Product Life Cycle Examples

Introduction to the Page

The standard product life cycle (PLC) depiction is idealized vision and is not always observed in practice. On this page, we present various PLC examples. Simple linear regression models are provided to better understand the underlying trend.

A few reminder notes about simple liner regression models:

• The independent variables (or the x variable) in these models is time, measured in years.
• The dependent variable (or the y variable) is a measure of industry volume. Due to data availability, we use various data to represent industry volume such as coal production in short tons to represent coal industry volume or commercial air passengers to represent air passenger industry volume.
• The R-square value represents the percentage of variance in the dependent variable explained by the model.
• The model-F is the ratio of explained variance to unexplained variance: a p<.01 means that the x variable and y variable relate in a meaningful way.
• The standard way of presenting a model is y = a + bx + e where:
  • a = the model intercept: note the intercept in our models cannot be interpreted since, for example, in the case of air passenger data, a is read as expected air passengers at the time of year zero or when Caesar Augustus was emperor of Rome.
  • b = rate of change between the x and y variables: Note that since our y variable is in years in increments of one, that b represents the average year over year average in the x variable. For example, air passenger model shows that a long run growth in air travelers of about 80 million per year.
  • e = the error term.
• We also present a second model in all cases accounting for relevant population growth. This is important as an increase in air passengers may simply reflect population growth. We need to ensure that we do not mistake product maturity rather than product growth.
• The important part here is to consider that a meaningful trend means that values in the data are increasing across time (a positive trend) or are declining across time (an inverse or negative trend).

Example 1: United States Coal Production (in the decline phase since about 2000)

Charts 1 and 2 respectively show United States coal production (in millions of short tons) and in millions of short tons per capita. These charts show long run production from 1850 through 2017. Data is not available for all years.

• In looking at the charts we can see that coal production with some variation appears to increased between 1850 and the end of the 2oth century. We therefore model the 1850-1999 and 2000-2017 time period separately.
• 1850 through 1999: The model shows a significant positive trend with short ton coal production increasing by an average of 7.744 million per year
• 2000 through 2017: The model shows a significant negative trend with short ton coal production declining by an average of 17.225 million per year.
• The models for coal production controlling for population growth provide a similar picture.

Major takeaway points: United States coal production, both in the aggregate and per capita experienced a century long introduction and growth phase spanning the 2000 through 1999 period. A period of maturity may be visually inferred from around 1980 through 2000. The post 2000 period is one of decline for the coal industry. Consumer demands for cleaner energy followed through with various state and federal legislative agendas appear to be pushing the industry, at least in the United Status toward diminished status.

Chart 1: Annual United States Short Ton Coal Production, 1850-2017 (in millions of short tons)

Regression Model: U.S. Coal Production (in millions of short tons); 1850-1999
R-square	.852
Model F (significance)	146.156 (p<.01)
Model	-14.650.691 + 7.744 x Year

Regression Model: U.S. Coal Production (in millions of short tons); 2000-2017
R-square	.731
Model F (significance)	18.345 (p<.01)
Model	35.639.106 - 17.224 x Year

Chart 2: Annual United States Short Ton Coal Production per Capita, 1850-2017 (in millions of short tons)

Regression Model: U.S. Coal Production (in millions of short tons) per Capita; 1850-1999
R-square	.516
Model F (significance)	19.644 (p<.01)
Model	1911.240 + 17.213 x Year

Regression Model: U.S. Coal Production (in millions of short tons) per Capita; 2000-2017
R-square	.885
Model F (significance)	54.334 (p<.01)
Model	2038.270 - 8.554 x Year

Example 2: Air Transport (long-term growth phase)

The next four charts illustrate historical trends in the air transport and passenger industry. Charts 3 and 4 show global air freight (in millions of ton – kilometers hauled) and global air freight per capita (in millions of ton-kilometers) from the early 1970s through 2017. Linear regression models show significant and continuous increases in air freight volume across time, both in total ton-kilometers hauled and in the amount when discounted by population growth. Indeed, year over year increases in air freight has typically increased by 47,671 million ton–kilometers per year between 1973 and 2017.

Charts 5 and 6 show air passengers (globally) and air passengers per capita during the same time period. Both simple linear regression models are statistically significant. Global air passengers are increasing by 71.756 million passengers per year. This is not a matter of population growth alone as the air passenger per capita model is also statistically significant.

Major takeaway points: Air transport is currently in a long term growth phase: i.e., both air freight and air passenger segments are in the growth phase of the product life cycle. Little evidence outside of these tables indicates that the trend will abate with entry into the maturity phase. Specific international routes are extremely valuable from a revenue perspective and will be sought out by carriers and protected by national interests. For example, total annual revenue on JFK (New York) – Heathrow (London) in 2019 reside at about $1 billion per year. The effects of the COVID-19 pandemic on the long term prospects in the air industry at the time of writing remain unclear.

