Faced with disruption, most companies reach for one of two levers: costs or prices. Both are now delivering diminishing returns. Input prices remain elevated, supply chains are more fragile and customers are more price-sensitive—each compressing margins and making profitable growth harder to sustain. Per AlixPartners Disruption Index, three out of five companies are holding more inventory to buffer geopolitical and supply chain shocks. The pricing power that once absorbed these pressures has largely faded.
The next durable—and controllable—source of value has to come from inside the business, and the largest is the cost of complexity. Complexity is easy to sense, but hard to pinpoint. It accumulates quietly as organizations add SKUs, enter new markets, win new customers, onboard suppliers and modify operating processes. Some complexity is essential: It creates scale, differentiates offerings, improves customer experience or creates operational resilience. But much of it also increases performance variability, raises operating and capital costs, and makes economic profitability harder to see. Over time, unmanaged complexity leaves companies larger but less profitable.
Most attempts to reduce complexity fail. They treat symptoms rather than cause, and focus on unit costs, headcount or isolated process improvements, without rigorously connecting choices about customers, offerings and service levels to their full economic impact across the P&L and balance sheet. To restore and maximize profitability—and prevent complexity from creeping back—leaders need a way to distinguish value‑creating (“good”) complexity from value‑destroying (“bad”) complexity, and to manage both through a customer‑back, economically grounded lens.
Understanding Complexity
Complexity grows exponentially. A Starbucks “triple shot half-caf oat milk sugar free vanilla latte” is not just a drink; it is a bottleneck. Starbucks reached a point where it offered 170,000 possible different drink combinations, before the company’s “Siren System” addressed the problem by reducing offerings and automating processes.
Each incremental decision introduces new demands on the system:
- Additional SKUs create inventory, production and quality issues
- New geographies bring additional regulatory requirements and needs to coordinate across functions such as procurement, distribution, finance, HR and sales
- New customers arrive with differentiated service expectations and demand patterns
- New suppliers require qualification, governance and ongoing management
- New production lines or capabilities increase inventory touchpoints, raise capital intensity and complicate planning and analysis.
Individually, each decision appears rational and even attractive, particularly to a function or business unit. Collectively, however, these choices create a web of interdependent activities that increase cost, slow execution and dilute accountability.
Worse, when these decisions are made in functional silos, disconnected from end‑to‑end economics, the complexity becomes difficult to track and compounds over time.
Good Complexity vs. Bad Complexity
Some complexity is essential to delivering differentiated value that customers recognize and will pay for. Seasonal product offerings are a good example. So are expedited delivery services for time‑sensitive orders, recyclable or sustainable packaging options that support customer ESG commitments or integrated product‑service packages that competitors can’t match.
Schneider Electric, for example, decided to take on additional complexity in pursuit of higher share of wallet from their large building clients. Schneider realized that building management was becoming overburdened by the difficulty of monitoring the health and maintenance of not just Schneider’s equipment but also adjacent components (e.g., Siemens HVAC, Honeywell Thermostats, etc.). Schneider offered to expand monitoring for the full hardware suite, adding complexity for itself but simplifying life for its customers. For this added complexity, Schneider was able to calculate the cost, price for an appropriate profit and target customers for whom the benefits outweighed the price. Schneider earned not just additional profitable revenue but the benefits of greater customer loyalty and insight into growth opportunities in adjacent product categories.
The critical question is whether the incremental revenue and strategic benefit from a given source of complexity exceed its full economic cost. Schneider was able to answer that affirmatively. Many organizations stop their analysis at direct costs, leading them to underestimate the true burden of complexity. But the true cost goes well beyond direct operating expense. It includes incremental overhead, additional working capital (inventory, safety stock, WIP), incremental fixed assets (dedicated tooling, equipment or facilities), and he cost of capital tied up in supporting those assets and inventories. Complexity adds value only when commercial terms fully reflect all these costs. When prices, contracts and service models fail to account for complexity, it (or “bad” complexity) silently destroys value, especially as volumes grow and “exceptions” become the norm.
Why Traditional Approaches Fall Short
Most organizations intuitively recognize that complexity is limiting performance. Few can pinpoint where it originates, how it interacts across functions or how it impacts economics at a segment or customer level. Two common pitfalls undermine traditional approaches.
