Southern States Material Handling (SSMH) hits 90% data accuracy and 85% better visibility with Microsoft Fabric and Kanerika

Southern States Material Handling (SSMH) is the dealer that sells, rents, and services Toyota and Raymond forklifts and other warehouse equipment. The company specializes in equipment sales, leasing, servicing, and fleet management, supporting a large network of service centers and warehouses that keep operations running smoothly for clients nationwide. Its business includes service management, parts inventory, fleet operations, and equipment sales and rentals. Each function plays a key role in maintaining efficiency and meeting customer needs in a competitive market.

SSMH’s operational challenges stemmed from a fragmented data environment. The company stored information in multiple locations, primarily SQL Server and SharePoint, but these systems didn't communicate effectively with each other. 

This fragmentation created silos that prevented managers from accessing the information when needed. The existing grid-based reporting system provided raw data but lacked the visual clarity and analytical depth required for strategic planning. Managers struggled to track resource utilization rates, project profitability, and service delivery timelines across departments. They couldn't identify workflow bottlenecks, spot underperforming branches, or allocate resources based on real demand. Additionally, leadership couldn't trust the reports because of inconsistent data quality, from manual entry errors and lack of standardization, and skewed KPI calculations, making it impossible to distinguish genuine operational issues from data problems.

The Push towards integrated analytics

Without a structured data warehouse, SSMH had no way to standardize, cleanse, or validate information before it reached decision makers. This meant that key performance indicators often reflected data anomalies rather than actual operational performance. The problem extended across multiple areas of the business.

Service managers lacked visibility into workforce efficiency and service backlogs. Parts managers faced similar challenges with inventory optimization, having limited insight into turnover rates, aging stock, or distribution efficiency across branches, consignment lockers, and service vans. Fleet operations suffered from inadequate tracking mechanisms, and warranty tracking presented another significant gap without a structured system for distinguishing between warranty-covered repairs and non-warranty work.

Real-time decision making was impossible when reports took days to generate, and by the time data reached managers, circumstances often changed.

Establishing a unified data foundation with Microsoft Fabric

SSMH partnered with Kanerika to build a modern data foundation that could support its operational needs. Kanerika, a premier data analytics and AI solutions provider, brought deep expertise in Microsoft technologies and enterprise data transformation to the engagement.

They implemented Microsoft Fabric as the foundation for comprehensive data transformation. Microsoft Fabric provided the integrated analytics platform SSMH needed to bring together data from disparate sources and create a single source of truth for the organization.

The solution was implemented using Microsoft Fabric, with OneLake serving as the centralized and governed data foundation. Data ingestion into OneLake was orchestrated through Azure Data Factory components, including pipelines and dataflows, enabling seamless integration from multiple source systems such as Azure SQL Database, SharePoint, and existing semantic models.

The initial data load comprised approximately 1 TB of historical operational and accounting data, followed by incremental ingestion on an ongoing basis. A master orchestration pipeline was designed to run every four hours, fetching updated and newly generated data from source systems to ensure the timely availability of critical business data. All ingested data was stored in the Lakehouse in delta format, providing scalability, optimized query performance, and ACID compliance. Data transformation and business-rule implementation were carried out using Fabric Notebooks with PySpark and Scala-driven logic to cleanse, normalize, harmonize across sources, and aggregate data. 

The transformed and curated datasets were subsequently surfaced via optimized semantic models, ensuring a consistent, high-performance analytics layer for enterprise reporting and decision-making. SSMH could finally trust that the metrics they were seeing reflected actual operational performance rather than data artifacts.

Creating a comprehensive reporting framework with Power BI

With the data infrastructure in place, Kanerika developed a comprehensive reporting framework using Power BI that addressed the specific needs of different roles and functions within SSMH. The approach followed a 1:3:10 methodology, creating one executive dashboard, three managerial scorecards, and ten detailed operational reports across key business areas. 

The framework included customized scorecards for Parts Managers, Service Managers, and Branch Operations Managers. Each scorecard focused on the KPIs most relevant to that role, with drill-through capabilities that allowed managers to dig deeper into specific work items, technician productivity, and resource utilization. 

