Corporate Real Estate Can Be a Profit Center

Microsoft made economic history on March 13, 1978, when management decided to relocate from Albuquerque, N.M. to Seattle, Wash. Seattle was chosen from a slate of candidates that ranked California first (favored by Bill Gates), with Dallas-Ft. Worth a distant second and Seattle third. Paul Allen championed Seattle because he favored a place where the gray skies encouraged people to work more, and where – compared to California – employees, once hired, were easier to keep. By December 1978, the entire company relocated to Bill Gates’s and Paul Allen’s home town, forever changing the economic histories of New Mexico and Seattle.

Microsoft’s relocation was done with very little of the cost analysis expected in traditional corporate site selection. No site selection consultants were used, and incentives apparently played no role in attracting the company to Seattle. It is easy to dismiss this choice as merely the personal whims of Bill Gates and Paul Allen. However, we contend that it actually reflected a high level of sophistication in understanding the fundamentals of their industry and in selecting the most potentially profitable location for their growing company.

Microsoft’s decision highlights how different high technology companies are from much of conventional industrial or commercial business. The modern high technology concerns that we study usually have sets of business fundamentals that are quite distinct from those of more traditional industrial or commercial enterprises. These differences drive them to site selection processes and development patterns that depart considerably from the familiar best practices and metrics of “industrial age” firms.

Exposed vs. Sheltered High Tech Industries

The high technology sector of the 1970s US economy was dominated by companies in areas like defense and aerospace, mainframe computers, and telecommunications equipment. These share a common feature – to a large extent, they are sheltered from exposure to broad competitive forces because of market restrictions and government regulation. These industry segments grew into tight oligopolies or monopsonies (several sellers, one or few customers) like the defense industry. Even in the more cyclical segments (like aircraft manufacturing) they tended to be relatively stable with fairly predictable single- or low-double-digit growth rates. Facilities demand, thus, was fairly predictable.

Today’s dominant high technology companies are in relatively new and fast-growing segments, such as microcomputers, software, the Internet, semiconductors, and personal communications. These are exposed to turbulent environments of rapid technology, market, and competitive change. A fundamental requirement for success is the ability to execute faster and more effectively than competitors accessing the same or similar technology bases. Time to market and time to money are critical factors. Facilities, in turn, can make a positive contribution to company profitability in these areas and can even function as a profit center.

A key distinguishing characteristic of the new, exposed high tech industries is their rapid and unstable growth rate. Over the past five years, some of the slowest growing industries in this segment – electronics equipment, for instance – have an average sustained growth rate of 10+ percent per annum. Semiconductors are growing at 16+ percent, microcomputer software at 30+ percent, and Internet services companies at triple digits. But this growth is often unstable. Recently, semiconductors experienced sales growth of 40 percent in one year and negative growth the next year. Rapidly growing, shrinking, and changing markets are inherent in this intensely competitive and turbulent environment.

The companies thus face a very different set of fundamentals from those of traditional industrial or commercial or even high tech companies. Boom times tend to generate tremendous wealth and with it, demand for infrastructure support from the communities in which the companies are located. In communities lacking sufficient slack capacity or that are unprepared for rapid expansion, that demand translates into severe pricing pressures on local resources in short order. Similarly, a bust can quickly devastate a community overly dependent on one product type, market segment, or employer.

Individual companies’ fortunes can vary more widely. Some companies that enjoyed explosive growth can achieve stability, such as America On-Line, Intel, and MCI. Many others, however, are now but footnotes in industry histories. Industry veterans know how precarious their fortunes are, and plan facilities accordingly.

During boom times, many high technology companies have high profit margins, which allow them to spend money on lavish facilities that can just as quickly become a millstone if conditions change. The reluctance of cash rich companies like Microsoft to own fixed assets or make long-term commitments to facilities is not uncommon. Dr. Andrew Grove’s dictum “Only the paranoid survive” has more than a grain of truth in it, but few communities truly understand the fragility of high-tech success.

‘Recycle’ Expertise

High technology industries tend to be glamorous. Success stories are common in the popular press, and industry people who “made it” are soon folk heroes. But the reality of high-tech start-ups is very different. The vast majority of high tech initiatives fail. Established venture capitalists will examine at least 20 proposals for every one actually funded, and fewer than one out of 10 of those start-ups is a success. Of the remainder, about two-thirds are described in terms like “walking dead,” and the rest are write-offs. That means there is an overall “hit” ratio of less than one percent. That and the high rate of business failure make a vibrant entrepreneurial environment a key variable in location decisions.

