Scale: Revealing How Big Things Behave Differently

Economies of scale are the cost advantages enterprises obtain due to their scale of operation. The output produced is higher, while the cost per unit is lower. Average costs start falling as output increases. This is manifest in the capital cost of manufacturing facilities and materials. You have felt the scale effect: buying food or consumer appliances is much cheaper in bulk. The same is true for business-to-business transactions up to a certain point. There are limits to how cheaply you can offer raw material, for example, such as wood in the paper industry.

The simplest definition of economy of scale is doing things more efficiently at larger proportion. Think bulk purchasing of materials through contracts, increasing the specialization of managers, obtaining lower-interest loans from banks when you borrow great funds, spreading the costs of advertising over multiple media outlets. Each of the mentioned reduces average costs while enhancing returns. It gives us a peak into how the behavior and performance of systems changes as their size increases or decreases overall. Processes that work efficiently on a small scale don’t operate the same way when scaled up, and vice versa. “Bigger” isn’t just a larger version of “smaller” — it brings unique challenges and opportunities.

The relationships between variables of a system change in non-linear ways as the size of it changes. Small systems could be predictable. Large systems can exhibit complex, emergent behavior. A large manufacturer can produce goods at a lower cost per unit compared to a small artisanal shop due to bulk discounts. But a very large company starts to struggle due to bureaucratic inefficiencies and communication breakdowns that don’t affect its mid-sized counterpart. A tiny toy model of a bridge functions flawlessly. Build a full-scale version and challenges like wind resistance, material stress, and load distribution result in collapse. A startup finds it easy to innovate and pivot when small, but as it grows, internal processes force it into new and stagnant operating procedures. A small shop has direct communication between all the employees, while a large corporation requires formal departments and hierarchical structures, making response times slower.

Real-world examples of how scale changes everything:

• Business and Organizations: small shops and big companies. A local bakery can make quick decisions, maintain personal customer relationships, and rapidly change. A national bakery chain, while benefiting from cheaper ingredients and huge ad spend, struggles with coordination across multiple locations.

• Product Design: the tiny toy versus the giant. A miniature toy can be designed with simple mechanics that function as intended at a small scale. Scaling the toy up to a large, interactive, human-sized version requires entirely different engineering approaches to ensure structural integrity and safety.

• Technology: startup and enterprise software. A small team can develop a simple, agile app that rapidly adapts to user feedback. Scaling that app to serve thousands of users in a multinational corporation requires extensive testing and additional layers of features that can dramatically slow performance.

• Urban Planning: large cities and small towns. In a small town, the infrastructure needs are simple; traffic, public services, and community interaction can be managed with minimal bureaucracy. In contrast, when mapping out a large metropolitan district, planners have to deal with complex transportation networks, diverse populations, and extensive regulations.

How you might use the scale effect as a mental model: (1) size matters — when planning a project or strategy, consider how scaling up or down changes its functionality, like evaluating whether the current processes can handle larger volumes when expanding a business; (2) aim for size savings — identify where scaling up could result in cost savings or improved efficiency, and invest where you can harness these benefits, such as bulk purchasing opportunities, automation, and specialization; (3) plan for failure — recognize that at a certain point, additional size makes things less efficient; (4) simulate the screw up — employ models to predict how changes of scale affect the broader system, like by simulating traffic flows under various population scenarios if mapping out a city; (5) change things — be ready to review and adjust as the system scales, such as a startup which implements more robust software architecture as its user base grows.