Chart 3: Global Air Freight 1973-2017 (millions of ton-kilometers)

Regression Model: Global Freight Air Miles in Millions of Ton-kilometers; 1973-2017
R-square	.982
Model F (significance)	1,187.924 (p<.01)
Model	-9,225,515 + 47,671.144 x Year

Chart 4: Global Air Freight 1973-2017 (millions of ton-kilometers per capita)

Regression Model: Global Freight Air Miles in Millions of Ton-kilometers per Capita; 1973-2017
R-square	.987
Model F (significance)	1,569.411 (p<.01)
Model	-1,178,218 + 598.063 x Year

Chart 5: Global Air Passengers (in millions) 1973-2017

Regression Model: Global Air Passengers (in millions); 1973-2017
R-square	.954
Model F (significance)	432.599 (p<.01)
Model	-141,592 + 71.756 x Year

Chart 6: Global Air Passengers per Capita (in millions/billions) 1973-2017

Regression Model: Global Air Passengers per Capita (millions/billions); 1973-2017
R-square	.963
Model F (significance)	553.513 (p<.01)
Model	-16,863 + 8.579 x Year

Example 4: United States Car Sales (excluding light trucks) (in the decline phase since the mid-1980s)

Charts 9 and 10 show Unites States car sales and car sales per capita from 1963 through 2017. The general trend for both measures of market size is one of a slow decline. However, separate models for pre-1986 and post-1985 show that: (1) car sales were flat before 1986; and (2) car sales showed a statistically significant decline after 1985. Indeed, the model for cars (in units) show a year over year annual decline of 113,308 cars. From 1986 (11.4 million) through 2017 (6.3 million), the number of cars sold in the United States declined by more than 44%.

Major takeaway points: Due to changing consumer preferences, the market for cars in the United States appears to have entered the decline phase of the PLC, a phase that apparently began during the mid-1980s. American automotive manufacturers has shifted resources and production from cars to light trucks and SUVs.

Chart 9 United States Car Sales (excluding light trucks) 1963-2017 (thousands)

Chart 10 United States Car Sales (excluding light trucks) per Capita 1963-2017 (units per thousand)

Example 5: Cottage Cheese (long-term decline since the mid-1970s)

Chart 11 and 12 show United States cottage cheese consumption (in millions of pounds and in pounds per capita) from 1970 through 2016. The origin of cottage cheese may be traced back to ancient Mesopotamia circa 3,000 B.C. Both charts show a significant decline in the market for cottage cheese: per capita consumption has fallen from 5.1 pounds per capita in 1970 to 2.2 pounds per capita in 2016, a decline of 57%. The annual expected decline across the relevant time period is 0.71 pounds per year.

Major takeaway points: Changing consumer preferences resulted in a the slow and steady decline in demand for cottage cheese in the United States. Other sources indicate that cottage cheese (a salty product) has been displaced by increased demand for yogurt (often times mixed with sugar), suggesting an economic substitution effect.

Chart 1`: United States Cottage Cheese Consumption 1970-2017 (millions of pounds)

Chart 11: United States Cottage Cheese per Capita Consumption (pounds)

Example 6: Global Smartphone Shipments (currently entering maturity phase)

Chart 13 shows global smartphone shipments between 2009 and 2017. The chart illustrates a “classic” PLC shape with identifiable introductory, growth, and maturity phases. The simple linear regression model is statistically significant and shows a year over year increase in shipments of 186 million units. Obviously, this model is not appropriate for forecasting a non-linear trend observed in the chart.

Major takeaway points: In 2017, global smartphone shipments were 1.465 billion units, or one shipment for every *** person on the plant. This is highly suggestive of market saturation and hence entry into the maturity phase. The lack of strong smartphone sales growth is reflected in Apple and other supplier stock price declines (as of February 2019) and to sentiments concerning the “sting of plateauing smartphones.” Apple and Samsung have increased their prices, but this pricing strategy may actually accelerate the plateau effect.

Chart 13: Global Smartphone Shipments 2009-2017 (millions)

Summary of PLC Analysis, Data File Usage, and Data Sources

Product Category	Region	PLC Stage	Comments	Data File
Coal	United States	Decline since about 2000	Withdrawal of resources; No new coal fired plants being built	Coal data
Air transport (freight and passenger)	Global	Long-term growth phase since at least the early 1960s	Continued investment by carriers and commercial air craft manufacturers	Air transport data
Cars	United States	Decline phase started during the mid-1980s; Displaced by light trucks and SUVs	American automotive manufacturers are abandoning the car market to foreign suppliers	Car data
Cottage cheese	United States	Decline phase since early 1970s; possible displacement by yogurt products	Rise of yogurt	Cottage cheese data
Cell phones	Global	Recent shift to maturity phase; Market saturation	Coping with the plateau	Cell phone data