Access to venture capital and sizable “surge tanks” (established firms or other start-ups) that can absorb and recycle talent from the failures without the cost and disruption of relocation can be important community competitive advantages. Many communities can support a single or even a few start-up companies, but few can sustain large clusters of companies that allow people to advance readily and change jobs as ventures come and go. While remote or rural locations can attract many high-tech professionals, the lure of major clusters is hard to ignore in the longer term.

Thus, “exposed” high-tech businesses routinely experience rapid boom-and-bust cycles. Even a successful company cannot assume its success is permanent – or even long-term. These fundamentals, in turn, redefine best practices in site selection and facilities optimization. Understanding these fundamentals can significantly improve the productivity and profitability of high technology facilities and firms.

Make Facilities A Profit Center

The essence of our approach to improving high technology facility profitability is to use a Life Cycle Cost of Ownership strategy. From this perspective, the question becomes: How do we translate the fundamentals into metrics for site analysis, selection, and development guidelines? To do so, one must be able to:

• understand a particular high-tech company and/or industry’s fundamentals
  
• be able to operationalize that into quantitative variables
  
• understand, in aggregate, how such a set of variables relates to the bottom line
  
• make sensible trade-offs between sets of site features
  
• build in flexibility and cover risks
  
• and select the best financial choice over the life-span of the facility.
  

Such analysis is technically feasible, but it requires a level of analysis and granularity of information well beyond those used in traditional site selection and analysis.

Technical Approach To Site Analysis

We began our work by studying the fundamentals of state-of-the-art semiconductor wafer fabs. We found that, aside from generalizations about such issues as quality of life, it is possible to identify, quantify, and relate site features directly to projected differences in productivity and profitability. This can be done for a particular company’s wafer fab for a particular product at a particular site.

The real benefit is only realized when such factors are related to the bottom line over the entire projected life-span of the facility. Facilities Life Cycle Cost of Ownership analysis proved capable of identifying sites offering combinations of high productivity, low costs, and low risks. We also found, for example, that just a one percent difference in projected life cycle productivity for a semiconductor wafer fab wiped out the benefits of most government incentives. We have since extended our work into other types of high technology operations with similar results.

Dynamic vs. Static Assessments

The key to this approach is the use of a dynamic modeling architecture that looks at the entire life cycle of a facility from initial conception, site selection, design, construction, ramp-up, maturity, and decommissioning. Just assessing overall cost structures and “weighing them” in a static model is not only insufficient, but deceptive. We found that many communities that appear similar at the outset, when modeled based on our dynamic modeling heuristics and metrics, actually have significantly different likely trajectories, and, hence, life cycle costs.

Because many high-tech industries are such intensive users of resources and grow rapidly, a modest population of relatively successful high-tech firms can readily “tap out” a community’s resources or hit severe growth constraints. When these walls are hit and cannot be bypassed, it forces the companies involved to make expensive relocations to other communities in order to continue to grow. Unlike industrial age relocations where moves could be made relatively inexpensively, a move that disrupts the lives of key employees for a month is a substantial hit when the life of a product can be as short as six months.

In our model, communities that have large resource “surge” capacities tend to come out well in life cycle costs. Second, communities that go out of their way to provide high quality services to support their local industrial base tend to score well. For example, the level of business taxation, per se, may not be as critical as the value-for-money that the firms involved receive for those taxes. Indeed, in our model, tax abatements that are likely to lower the level of critical services can raise project risks or have a negative impact on life cycle productivity.

Taking this dynamic approach, our model identified the following important factors in profitability impacts on high-tech industries.

• System speed and cycle time is critical.
  
• Opportunity cost is a significant issue in making trade-offs between facility and site choices.
  
• Productivity is most critical, and overall costs second. Productivity improvements must be feasible and worth the additional costs.
  
• Productivity must be viewed and measured in dynamic terms.
  
• Strategy of flexibility is necessary so facilities can be expanded or contracted with changing market conditions and needs.
  
• Facilities must fit tightly with business plans.
  

Companies selecting sites and facilities based on this approach tend not to choose the cheapest sites, but rather those offering a combination of high productivity and modest costs with low risks and considerable flexibility. Our study of semiconductor wafer fabs found that these attributes, over the life of the facility, gave companies considerably lower life cycle costs and higher profitability from their sites.

In some of the cases we studied, the company chose a site that experienced so many delays that they missed market opportunities for an entire generation of products, resulting in cost overruns that greatly exceeded the incentives they received. Because much of these costs tend to be opportunity costs, they are not always obvious. Only an in-depth dynamic modeling approach has proved capable of estimating these kinds of likely differences in profitability between sites.

