WMS Redesign Consulting to Save Up to $170K in Annual Employee Time

About Our Client

The Client is a US provider of logistics and transportation services and solutions.

Custom Warehouse Management Solution (WMS) Needed Evolution

The Client had a custom on-premises solution for warehouse management. User apps for warehouse workers and supervisors were part of the WMS solution. The system was integrated with third-party software (e.g., a help desk platform). As the Client’s business grew, the solution’s scalability and performance became insufficient for the increased diversity of logistics operations and the number of new clients. For instance, warehouse workers would wait for up to 15 seconds for the next page of their app to load. According to the Client’s calculations, the compound wait time accounted for $170,000 of the workers’ compensation costs per year.

The Client’s internal IT team was gradually evolving the WMS. The goal was to upgrade the applications for warehouse workers and supervisors, then evolve the remaining modules and turn the solution into a subscription-based SaaS product by 2030. However, the process was inefficient due to the team’s commitment to other routine IT tasks, the absence of established project management workflows, and the gaps in practices for software testing. In addition, the defects in new WMS versions lead to an increased number of user support tickets and a low issue resolution rate.

The Client turned to ScienceSoft to perform an audit of its WMS and design a roadmap for a strategic system revamp. With a proven track record of IT solutions for logistics and transportation, ScienceSoft had the skills and experience to drive the Client’s initiative to success.

WMS Solution Audit and Review of Project Management Practices

ScienceSoft conducted multiple interviews and workshops with the Client’s upper management and representatives of the IT department to get a clear picture of their warehouse operations, processes related to WMS evolution project management, and user support workflows. We also examined the new solution architecture designed by the Client’s IT team.

As a result, our team provided the Client with a high-level plan that covered the milestones, cost and time estimates, and talents required to redesign the apps for warehouse workers and supervisors within 18 months, which was the deadline set by the Client. ScienceSoft also documented the inefficiencies and respective remediation measures across the following critical areas:

WMS architecture and infrastructure

ScienceSoft examined the software architecture and the on-premises IT infrastructure design developed by the Client’s IT team and concluded that it was solid enough to support the internal WMS and only needed minor fine-tuning. We provided the following recommendations:

• Adopt DevOps practices, starting with container orchestration. Our team suggested using Kubernetes, Rancher, and Helm to ensure zero downtime during deployment, easy rollbacks, and autoscaling.
• Duplicate the existing Microsoft SQL Server and ensure continuous data replication to account for the potential failure of the primary server.
• Transfer the business intelligence (BI), reporting, and testing workloads to the duplicate SQL server to decrease the burden on the main server.
• Implement monitoring tools (e.g., Zabbix Server, Uptime Kuma) to track the infrastructure performance metrics such as request rate, latency, error rate, connection rate, throughput, and connection counts.

Database performance optimization

ScienceSoft found that slow database stored procedures caused the WMS performance issues and 15-second page loading time. To determine the exact bottlenecks, the Client would need to monitor database performance logs and investigate each detected issue. To facilitate this process, ScienceSoft shared with the Client:

• Best practices for identifying slow stored procedures with a list of the most common issues (e.g., outdated table statistics, long-running transactions, sorting issues) and the optimal steps to mitigate each of them.
• Performance optimization measures to ensure high performance of the solution’s SaaS version (e.g., utilizing table partitioning and implementing memory-optimized tables for frequently accessed data).

Project management optimization

Our consultants provided instructions for the following project management areas:

• Scope management. ScienceSoft suggested creating a prioritized product backlog, and utilizing user stories to ensure efficient sprint planning.
• Change management. We designed a clear change request management workflow to avoid scope creep and ensure timely implementation of feasible changes.
• Resource allocation. In addition to sharing best practices for resource selection, ScienceSoft provided a list of roles the Client would need to cover the development needs within the next 18 months. The list included the description of new roles’ responsibilities and the estimation of their involvement duration.
• Stakeholder management. Our team recommended classification models for stakeholder grouping like power/influence and power/interest grids and shared sample stakeholder management tables.

QA process optimization

ScienceSoft recommended:

• Introducing load and stress testing into the QA pipeline to evaluate system performance under varying conditions.
• Integrating automated tests into the existing CI/CD pipelines for immediate feedback on code changes.
• Differentiating between software defects identified during production and the ones found by users for better resolution planning and accountability.
• Incorporating security and reliability testing into the testing framework.

Help desk optimization

ScienceSoft suggested:

• Implementing regular manual ticket review sessions to identify and clear out outdated or irrelevant tickets.
• Adding new ticket groups for better accountability and improved case resolution since many tickets related to new software features would pile up in the ‘Other’ category.

High-Level Consulting on Cloud Migration for the SaaS Version of the WMS

ScienceSoft colcluded that to satisfy the scalability needs of the solution’s future SaaS version, it will be necessary to migrate the system to the cloud.

Taking into account the tech stack of the Client’s current WMS architecture, ScienceSoft suggested Azure as the most suitable cloud platform for future SaaS WMS deployment. Our team provided the Client with cloud cost estimates for two potential pricing models: pay-as-you-go (on-demand instances) and yearly packages (reserved instances). The calculations proved that reserved instances would be 30% more cost-effective, given that the Client did not expect significant fluctuations in cloud usage patterns of the WMS SaaS.

Next, ScienceSoft’s engineers analyzed the available data intake and processing options for the SaaS and concluded that it would benefit from a robust message bus. Our team documented a detailed comparison of optimal message bus techs (e.g., NATS, Apache Kafka, NSQ, RabbitMQ) against the most vital parameters, including delivery and ordering guarantee, throughput, persistence, replayability, and scalability. Based on the analysis, we recommended Kafka as the best option.

We also provided the Client with instructions on ensuring security in the cloud, including setting up role-based user permissions and access and implementing multi-factor authentication using Azure Active Directory.

WMS Evolution Roadmap and Performance Enhancements with the Potential to Save $170K a Year

In just five business days, the Client received a full audit of the new WMS architecture. ScienceSoft provided the Client with recommendations on optimizing the architecture and delivered a detailed roadmap of evolving WMS apps for warehouse workers and supervisors. ScienceSoft instructed the Client on how to improve the performance of its on-premises WMS. The implemented performance enhancements can save the company up to $170,000 a year.

Our team also analyzed the Client’s project management, QA, and help desk workflows and provided recommendations for their improvement. In addition, the Client also received strategic consulting on WMS migration to the cloud for its further development as a SaaS.

Technologies and Tools

Microsoft SQL Server, Azure Cloud, Azure Active Directory, Node.js, Zabbix Server, Uptime Kuma NATS, Apache Kafka, NSQ, RabbitMQ.