Chapter 4

Porter’s Five Forces Model, Part 1

Barriers to Entry

Introduction

The economic theory about industry structure that you read in chapter 1 implies that profitability in an industry should go to zero in equilibrium (i.e., the accounting profits should equal the opportunity cost of the investment). However, we know that some industries appear to be exceptionally profitable while some are desperately troubled. For example, Table 4.1 lists the top five and bottom five industries in terms of profitability (return on sales [ROS], a profit margin calculation) from 2007 according to Fortune magazine.¹ Clearly, the industries differ pretty widely, but are these interesting results from that economic point of view? What explains the differences in outcomes? With a little work, we can estimate the expected return for the industries (i.e., the opportunity cost) and determine whether some industries experience above- or below-average returns.

Aswath Damodaran presents the following easy equation for this: expected return = risk-free rate + (β) (risk premium).² For 2007, the risk-free rate was about 4.6% and the risk premium (average stock market return – risk-free rate = 5.5% – 4.6% = 0.9%). He also estimates industry betas (β) or volatility indices. His data indicates, for example, that the oil production, pharmaceutical, grocery, and semiconductor industries have betas of 1.30, 1.14, 0.78, and 2.11, respectively.³ Given the previous equation, we should expect returns for these industries to range from 5.3% to 6.5%, yet the actual results are vastly different. Some industries (the top five) earn returns greatly in excess of the opportunity cost while others earn far below. Why? Michael Porter argues that industry-specific structural differences can generate these results.

Porter’s Five Forces model is one of the best known and most used techniques for assessing the factors that most directly affect the profitability of an industry. Porter was heavily influenced by economists (particularly J. S. Bain and his protégé, E. S. Mason) working for the U.S. government in the 1940s, ’50s, and’60s. These economists, while investigating industries that were believed to be generating unusual profitability, developed an approach that emphasized the relationship between structural characteristics of the industry and subsequent conduct and performance of the industry. Porter systematized this as five forces.⁴

The basic idea draws heavily on rejecting the key assumptions made earlier in the microeconomic discussion about industry structure. That is, if we were to relax those assumptions, such as free entry and exit, what would happen? Depending on the structural force, Porter showed how industry profits could be protected (as in the top five industries, perhaps) or drained away to benefit others outside the industry.

In this chapter, we’ll focus on analyzing one of the forces: barriers to entry. We’ll address the other four forces in chapter 5. The approach, as in using the PEST model, is to ask and answer particular questions and then analyze what the answers mean. At the end of this chapter, you’ll be able to describe the extent to which you expect potential entrants could dissipate industry profits.

Barriers to Entry

Barriers to entry are structural industry conditions that deter potential entrants because the cost of overcoming those conditions exceeds the profits the entrant anticipates. Recall how the presence of above-average profits—those greater than the opportunity costs—incites entrants because they want some of those profits. However, if the potential entrant concluded that costs are greater than profits, is it rational to enter? Such high costs keep rational potential entrants out and the industry incumbents continue to enjoy high returns. Our objective here is to understand whether this deterrence exists. There are many potential barriers to entry, but we’ll analyze in detail a set that includes economies of scale, differentiation, access to distribution, learning curves, switching costs, government policy, and the threat of retaliation. However, in doing so, you’ll develop, or model, an approach that can be extended to any other barrier. In assessing each barrier, we’ll first establish what it is (a definition or standard; what it is we look for) and why it creates costs for entrants. Then, we’ll focus on the key questions, such as, “Does the barrier exist in this industry and is it significant?” Finally, we’ll examine how to analyze the results and draw appropriate conclusions.

There are a couple of key points to keep in mind as you go forward. First, this is a tool for analyzing an industry as a whole, not an individual firm. Second, establishing what firms are members of the industry is critical, and this is why industry definition (chapter 2) is so important. An overly broad or narrow definition can radically alter (and weaken) your findings.