Opportunity Costs

One of the truly distinctive characteristics of exposed high technology fundamentals is the exceptionally high opportunity costs in the industry. Many high-tech products are aimed at a narrow market window. If a product is delivered on schedule, it makes a large profit margin that enables the firm to go on to pay for future product development. Should the product be as little as six months late, it could see its profits evaporate as competing products beat it into the market. A company that misses the peak profit period can find slim profits or even losses for years until a new market opportunity turns up.

Another source of high opportunity costs is the exponential growth rate experienced by many new high-tech product markets – from 20 percent to 100 percent or more a year. These markets grow fast and then mature with a “shake out” that ends with fairly stable market shares among the survivors. Having available product on hand to ship is critical to capturing market share during the rapid growth phases of a new product. Moreover, many high-tech products tend to “lock in” their users. Thus, for example, once a critical component or piece of software is adopted by a customer, there is a tendency to buy upgrades from the same vendor. Therefore, losing market share as a result of non-availability of a critical component or a missed schedule, can be critical in terms of both present and future profitability.

All of these factors point to one key high-tech industry fundamental: high opportunity costs when schedules slip. Slipped schedules, in turn, mean both lost short-term profits as well as potential loss of future revenues. High opportunity costs mean that companies especially value speed and cycle time. In terms of the overall cost structure, conventional measures of facilities and location-related costs can be much less important than the ability of a community’s infrastructure to respond quickly to company needs.

Our assessments and modeling of likely responsiveness of various sites found that there are dramatic differences in such performance. The best sites are able to respond quickly not only initially, but over the life of the project as needs and priorities change. Indeed, one of our startling observations is that sites that start out low in static rankings can, over time, exceed the performance of incumbent sites with higher initial rankings. This throws into question the value of classical site selection tools like published rankings of sites for high-tech locational decision making.

Productivity First, Cost Second

Our research into locational and infrastructural factors impacting productivity in high technology industries demonstrates that productivity varies greatly among different locations and different companies, managements, and products. What separates companies is how they integrate and make use of widely available technology toolkits. Although they have the same basic access to know-how, the performance of high-tech companies varies widely. Our research indicates that not only is a company’s management skills critical to productivity, but so is a community’s, region’s, or nation’s infrastructure. In our study of such infrastructure, we found that significant contributions to productivity can be made by improving and tuning a community’s infrastructure to service local industry needs. Differences in productivity among communities form a self-reinforcing pattern that strengthens high-tech clusters in particular locations. The classic example is San Jose, where the density of expertise, talent, capital and other infrastructure forms a high productivity environment for many technology industries. Much of this impact of community infrastructure and related firm clusters on productivity can only become apparent with dynamic modeling.

Do Facilities Fit the Business Plan?

High technology products that sell into rapidly changing markets put a premium on having facilities that “fit”. Excess or inadequate facilities are good ways for a high technology company to lose money or to incur opportunity costs. Because it is virtually impossible to forecast with certainty where a business is going to be more than two or three years out, business plans are in a constant state of flux. As they change rapidly, so must facilities and facility planning. Thus, many high-tech companies avoid long term, low cost, fixed leases in favor of relatively higher cost, short-term contracts. This is the case even in the highly capital-intensive segments of the semiconductor industry. The best practices of the firms we studied aim for a large degree of freedom in facility commitments, in such things as options for expansion or low-cost exit strategies. Thus, commitments that give large “signing bonuses” for long-term commitments tend to be regarded as potentially risky.

High technology industries exposed to intense competition have very different facility needs from traditional industrial concerns. Their needs, in turn, can best be understood in terms of infrastructure that matches their distinctive business fundamentals. These fundamentals can be summed up as: high opportunity costs, rapid business growth and change, and the need for flexibility as business plans change.

Communities that understand this and can provide an infrastructure that is responsive, flexible, and tightly integrated with their local firms’ needs have an advantage over others. If a firm can achieve higher productivity than their competitors at any given cost structure at a particular location, they have a considerable advantage.

Communities that offer only traditional tax incentives and other up-front bonuses to attract new firms tend to overlook these upside opportunities. Companies that select sites on the basis of a dynamic assessment have the opportunity to capture and monopolize sites that are particularly more productive and profitable. Dynamic modeling of a company’s Life Cycle Cost of Ownership clearly identifies those sites with long-term competitive advantages. SS