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Productivity: A Moving Target

She moves in mysterious way…—U2

A couple of issues back, I cited a number of contingencies—“demons,” I called them—that regularly plague even the most incisive bidmeisters. And while job-specific contingencies (concealed costs) can be maddeningly elusive, they are by no means the slipperiest set of factors that an estimator typically contends with. Ambiguity in the bid docs (gotchas!), skyrocketing material prices, lowball competitors, surprise addenda and technical SNAFUs all compete in vain to be the slimiest concern facing estimators today. I say “in vain” because none of these evasive issues can hold a candle to labor productivities—the presumptive winner of the greased pig award for countless years running—when it comes to mystery.


I focus on productivity not only due to its nebulous nature but also due to its extreme effect on cost. As every bidmeister knows, labor is by far the single most cost-intensive factor in any estimate, as well as the most difficult to quantify. Arduousness notwithstanding, the laborious task of quantifying the predicted productivity on a given project befalls the estimator on each job that comes before him. Now, from the way I’ve described the shifty quality of productivities, one might think that the best approach to predicting quantities completed per day might involve a crystal ball, a pair of dice or a Ouija board. Fortunately, the astute bidmeister brings something a bit more substantive to the task: a set of historical data—a summary of how craftsmen have performed on certain assemblies and conditions in the past. Unfortunately, these tables cannot be rock-solid predictors of future performances because they are subject to a number of mitigating factors. These factors include type of construction, labor market conditions, location, turnover, project schedule, seasonal concerns and GC coordination, among others.


Most labor tables of productivity are based on either average or optimum conditions and are subject to adjustment for the kind of extenuating factors as listed above. Type of construction may be the most significant factor in its effect on productivity. An extreme example of this factor can be found in a comparison between, say, health care projects and office build-outs. Clearly, a journeyman framer/hanger will rack up better daily footages in the office build-out, with its repetitions of easy penetration walls between offices, long, unobstructed corridor runs and knock-down door frames, as opposed to a hospital floor with full-height fire and sound walls, a predictable train-wreck of overhead obstacles, required specialty board products (cement, high-impact, moisture-resist) and welded one-piece hollow metal door frames. Bidmeisters can safely predict that labor efficiency will be reduced in the health care setting by 20 to 30 percent over the office build-out.


Then again, a GC’s ability (or inability) to coordinate a project can and will influence the drywall sub’s productivity every bit as much as construction type can. It is critical that the GC superintendent and his cohorts provide an unobstructed path of progress to the framer/hanger crew—one where predecessor trades have completed their work in an entire area—for that crew to maintain even average productivities. Alas, many GC supers fail to comprehend the consequence of poor coordination, allowing numerous starts and stops by changing the critical path whimsically. Such deficiencies in organized management not only affect the productivities of all of the subs, but they hamstring the progress of the entire project. Sad to say, poor coordination is almost impossible for an estimator to predict or quantify unless he is aware of some egregiously derelict super’s association with the project he is bidding (rare), or unless he is clairvoyant (extremely rare). Otherwise, this is a production killer and a budget-buster that will blindside even the best of estimators.


A related way in which a GC can affect productivities lies in the project schedule. Every project manager knows that schedule shortfalls are commonplace, while schedule end dates are rarely adjusted, if ever. The suggested remedy usually lies in overtime work for the subcontractors. And even in cases where the GC offers to pick up the premium time, that reimbursement rarely compensates the sub for lost productivity, which can be significant. Studies show that productivity can be reduced by as much as one-third for time on task spent over and above an eight-hour day.


Even the seasonal elements can have a significant effect on daily productivities. Several studies have indicated what seems intuitive, especially to the craftsman in the field: extreme heat or extreme cold drives down the worker’s performance level. No kidding. Like those of us who have worked through Arizona summers and Colorado Rocky Mountain winters needed a study.


Apparently, the factors cited thus far are external ones—outside obstacles to efficiency that confront the average worker. But what about the workers themselves? Does it not stand to reason that an environment in which the labor pool is shrinking while the demand for workers is expanding might negatively affect the skill level (and thereby the productivity) of the average worker? (Now there’s a theory that cries out for a study!) If this is indeed the case, then historical data will prove to be misleading in the present atmosphere.


Clearly, all quantifiable factors must be considered while attempting to predict productivity. But then there are those issues that elude prognostication. That’s what makes labor productivity the moving target that often eludes the estimator’s best aim.

Vince Bailey is an estimator/project manager working in the Phoenix area.

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