BIBLIOGRAPHY AND REFERENCES
5. Net Debt
4.3 FORECASTING SALES
as shown in Exhibit 4.17 taken from the 2003 edition of Diffusion of Innovations.4 We can see that new adopters are presented in a bell-shaped curve whereas the rate of adoption (market share) takes the form of a so-called S-shaped diffusion curve. Innovators are people that are willing to trial new products/services. Typically, the excitement and personal satisfaction of being one of the few to be actually using the product/service are the main reasons for take-up.
For example, when technology-based companies release early versions of software, innova-tors are the target market and the catalyst for penetration. Early adopters are closely tied to Innovators and are strong opinion formers within their social networks. They typically give advice and recommendations to their friends and colleagues and when they have found something they think is of value they often become brand advocates. The early majority are pragmatists, comfortable with moderately progressive ideas, but unwilling to act without solid proof of benefits. They are followers who are influenced by mainstream fashions. The late majority are conservative pragmatists who hate risk and are uncomfortable with new ideas.
Practically their only driver is the fear of not fitting in. Finally, laggards are people who see a high risk in adopting a particular product or service and are the last individuals of a social network to try it.
Among the various types of S-shaped diffusion curves we could use, we will elaborate on the Gompertz Curve named after Benjamin Gompertz, who derived it back in 1825 in order to estimate human mortality. The formula of the Gompertz Curve is the following:
F t
( )
=S e(−K e( )−qt)EXHIBIT 4.17 The diffusion of innovations adapted from Rogers
With successive groups of consumers adopting the new technology (bell-shaped curve shown in dark line), its market share (S-shaped curve shown in gray line) will eventually reach saturation level4
where
F(t): the number of people who have already adopted the technology, S: the number of people who will eventually adopt the technology (potential market),
K: a change in K shifts the diffusion curve horizontally without changing the speed of the diffusion process,
q: a decrease/increase in parameter q slows down/accelerates the diffusion pro-cess, and
T: time (e.g. in years).
Let us use the Gompertz Curve to verify the curves of Exhibit 4.17. If we set the follow-ing values – S equals 1, K equals 15, q equals 0.5 – and plot the formula below for 18 periods (let us say quarters) then we get the curve of Exhibit 4.18 (dark line; refers to left hand axis).
F t
(
=1 18..)
=1*e(−15*e(−0 5.*t))A detailed description of how the coefficients K and q can be derived according to the maximum penetration and the number of periods (t) can be found in [2].The gray line of Exhibit 4.18 (refers to the right hand axis) represents the number of new adopters at any point in time (t) which can be calculated by subtracting from the number of adopters who have already taken up the new product or service at point (t) the number of adopters who had taken up the new product or service at point (t-1). That is, the new adopters of the period (t) are derived from the following equation:
New adopters (t) = F(t) − F(t−1)
For example for t=3 (i.e. the third quarter from the launch of the product or service), the diffusion is:
F 3 e 15 e
0 5 3
( )
=(
− *(−.*))
= 3.5%of the potential market, and the new adopters for the third quarter are:
New adopters (t=3) = F(3) − F(2) = 3.5% − 0.4% = 3.1%
14.0%
16.0%
18.0%
20.0%
70.0%
80.0%
90.0%
100.0%
4.0%
6.0%
8.0%
10.0%
12.0%
20.0%
30.0%
40.0%
50.0%
60.0%
0.0%
2.0%
0.0%
10.0%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Gompertz diffusion curve (LHA) New Adopters (RHA)
EXHIBIT 4.18 Plotting Gompertz Curve (dark line) and new adopters (gray line) for 18 periods
Now it is time to determine the number S of people who will eventually adopt the new product or service. New adopters (potential market) are always a subset of the addressable market of the new product or service as shown in Exhibit 4.19. For example, if the total mar-ket is 100% and (a) only 50% of the marmar-ket has sufficient income to consume our product or service and (b) 60% of the market is in the age range 18–45 that will most probably adopt our product or service, then the addressable market is 50%*60% = 30%. To forecast the potential demand over the years we will multiply the adoption rate from the Gompertz Curve by the addressable market. So for a country with population of 50 million, and an addressable market of 30%, the number S of people who will eventually adopt the new product or service are:
50m × 30% = 15 million people
Then the potential demand at the 10th quarter of the forecast will be 91% and the number of potential customers will be:
Potential Market = Market Size (population) × Addressable Market (%) × Adoption Rate (%), or
Potential Market = 50m × 30% × 91% = 13.6 million customers
If we want to forecast sales revenues for the 10th quarter of the forecast then we should multiply:
Potential Market (Potential Customers) × Sales/Customer/Quarter (units) × Unit Price
Following the above method we have broken down sales revenues into the following components:
Sales Revenues = Market Size (population) × Addressable Market (%) × Adoption Rate (%) × X Sales/Customer/Quarter (units) × Unit Price
Total population 100%
60%
In scope population (i.e 18–45 years old) 50%
With sufficient income
100%
Potential market
30%
0%
50%
1 5 9 13 17
Addressable market
EXHIBIT 4.19 Deriving the potential market of a new product or service
4.3.1 Bottom-up Versus Top-down Forecasting
The revenue forecasts described so far are the so-called “bottom-up” forecasts since they include the units that will be sold at some point in time.