Economies of scale (EOS) exist when the average cost of production decreases as output increases. It is useful to remember some basic accounting equations here:

,

where AC = average cost, TC = total cost, and x = units of output or production. We can break this down a bit further:

,

where VC = variable cost (usually material and labor) and FC = fixed costs (usually capital equipment dedicated to production). Fixed costs (FCs) are those that must be incurred even if production drops to zero. Plug this into the first equation to get

.

This is equivalent to

or

.

In other words, the variable cost component of average cost remains the same (as long as the technology doesn’t change), but the FC/x element continues to shrink as output increases because that cost is spread over more units. The associated cost curve will look like Figure 4.2. Obviously, EOS are most often found in industries where production has been highly automated.

Another key point about Figure 4.2 is the point labeled MES. This stands for minimum efficient scale, or that production level where the cost curve essentially flattens out. Firms that produce at or beyond this level face about the same AC, but those firms that produce at a lower level face higher—sometimes much higher—average costs. MES is a function of where the product (or service) is produced, which is usually a plant.

EOS constitute a barrier to entry for several reasons. Let’s assume for now that industry firms are price takers, which means that products are basically the same and buyers care most about price. This is captured by pricing at the P1 level in Figure 4.2. Note that if a potential entrant comes in with a production facility that is not at MES (some output to the left of MES), then the product cost may be too high. In fact, cost might exceed market price, which means losing money. If the cost penalty for entry under MES is high enough, the entrant faces losing all the potential profits through excessively high production costs. Therefore, a rational manager would not want to enter this industry if that were the only choice.

Could a firm enter with production at MES? Yes, but recall from chapter 1 what happens: The supply curve shifts to the right and the prices drop. In fact, if MES is large enough, the supply curve shifts out quite far and drives pricing to unprofitable levels. Again, a rational manager would not want to enter.

There are two key questions to ask about EOS as a barrier. The first is, “Do EOS exist in this industry?” You will need to understand the economics and the firms of the industry well to make this determination. You might look for EOS from the following sources:

• Technical economies. These are usually by far the most significant (and are reflected in the discussion of plants and MES). Technical economies arise from the replacement of labor with capital, so we should expect to see them in industries that are highly automated or capitalized.
• Purchasing or commercial economies. This is the benefit of bulk buying.
• Financial economies. Sometimes the sheer size of firms can give them clout in borrowing money at lower interest rates than those available to smaller firms. What would you need to know here?
• Marketing economies. These can be achieved by spreading the high cost of advertising on television and in national newspapers across a large level of output.
• Managerial economies. The administration of a large firm is improved by dividing management jobs and by employing specialists such as accountants, sales reps, and so on. Is this common or uncommon?

What you’ll be looking for here are industry references, such as those in industry journals or trade association magazines, to topics such as purchasing terms or the scale of new plant construction. You might even find stories about single firms that appear to address EOS issues. These data can be cautiously extended to the entire industry if the firm is fairly typical in terms of size and configuration. (Although, note that anything characterized as unique to the firm or interesting is likely not extendable!)

If you do find evidence forEOS, the second question you must answer is, Are they important?” What you want to accomplish here is an assessment of the potential problems with entry at scale and under scale. For the first, you are assessing the relationship between the MES for the EOS effect you’ve found and industry supply. The point is, if the MES is large as compared to current output (or if there appear to be oversupply problems in the industry), then an entrant is likely to find entry unprofitable. If the MES is small, though, entry is less likely to distort supply enough to cause profitability problems. Here are some examples.

