A computer-implemented system, platform, computer program product, and/or method for optimizing a data analytics suspicious activity detection pipeline that includes identifying a data analytics suspicious activity detection (SAD) pipeline for optimization; inputting desired key performance indicators for the data analytics suspicious activity detection (SAD) pipeline; gathering key performance indicators from previous runs of the data analytics suspicious activity detection (SAD) pipeline; identifying candidate pipeline configurations for simulation runs; running simulations of the candidate pipeline configurations; analyzing the simulations of the candidate pipeline configurations; and identifying the optimal pipeline configuration for the desired key performance indicators.

The present disclosure is directed, in part, to improving existing technologies by selecting a configuration for an asset array based on features of both the assets and a loading area. In order to select the configuration, a plurality of configurations may be generated and scored. The scores may be based on any number of factors, such as the number of return trips required, the time required to deliver all packages, or another aspect of asset loading and/or delivery. Based on the scores, a particular configuration is selected for the asset array. Instructions are provided for loading the vehicle in accordance with the set of loading area arrangements associated with the selected configuration, such as step-by-step instructions for loading each asset in a particular position and orientation may be provided to a computing device.

A method for improving performance of a computer procurement application includes using the procurement control system computer, determining a peer group associated with a first entity; using a procurement control system computer, obtaining, from client computers respectively associated with the entities, configurations that instruct a procurement application how to perform procurement tasks for the respective entities of the entities in the peer group; using a procurement control system computer, obtaining, from a first client computer associated with the first entity, a first configuration that instruct the procurement application how to perform procurement tasks for the first entity; obtaining a desired end result from the first entity; using the procurement control system computer, using a machine learning algorithm, determining configurations of the peer groups that have a causal relationship to the desired end result; providing a description of the configuration value to the client computer associated with the first entity.

The present disclosure relates to systems and methods that provide recommendations to service owners on what actions to take to modify a service of the service owners. The systems and methods analyze the service owner’s workload and telemetry from the services worked on by the service owners. The systems and methods provide recommendations with actions to take to modify the service based on a predicted outcome of the recommendations.

Systems for optimizing a delivery process according to various aspects of the present technology may comprise a user device, a ranking engine, a delivery driver management platform communicatively linked to the ranking engine, a database, a server, and a display. The user device may be configured to receive customer data and delivery driver data. The ranking engine may be configured to compute a plurality of rank scores according to the customer data and the delivery driver data. The ranking engine may also be configured to generate a plurality of customer profiles and a plurality of delivery driver profiles according to the customer data and the delivery driver data, respectively. The ranking engine may be further configured to store the plurality of delivery driver profiles and the plurality of rank scores in the database. The server may be communicatively linked to the database over a communication network, wherein the server may be provided with access to the plurality of customer profiles, the plurality of delivery driver profiles, and the plurality of rank scores. The display may be configured to present the plurality of delivery driver profiles according to the plurality of rank scores.

Disclosed herein are systems and methods for contractor analysis. A system receives contractor information associated with a contractor. The contractor information includes financial information about the contractor and business information about the contractor. The system generates a contractor quality score based on the contractor information. The system establishes an account credit offer based on the contractor information, and adjusts the account credit offer based on the contractor quality score.

Most of the business intelligence and analytics applications uses a data model. Any change in the source field or in the key performance indicator (KPI) calculation changes result in long turn-around time and complex changes in the background coding of the data model. A method and system for handling the source field change and the key performance indicator (KPI) calculation change in the data model has been provided. The disclosure provides a data modelling design, in particular, for handling source field changes or additions and target KPI calculation changes without any impact on the data model. The solution section is divided in two areas so as to tackle the technical problem statement points. First part is the data ingestion and second is data reporting.

Techniques for benchmarking performance in a contact center system are disclosed. In one particular embodiment, the techniques may be realized as a method for benchmarking contact center system performance comprising cycling, by at least one computer processor configured to perform contact center operations, between a first contact-agent pairing strategy and a second contact-agent pairing strategy for pairing contacts with agents in the contact center system; determining an agent-utilization bias in the first contact-agent pairing strategy comprising a difference between a first agent utilization of the first contact-agent pairing strategy and a balanced agent utilization; and determining a relative performance of the second contact-agent pairing strategy compared to the first contact-agent pairing strategy based on the agent-utilization bias in the first contact-agent pairing strategy.

A system and method may be used to indicate change to a key performance indicator (KPI). The method may include receiving data regarding operation of an enterprise, generating the KPI based on the data, assessing a rate of change of the KPI, and initiating notification of a user regarding the rate of change of the KPI. The method may further include modeling the KPI to obtain an ordinary rate of change of the KPI, or a KPI element incorporated into the KPI, over time, comparing the rate of change with the ordinary rate of change, and, based on a difference between the rate of change and the ordinary rate of change, determining that the notification is to be initiated. The method may further include determining whether to notify a user of the change to the KPI based on materiality of the KPI to the user.

Methods and systems for controlling production resources in a supply chain are described. The system automatically generates predicted supply chain operational metrics across a nodes of a supply chain. The system automatically infers causal factors that impact the predicted supply chain operational metrics. The causal factors include a change to a utilization of the production resource. The system communicates a user interface including production runs being scheduled on the production resource including a user interface element representing the scheduling of the production run associated with a value at risk. The system receives input causing a change to the utilization of the production resource. The change to the utilization of the production resource impacts the predicted supply chain operational metrics including the value at risk associated with the scheduling of the production run.