Product Life Cycle Examples
Introduction to the Page
The standard product life cycle (PLC) depiction (see the Product Life Cycle Theory page) 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 linear 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.[1] 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 3: United States Car Sales (excluding light trucks) (in the decline phase since the mid-1980s)
Charts 7 and 8 show Unites States car sales and car sales per capita from 1963 through 2017. A visual analysis of the data appears to indicate that car sales increased slowly or not at all from 1963 to the mid-1980s. After the mid-1980s, car sales appear to be in decline. Using 1985 as a break point in our modeling, the results suggest the following:
- 1963-1985: The model predicting car sales is not significant (the model F of 2.838 has a p>.10). This is supports the idea that the industry was in the maturity phase during this period.Average annual sales were about 9.5 million units per year.
- 1986-2017: the model for this time period shows a significant decline in car sales (Model F = 57.416, p<.01). Year over year, can sales declined by an expected 113,308 units. This represents a decline in total units across the period of 3.667 million cars. This represent a drop of about 38% from 1985 to 2017. The decline represents the productive output of about 12 larger automobile assembly plants, based on a capacity of about 300,000 car per annum per plant.
- Data for the car sales on a per capita basis show similar trends.
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 7 United States Car Sales (excluding light trucks) 1963-2017 (thousands)
| Regression Model: United States Car Sales (excluding light trucks) in thousands; 1963-1985 | |
| R-square | .345 |
| Model F (significance) | 2.838 (p>.10) |
| Model | -107,706 + 59.430 x Year |
| Regression Model: United States Car Sales (excluding light trucks) in thousands; 1986-2017 | |
| R-square | .810 |
| Model F (significance) | 57.416 (p<.01) |
| Model | 23,861 - 113.308 x Year |
Chart 8 United States Car Sales (excluding light trucks) per Capita 1963-2017 (units per thousand)
| Regression Model: United States Car Sales (excluding light trucks) per Capita (in thousands); 1963-1985 | |
| R-square | ..217 |
| Model F (significance) | 1.034 (p>.10) |
| Model | 369.007 - .164 x Year |
| Regression Model: United States Car Sales (excluding light trucks) per Capita (in thousands); 1986-2017 | |
| R-square | .909 |
| Model F (significance) | 141.862 (p<.01) |
| Model | 1,445.393 - .708 x Year |
Example 5: Cottage Cheese (long-term decline since the mid-1970s)
Charts 9 and 10 show United States cottage cheese consumption (in millions of pounds and in pounds per capita) from 1970 through 2016. As an interesting fact, the origin of cottage cheese may be traced back to ancient Mesopotamia circa 3,000 B.C.
- The model for per capita consumption shows a significant decline with a year over fall of .071 pounds per year (.071 the 'b' or rate of change in the regression model.
- Expected per capita consumption fell from 4.481 pounds in 1970 (this equals 144.351 - 071 * 1970) to 1.144 pounds in 2016 (this equals 144.351 x 2016.
- The above decline represents a drop of 74%. This equals (4.481 - 1.1440)/4.481.
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.[2]
Chart 9: United States Cottage Cheese Consumption 1970-2017 (millions of pounds)
| Regression Model: U.S. Cottage Cheese Consumption (in millions of pounds); 1970-2017 | |
| R-square | .922 |
| Model F (significance) | 253.940 (p<.01) |
| Model | 19,358 - 9.290 x Year |
Chart 10: United States Cottage Cheese per Capita Consumption (pounds)
| Regression Model: U.S. Cottage Cheese Consumption per Capita (in pounds); 1970-2017 | |
| R-square | .965 |
| Model F (significance) | 617.622 (p<.01) |
| Model | 144.351 - .071 x Year |
Example 5: Global Smartphone Shipments (currently entering maturity phase)
Chart 11 shows global smartphone shipments between 2009 and 2017.
- The chart illustrates a classicPLC shape with identifiable introductory, growth, and maturity phases.
- The simple linear regression model is significant and shows a year over year increase in shipments of 186 million units. Obviously, this model is not appropriate for forecasting as this PLC exhibit a non-linear trend.
Major takeaway points: In 2017, global smartphone shipments were 1.465 billion units. Based on a global population of 7.55 billion, this represents one shipment for every 5.15 people on the plant (7.55 / 1.465. Clearly gowth at this rate is not sustainable and suggest saturation: hence entry into the maturity phase. The lack of strong smartphone sales growth is reflected in Apple and supplier issues concerning the “sting of plateauing smartphones.” Apple and Samsung have increased their prices, but this pricing strategy may actually accelerate the plateau effect.[3]
Chart 11: Global Smartphone Shipments 2009-2017 (millions)
| Regression Model: Global Smartphone Shipments (in millions); 2009-2017 | |
| R-square | .974 |
| Model F (significance) | 127.403 (p<.01) |
| Model | -372,610 + 185.565 x Year |
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 |
Notes
- ↑ https://data.worldbank.org/indicator/IS.AIR.GOOD.MT.K1?end=2017&name_desc=true&start=1970&view=chartSimple
- ↑ https://www.npr.org/sections/thesalt/2015/07/16/423207704/the-fall-of-a-dairy-darling-how-cottage-cheese-got-eclipsed-by-yogurt
- ↑ https://www.theverge.com/2019/1/3/18166399/iphone-android-apple-samsung-smartphone-sales-peak