In the brewing industry, MES has been estimated at around 5 million barrels per year at the plant level and as high as 21 million barrels per year at the firm level (The distinction between plant and firm MES is due to the fact that competition is national in scope though plants have a limited geographic service reach—hence, larger, multiplant firms). Overall national production in the industry about 200 million barrels so a plant at MES represents about 2.5% of the supply. A firm trying to enter on a national scale at MES needs to add about 10% of existing supply. Both of these figures are large (especially since brewing industry growth has been flat to slightly declining for quite a few years).⁵ Entry at scale will shift supply significantly and likely drive down prices and profitability, so EOS are a relatively high barrier to entry. In banking, MES has been calculated at about $2 billion in total assets. According to the Federal Reserve, assets for all U.S. commercial banks total in excess of 11 trillion dollars, so the $2 billion MES is far less than 1%.⁶ Therefore, a new bank can enter without substantially shifting supply, and EOS are not a significant barrier. In long-haul trucking, MES has been estimated at around 500 million ton-miles, which is about one half of 1% of the overall market.⁷ Shearer and his colleagues collated data from several industries and showed that the MES as a percentage of the U.S. market varied widely: The MES for refrigerator manufacturing was around 14% (this would be significant!), but the shoe production MES was only 0.2%, and cement was 1.7%.⁸

A caution, though if production technologies change, what incumbents do from an EOS point of view might not matter. An example is the steel industry, where for decades steel was produced at highly automated integrated mills (like U.S. Steel or Bethlehem). These could begin with raw material of iron ore and coke and finish with rolled steel. The EOS here were enormous (several million tons of steel per plant), and for quite a while, they constituted a significant barrier to others wishing to enter the industry. However, in the 1970s, new technology emerged in electric furnaces that permitted the production of certain grades of steel at much smaller scales. Firms like Nucor or Chaparral Steel then entered with plants at one-tenth the scale of integrated firms.⁹

If the MES is high, an alternative for a potential entrant is to come in under scale. Clearly, given the shape of the curve in Figure 4.2, this means the entrant will produce at a higher cost—but how much? No two industry cost curves are alike, and the flatter the curve, the lower the cost penalty. As an example, in the brewing industry, unit capital costs for a plant at one half of the MES increase by 33%.¹⁰ Similarly, in refrigerators, plants at two-thirds MES suffer a 6.5% cost penalty and in steel, plants suffer an 11% penalty.¹¹ These are pretty high cost increases, particularly if there is fairly standardized product pricing across the industry. Entering under scale in these industries can put entrants at a cost disadvantage, dissipating profits and making it less rational to enter.

Ultimately—and this is true of your assessment of all parts of this model—you will want to formalize a conclusion about your findings regarding EOS as a barrier. If you found that EOS do not exist in the industry, what would you conclude? If EOS did exist and the MES was a sizeable portion of demand, what would you conclude?

Differentiation is a popular word, and people use it in very distinct ways (from simply meaning different to a technical term from calculus). The word also has a very specific meaning in strategic analysis. For us, differentiation means that customers find extra value in a product or service and are both willing to pay more for it and are brand loyal. Note that for whatever reason when people care about a product or when they are loyal, consumers suspend their microeconomic assumptions that all products are the same and that firms are price takers.

For instance, some people are Coca-Cola drinkers, while others prefer Pepsi, and some have no preference at all. Are soft drinks an example of a differentiated product? Let’s see how well this industry fits the definition. Are there different price levels or strata in this industry? Yes, in that Coke and Pepsi are premium priced products, generic soft drinks form the lowest price level, and in between there is a set of products that often fall between the two extremes in price (such as RC Cola, Orange Crush, Schweppes, and so on). Are soda drinkers brand loyal? We define loyalty as the willingness to purchase the same product or service when free to choose otherwise. In general, Coke and Pepsi (and many other competing products) are equally available for consumers (bearing in mind that restaurant chains often exclusively contract with one or the other major brand). If total market shares for differentiated products remain constant or even grow, then we can conclude that buyers are loyal. Is that the case here? The combined market shares of Coca-Cola and Pepsi increased from about 54% in 1966 to 74% in 2004 but slumped somewhat sharply since then to 60%.¹² Therefore, we can conclude that for most of the period, buyers were brand loyal, which makes these products differentiated. Still, if the data are correct about current trends, this is less the case now than it was just 5 years ago.

