Building a profitable franchise requires executives to measure the right things, make the right decisions and keep their eyes on the ball.
In 2002 the Oakland Athletics were confronted with a conundrum. Player salaries had ballooned to astronomical levels, and large-market franchises, such as the New York Yankees, New York Mets and the Boston Red Sox, with a seemingly endless fan base, lucrative television rights and merchandise outlets possessed the ability to gobble up signature players. Smaller-market teams were forced to be more frugal with their expenses and balance their payroll against lower income.
In many cases, talented players who developed quickly and became media darlings in these small markets moved to the mega franchises for their payday. Oakland qualified as one of these small markets. Lacking the financial means of the New York Yankees, the Athletics were required to use a nontraditional means of evaluating talent. Enter Billy Beane.
Beane’s previous teams witnessed a mass exodus of talent, victims of baseball’s free agency system. Players found the glitz of big cities more attractive than Beane’s Oakland community. As a result, Beane enlisted the services of Peter Brand, an economics graduate from Yale. Economics? Yes. Baseball scouts had typically relied on intuition and baseball acumen to grade talent as it entered the major leagues from high school or college. In most cases, these talented ball players commanded higher salaries for an unproven product. Beane, himself a mediocre ball player in a previous life, was keenly aware of how scouts gauged talent and how qualitative metrics (how a player looks on the field) received higher marks than quantitative metrics (on-base percentage). Finding undervalued players through a statistical mechanism known as saber metrics — a specialized analysis of objective, empirical data gathered through in-game activity — became Beane’s hallmark. Scouts and baseball pundits scoffed at Beane’s process, calling it a media gimmick. However, in finding unorthodox players who excelled largely in previously unheard of statistical categories, Beane created franchise gold — winners on the field and on the income statement.
The construction industry today is incredibly competitive in two areas. The supply of contractors greatly exceeds the demand of construction projects. Market sectors are saturated with contractors of all talent levels and emerging markets rarely remain a secret for long. More importantly, even in a recessionary market, contractors struggle to find capable estimators, managers, superintendents and foremen to enhance margins, reduce risk and preserve the bottom line. “We’re looking for strong estimators and managers.” “We need managers who can make money.” “We need field managers who have a can-do attitude.” While these are notable characteristics, this is a little like a general manager looking for a shortstop with “hustle” and a right fielder with a “gun”. Is there a way to evaluate construction professionals based on specific criteria that correlates to enhancing margin, improving productivity and hedging construction risk?
Statistics surround the construction industry. There is no shortage of data in the plethora of financial information that comprise the construction business. However, in an industry predicated on a lean operating model (not to be confused with a lean construction model), finding and evaluating the right data proves elusive. Much like the 2002 Oakland Athletics, construction firms are confronted with finding the right variables to the successful profitability equation.
FIRST BASE — THE ESTIMATING MODEL
Construction executives have long benchmarked their estimating talent to hit ratio — the higher the hit ratio, the greater the success. Common wisdom would indicate that without work, there is no money to be made. However, these supposed “wins” are tallied on bid day rather than at their conclusion. This is analogous to determining the winner of any game at the end of an inning, quarter or period rather than at the final whistle. Similarly, Beane viewed batting average as a weak metric on which to base a player’s contribution to wins and losses. A player may exhibit strong hitting prowess, but this does not necessarily translate to runs, which is a greater leading indicator to winning. Rather, Beane used nontraditional metrics or key performance indices, such as on-base percentage and slugging percentage. Ultimately, home runs translate to winning, not simply hits.
Basing estimating success simply on jobs awarded is a flawed metric. In the end, jobs must translate to bottom-line profitability. Hit ratio and profitability are not necessarily correlated. Put another way, just because a contractor is low on bid day does not mean it will achieve desirable margins. However, an organization that emphasizes the importance of a high hit ratio will normally see its hit ratios will increase, often at the expense of profitability. Estimators are evaluated best on their accuracy. Maybe the flaw in this position lies within its title — should estimators be reclassified as “accurators?” Consider the following analysis of an estimator’s performance as shown in Exhibit 1.
