New Product Forecasting in Volatile Markets
By
Alexander Baldwin
B.S. Economics, B.S. Political Science, University of Oregon 2008
And
Jaesung Shin
B.E. Industrial Engineering, Korea University 2007
MASSACHUSETTS INSTITUTE
OF TECHNcLOC-Y
JUL 17 2014
LIBRARIES
Submitted to the Engineering Systems Division in Partial Fulfillment of the
Requirements for the Degree of
Master of Engineering in Logistics
at the
Massachusetts Institute of Technology
June 2014
C2014 Alexander Baldwin and Jaesung Shin. All rights reserved.
The authors hereby grant to MIT permission to reproduce and to distribute publicly
paper and electronic copies of this thesis document in whole or in part in any medium now
known or hereafter created.
Signature redacted
.......................................
Signature of A uthor ... , . ............... .................... ;' -....................
Master of Engineering in Logistics Program, Engineering Systems Division
May 13, 2014
Signature redacted
Signature of A uthor ............... ........................................ E;, ... *.............................
Master of Engineering in Logistics Program, Engineering Systems Division
May 13, 2014
Certified by ..................................
(
Siqnature redacted
................
Shardul Phadnis
Postdoctoral Associate, Center for Transportation and Logistics
Thesis Supervisor
ninature redafed
.............
Wg
A ccepted by ...............................
I
....................................................
Prof. Yossi Sheffi
IV Director, Center for Transportation and Logistics
Elisha Gray II Professor of Engineering Systems
Professor, Civil and Environmental Engineering
New Product Forecasting in Volatile Markets
By
Alexander Baldwin
And
Jaesung Shin
Submitted to the Engineering Systems Division in Partial Fulfillment of the
Requirements for the Degree of
Master of Engineering in Logistics
at the
Massachusetts Institute of Technology
ABSTRACT
Forecasting demand for limited-life cycle products is essentially projecting an arc trend of
demand growth and decline over a relatively short time horizon. When planning for a new
limited-life product, many marketing and production decisions depend on accurately
predicting the life cycle effects on product demand. For products with stable market
shares, forecasting demand over the life cycle benefits from the high degree of correlation
with prior sales of similar products. But for volatile-share markets, rapid innovation
continually alters the shape of available features and performance, leading to products
with demand patterns that differ greatly from prior generations and forecasting techniques
that rely more on judgment and naive expectations. In an effort to understand
opportunities and limitations of quantitative forecasting in a specific volatile-market
context, we hypothesized certain characteristics about the shape and volatility of the
demand trend in volatile-market product, and tested them using sample stable and volatilemarket data from a partner firm. We found significant differences in quantifiable
characteristics such as skew and variance over the life cycle, presenting an opportunity for
supply chain stakeholders to incorporate life cycle effects into forecasting models.
Thesis Supervisor: Shardul Phadnis
Title: Postdoctoral Associate, Center for Transportation and Logistics
1
Acknowledgements
We would like to thank the following people for their contributions to our research:
" Our adviser, Shardul Phadnis for his guidance and advice
"
Employees of ElectricCol, who have responded our inquiries on an at-least weekly basis
and shared a wealth of knowledge and experience about their supply chain
*
Our families and friends, especially the SCM class of 2014, for providing knowledge,
nourishment, and emotional support!
ElectricCo is the firm with which we collaborated on the project. The name has been changed.
2
CONTENTS
......................................................................................
List of Figures ..................................
List of Tables..............................................................................................................................
Introduction .............................................................................................................................
1
1.1
1.2
1.3
2
Stable and V olatile M arkets...........................................................................................
M otivation: Impact on the Firm ..................................................................................
Research Objective .........................................................................................................
Literature Review ................................................................................................................
Forecasting M odels and Methods ...............................................................................
2.1
...................................................................................
Expert Judgm ent
2.1.1
Hybrid and Survey Judgm ent M ethods ...............................................................
Econometric Methods.............................................15
2.1.3
M ethods.............................................................................................................................
2.1.2
3
7
7
10
12
13
13
14
15
17
18
Evaluating Shipment Behavior .......................................
M easuring Skew ...................................................................................................
3.2.1
M easuring Rate of Growth/D ecline....................................................................
3.2.2
M easuring V ariance.............................................................................................
3.2.3
20
3.1.1
3.1.2
3.2
M easuring M odality ............................................................................................
Results................................................................................................................................
3.2.4
4.1
4.2
18
19
21
22
24
24
26
Segm entation ................................................................................................................
Skew ...........................................................................................................................
26
Rate of Growth/D ecline .............................................................................................
V ariance ....................................................................................................................
M odality .......................................................................................................................
30
4.3
4.4
4.5
5
6
...........................................................................
Segmentation ........................................................................................................
D e-Trending .........................................................................................................
p
3.1p
4
5
28
31
33
D iscussion..............................................................................................................................
Segmentation ...................................................
5.1
Skew ...............................................................................................................................
5.2
35
Skew - Implications for Sales Forecasting .............................................................
