a transparent, efficient, and automated SaaS contract allocation system‍‍

the cicero allocation system tackles inefficiencies in contract allocation for manufacturing enterprises by automating repetitive tasks, resolving delays, and ensuring equitable distribution.

enterprises expect improved efficiency in contract allocation because manual workflows cause delays and uneven workload distribution, by implementing automated processes and standardized allocation logic at the contract management stage.‍‍

our clients‍‍

traditional manufacturing enterprises seeking to optimize contract allocation processes, particularly departments that currently rely on manual handling to manage high-frequency contract assignments in their workflows.

our clients' goals‍‍

• manual contract allocation causes delays and inefficiencies, slowing down workflows.
• uneven workload distribution and lack of transparency in allocation processes lead to operational challenges and decreased trust.
• high operational costs and inefficiencies in order processing hinder competitiveness.
• offline data is untrackable and unpredictable, limiting scalability and long-term growth potential.

disclaimer: as a UX designer, my goal is to enhance efficiency and usability; however, successful implementation still relies on the holistic involvement of PMs to ensure alignment with business goals and operational needs.

current workflow‍‍

potential obstacles‍‍

the issues are primarily concentrated in the contract allocation stage, with the following specific challenges:

• manual contract refreshing every 15-20 minutes causes inefficiencies.
• allocation logic lacks transparency, leading to uneven workload distribution.
• urgent contracts disrupt workflow rhythm due to re-prioritization.
• manual notifications result in communication delays.
• difficulty matching tasks to specialists’ skills effectively.

we need to identify key statistics as core considerations for automation allocation logic to drive the transition from manual workflows to automation.

aiming to measure the success of the project, the following are the key metrics to evaluate its effectiveness:

• workload distribution fairness & transparency: percentage of tasks allocated evenly across breakdown specialists based on skills and availability.
• order processing efficiency: reduction in the average pre-order processing time (target: 50% reduction).every 15-20 minutes causes inefficiencies.
• automation Impact: increase in the number of contracts processed per unit of time (target: 10% improvement) and reduction in manual interventions.

the logic of the automation system builds upon the original manual allocation process, digitizing key considerations and subjective judgments previously based on experience, while removing reliance on manual intervention. below are the main process stages, with specific allocation rules adapted to different scenarios: common contract allocation logic, changed contract allocation logic, and urgent contract allocation logic.

the service blueprint aims to map the workflow changes introduced by the automation system and redefine the required datasets at each stage of the contract allocation process.

mid-fidelity prototype‍‍

the service blueprint aims to map the workflow changes introduced by the automation system and redefine the required datasets at each stage of the contract allocation process.

final design‍‍

Visualization of Allocation Factors

Clearly presents equipment-related and personnel-related parameters to ensure transparent allocation rules and reasoning.

AI-Assisted Optimization

Leverages historical data to recommend optimized parameter configurations, enabling scientifically fair adjustments to equipment and personnel assignments.