Differentiation can create a barrier in that entrants have to convince current product buyers to switch in order to gain business. However, if buyers are loyal, what motivation do they have to switch? In some way, entrants have to alter the value proposition for their product and both convince and inform customers about it. Our issue is really the second point. How would a new firm get this message across? Attracting new customers is substantially more costly than retaining them. A rule of thumb is a 5:1 ratio on costs of acquisition to retention, though in some industries, it can be much higher (e.g., 12:1 in the telecom industry).¹³ Therefore, if entrants had to persuade loyal customers to switch through intensive advertising, samples, price cuts, and so on, then they would face incurring costs that incumbents do not. Therefore, potential profitability erodes and the likelihood of entry diminishes.

The two questions you need to assess for differentiation as a barrier are, first, is this industry characterized by differentiated products or services? Are customers loyal and willing to pay more? Based on the previous example, you know to seek evidence of price level differences or strata as well as some indicator of loyalty. Make sure that the higher prices reflect real choice and loyalty. If there is no real choice (limited competition as a cause, for example), people will pay more, but they will not like it. For example, in the air carrier industry, business travelers flying on short notice and in the middle of the week usually pay much more than passengers who have purchased tickets well in advance. The business travelers are not paying more out of loyalty but out of necessity because that flight is the one that gets them where they need to go and at the right time. Also, some consumers may be very willing to spend more but definitely not be loyal. Think about consumers of craft beers or wine who will willingly pay a price premium for a product. Are they loyal to a brand? Alan Newman, president of Magic Hat Brewing Company, has estimated that only about 10%–20% of the consumers of his beers drink Magic Hat 80% of the time. Rather, he characterizes these consumers as more loyal to a category of products.¹⁴ Wine drinkers also do not exhibit significant loyalty to a specific winery. Rather, they tend to be consistent in their purchases of a particular style of wine (such as Chardonnay or Riesling) or a region (such as wines from California or Italy).¹⁵ The second question is about the extent to which differentiation (if it exists) characterizes the industry. Here, imagine what an entrant would face in the soft drink market if, as loyal as Coke and Pepsi drinkers are, they comprised only 10% of the buyers? What would be the difference to an entrant if the loyal buyer share were 90%? In general, the more the market is made up of the loyal purchaser, the higher the barrier differentiation is going to be.

Again, draw a conclusion based on your findings about differentiation as a barrier. If you found that differentiation does not exist, what would you conclude? If it did exist and the share of loyal buyers were a small component, what would you conclude? What if the share of loyal buyers were high?

Access to distribution assesses the channels of distribution potential entrants have to use in order to provide their products or services to buyers. For instance, grocery stores are a distribution channel for foodstuff producers. In the brewing industry, beer distributors are the first part of the channel, then come retail outlets such as stores and taverns (though, to a limited degree, brewers can go directly to some retail outlets). In the auto industry, dealers are the distribution channel. The key issues for us are, first, describing the channel correctly and, second, determining if there are constraints, or bottlenecks—or the degree to which the channel is controlled by industry incumbents.

In the auto industry, for example, potential entrants need to find some way to put cars in front of prospective buyers. Historically, this has meant working with auto dealerships, as they are the immediate sales medium. What problems might a new auto manufacturer find in trying to line up dealers? First, most new car dealers have a flagship brand, such as GM or Ford or Toyota or Honda. They often have additional brands to complement the first but not always. Chances are a one-brand dealership will not take on other lines. Multibrand dealers might, but at what cost? Given space, capital, and personnel constraints, dealers that accept a new line will have to cut back on inventory (and sales opportunities) of existing brands. If the new brand didn’t sell well, the dealer would be damaged—so how willing would they be to take on new, unproven lines?

