OTB constraints: Flexible financial planning for dynamic markets

Nov 20, 2024 • 5 min

Is your budgeting process stuck in the past?

The Merchandise Financial Planning (MFP) and Open to Buy (OTB) processes have remained essentially unchanged for the past two decades, relying heavily on manual tasks. These processes typically involve creating sales plans and allocating budgets years in advance, often based on historical performance data. This can be highly inaccurate as budgeted figures and actual ordering needs are frequently kept in different systems, leading to inefficiencies and misalignments.

Budgets conceived in a traditional MFP process often do not align with the real-world requirements of field operations. Budget planning methodology tends to be more aggregated, less data-driven, and based on a longer-term frame of reference. While this doesn’t necessarily make all budgets wrong, it can frequently require planners to adjust budgets as they gather new information in season. The result is a time-consuming and inefficient process when straddling multiple systems.

Constraining your replenishment orders based solely on budget limits can also be problematic if you fail to adequately apportion the top-down budgets using the in-season bottom-up data you’ve recently gathered. Failing to do so drives unnecessary stock and lost sales simultaneously.

As if that weren’t enough, the traditional budgeting process lacks collaboration. Individual planners frequently work independently without sharing information or collaborating. Since each planner only has a limited view of the overall supply chain, they inevitably make decisions that benefit one area but harm another.

Leveraging AI for budgeting and inventory management

While the traditional budgeting process has remained static, modern MFP and OTB solutions have evolved significantly.

Artificial Intelligence (AI) can now play a crucial role in making more specific and efficient budget decisions by analyzing large data sets.

Top-level budgets can be apportioned to individual product and location groups based on the potential lost sales and margin impacts in each planning period. Subsequently, the replenishment plan can be automatically adjusted in line with these optimized limits while respecting all MOQs, delivery schedules, etc.

Effectively, the same bottom-up scrutiny applied in replenishment calculations is extended to the traditionally top-down OTB budgeting process.

In a budget scarcity situation, the net effect is that products (where order need is driven by customer demand) are protected, and items with a larger share of buffer stock are reduced. In the opposite situation with a budget surplus, a more granular view of the replenishment plan reduces the chances of over-ordering less performant stock.

By modeling the lost sales impacts, there is direct feedback into the budget process. AI-driven tools can help determine when to adhere to or question the budget by effectively diagnosing true demand sources against budget constraints.

Balancing OTB budgets with forecasts

OTB constraints are crucial for helping retailers stay within budget. Still, sometimes it’s wise to question the budget and trust the bottom-up forecast. Diagnostic metrics are a valuable way of assessing the nature of your OTB discrepancies and whittling down whether a surplus is due to genuine customer demand or supplementary replenishment settings, like buffer stocks, investment buys, or presentation stock-builds.

Imagine a discounter that has set future OTB budgets for its toy categories based on historical sales data. As the year unfolds, there is an unexpected increase in action figure sales due to a related movie release. In turn, the AI-driven forecasting tool indicates that the customer demand for these will be higher than predicted pre-season.

If the planner rigidly adheres to the original budget and applies blanket changes across all toys, they will sacrifice margin or customer demand. In this instance, a retailer could likely achieve optimal profitability by re-apportioning the budget in-season by reducing orders on slower-moving soft toys with lower margins. It may also be an opportunity to flag these products for attention when assembling the plan for next season.

The benefits of AI in budget planning

Leveraging AI can not only streamline budgeting processes but also enhance decision-making capabilities. By integrating robust AI tools, companies can optimize inventory management, improve supplier relationships, and boost profitability, setting the stage for sustainable growth.

Consistency in planning

Businesses can trust that their replenishment plans are based on solid bottom-up data while still governed by important financial restrictions. The ability to plan and apply constraints before the last minute makes it easier to predict your labor and supply chain needs, reducing your overall cost to serve.

Additional sales

AI can reallocate budgets to meet the most recent fluctuations in demand. This ensures inventory levels are optimized to capture sales opportunities otherwise missed. When budgets cannot be reallocated, the system can provide purchasing recommendations to maximize sales potential. Proactive adjustments can maximize revenue and customer satisfaction, even when difficult choices must be made during budget scarcities.

More profitable stockholding

If budget reconciliation (in surplus or shortfall) is system-led and done using the most up-to-date bottom-up data, the process is less likely to result in ‘dead’ stock. Automated constraining logic is most likely to cut order volumes where the probability of a sale is low unless the margin is high enough to justify taking a chance on the extra stockholding.

Automation of manual work

Traditionally, an Open-to-Buy process involves dividing time between different systems or spreadsheets with rarely synchronized figures. Integrated systems can eliminate many of these time-consuming comparisons. This allows planners to devote their time and energy to more strategic and high-value decision-making, such as approving constrained replenishment plans or budget amendments.

While AI can handle many tasks, it cannot replace human critical thinking, especially if certain long-term business decisions have low data availability.

Better relationships with suppliers

Achieving a replenishment plan synchronized with your budgetary limits ensures your suppliers receive a consistent and realistic set of projections. This increased collaboration strengthens supplier relationships and contributes to a more cohesive supply chain.

Financial planning for the future

Traditional budgeting processes often depend on historical data and fixed assumptions, which do not consider the market’s dynamic nature. As a result, sudden changes—such as unexpected supply chain disruptions or shifts in consumer behavior—can significantly lower a budget’s accuracy.

Integrating AI into business operations alongside human planners is no longer just a trend; it is becoming necessary for companies that wish to remain competitive. The benefits are clear, from boosting accuracy and sales to automating manual tasks and fostering better supplier relationships.

Embracing the right AI tools can lead to more thoughtful decision-making and more efficient operations, ultimately driving your business toward long-term success.

You need more dynamic and integrated budgeting processes that can adapt to real-time data and ever-changing market conditions to keep up.

Learn more about why it’s time to bring MFP into the digital age