Skew - Implications for Capacity Planning and Inventory Management .......
5.2.2
...........................................
Rate of Growth/Decline
5.3
36
5.2.1
5.3.1
Rate of Growth/Decline - Implications for Sales Forecasting ............................
3
35
35
37
37
38
5.4
V ariance.......................
5.4.1
5.4.2
. --.......
............ ...
............................................................
Variance - Implications for Inventory Management...........................................
V ariance - Implications for Capacity Planning ..................................................
5.4.3
39
40
40
V ariance - Implications for Sales Forecasting ....................................................
M odality.........................................................................................................................41
41
41
5.6
M odality - Implications for Capacity Planning ..................................................
Volatility and Capital-Intensiveness ..........................................................................
5.7
Additional Lim itations & Future Research...............................................................
43
5.7.1
Product and Industry Choice ...............................................................................
43
5.7.2
V ariation and Exogenous Effects ........................................................................
V alidity of Judgm ent M ethods ............................................................................
43
5.5
5.5.1
5.7.3
5.7.4
42
44
Sales Forecasting, Capacity Planning, and Inventory Management Strategies...... 45
5.8
Conclusion .....................................................................................................................
46
References.....................................................................................................................................47
Glossary of Terms.........................................................................................................................49
4
LIST OF FIGURES
Figure 3-1: Research D esign......................................................................................................
17
Figure 3-2: Example of De-Trending Effect ............................................................................
20
Figure 4-1: Monthly Shipments, Product 1 ..............................................................................
34
Figure 5-1: Conceptual Rule-Based Range Forecast...............................................................
36
Figure 5-2: Conceptual Slope & Segment-Based PLC Forecasting ........................................
39
5
LIST OF TABLES
Table 4-1: Summary of Results .................................................................................................
26
Table 4-2: Descriptive Statistics and Segment Lengths ..........................................................
27
Table 4-3: Relative Lengths of Product Segments ...................................................................
28
Table 4-4: Skew Statistics.............................................................................................................
29
Table 4-5: Average % Rate of Growth/Decline (EMA) - Stable Products............... 30
Table 4-6: Table 4-6: Average % Rate of Growth/Decline (EMA) - Volatile Products.......... 31
Table 4-7: Hypothesis Test Results - Permutation....................................................................
31
Table 4-8: Coefficients of Variation........................................................................................
33
Table 4-9: Hartigan's Dip Test for Modality.............................................................................
34
6
INTRODUCTION
Imagine that your company is introducing an innovative new durable product to market.
You have been involved in this market for more than a decade, with a stable, leading
position relative to the competition, so you have a large degree of influence on product
technology and trends. In such a stable market, forecasting and managing product demand
is a relatively straightforward task, with gradual changes in products and demand trends.
With this level of stability, historical sales data accurately predicts demand for new
product introductions. On the other hand, consider introducing an innovative durable
product into a more volatile market where product life cycles are shorter, technological
changes are revolutionary, and as a result each firm's share of the market varies greatly
between product generations as consumers polarize around feature sets in a manner that is
difficult to predict. How many products do you need to product in this sort of market?
Many important decisions will be made as a result of your forecast: new capital
investments will be made in a factory for the new technology, a sales channel will be
created to attract new customers, and your distribution network will have to be modified to
deliver the product to those customers. If your product is successful, you will need to
quickly add capacity along the supply chain. If your product is not successful, you will
need to sunset the product in order to free capital resources to focus on the next generation
of the product or other markets.
1.1
STABLE AND VOLATILE MARKETS
A good deal of supply chain theory is based on assumptions of relative stability in market
share. This is evident in the prevalence of econometric forecasting models that use
historical sales data for a certain product to predict its future demand. This approach relies
7
on the implicit assumption that the future demand will exhibit the same pattern(s)
observed in the past - an approach that does not work for products whose features and
performance evolve quickly, and the resulting harder-to-predict volatile reception from the
market.
We will develop these two archetypes: stable versus volatile markets, to help illustrate the
additional challenge of planning, producing, and distributing new products outside of the
classical stable market bounds. In order to do this, we will first explore volatility as the
primary independent influence on product demand, and will go on to perform a
quantitative comparison of volatility-dependent demand patterns in the Methods chapter.
From the long-term perspective of the product life cycle, one way to approach volatility in
demand is by defining it as a function of volatility in market share - the relative share of
each product in the market. This is an appealing independent variable because it is
relatively easy to quantify for most durables industries. But we acknowledge several
exogenous effects that influence volatility in market shares (Armstrong 2001a):
*
The rate of change in productfeatures and technology, which is closely related to
the length of the product life cycle. When this rate is high, frequent new entry into
the market constantly shifts the array of features available to the consumer, making
it difficult for the firm to estimate where its product's features will fall in relation
to other products. Due to this uncertainty, prior products' sales history does not
correlate well with new product demand.
*
Consumer understandingofproduct category - much as the rate of change in
features confounds producers' expectations about the market, rapidly changing
features impact consumers' purchasing decisions - for instance, in the smartphone
8
market, a new product with a voice-control mechanism might initially cause
consumers to polarize around it, only to find out after several months' experience
that the feature is not that useful.