In this example, Estimator #1’s portfolio of awarded projects is examined by overall accuracy (estimated costs compared to actual costs) and the relative labor expenditure on said projects. When the projects are sorted according to size, a pattern quickly emerges. As the project size decreases, Estimator #1’s overall accuracy improves. As seen in Exhibit 1, this estimator’s “sweet spot” — and variance of +/- 10% — is best demonstrated on projects where the labor costs do not exceed approximately $500,000. One could argue that this player’s contribution to the team needs to be aligned more with estimating projects within this sweet spot. Lastly, Estimator #1’s overall accuracy is -14% based on this year’s data set. From a different perspective, it is likely that Estimator #1’s projects will experience some sort of margin erosion based on this predictive index. Conversely, it would be equally concerning to see an estimator with a positive variance. While the organization will likely improve on its labor budget, it is also likely that the organization will lose work due to inflation of estimated costs.
The next step in the analysis is to examine the entire pool of estimators and determine overall accuracy of the group. Exhibit 2 examines an entire estimating department’s accuracy.
Estimator #4 is the most accurate estimator within this firm. In addition to a high accuracy score, this person also has the highest number of projects that fit within the acceptable range.
The last element to examine is overall profitability. Within Exhibit 3, the metrics of hit ratio, accuracy and final project profitability are compared.
Based on the traditional metric of Hit Ratio, Estimators #4 and #5 both exhibit strong hit ratios. However, Estimator #5 not only is less profitable, but also demonstrates a “negative accuracy”, most likely causing the lower overall profitability. Additionally, Estimator #1, who demonstrates a gaudy 18% gross margin, has one of the worst accuracy ratings and consequently, a low hit ratio.
Metrics and analyses such as these are not complicated and shed light on important strengths and deficiencies within an organization. Furthermore, they provide data points that require further edification and clarity. For example, subsequent actions resulting from this analysis may be as follows:
- Replicable Systems: What processes, systems or knowledge does Estimator #4 have that the balance of his/her estimating team lacks?
- Training: What training is required to improve the collective accuracy of the group?
- Sustainability: As markets continue to evolve, is the success of Estimator #4 sustainable or an anomaly brought about by a niche, customer or simply timing?
SECOND BASE — THE PROJECT MANAGEMENT MODEL
Project managers are often the conduit or link between all elements on a project — from office to the field, estimating to the site, trade partners to the customer, customer to the firm. Consider the project manager as the catcher, communicating with the pitcher, signaling to the outfield and aligning the infield according to the hitter and pitches to be thrown. In many cases, managers’ project success or failure is correlated directly to how well they communicate across these different parties. Often it is not the individual communication with these entities that breeds success, but alignment of the communication across these entities that is the true measure. For example, a project manager may possess extraordinary ability to converse with the field. However, this same manager may lack finesse when dealing with a sophisticated customer. The best managers are effective communicators at each level.
The answer lies in finding the appropriate statistical criteria for a project manager. A project manager who achieves a high gross margin is often lauded as a success. However, can a manager make money but fail at achieving the “bigger strategic picture” for the organization? Final profitability on projects provides short-term success, but in an industry predicated on relationships and the ability to communicate with customers and trade partners, what defines long-term success? Consider the following example as shown in Exhibit 4.
Within Exhibit 4, a firm’s project managers are graded according to two external criteria: customer satisfaction and trade contractor coordination. Customer satisfaction, garnered at the conclusion of the project, may be based on timeliness of documents, quality of communication, ability to perform to predetermined standards, etc. Trade contractor coordination, also gathered at a project’s conclusion, may be based on adherence to the baseline schedule, proactive communication and planning processes employed, and overall fairness. The 10 project managers were also measured on their final profitability. Project manager #4 exhibits the highest final margin. However, this person also exhibits some of the lowest scores in both the customer and trade partner community. One might ask, “Is this profitability sustainable in the market in which this contractor exists?” For example, what if this contractor operates within a small metropolitan area with limited trade partners and within a niche that has a small, finite list of customers? Might this contractor begin to wear out its collective welcome?
It is important to examine this analysis within the context of the firm’s overarching strategy. For instance, consider the four quadrants within Exhibit 4. It is likely that the managers in quadrant #4, while lower in overall profitability when compared with their peers, may be in a very customer-centric market (i.e., design- build, negotiated market, etc.). On the other hand, the managers within quadrant #1 may be suited for a hard-bid, highly competitive market. If this firm operates within multiple markets/niches, it will be important to use this tool to find the appropriate role for each individual. However, if this firm operates within one market or niche, there is distinct misalignment that may lead to customer attrition, poor market perception and, possibly, long-term profit erosion. Additionally, with as much variability as exists within this firm, it may be important to examine overall company standards relative to operating procedures. Does one firmwide standard exist, or is there an array of 10 sets of process, procedures and tools? This would be like a team such as the Yankees all wearing different uniforms, players playing whatever position they want and ultimately implementing individual game tactics rather than having one consistent, organizational-driven strategy for executing the game plan.
