Transportation and logistics companies have long used analytics and machine learning to optimize routes, forecast demand, manage fleet assets, and improve warehouse operations. The next opportunity is to connect activities that have traditionally been planned and analyzed separately. 

For finance leaders, this matters because an operational change rarely affects only one part of the business. A shift in customer demand can simultaneously affect routes, warehouse activity, equipment utilization, and labor requirements; with consequences for costs, capacity, service levels, and margins. 

The planning challenge is therefore not simply forecasting an individual variable. It is understanding how multiple operational and financial variables interact, particularly when business conditions change at the same time. 

For third-party logistics providers, connected planning can bring these relationships into the financial model. By combining information from across the business, finance can model changing conditions, assess their financial impact, and evaluate potential responses. AI-enabled decision intelligence can extend this analysis by identifying gaps and presenting options for management to consider. 

Connect operational and financial planning 

Finance teams often draw planning information from multiple systems, including asset-tracking platforms, transportation management systems, warehouse management systems, and financial reporting systems. When this information remains separated, it can be difficult to see how a change in one area affects the rest of the business. 

Predictive maintenance provides one example. Maintenance requirements may be analyzed independently, but they are also affected by changes in routes, customer demand, and asset utilization. Similarly, changes in warehouse activity can alter labor and transportation requirements. 

Bringing these relationships into the planning process allows operational changes to become inputs to financial forecasts and scenarios. Finance can then analyze how changes across the business may affect costs, margins, and the broader financial plan. 

This represents an important shift in planning. Operational assumptions are no longer treated only as background information. They become measurable drivers of financial performance. 

Bring 3PL operating drivers into the financial model 

For 3PL finance teams, connected planning begins with the operating drivers behind revenue, cost, and capacity. Depending on the business, these drivers may include: 

• • Transportation: Miles driven, loads, number of trucks, fuel rates, and cost per mile 

• • Warehouse operations: Occupancy, pallet throughput, and labor productivity 

• • Workforce: Labor hours, overtime, contract labor, and labor costs 
  • Customers and services: Customer volume, storage and handling rates, product or service mix, and line of business 

Adaptive from Workday can bring these measures together with data from the general ledger, HR or human capital management systems, WMS, TMS, and other source systems. Finance can build forecasts around operational assumptions and analyze results by dimensions such as customer, product, or line of business. 

In one scenario modeled in Adaptive by Workday, a warehouse customer’s throughput increased by 25%, enabling finance to assess the resulting impact on labor requirements and profitability. By adjusting the underlying drivers, finance could evaluate whether the additional business remained attractive at the existing price. 

The broader insight is that growth does not automatically translate into greater profitability. Additional volume may also create new labor, capacity, and service requirements. Driver-based planning helps finance evaluate those downstream effects before management decides how to respond. 

Use AI to evaluate scenarios and potential responses 

Planning becomes more complex when several operating conditions change at once. An increase in customer volume, for example, can affect labor requirements, capacity, service demands and margin simultaneously. 

In another Adaptive by Workday scenario, a fourth-quarter volume spike for an e-commerce customer coincided with constrained labor availability. The customer required labor-intensive services, including kitting, case picking, and shrink wrapping. The model incorporated the additional work hours and labor requirements, along with the potential financial impact and the risk to service-level commitments. 

AI-enabled decision intelligence can extend this type of analysis by identifying gaps and presenting potential responses for management to evaluate. Depending on the scenario, finance could explore questions such as: 

• • What happens to margin if labor costs or throughput change? 

• • Could pricing changes offset higher costs? 

• • What is the financial impact of protecting a customer service level? 

• • How would shifting carriers affect the scenario? 
  • What would happen if the business moved to another warehouse? 

The value of this analysis is not limited to producing another forecast. It can help finance bring the operational and financial tradeoffs of different responses into the conversation while management is considering its options. 

AI does not eliminate the need for management judgment. Instead, it can help structure the decision by showing how changing assumptions may affect costs, margins, capacity, and service commitments. 

Move from spreadsheets to connected planning 

For Fidelitone Logistics, the path toward more flexible planning began with a familiar challenge: “spreadsheets, spreadsheets, spreadsheets.” 

The 3PL was managing more than 100 P&Ls across multiple legal entities, business segments, and regions. Its budgeting process was relatively flat, even as the business needed to account for changing customers and locations, parcel and LTL services, leases, capital expenditures, and labor. 

As the planning process became more complex, Excel also became more difficult to manage. Links could break and had to be recreated. Accounting staff entered separate ERP systems, gathered information from individual companies and manually combined it. 

Fidelitone implemented Adaptive from Workday (then called Workday Adaptive Planning) in 2023, initially for budgeting. Its planning environment now includes several connected capabilities: 

• • Integrated financial data: General ledger actuals and transaction activity from its ERP systems are loaded daily. Finance can roll up information across companies and drill into underlying transactions. 

• • Sales and workforce planning: The company can budget by client and revenue stream while incorporating workforce information related to new hires, separations and changing labor needs. 

• • Capital and lease planning: Fidelitone has built tools for capital planning and for projecting building expenses across more than 40 locations. 

• • Scenario planning: Finance can manage allocations and create different versions of the plan as customers and locations change.
  • Finance-led maintenance: Once the integrations were established, finance could manage much of the ongoing mapping and model maintenance with limited IT support. 

The implementation began in June 2023, and Fidelitone produced its first Adaptive budget that October for the 2024 budget year. The finance team participated in training and hands-on implementation and continued refining the model after its first planning cycle, including changes to its sales and cost-of-sales setup. 

The experience also points to a broader planning lesson: implementing a platform is not the end of the process. Organizations must be prepared to participate in training, work directly with the model, and refine it as they learn from using it. 

Build the foundation for AI-enabled planning 

Fidelitone has identified routing and scheduling as potential areas to explore for implementing AI capabilities as it continues developing its use of the platform. 

Its experience illustrates an important foundation for AI-enabled planning. Before finance can evaluate complex operational and financial relationships, it needs access to connected, usable data. If information remains spread across separate systems and manually assembled spreadsheets, finance may spend more time bringing the data together than analyzing what it means. 

Connected planning addresses that foundation by bringing operational and financial information into a shared environment. AI-enabled capabilities can then build on that foundation by helping finance examine changing conditions, identify gaps and bring potential responses into management discussions. 

For transportation and logistics leaders, the opportunity is not simply to produce forecasts more efficiently. It is to create a planning process that reflects how the business operates, where changes in customer demand, labor, capacity, services, and pricing are interconnected. 

As AI-enabled planning capabilities develop, the quality and connection of financial and operational data will remain central to how effectively finance can use them. Organizations that strengthen that foundation will be better positioned to evaluate change, understand its financial implications, and support more informed decisions. 

For an even deeper dive, watch the full webinar: AI Planning with Workday Adaptive Planning