Service Time-Risk-Assignment-Knowledge Ensemble

Service operation has always been a conundrum for asset-intensive industries: while high uptime is critical to ensure return on assets, these industries often involve difficult and unpredictable circumstances. the growing human-level inefficiencies in-between decision-making systems and choice-making agents is how to draw on the knowledge of an accurate understanding of verifying service rendered that customer value, and would value, to gain marketplace advantages; and how to find patterns and constrain the learning so that leaders serve when they get a wider, without assuming only linear connections?

How would service organizations represent knowledge of on-chain and off-chain service verification for choice-set offered and rendered service rewards at each touch-point to measure decision-effectiveness? How would they automate this learning of their customers, employees, suppliers and then communicate back on what’s working versus what’s not working? How would they increase product lifetime by re-marketing equipment, re-pricing parts more dynamically, or service to overhaul and modernize a customer’s assets through hardware or software upgrades?

Service Time-Risk-Assignment-Knowledge Ensemble (STRAKE) is one-of-a-kind platform for next-generation service networks and operations, founded on knowledge reasoning and inferencing engine – a foundation model ensembles – that allows decision-makers organize signals of values and belief cues of choice-set for end-customers, based on Self-Organized Cognitive Algebraic Neural Network (SCANN®) and Dynamic Algebraic Causal Subsequence (DACS®), our AI/ML learning methods, that are designed to predict failures, densities and demand flow into the network; find causal relations, perform object and event detections, optimize values, speed, risks and scale of operations through throughput, resource capacity and assignment and build possibilities with choice-set at each phase in customers’ journey of experience.

What does Strake do?

Forecast Demand Flow and likelihood of density: Designed to manage network demand flows in consumer journeys that organize decisions such as pricing, promotion, part assortment, resource assignment, etc.

Optimize Multi-layered Multi-dimensional latent Variables for Options: Optimization of value, speed and scale through augmented approach to maximize yields, throughput, resource capacity and dynamic resource assignments

Knowledge Reasoning and Inferencing, AI/ML Enabled: This improves experience and accelerates service, augmenting human intellect in minimizing interventions for the company. Knowledge diffusion entropy is highly scalable, supporting a virtually unlimited number of customers without adding more support personnel. Promising use cases include keeping customers up-to-date on their use versus their plan allowance; automatically monitoring service quality and proactively reaching out to resolve detected issues (via self-service wherever possible)

Human Augmentation Seize The Opportunities: Multiple converging factors have made the case for AI/ML-based service engineering. Among the most important: increased customer acceptance of (and even preference for) machine-led AI “Service-Assist” interactions. So that behaviors become more understandable with the relentless expansion of data that are created new human augmenting platform

Every Company Becomes An Intelligent Company: Improve quality of decisions by setting choice-sets for end-customers that autonomously determine the optimal options for a set of decisions in a given business environment and business target, thus recommend consistent feasible choices for end customers. For AI/ML-based system to work autonomously, they need a reasoning and inferencing in their flexible tech architecture. Core components of this architecture include ‘forward-backward’ learning of segments, contexts and market conditions, even in uncertainty

Central pattern generation in diffusion are constructed to de-noise knowledge, reasoning and inferencing needs of customers and for employees to derive causality of any event, interpret inferences, predict consequences and find optimal choice-sets.

Workflows are orchestrated to cleanly segment dimensions, latent features and attributes and phases in the customers’ choices so that multiple assets or services that sharing a degree of similarity, are enabled for scale advantages. This multi-dimensional structure reflects the preferences, features, capabilities, etc. to deliver the proactive, personalized service that customers want, when and how they want it – sometimes even before they know they want it – so that additional production converts into additional sales. Companies bring together capabilities from multiple groups (e.g., digital, manufacturing, operations, etc.) to dramatically expand the available predictivity in reasoning and explainability in inferences to solve a large and diverse range of downtime root causes. Building such digital service operations is not a goal, it’s an enabler of productivity, experience, resilience – primary decision effectiveness that are measured.

Building a digital (AI) “Service-Assist” for after-market operation has at least three major benefits:

First, and most directly, it provides end users with a better interpretability of causalities, infer-ability of reasoning, proactivity in choice-setting and explainability of choice-set. The STRAKE is designed to reduce cycle of innovation and increase revenue

Second, the STRAKE helps maintain relationships with customers and partner networks. This enables the company to find knowledge reasoning, inferences and diffusion to de-risk the services with ground data, capabilities and functional models for risk exposures

Finally, STRAKE pleases decision-makers and ground-staffs to create options for end-customers to determine choices that improve reward functions minimize costs and neutralize risk exposures. The team only focus on operating service functions