Grocers face a similar problem. Shelf space is limited and including a new product almost always means getting rid of some other product. This is a risk because the new brand may not sell. Some estimates are that 70% of new grocery product introductions fail,¹⁶ representing an opportunity cost for the grocer. This is why they usually charge (often quite substantial) slotting fees, which can vary with the attractiveness of the shelf space.

Brewers, as mentioned, also have some channel issues. Federal law mandates that brewers work through distributors. Distributors are generally limited to a specific geographic area and, like car dealers, have a flagship brand or brewer. Anheuser-Busch’s (A-B) distributors, for instance, often carry only A-B products or those they are authorized by A-B to carry.

You can see that gaining access to the channels of distribution may not always be easy or inexpensive. In general, the fewer channel outlets there are (because they are geographically bound or limited by law) or the more existing competitors control them, the more costly it is to gain access. Since accessing channels is a cost entrants bear, which incumbents do not, this erodes the potential profits of entry, constituting a barrier. Certainly, in some cases, entrants could attempt to create their own channels, but this would be very costly and certainly something incumbents do not have to do.

The key points to assess for access to distribution deal with understanding precisely how new entrants can place products or services before the buyer and identifying the control and constraints, if any. To the extent that incumbents do control the channels or law or structure (such as sparsity or geography) limit access, then the channels will require expenditures to access. For example, a new car manufacturer can implement large-scale advertising campaigns or underwrite extensive guarantee programs could help sales, but these are costly and erode potential profits. Alternatively, a potential entrant can consider creating its own distribution system, but this too could be costly and time consuming. And as the costs increase, so too does access to distribution as a barrier. As always, draw a conclusion from your findings.

Learning curves (LCs)look like EOS—at least graphically—and create a similar cost disadvantage for prospective entrants, but the source of cost savings is completely different. In EOS, the source of cost differences lies in the way average cost diminishes as fixed costs are spread across higher output. The key issue in LCs is that the cost reduction comes from reduced labor (i.e., the benefit is in variable costs).

LCs were described first in the aeronautical industry as a way to explain why some frame manufacturers were consistently able to bid and build planes at lower costs than other manufacturers were. Inquiry found that workers who consistently did the same sorts of tasks got better (i.e., more efficient—that is, less time required, hence less labor cost). If you’ve ever put together a bicycle or other object complex enough to require instructions, you’ve probably seen that if you had to do it again, you could probably do it faster—which demonstrates an LC. Thus, firms that have experience will face lower cost structures than those that do not and this is not easily overcome. Figure 4.3 shows an average cost curve for an industry. Let’s suppose that incumbents are already operating along the flat, lower portion of the curve while entrants have to start at the top left portion of the curve. If prices were already established at P1, then new entrants would have to sell at that price. In other words, until entrants get enough experience to move down the cost curve, they sell at prices lower than their average costs. The gray shaded area of Figure 4.3 shows the losses entrants incur to get to efficient production levels—and if those losses are greater than the expected profits, firms are dissuaded from entry.

Technically speaking, LCs capture efficiency gains in terms of how much labor is required as output doubles. An 80% LC means that every time production is doubled, the labor required to produce output at the point is decreased by 20% (or is just 80% of what was previously required). Not all industries (or even many) exhibit significant LCs. What really matters here is the task or process. Processes that meet three criteria are likely to exhibit LC effects. First, the process must be labor intensive. That is, if you analyze the

equation, FC has to be low, and since VC = (M+L), the L component (labor) has to be comparatively large.

Second, the process or task has to be complex. This is important because simple tasks do not permit or require much learning (that’s why they are simple). Lots of jobs have a high labor component but are too simple for learning to reduce significantly the time spent per iteration. Third, the process must be repetitive. The core process has to be done the same way every time. Thus, there are jobs that are labor intensive and complex but differ in greater or lesser degree every time they are done (construction might be a good example). If these three conditions are met, then it is likely the process exhibits significant LC effects.