Managers must have a consistent standardized model from which they govern their work. Scoring within firms focuses largely on end results rather than contributing behaviors to success. For example, examining “downstream” results, such as profitability, direct cost variance and safety scores, provides a historical benchmark but does not provide a great indicator on the systemic reasons for success and failure. Consider the baseball analogy. A team may win a ballgame, but the result may be skewed by the other team’s poor performance (i.e., numerous errors, injuries to key players, etc.). Put another way, the team may win in spite of itself. Downstream results, such as wins and losses, are no different from a construction firm’s profit and loss statement. Both are important, but it is difficult to make measureable change simply from these metrics.
A better indicator of team health may be to measure the best practices that translate to winning on the field — practices, film sessions, etc. In the case of a contractor, an organization must first measure “upstream” metrics such as process compliance. Did a project team in fact effectively plan the work and prepare the front line for the project? Did the project manager continue the planning through to the end via a project exit strategy session? Did the manager conclude the project with a post-job review or project post mortem to determine winning project tactics? Measuring adherence to firmwide standards is an excellent indicator of performance — projects that make money often are planned from beginning to end. Measuring the upstream (processes) achieves the desired downstream (high-margin) results. Exhibit 5 demonstrates measurement of both upstream and downstream key performance indicators.
The firm modeled in this graph demonstrates a steady linear adoption or adherence to the firmwide pre-construction planning standard. For example, in March, the firm completed pre-construction planning on 50% of its projects. Additionally, the firm’s labor expenditures are also declining. As the firm plans more, the overages on its direct labor decline. One point of interest is the comparison of the labor in terms of dollars and hours. During January, the firm spent 120% of its labor budget in terms of dollars and 115% of its labor budget in terms of hours. Through this analysis, two major conclusions are drawn. As the firm’s managers improve their planning capabilities, their costs decrease. Additionally, the variance in dollars and hours exposes a potential flaw in the firm’s estimating rates. Could the estimators be using a higher blended labor rate than actually used in the field? A firmwide “upstream” scoreboard illustrating the right behaviors for managers to exhibit emphasizes and reinforces the right standard to make long-term improvements. Just as practicing improves a team’s performance, planning improves a firm’s profitability. The team also knows the standard by which it is measured, leaving little subjectivity in the management of people.
THIRD BASE — THE FIELD MODEL
The superintendents and foremen — the front line of any firm — are the true clean-up hitters. The game ultimately is won or lost by their actions. Success is predicated on how well these players understand and implement the game plan. As illustrated in Exhibit 5, examining “downstream” results, such as profitability, direct cost variance and safety scores, provides historical benchmarks but is not a great indicator on the systemic reasons for success and failure. Just as an organization measures management’s adherence to pre-construction planning, it must also measure the superintendents and foremen’s compliance of short-interval and daily planning. Every player must be held accountable to maintain the firm’s standard operating procedures. Beane’s players, however seemingly ubiquitous or anonymous, were expected to play to the Athletics’ standards, every inning of every game.
One of the greatest individual metrics for project performance is the concept of earned value. Earned value is simply a comparison of estimated units/put-in-place units for a particular phase of work and the estimated hours on said work. Through this mathematical equation, the number of “earned hours” is compared to the actual hours spent to determine the overall efficiency. Exhibit 6 shows a brief illustration of basic earned value (with the formulas to guide the mathematics).
Within this example, the chilled-water piping is 30% complete (300 LF of the 1,000 LF estimated). However, this firm has spent 50% of its labor hours. The P-Factor (Productivity Factor) or efficiency rate is currently 60%. Comparatively, the 6” sanitary sewer installation is operating at a 143% efficiency rate. These simple calculations rely on the following data:
- Accurate Labor Reporting From the Field: Hours must be assigned to the correct labor code.
- Accurate Unit Reporting: Units must be “claimed” as complete in a universal, firmwide fashion.
- Timeliness: Reporting must be made frequently to provide timely feedback, allowing for corrective action to take place.
Whether used in examining the performance on individual tasks or entire projects, the power of earned value as an overall efficiency rating cannot be understated. While profitability may indicate a project’s overall success or failure, a superintendent or foreman’s true measure is the ability to control direct project costs, thus enhancing the final margin through effective utilization. One of the main reasons that earned value is far superior in grading superintendents than simple profitability is because most firms put their best superintendents on the most challenging projects that may not be the most profitable projects.