1. Underestimating Interdependencies
Leaders often frame the problem in narrow terms:
- “Do we just have too many SKUs?”
- “Are we wasting resources by serving too many customers or markets?”
- “Have quality, compliance or administrative processes become too burdensome?”
In reality, complexity rarely resides in a single layer of the business but rather from interactions among them. An incremental SKU, for instance, may trigger changes in forecasting, supplier management, plant scheduling, inventory policies, quality testing, and field service—none of which is visible if each team looks only at its own metrics.
Traditional efforts that target only one dimension—such as SKU reduction in operations—leave the structural drivers of complexity in place, and well‑intended changes in one function are offset by compensating behavior in another.
2. Ignoring Total Economic Impact
Because complexity is often most visible in operations—in scheduling, working capital and plant utilization—many companies start there. This rarely works for two reasons. First, it frames complexity primarily as a cost problem rather than a value problem. Second, because “the customer is always right,” commercial teams often re‑introduce complexity to meet customer requests, causing it to grow back once the initiative ends.
Indeed, when organizations launch complexity‑reduction initiatives, the sponsoring function strongly shapes the lens. Operations‑led efforts often emphasize SKU rationalization, volume and contribution margin, or apply Lean Six Sigma to reduce process variation. Commercial‑led efforts may focus on simplifying pricing or discount strategies but underweight operational, overhead and capital implications. Efforts to reduce SG&A costs through centralization, outsourcing or offshoring can reduce functional effectiveness and add work in other units. While these approaches can be valuable, they tend to:
- Miss indirect and capital‑related costs, which often represent a large share of the complexity burden.
- Ignore structural drivers such as sales coverage models, service levels and contract terms that encourage uneconomic variation.
- Treat all complexity as negative, even when some drives strategic differentiation or value for customer.
- And, efforts to coordinate these various initiatives—often through teams or a multi-dimensional matrix – both consume time and dilute accountability for business results.
A Customer‑Back Approach to Complexity
A more effective approach starts with the customer and works backward through the organization. Instead of asking “Where can we cut?” it asks, “Which customers, offerings and service elements truly require complexity—and are we being paid for it?” This approach is grounded in three fundamental questions:
- What are the customer needs (both today and tomorrow) that must be addressed? (ideally to grow profitably faster than competition)
- What products and services are core strengths? Which are incremental or non-core?
- How differentiated is the current offering? Will incremental ones truly add profitable differentiation?
- What drives complexity in the operating model?
- What does a “baseline” operating model look like in this industry—i.e., is there a less complex way of serving comparable demand?
- Which incremental activities, processes, and design choices introduce complexity relative to that baseline? How do those choices connect across commercial, operational and support functions?
- What is the full economic impact?
- How does complexity affect the P&L and balance sheet relative to a lower‑complexity baseline (e.g., through higher overhead, inventory, capital expenditures and risk)?
- How do different complexity drivers interact and compound one another?
- Are customers paying for it?
- Do prices, contracts and commercial terms accurately reflect customers’ willingness to pay for the incremental value provided by complexity?
- Where are there gaps between economic cost and realized price or margin?
By grounding decisions in customer-back needs and end-to-end, leadership teams can preserve complexity that genuinely supports differentiation and strategic positioning, while systematically removing or repricing the rest. This shift requires close collaboration across commercial, finance, operations and support functions, but it creates a more stable foundation for profitable growth.
Case Study: Restoring Profitability Through Complexity Management
The Context: Consider how this approach worked with one client. The largest global manufacturer of a major component for automobile companies, it had found that sales to one of its biggest customers had become unprofitable, despite multiple improvement initiatives—including SKU rationalization, product redesign, automation and Lean Six Sigma efforts—the segment remained unprofitable.
The Challenge: Complexity was the obvious culprit. The customer wanted lots of variation and customization, and fast, special-order service. But solutions were elusive without a clear, fact‑based understanding of how complexity entered the organization, what its full costs were and which elements were value‑adding versus value‑destroying. The company addressed the problem in a three-step process.