It is probably apparent that LCs differ across industries. Some industries like aerospace or shipbuilding (or even locomotives) demonstrate LCs in the range of 80%–85% because the jobs are very complex and very labor intensive. Moreover, workers build the same model repeatedly. Other industries or processes (e.g., welding or electronics manufacturing) show LCs of 95%.¹⁷ Some industries (especially bulk and continuous processing where automation investment is high) show virtually no LC effects. Certainly, service industries show little to no LC effects, as each transaction is unique. Deep curves (like shipbuilding) mean there are significant cost advantages for incumbents. If the curve is 95%, then it is shallow and incumbents have less cost advantage. The deeper the curve, the more difficulty entrants have in overcoming the lost profits. Remember to draw a conclusion from your findings.

Switching costs are the sunk cost of investments customers have to abandon or absorb in order to switch to a new product. These are often expenditures for complementary goods or services to make a focal product work and are above and beyond the cost of the product itself. For example, if you owned a PC and decided to switch to a Mac-based machine, would you face particular transition costs to make the move? For example, would you also need new software? New peripheral devices? Don’t forget that you would still have to convert data in old formats to new formats (or find and buy a bridge program that did it for you). Are training costs involved (even if not actual expenditures, then the cost of time you might have spent learning how Word for Vista differs from Word for XP)? This might be aggravating for individuals, but imagine the cost to a firm in making a similar transaction. So, if sunk costs and complementary investments are high, switching costs tend to be high.

In fact, if the cost of switching is high enough, customers will not be motivated to make the move unless the new entrant can make it less costly. They can do this by providing training or installation, or coordination conversions, or any of a host of other ways, depending on the product or service. What these solutions have in common is that they all cost the entrant money that incumbents don’t have to spend. This erodes the potential profits that accrue to entry, thereby making the industry less attractive. The key test for this, then, is to consider whether the focal industry product or service is complemented by other investments in hardware, software, learning, or other difficult to reverse investments of money or time. Note that the prior investment would have to be useless in the new context!

Restrictive government policy also affects the ability of firms to freely enter the market. This is clearly the case in regulated industries, such as the air carrier industry prior to 1978, public utilities, or even the restaurant and tavern industry in most states. To illustrate, if a person or a firm wants to open a business where alcohol is consumed, a liquor license is required, and getting one isn’t just a matter of asking for it. In Pennsylvania, for example, the Liquor Control Board of Pennsylvania restricts the number of licenses available in a county to roughly 1 per 3,000 residents. If all available licenses in a county were issued, then potential entrants would be out of luck. This is not to say that one could not be transferred or purchased (there are a number of firms that could coordinate the transaction), but the cost is reportedly high, ranging from $5,000 to $400,000 depending on location.¹⁸ Similarly, the City of New York limits the number of taxi medallions and auctions them off. As of last year, the price to be a legal taxi operator in the city had risen to $766,000.¹⁹ Thus, government policy can significantly affect the cost of entry.

Policy can also dampen competition through mechanisms like patents. In the pharmaceutical industry, for instance, as long as a drug is still under patent, no potential entrant can produce the identical compound. Similarly, government policy may preclude entry by foreign firms in key domestic industries altogether.

The key questions are, is this industry regulated by government? Are there restrictions on entry, and what is the cost of meeting or working around those restrictions (if possible)? Remember to draw a conclusion from your findings.

The final barrier we’ll examine is the threat of retaliation from industry incumbents. Remember that new entrants reduce the profitability for all, so in some cases incumbents may be motivated to threaten to act to reduce the attractiveness of entry by reducing profitability prior to entry. There are, as we’ll see, many ways to do this but the key point is that if the potential entrant believes the threat, he or she perceives the industry to be less profitable even before entry and certainly even worse afterward. Therefore, the likelihood of entry is reduced.

Note carefully how this is worded: the incumbent does not actually do anything. (In fact, the incumbent really doesn’t want to do anything. Doing something really does hurt profitability!) Still, retaliation is a more credible or believable threat under certain conditions.

