Aircraft W&B - Part 3 - Extracting efficiency from weight and balance
In Part 1 and Part 2, we discussed the foundational requirements of aircraft weight and balance, emphasizing their importance for aviation safety and operational efficiency. A key takeaway is that every flight has an optimal way to safely distribute its load—maximizing performance and minimizing costs.
Airlines face the daily challenge of consistently achieving this optimal and safe load distribution. True efficiency requires balancing in-flight fuel performance with ground operational speed and accuracy—two goals that can sometimes conflict. Ignoring the impact of weight and balance on efficiency leads to performance penalties and higher operational costs. The golden nugget in aviation lies in operating flights that are both safe and efficient.
In practice, however, airlines often fail to apply consistent effort toward optimizing weight and balance for maximum performance efficiency. This shortfall largely stems from inadequate tools and heavy reliance on human judgment.
If an airline wishes to maximize weight and balance efficiency, it must examine its Load Control process—specifically how the critical decisions that determine attainable efficiency are made.
Load Control is the process through which airlines ensure that aircraft meet all weight and balance limitations before each flight. It’s also the point at which key decisions about load distribution are made, directly influencing achievable performance efficiency. Any operational improvements in this area must be implemented through the Load Control process itself.
Typical Load Control Setups
Local Load Control (LLC)
Because weight and balance must be determined before each flight, the most immediate setup involves a local person or entity performing this task at the departure airport. This configuration is known as Local Load Control (LLC).
LLC is a common service offered globally by Ground Handling Agents (GHAs). A team of load control agents employed by the GHA uses airline-provided or contracted software to perform weight and balance calculations. While manual calculations using paper charts have largely disappeared, they remain a fallback in contingency situations.
For airlines with extensive scheduled operations and dependencies between flights (e.g., transfer loads), LLC presents several challenges to achieving consistent efficiency through weight and balance.
Airlines remain accountable to regulators for compliance. Service providers report to the airline, which in turn must demonstrate compliance to regulators—forming a pyramid model of oversight. Because W&B is subject to audits, relying on numerous LLC providers requires an airline to ensure quality and compliance at every location—an approach that is both costly and inefficient.
Ensuring adequate performance from multiple suppliers is also difficult, if not impossible, without considerable expense.
Centralized Load Control (CLC)
Many of the drawbacks of LLC—particularly around quality, compliance, and performance—can be mitigated by Centralized Load Control (CLC). Instead of local load control at each airport, this model consolidates operations in a single location. A dedicated team of load controllers performs all load control tasks centrally for one airline or a group of airlines.
This setup is ideal for specialized CLC providers offering centralized services with either airline-supplied or proprietary software.
From the airline’s perspective, CLC is easier to manage because only one supplier needs to be monitored for compliance with W&B regulations rather than one per destination. Process costs are generally reduced through economies of scale.
However, a practical challenge arises when managing local loading problems remotely. Loading an aircraft is inherently a local task. Effective CLC therefore depends on reliable data and voice communication channels between the central office and airport personnel. Because loading issues typically occur close to departure, any communication delays can be costly. A well-designed CLC process must include tools and protocols that support fast and reliable collaboration across locations.
Integrated Load Control (ILC)
An Integrated Load Control (ILC) setup involves an airline establishing an in-house CLC unit staffed by its own employees who exclusively manage load control. This structure is typically embedded within, or closely linked to, the airline’s Network Control Center (NCC), which oversees network-wide operations around the clock.
This setup offers greater control over quality and performance than LLC or outsourced CLC models. Although costs may be slightly higher due to limited economies of scale, they are generally lower than the distributed LLC model.
Some smaller airlines adopt a pilot-based ILC approach, where flight crews perform load control using Electronic Flight Bags (EFBs). Upon landing, the local GHA provides load data or follows a predefined standard load plan. This method, however, is suitable only for simple operations without significant cargo or transfer loads, where advance planning is minimal.
Complex operations involving high payload variability or sensitive cargo require load distribution planning well in advance. This ensures that all load fits onboard and that GHAs can prepare accordingly.
Extracting efficiency from Load Control
The key element in Load Control - optimal decision making
The single most important element of load control is the decision on how to distribute the load within the aircraft. This decision dictates the achievable operational performance of each flight, including fuel efficiency and turnaround time.