Step 1: Mapping complexity and its cost. Complexity is hard to see in financial reports, but relatively easy to spot at the activity level. The team documented approximately 60 process steps across commercial, engineering, operations, quality and supply‑chain functions. This created a granular fact base showing exactly which incremental activities, handoffs and resources were required to serve this customer compared to others.
- These customer‑specific requirements and their associated costs were benchmarked against a lower‑complexity model serving similar demand. Comparing the two made it possible to identify where the client’s complexity significantly exceeded industry norms.
- Each high‑cost complexity driver was then linked to both direct and indirect costs across the P&L and balance sheet. In addition to the obvious items, those considered how additional SKUs increased inventory, planning time, testing and capital investment; expanded delivery locations drove higher logistics and service costs; frequent design changes raised engineering workload and led to obsolete inventory.
The analysis also revealed that complexity drivers compounded one another—and it uncovered a cultural problem: A well‑intended “customer‑first” mindset led individual teams to add complexity independently, often without adjusting prices or terms.
Step 2: Separating good from bad complexity. With the economics clearly mapped, each complexity driver was evaluated to determine whether the customer value it created exceeded its incremental cost.
- Example of good complexity:
The company was able to offer and accommodate a much wider range of product offerings than any of its competitors. Customers valued that and were willing to pay for it.
- Example of bad complexity:
A high rate of product returns—often driven by the customer’s own actions—created substantial rework, logistics and inventory costs with little strategic benefit.
This clear distinction between good and bad complexity enabled management to focus on profitable growth without undermining customer relationships or strategic differentiation.
Step 3: Identifying solutions. Cross‑functional workshops with leaders from commercial, finance, R&D and operations then translated these insights into action. For each major complexity driver, teams aligned on origin, value contribution, customer impact, and the degree to which current terms compensated for costs. Solutions fell into several categories, summarized below.
| Solution Lever | Action | Outcome |
| Price and Offer Alignment | Increase prices on the most complex SKUs | Customers paid for the full value received; low‑value SKUs were discontinued |
| Predefined Bundles | Package features commonly bought together | Simpler purchasing process, easier fulfillment, fewer SKUs |
| Unbundled Pricing | Define standard services and price add‑ons | Preserved revenue while ensuring value‑add services were profitable |
| Contract Adjustments | Remove contractual constraints that locked in excess complexity | Reduced inventory and operational inefficiencies |
| Terms Enforcement | Enforce contractual terms and charges on customer‑driven returns | Decline in customer‑driven rework; revenue capture on remaining returns |
| Operational Alignment | Right‑size quality and testing to contract requirements | Maintained quality standards at lower cost |
| Governance | Tighten internal process and approval controls on out‑of‑scope requests | Reduced risk of complexity creeping in without corresponding price changes |
These levers worked together. Pricing changes ensured customers paid for value‑adding complexity. Contract and governance changes curbed uneconomic complexity at the source. Operational changes aligned internal processes with the new commercial model, reinforcing the economics over time.
- Showing where the customer’s complexity unnecessarily diverged from industry standards
- Collaborating to identify and remove non-value add complexity
- Unbundling and variable pricing to allow the customer to choose complexity up to the level they value
- Delivering consistent messaging to the customer from each functional silo
Combined, these changes returned the customer segment to profitability as complexity shifted from an unavoidable byproduct of growth and high service levels to a strategic offer that the client could provide their customer at the exact level it valued. This also shifted the relationship from a transactional one to a strategic partnership, where both sides are increasingly collaborating on win/win outcomes.
Through the process, the client established a repeatable capability to identify, measure and manage complexity. Cross‑functional teams, standardized baselines and enhanced governance now help prevent future erosion of value. Complexity is periodically re‑assessed and repriced or redesigned, rather than allowed to accumulate unchecked.
As pricing power moderates and disruption becomes a permanent feature of the landscape, managing complexity is emerging as a critical lever for sustained performance. A holistic, customer‑back approach—grounded in cross‑functional insight and financial rigor—can unlock material value where traditional efficiency efforts plateau.
Organizations that build this capability will not only reduce cost. They will create a more resilient, scalable and profitable operating model—one in which complexity is intentionally designed, priced and governed rather than simply inherited and endured.