Parts Inventory Management received particular attention. The new reports covered inventory turns at multiple levels, including branches, consignment lockers, and service vans, tracking both volume and value metrics. Moreover, the Parts Aging Report provided insights based on sales and internal usage patterns, helping managers identify slow-moving stock and make informed decisions about inventory optimization. 

Work-In-Progress (WIP) tracking transformed service operations. WIP Aging Reports identified blockers in the service pipeline, making it visible where jobs were getting stuck and why. Warranty Summary Reports broke down repairs by warranty status, distinguishing between covered and non-covered work to improve cost control. 

One particularly valuable addition was the Technician Productivity Reports. These tracked not just basic productivity metrics but also highlighted technicians who identified additional repair needs during scheduled maintenance work. orders. This proactive service identification represented significant value for customers while creating additional revenue opportunities.

Similarly, Service Coordinators now benefit from the Awaiting Work tool. It helps planning by clearly showing Scheduled Maintenance waiting for assignment and new work orders waiting for technician availability. Furthermore, it highlights units under repair with upcoming Scheduled Maintenance, allowing both tasks to be done together. This cuts customer downtime and improves SSMH results by finishing maintenance during the repair visit. 

Additionally, Financial Reporting received equal attention with comprehensive Accounting Reports, including Balance Sheets, Profit & Loss Statements, and Income Statements. All these reports were built in Power BI, taking full advantage of its visualization capabilities. The shift from static grid-based reports to dynamic, visually enriched dashboards made a huge difference in how quickly managers could absorb information and act.

Real-time insights drive operational excellence

The implementation of Microsoft Fabric and Power BI fundamentally changed how SSMH operates. The shift from batch reporting to near real-time updates dramatically improved decision-making efficiency. Managers no longer had to wait hours or days for reports to run. They could access current information whenever they needed it, enabling rapid response to emerging issues. 

Service managers gained a structured, data-backed approach to tracking work-in-progress, scheduled maintenance, and resource allocation. The drill-through capabilities provided granular visibility into technician productivity and work item status. Parts managers experienced similar improvements with real-time insights into inventory turnover, aging, and cycle counts, helping reduce excess stock while ensuring availability for critical repairs. 

Improvements in data accuracy have had ripple effects throughout the organization. When managers and leadership could trust the numbers they were seeing, they made better decisions. The comprehensive data cleansing and validation process eliminated the distorted figures that had previously undermined confidence in reporting.

A platform built for growth

The move from scattered reports to a single analytics setup marked a major shift in how SSMH works. It also sparked a new focus on data-based decisions. Managers who once leaned on instinct now had clear facts to guide them. This boosted trust in reporting and led to 85% higher operational visibility, giving teams a clearer view of service, parts, and fleet activity than ever before. With this new clarity, managers could confirm what they suspected and spot issues they may have missed before. 

The new data structure also raised the accuracy of key measures. After cleanup and checks were put in place, SSMH saw 90% better data accuracy and KPI reliability, enabling leadership to rely on the numbers with confidence. The solution on Microsoft Fabric is built for the long run. It can bring in more data sources and support more teams as the company grows. As SSMH adds new reporting needs, the platform grows with them. This setup allows 100% scalability and support, making it easy to add new reports, adjust current ones, or bring in fresh data whenever the business needs it.

• Azure Data Factory now handles data loads much faster than the old process, so teams get updated info with less delay.

• Fabric Spark helped cut down the time spent on heavy data tasks, making it easier to process large files and run complex jobs without long waits. 

• With Azure Data Factory in place, data loads are about 25% faster, helping teams access near real-time operational insights.

• Fabric Spark helped reduce data transformation time from weeks to minutes in some workflows, making large-scale processing much faster.

“The ability to bring in many data sources and shape a strong analytics setup will be a game changer for SSMH. Kanerika’s flexibility in matching Microsoft Fabric with our goals ensures we are building a system that will lead to even better results across our operations.” says Delano Gordon, CIO at SSMH.