Here we share our unique perspective with the world. All things data, process optimization and AI, but always from the perspective of the employee and customer experience.
OTIF Scoring: Putting it all together
Combining a Unified Data Platform, Robotic Process Automation (RPA), and Artificial Intelligence (AI) to improve On-Time, In-Full (OTIF) scoring can be seen in the case of a company that used a bot-building platform to overcome challenges related to data consolidation across production, inventory, and sales.
Overview of concepts and industry jargon.
An Integrated Data Platform is a comprehensive solution that combines various data sources and tools into a single unified system. It enables businesses to efficiently manage, analyze, and derive insights from their data. By integrating different data sets, such as structured and unstructured data, it provides a holistic view for informed decision-making.
A Semantic Model is a representation of data that captures the meaning and relationships between different elements. It provides a structured framework for organizing and understanding data, enabling more effective analysis and interpretation.
Information Architecture is the practice of structuring and organizing information to make it easily accessible, intuitive, and user-friendly. It involves designing navigation systems, categorizing content, and creating clear hierarchies to optimize the way users interact with information and navigate digital interfaces.
Process discovery is a systematic approach to understanding and documenting existing workflows within an organization. It involves analyzing data, interviewing stakeholders, and mapping out activities to identify inefficiencies, bottlenecks, and opportunities for improvement in order to streamline operations and enhance productivity.
Process mining is a data-driven approach that extracts knowledge from event logs to visualize and analyze real-time processes within an organization. It enables identifying inefficiencies, bottlenecks, and opportunities for optimization to enhance operational performance and drive continuous improvement.
Task mining is a data-driven approach that captures user interactions and analyzes them to understand how people perform tasks on their digital devices. It enables organizations to identify inefficiencies, streamline processes, and improve user experiences through actionable insights gained from the analysis of task-level data.
Augmented automation combines artificial intelligence and advanced automation technologies to enhance and optimize business processes. It leverages AI algorithms, robotics, and machine learning to streamline operations, increase efficiency, and improve decision-making, ultimately driving digital transformation and organizational growth.
Applied AI refers to the practical implementation of artificial intelligence technologies in real-world scenarios, where algorithms and machine learning models are used to solve specific problems and enhance decision-making processes across various industries and sectors.
Generative AI is an advanced technology that uses machine learning models, such as Language Models (LLM), to create original content, such as text, images, or even music, by analyzing and learning patterns from existing data. It enables the generation of new and creative outputs based on the learned knowledge.
Data and AI governance refers to the framework and practices that ensure responsible, ethical, and secure management of data and artificial intelligence technologies. It involves creating policies, procedures, and safeguards to protect privacy, mitigate bias, ensure transparency, and promote accountability in data-driven decision-making.
Responsible AI entails the ethical development and deployment of artificial intelligence systems, prioritizing transparency, fairness, accountability, and privacy. It involves mitigating bias, ensuring safety, and promoting the positive impact of AI on society.