Conventional wisdom holds that bigger is better, and there are numerous factors that support this perspective. Consider that when you’re an organization or a business and you’re doing better, then you’re going to get bigger — so, therefore, bigger is better!
And certainly issues such as bulk buying discounts or other economies of scale favor size.
But, the tumultuous forces of an uncertain world might just favor agility. Consider that while a cost/benefit analysis will typically reveal that unit cost is much lower for a single large-capacity piece of equipment than for several smaller machines, rarely does that analysis incorporate the impact of uncertainty and variation on total cost; and, in reality, variation in either market demand or supply (such as machine downtime), or the relative inflexibility of a single large capacity machine can drive inefficiencies that greatly offset the lower unit cost that was calculated when a purchasing decision was made.
For example, a commercial bakery could purchase one large capacity mixer that could produce 100,000 loaves for far less cost per loaf than two smaller mixers. The large mixer produces large batch sizes; that’s how it gets its great efficiencies. But if the market is looking for variety, none of which is ordered in bulk, the large mixer results in the worst of both worlds: you either produce large batch sizes and have a lot of scrap if the demand does not materialize in time, or you waste the purchased capacity by preparing batch sizes more closely tied to current demand for the product variety. Either way, you can never really produce enough variety for the market, because the equipment produces only one variety at a time.
Capacity to produce must be as flexible as the market is variable and dynamic. Often this runs directly counter to economies of scale. Optimizing the machine-cost-per-unit can sub-optimize the profitability of the process as a whole.
So, ultimately, to effectively answer the “is bigger better” question, we must go well beyond conventional wisdom or what we assume to be true, and instead consider a wider range of variables. Possibly this well-known quote, sometimes attributed to Mark Twain but also to Josh Billings, sums-it-up best: “It ain’t what you don’t know that gets you into trouble. It’s what you know for sure that just ain’t so.”