Potential entrants might more reasonably be concerned about retaliation if prior entrants have stimulated a strong response, if industry growth is slow, if there are high FCs, or if they appear to have the resources required to sustain the response. We’ll explore these conditions more when we discuss rivalry in the next chapter (for these conditions tend to lead to higher internal industry rivalry as well), but we can get a sense of the logic here by focusing on one element. Consider the FC problem. If FCs are high, then typically the incumbent firms want lots of volume to cover those costs and reduce average costs. New entrants push the supply curve to the right, creating excess capacity in the industry. This means that there is less volume per firm (all else being equal) so average cost increases, and since equilibrium prices drop with the new entrant, profits really dive.

How can incumbents threaten to retaliate? Mostly, they get the point across through public announcements and tactical actions. For example, in an industry where FCs are high, a firm might announce that it is proceeding with plans to add a new plant. (What would this do? Why is it a warning to potential entrants?) Tactically, firms might engage in price wars. Dropping prices without altering the cost structure of the industry automatically reduces profitability for entrants. The nice thing about price wars is they can (usually) be easily reversed once the threat diminishes.

The key issues here are to diagnose the condition of the industry and incumbents as described earlier. The more of these that apply, the more likely entrants believe they would face retaliatory actions, thereby increasing the barrier to entry.

What we’ve covered here is by no means an exhaustive list of barriers to entry Barney²⁰ covers issues like proprietary technology, favorable access to raw materials or geographic locations, or plain know-how among incumbents. Porter also addresses capital requirements (the idea being that the more money is required to start up operations, the less likely potential entrants will attempt entry) and cost disadvantages independent of scale. These include control of proprietary technology, favorable locations, and government subsidies, among others.²¹ An online search brings up yet more possible barriers. As you learn more about your industry, you may refine your list of barriers and include some that may be quite specific and exclude others. The key is to realize that you know which questions to ask: What does this barrier mean (i.e., what are the definitions or standards); why is it a barrier; does it exist in this industry; and if it does, to what extent is it a barrier?

Understand that just analyzing a barrier or reaching conclusions about a set of barriers is not enough. Ultimately, the idea here is to assess all the barriers (or those in your experience you think relevant) and reach a conclusion for the industry as a whole. When you analyze a barrier, you must reach a conclusion about whether it is strong or weak with respect to inhibiting entry. If a barrier is strong (or high), entry is more difficult and profits for entrants diminish. You should do the same for barriers as a class. This is, at least for now, not difficult. It is not crucial to be overly precise or worry about whether one barrier is stronger than another. We are interested in an overall view of the industry and still have four forces to go. Therefore, I recommend a simple (–1/0/1) scoring system for each force where –1 = weak and 1 = strong. If, for example, EOS exist in your industry and you deem them to be significant, score it a 1. When you do this for all barriers, you get a sort of composite and easy to understand score. In Table 4.2, I’ve analyzed an imaginary industry and found that four barriers are strong or high (or deter entry), one is low, and I am neutral on two. The highest score (exceedingly high barriers) is 7, the lowest score (easy entry) is –7, and 0 is neutral. How do you assess this industry? Is it easy to enter? Are profits being drained away to new entrants?

As a final observation, it is clear that if you are a manager for an industry incumbent, you want barriers to entry to be high to prevent others from competing profits away. It is a bit counterintuitive, but if you are managing a potential entrant, you also want high barriers. Under the assumption you can find a way to circumvent the barriers and enter, profitability will be enhanced because others are still barred.

Summary

In this chapter, you learned that certain industry structural characteristics act as barriers and can deter entrants and protect profitability. We covered seven such barriers. The key to applying a barriers analysis is to make sure you understand what the standard for each barrier is (i.e., what you are looking for), find the evidence for existence and relevance, and then draw an appropriate conclusion. No matter what you find here, though, recall this is just one (albeit the most complex) of the five forces. We’ll cover the remaining four in the next chapter.