lead time compared to official weather forecasts. We have successfully implemented the predictive component at the OCP Safi site, which is Morocco's largest chemical industrial plant, and are currently in the process of deploying the prescriptive component. Our framework enables sustainable industrial development by eliminating the pollution-industrial activity trade-off through data-driven weather-based operational decisions, significantly enhancing factory optimization and sustainability. This modernizes factory planning and resource allocation while maintaining environmental compliance. The predictive component has boosted production efficiency, leading to cost savings and reduced environmental impact by minimizing air pollution.

In the 1990s, business process reengineering was all the rage: Companies used budding technologies such as enterprise resource planning (ERP) systems and the internet to enact radical changes to broad, end-to-end business processes. Buoyed by reengineering's academic and consulting proponents, companies anticipated transformative changes to broad processes like order-to-cash and conception to commercialization of new products. But while technology did bring major updates, implementations often failed to live up to the sky-high expectations. For example, large-scale ERP systems like SAP or Oracle provided a useful IT backbone to exchange data, yet also created very rigid processes that were hard to change past the IT implementation. Since then, process management typically involved only incremental change to local processes -- Lean and Six Sigma for repetitive processes, and Agile Lean Startup methods for development -- all without any assistance from technology.

Every day, small and large companies alike make strategic and operational decisions that influences the bottom line. Strategic decisions are typically made by the C-suite, and these generally are one-off decisions that are made over time and only after careful study of curated information from several sources and consultation with experts. Examples of strategic decisions include mergers and acquisitions and large capital expenditures. Operational decisions on the other hand are made every day by workers and operations personnel. For small organizations, this can mean dozens, if not hundreds of decisions every day.

Eugenie was incepted with an intent to solve two of the biggest problems the process industry faces today. First was the democratization of data-driven insights to drive complex operational decisions with a bottom-line impact. Second, to build an eco-system that leverages both human and machine intelligence to improve reliability, efficiency, and sustainability of assets as well as processes. Eugenie's unique decision intelligence and execution platform enables enterprises to make efficient and optimal operational decisions about their assets and processes. The company addresses issues related to anomalies in operations such as unscheduled downtime detection, production quality issue detections, process-deviation detection, etc., using its descriptive, prescriptive, and predictive analytics products.

Artificial Intelligence (AI) and Machine Learning have enormous potential to transform businesses and disrupt entire industry sectors. However, companies wishing to integrate algorithmic decisions into their face multiple challenges: They have to identify use-cases in which artificial intelligence can create value, as well as decisions that can be supported or executed automatically. Furthermore, the organization will need to be transformed to be able to integrate AI based systems into their human work-force. Furthermore, the more technical aspects of the underlying machine learning model have to be discussed in terms of how they impact the various units of a business: Where do the relevant data come from, which constraints have to be considered, how is the quality of the data and the prediction evaluated? The Enterprise AI canvas is designed to bring Data Scientist and business expert together to discuss and define all relevant aspects which need to be clarified in order to integrate AI based systems into a digital enterprise. It consists of two parts where part one focuses on the business view and organizational aspects, whereas part two focuses on the underlying machine learning model and the data it uses.

In the first part we discussed the importance of making decisions the front-line focus of day-to-day operations in organizations. In this post we look at the high-level steps required to achieve this outcome. We also examine the advantages of a decision-centric approach once it is adopted within an organization. Every day, businesses make operational decisions. Not a day goes by without hundreds or even thousands of business decisions being made. Adopting a decision-centric approach changes the focal point from processes, applications and systems to pragmatic business outcomes.

John Rymer and Mike Gualtieri of Forrester Research have just published a new piece of research – The Dawn Of Digital Decisioning: New Software Automates Immediate Insight-To-Action Cycles Crucial For Digital Business. This is a great paper – not only does it mention some Decision Management Solutions' clients as examples, it makes some great points about the power of Decision Management and some great recommendations about how best to approach Digital Decisioning. In particular I liked the paper's emphasis on keeping business rules and analytics/ML/AI integrated and its reminder to focus first on decisions (especially operational decisions) and not analytic insights. These are key elements in our DecisionsFirst methodology and platform and have proven themselves repeatedly in customer projects – including those mentioned in the report. Our DecisionsFirst approach begins by discovering and modeling these operational decisions, then automating them as decision services and finally, as also noted in the report, creating a learn and improve feedback loop.

OpsVeda today announced that it will participate at SAPPHIRE NOW and ASUG Annual Conference being held May 16–18 in Orlando, Florida. The company will be showcasing its platform that leverages machine learning techniques for proactive detection and remediation of potential operational disruptions. OpsVeda presents the full stack real-time operational intelligence platform, to power automated operational decision making in the enterprise. It helps the operations team to re-capture an estimated 10-20% of the revenue and margin leakage due to out of stocks, chargebacks, changes in customer buying behavior, expedites, missed deliveries and inventory obsolescence. Many of these issues go undetected until it is too late for any corrective action.

Today we hear more and more about Artificial Intelligence and the increasing role of robots in industry and society. I hear many concerns about the safety of these systems, but I also hear worries about jobs and decision-making. Algorithms are getting better and better are solving quite complex problems. So, in the future, who will make decisions, human or machine? And if an algorithm is making the decision, what happens to our jobs?