Consider the analysis in Exhibit 7. On the surface, Superintendent B exhibits the highest final gross profit margin. However, it may be the result of any number of factors outside of his/her control. On the other hand, Superintendent A has the highest efficiency rate but falls within the firmwide average on final margin. As a potential scenario, it may be likely that Superintendent A’s projects are more challenging, and in the hands of a lesser individual, such as Superintendent D, the project would fail miserably. However, simply examining final profitability may fail to yield the true source of the firm’s most productive “hitters”.
Punch lists or deficiency lists provide another indicator of superintendent performance. In addition to the costs associated with errors in construction, unfinished items and unacceptable quality, there is the intangible erosion on the customer relationship and long-term confidence. There is a distinct correlation between how a project finishes and its margin gain or erosion.
Exhibit 8 shows a summary of a firm’s completed projects and the punch lists generated. Additionally, the corresponding margin gain and erosion were plotted on a secondary axis. Just as the estimator’s sweet spots were found in a previous analysis, this firm’s “superintendent strike zone” is between 60-70 punch list items. Much as a coach monitors pitch count as a predictive index for a pitcher’s deteriorating performance as a game wanes, metrics such as the “superintendent strike zone” provide a target and visual depiction of quality or completion standards. While it is not provided in this graphic, the firm could easily plot customer satisfaction scores in lieu of profitability. While each customer has his or her own expectation of quality, a firm could glean critical connections between its ability to finish with limited deficiencies and its overarching customer satisfaction.
Farfetched as it may seem, a great superintendent can easily resemble a weak superintendent without proper planning. Business developers, estimators and managers may spend the requisite amount of time planning a project, but how much time is dedicated to preparing the field manager? Would the coaches and managers develop a game plan and then fail to share it with the starting pitcher? The effect of poor planning usually rears its head at the most inopportune time. Unfortunately, the project’s warts often percolate to the surface at the end, when deadlines approach, crews are tired, and relationships are strained. Profitability declines, and the field manager wears the brand of weak leadership. In the spirit of sports analogies, consider the 1986 World Series. Bill Buckner’s infamous error became the most recognizable mistake in the sports world and was largely believed to have cost the Boston Red Sox the crown. However, it is important to note that this was not the final game of the World Series. Furthermore, this was a seven-game series, which means the Red Sox did have ample opportunities in other situations to win. Superintendents on poorly planned projects often become the “Bill Buckner” — scapegoats for errors demonstrated in the first inning.
Metrics permeate throughout the construction industry. Whether it is measuring the slump of a concrete yield or the recordable safety incidents annually, managers, executives and business leaders are inundated with numbers. With the saturation of data, it is easy to become numb and fail to recognize patterns and the true indicators of performance. Billy Beane revolutionized the game of baseball and revitalized a franchise up against insurmountable odds, on and off the ball field. The construction industry’s context is ever-shifting, and building a profitable franchise requires executives to measure the right things, make the right decisions and keep their eyes on the ball. ■
Moneyball: The Art of Winning an Unfair Game by Michael Lewis (New York: W.W. Norton, 2003) tells the story of how the Oakland A’s achieved a remarkable winning record in 2002, despite having the smallest player payroll of any other major league baseball team. In 2004 Columbia Pictures bought the rights to Lewis’ book, and “Moneyball” the film debuted on September 23, 2011. Brad Pitt stars as Billy Beane, the A’s general manager, who is tasked with assembling a competitive team despite the dismal budget. The film was nominated for six Academy Awards, including Best Actor and Best Picture.
William Joseph “Bill” Buckner is a former major league baseball first baseman who had more than 2,700 hits in his 20-year career, won a batting crown in 1980 and represented the Chicago Cubs in the All-Star game in 1981. In spite of that, he will be remembered for one bad play in Game 6 of the 1986 World Series —an error that ultimately cost his team, the Boston Red Sox, the championship against the New York Mets.
Boston was up three games to two over the Mets. Game 6 went into extra innings, and the Red Sox scored two runs in the top of the 10th inning. When the Mets came to bat, Buckner, despite being hampered by leg problems, was sent to take the field. The Mets came back to tie the game, and then Mookie Wilson hit a groundball to Buckner at first base. Buckner rushed the ball, which went through his legs and into right field, allowing the Mets to score the winning run. Even though mistakes were made by other Red Sox players throughout the series, Buckner became the scapegoat for a disenchanted fan base.
Gregg Schoppman is a principal with FMI Corporation. He may be reached at 813.636.1259 or via email at email@example.com.