An overview of the client

For consumer packaged goods (CPG) businesses, LogiTech is a mid-sized third-party logistics (3PL) supplier that specializes in distribution, warehousing, and transportation services. Established in 2005, the business serves more than 50 customers, including national and regional businesses, by running eight distribution centers and a fleet of 120 trucks throughout the United States. Before our involvement, LogiTech was expanding quickly but was having trouble with operational inefficiencies that were preventing company from becoming more profitable and expanding.

The difficulty

Midway through 2021, LogiTech came to us with a number of serious issues that were jeopardizing their capacity to continue growing and making money:

• Growing Operational Costs: Due to the sharp rise in labor, fuel, and warehouse expenses, already slim margins were being squeezed.
• Wasteful Use of Resources: There were a lot of empty backhauls with trucks operating at an average of 72% capacity.
• Manual Methods of Planning: Manual procedures and indigenous expertise were crucial to route planning and warehousing operations.
• Restricted View: There was no real-time visibility into shipping status and inventory levels for either LogiTech or its clients.
• Service Level Difficulties: The industry standard of 95% was surpassed by 86% on-time delivery rates.
• Expandability: The inability of current procedures to accommodate anticipated expansion without corresponding increases in personnel

Because LogiTech was in a tight spot, their clients were putting pressure on them for cheaper prices because of their own profit margin limitations, while also wanting better service levels and more visibility. This made the situation more difficult. LogiTech CEO David Chen says, "We were being squeezed from both sides." Our manual processes were crumbling under the weight of expansion, our clients demanded more for less, and our expenses were increasing. We were aware that we couldn't simply make small adjustments to our processes; we needed to completely overhaul them.

How We Proceed

Following a thorough analysis of LogiTech's business processes, technological setup, and competitive positioning, we created a comprehensive supply chain optimization plan that focuses on using automation, real-time visibility, and advanced analytics to boost productivity and service quality.

Phase 1 (three months): Data Foundation and Visibility

First, we implemented real-time supply chain visibility and built a strong data foundation:

• IoT implementation: installed IoT sensors in warehouses and GPS trackers in every car to monitor location and condition in real time.
• Data Integration Platform: To combine data from various systems (such as ERP, WMS, and TMS), a cloud-based data integration platform was put into place.
• Supply Chain Control Tower: Implemented a control tower system that makes inventory, shipments, and possible interruptions visible in real time.
• Customer Portal: Created a safe customer portal that allows clients to view their inventory and shipments in real time.
• Establishing procedures and instruments to guarantee data completeness and accuracy is known as the Data Quality Framework.

Teams had to manage a lot of change throughout this phase as they adjusted to new technologies and more data-driven procedures. "At first, there was some resistance," says Sarah Johnson, VP of operations at LogiTech. It was common for people to rely on their intuition and past experiences. However, as they realized that the data could help them avoid issues and make better decisions, they became fervent supporters.

Phase Two of the Supply Chain Control Tower: Advanced Analytics and Optimization (four months)

We put extensive analytics and optimization capabilities into place after establishing the data foundation:

• Route Optimization: AI-driven software for route optimization was implemented, taking into account a number of limitations (such as time limits, driver hours, and traffic patterns).
• Consolidating loads: optimized load consolidation and decreased empty miles with the implementation of algorithms
• Utilizing analytics, warehouse slotting optimization maximizes product placement in warehouses according to physical attributes, relationships, and velocity.
• Predictive maintenance was used to minimize breakdowns and improve maintenance schedule for the truck fleet.
• Demand Forecasting: To increase the accuracy of demand forecasting for improved resource planning, machine learning models were developed.

To make sure the models represented real-world limitations and business regulations, our data scientists and LogiTech's operational teams had to work closely together to execute these capabilities. "Achieving the ideal balance between mathematical optimization and operational practicality was crucial," says Michael Rodriguez, our project's Lead Data Scientist. "A theoretically optimal route isn't truly optimal if drivers find it too complex to execute or if it doesn't account for real-world conditions."

Phase 3: Three months of process automation and workflow optimization

Building upon the analytics framework, we automated processes to get rid of human labor and improve workflows:

• Automated scheduling that is constantly optimized in response to current circumstances and limitations is known as dynamic scheduling.
• Exception Management: Automated processes for detecting and resolving exceptions were implemented.
• All shipping documentation has been converted to digital format and is automatically generated, distributed, and archived.
• Warehouse Automation: To increase warehouse productivity, automated sorting and pick-to-light systems were implemented.
• Delivery confirmations, exceptions, and shipment updates are sent to clients automatically using proactive client communications.

This phase reduced errors and eliminated manual chores, which resulted in significant efficiency increases. Jennifer Martinez, Customer Service Director at LogiTech, says, "Previously, we had a team of 12 people just handling documentation and customer updates." "Now those processes are largely automated, and we've been able to reassign most of that team to higher-value customer support activities."

Phase 4: Innovation and Continuous Improvement (Ongoing)

We created a framework for ongoing innovation and development after establishing the fundamental optimization capabilities:

• Performance Analytics: Detailed KPI dashboards with drill-down capabilities were put into place for continuous performance tracking.
• Tools for scenario planning have been developed in order to assess the possible effects of changes (new clients, network adjustments, etc.).
• Sustainability Initiatives: Tools to quantify and improve logistics operations' environmental impact were put into place.
• Innovation Pipeline: a methodical procedure for assessing and putting novel methods and technology into practice
• Implementing collaborative planning procedures with important clients in order to find chances for mutual optimization

Impact and Outcomes

The supply chain optimization project has given LogiTech substantial, quantifiable advantages:

Numerous qualitative advantages have resulted from the transition, in addition to these headline metrics:

• Better Customer Service: Increased customer satisfaction and increased visibility have improved customer service and decreased attrition.
• Advanced capabilities have set LogiTech apart from its rivals and allowed for premium pricing for services with extra value.
• Scalable Growth: LogiTech has been able to raise revenue by 35% without corresponding increases in manpower thanks to optimized processes.
• Environmental Impact: By reducing empty miles and optimizing routes, carbon emissions have dropped by 28%.
• Employee satisfaction: Routine task automation has freed up staff members to concentrate on higher-value, more fulfilling activities.

"Our business has undergone a significant transition. We are now proactively streamlining our operations and providing outstanding service instead of battling with low margins and fighting fires. In addition to increasing our productivity, the visibility and analytics features have changed the way we interact with our clients. In addition to being a service provider, we are now regarded as a strategic partner. Most significantly, we have created a foundation for long-term growth that goes beyond merely increasing our workforce and resources."

— David Chen, CEO, LogiTech

Takeaways

The LogiTech optimization project produced a number of insightful findings that other supply chain and logistics companies might use:

• Start with Visibility: The cornerstone of any optimization endeavor is real-time visibility throughout the supply chain.
• Maintain equilibrium Efficiency and Reliability: In practical operations, the best solution isn't usually the one that is theoretically optimal.
• Pay Attention to Change Management: People's adoption of new work practices is just as important to supply chain optimization success as the technology itself.
• Incorporate Customer Views: By including clients in the optimization process, connections can be strengthened and mutual benefits can be found.
• Build for Continuous Improvement Supply chain optimization is a continuous capacity that needs to change with the business, not a one-time endeavor.

The LogiTech story, maybe most significantly, shows that mid-sized logistics companies can use cutting-edge technologies to effectively compete with larger competitors by being more responsive, agile, and client-specific optimization possibilities oriented.