Because airlines typically aim to carry as much payload as possible, the key variable is how that payload is distributed. Load controllers determine the distribution and provide loading instructions to GHAs. To ensure consistent and optimal results, airlines must both train and monitor their load controllers—and, ideally, quantify the efficiency of these decisions through post-flight performance analysis.
The optimal decision should ideally be determined computationally using a mathematical model that incorporates all operator preferences and aircraft limits. Removing the human element from this decision reduces inherent variance and produces a mathematically justifiable optimal solution—either for direct implementation or as a performance benchmark.
Efficiency and Consistency - The cost of variance
Efficiency in large-scale operations cannot exist without consistency. Temporary or sporadic improvements will not meaningfully affect long-term performance averages. Sustainable efficiency requires consistent optimization.
Human-based processes may yield acceptable averages but are always accompanied by high variance. Variance equals inefficiency—and therefore unnecessary cost. Consistency, achieved through automation, minimizes this variance.
Standardized loading procedures may reduce human error but will not achieve true optimality. Each flight has its own optimal load distribution given its specific constraints; using a one-size-fits-all approach introduces variance and suboptimal performance.
Human-centered decision-making also entails additional costs through training, supervision, and auditing. Automating or algorithmically supporting load distribution decisions is the only scalable way to reduce both performance variance and operational overhead.
Can’t improve what can’t be measured
Given the need for optimality and the challenges of human-based processes in achieving it, how do we determine if we are indeed optimal? This is a key problem for an airline to solve.
While we can statistically analyze historical weights and center of gravity (CGs), this doesn’t tell us if the derived mean and standard deviations are good or bad. We need an optimal reference point for this comparison to yield any usable output. This reference point allows us to “measure” the quality of the load distribution on each flight, similar to measuring temperature on a relative scale (e.g., Celsius, Kelvin). Without a reference, any inference is guesswork at best and highly subjective.
The cost of Load Control
To achieve optimization, airlines must first be able to measure how close each flight’s load distribution is to the theoretical optimum. Without such a reference point, performance assessments are subjective and largely meaningless.
Historical analysis of center-of-gravity (CG) distributions can provide insight, but without an optimal baseline, these statistics cannot indicate whether the process is efficient or not. Establishing such a reference enables airlines to quantify and track efficiency over time, similar to how temperature is measured against a fixed scale.
The Cost of Load Control
Weight and balance is a mandatory, regulatory process—so airlines naturally seek to accomplish it as economically as possible. However, since the outcome directly affects operational performance, the goal is not just to minimize cost but to maximize efficiency at the lowest possible cost.
The primary cost components are:
- Software costs, including any associated communication fees (ACARS, Type B, etc.).
- Service costs, primarily labor.
- Overhead, including internal management, quality control, and audits.
Software costs are relatively fixed; airlines must use tools that suit their operation and chosen setup. Cost reductions typically come from changing the process model—e.g., moving from LLC to CLC.
Service costs dominate, as the process is heavily labor-dependent. Substantial savings can be realized by reducing human involvement.
Overhead is necessary because airlines must demonstrate regulatory compliance and maintain performance monitoring. However, human decision-making inherently requires additional supervision and training, which add cost.
How to reduce cost?
Currently, most available load control and W&B software tools rely heavily on human interaction for both decision-making and execution. Few include intelligent or automation-driven features capable of meaningfully reducing labor requirements.
A process dependent on human operators for both actions and decisions cannot consistently achieve optimal results. Ensuring consistent performance under such conditions demands additional administrative oversight, which is costly and often ineffective.
The only way to meaningfully reduce costs — and improve consistency - is to reduce the dependence on human labor and automate and optimize the decision-making process. Automation will not improve anything - unless it is optimized.
Conclusion
Consistent efficiency cannot be extracted from the weight and balance process as long as it relies on human-based decision-making. Such dependency inherently limits process efficiency because humans cannot consistently make optimal decisions.
True consistency requires computational logic that captures airline-specific preferences and aircraft limitations to define optimal conditions for each flight.
Moreover, performance improvement depends on the ability to measure. Airlines must be able to take the temperature of their process—to determine how close each flight is to optimal. Establishing a computational benchmark for every flight enables airlines to derive global KPIs, assess process performance, and implement targeted improvements to minimize deviation from optimality.