An alternative to bottom-up models is to forecast total revenue directly, without examin-ing the individual elements that comprise total revenue. A forecast based on this approach is sometimes referred to as a “top-down” forecast [2]. In practice, many modellers adopt both top-down and bottom-up approaches when faced with an uncertain future market. To add con-fidence to a forecast, it is often advisable to use a number of different approaches. For example, when we want to forecast the revenues of a start-up business that offers new technology prod-ucts or services, we may be forced to adopt both approaches. Imagine the uncertainties asso-ciated with the new product or service adoption and the difficulty we will face if we try to forecast revenues on a long-term horizon of more than 7 years. Bottom-up models are useless in this case. Consequently, the forecasts have to be derived from an analysis of customers’
disposable income and the percentage of it that could be allocated to the product or service of interest. For example, if we want to model the revenue of a new mobile entertainment application, we will have to make assumptions concerning the level of current entertainment expenditure on other similar applications and then multiply this figure by the potential market as we did above. Thus, if we know that 5% of the disposable income of a customer between 18 and 45 years old goes on mobile entertainment and his or her disposable income is €200 per month, then the potential market for 15 million people, described in terms of value, would be:
€200 × 5% × 15 million = €150 million
A top-down approach would consider that the revenue of the new mobile entertainment application will be a tiny percentage, e.g. 4% at the first year of the launch followed by a higher percentage year on year of the total pie of €150 million.
4.3.2 Forecasting Sales of Existing Products or Services
If we come back to SteelCo’s case, since it is an existing company with a certain history of sales data, we will use regression analysis as described in Chapter 3 to forecast revenues. We have already applied regression analysis to forecast the growth rate of SteelCo’s sales volume for years 2014 to 2017. We then estimated sales revenues indirectly. We could forecast sales revenues for 2018 using the same technique we did for 2014–2017. Nevertheless SteelCo already faces 2 years of reduced sales (2012–2013) and we have forecast another 4 recession-ary years (2014–2017). As we will see in Chapter 5, recent research has shown that the aver-age duration of a downturn is between 5 and 6 years. So in our view we should use a different forecasting technique that will assume that 2017 will be the last year of the downturn and from 2018 SteelCo will return to growth. As we mentioned previously, polynomial trendlines assume a curvilinear relationship between variables that shows a minimum in the curve. If we examine carefully the curve of Exhibit 4.16 we can see that the minimum could be at the 7th period (year 2017) and if we had a function that described the relationship of the data point plotted on the chart we could easily forecast the value of Y for the 8th period (year 2018). But this function is presented in the right top corner of Exhibit 4.16 and is as follows:
Y = 2,406.8 * x2−34,206*x + 215,424
where x equals the period under forecast and ranges from 1 to 7. So if we want to forecast (extrapolate) sales for 2018 then we should use the above formula for x = 8 (Exhibit 4.17):
Sales 2018= 2,406.8 * 82− 34,206*8 + 215,424 = €95,811k
183,625 156,639
134,467 140,000
160,000 180,000 200,000
117,109 104,564
96,833 93,915 95,811
60,000 80,000 100,000 120,000
20,000 40,000
1 2 3 4 5 6 7 8
EXHIBIT 4.20 Forecasting sales for 2018 based on a polynomial quadratic function
It is clear from the curve shown in Exhibit 4.20 that the sales of the 7th period (year 2017) are the curve’s minimum point.
An alternative approach would be to use a “top-down” model as described in the previous section, to try and forecast the evolution in the size of the steel market up to 2018 and then estimate SteelCo’s share of that market. Multiplying the addressable steel market by SteelCo’s market share we could arrive at a sales estimate for a certain year. For example, assuming that the addressable steel market in the country in which SteelCo operates will be 1,750,000 Metric Tons (MTs) in 2014 and SteelCo’s market share will be 10%, then SteelCo’s sales in 2014 would be the following:
1,750,000 MTs × 10% = 175,000 MTs
a figure that is close enough to the one derived in Chapter 3 through a different logic. To forecast activity in the steel market for the years to come, we could find correlations between it and some macroeconomic indices (such as GDP growth, private consumption, consumer confidence, home sales, industrial production, unemployment rate, etc.) that are published and forecast by official bureaux (IMF, World Bank, Central Banks, etc.). This kind of forecast is called a macroeconomic or econometric forecast and could be derived from an equation of the following form:
St= +a b GDPt−1+c Pt+d Ht+et
where
St is the steel market in year t,
GDPt-1 is the Gross Domestic Product during the previous year, Pt is the industrial production forecast for the year t,
Ht is the home sales forecast of the year t, and
et is an error term measuring the extent to which the model cannot fully explain the steel market.
Then the objective of the macroeconomist or the econometrician would be to obtain estimates of the parameters a, b, c, and d; these estimated parameter values, when used in the model’s equation, should enable predictions for future values of the steel market to be made contingent upon the prior year’s GDP and future year’s production and home sales. I would like to stress here that the construction and preparation of macroeconomic forecasts is one of the most publicly visible activities of professional economists as they concern many aspects of economic life including financial planning, state budgeting, and monetary and fiscal policy. The production of these kinds of forecasts requires a clear understanding of the associated econometric tools even though it results in forecasts which tend to be little better than intelligent guesswork – and that is a fact. During the years before the Great Recession of 1929 forecasts might have appeared to improve, but that was only because most economies became less volatile. As is well known, the Great Recession was completely missed, not to mention the 2007 financial crisis which started as an asset bubble. In any case macroeconomic or econometric forecasting is far beyond the scope of this book.
We have now adequately covered the topic of forecasting sales for both new and existing businesses, products, and services and it is time to move on to forecasting costs.