*
The rate of entry and exit in the market. Products with shorter life cycles and new
technology are more of a gamble for firms. On the upside, volatility in the market
can work to the firm's advantage if judged accurately, and the relative length of
payback period on an investment is shorter - both features that entice firms to
enter the market. However, there is more risk involved, lowering the overall
expectation of return and increasing the frequency of financially-motivated exits.
Regardless of the level of volatility, firms seek quantitative forecasting techniques in order
to accurately manage activities and assets on a large scale. The alternative is the use of
naive and judgment methods, which may complement quantitative forecasting methods
but are known to be ineffective as standalone measures. We generally found that the
established methods (which will be surveyed in detail in Chapter 2, LiteratureReview) are
heavily biased toward stable-market conditions, leaving a motivational gap: for limitedlife cycle products in volatile markets, what specific quantitative analysis can be
conducted given the only arm's-reach data is the recent sales data for the new product in
question? The fact that demand for limited-life products begins and ends at zero within a
clearly-defined time frame means that a life cycle-driven trend between each data point
signals something about trends in the near future. If we can identify meaningful life-cycle
characteristics of the demand line, it implies a forecasting framework that draws
quantitative significance from the trend in demand itself.
9
1.2
MOTIVATION: IMPACT ON THE FIRM
All activities related to marketing, producing, and distributing products are impacted by
trends in demand new products throughout the product life cycle. Vernon (1966, 1979)2
first described phases of the cycle: In the introductionphase of the product, firms have
finite time frame opportunity to adjust output in line with the market's reaction to the
newly-released product; during the subsequent growth and maturity of the product, firms
anticipate a peak in sales that will not only determine long-run profitability, but set a
definitive trend for the future; and during the decline at the end of the life cycle, a timely
decision needs to be made about when to phase the product out in favor of the next
generation.
In order to satisfy the motivational goal of identifying meaningful demand signals, we first
identify who might benefit from greater accuracy in the processes generally referred to as
"forecasting". We group these stakeholders into three lenses through which the supply
chain is perceived and managed:
The first is long-range salesforecasting, which is largely concerned with the overall
financial performance of a product. This group is less interested in short-term sales
variation as it is matching overall sales volume to quarterly demand expectations. As such,
an important feature to this group may be the skew in sales volume: when in the product
life cycle will the firm achieve the expected level of demand? If there is a reliable
expectation of high sales early on, and that level is not obtained, then forecasters can
2 Vernon's
work focused on the effects of a technology's product life cycle on international trade; however,
the underlying assumptions about the nature of products' growth and decline are frequently generalized to
specific market segments or products. See Porter (1980) as an example.
10
adjust their expectations accordingly, rather than hoping they will make it up in the next
quarter!
The second group is capacityplanning, which is concerned with making the right level of
production capacity available in a timely fashion. This may include the construction of
facilities, hiring of labor and procurement of equipment required to produce the product.
In order to be profitable, production management must keep asset utilization high while
not running out of capacity. Consequently, the rate ofgrowth/decline in product demand is
very important - how fast will the product ramp up, and how will capacity be made
available to meet this ramp? Additionally, the modality of the product becomes relevant;
can capacity planners expect a single peak in demand for a smooth transition, or should
they expect multiple peaks - key questions to determining how much flexibility is
required in the supply chain.
The third and final group is inventory management, a discipline which extends across the
supply chain, from the management of stocks of inbound of raw materials and
components, to the maintenance of these stocks in production buffers, to the stocking of
products in outbound channels to ensure availability to customers. Inventory management
is largely concerned with the variancein sales on a short-term basis, so it can position
inventory to meet the demand of the market while limiting unnecessary expenditure of
resources in doing so. Choosing appropriate levels of inventory stocks is vital at the
introduction of a new product, as an early shortage can harm the overall success of the
product, especially in volatile markets. Other important questions arise here too, such as
whether will variance in demand will decrease or remain volatile throughout the product
lifespan.
11
1.3
RESEARCH OBJECTIVE
In the prior discussion, we identified skew, rate ofgrowth/decline, variance, and modality
as important demand signals to three key supply chain groups. The key research question
is thus: do volatile market products behave consistently along these criteria, and if so, to
what degree do they differ from stable market products? To answer this question, we will
establish hypotheses around each signal in Chapter 3, Methods, and test them in a case in
Chapter 4, Results.
If we can reliably establish significant gap between stable and volatile product behavior, it
implies a framework for making decisions about producing new products. For instance, if
a firm can expect a certain direction of skew for new products in volatile markets, it can
adjust its long-range forecasting process accordingly depending on the expected level of
volatility. Similar decisions can be made for capacity planning decisions regarding rate of
growth/decline and modality, as well as for inventory management regarding variance.
12
2
LITERATURE REVIEW
The foundational principle that applies in forecasting new products is that "new" is but
one part of the larger product life cycle; that is to say that all product life cycles have
introduction, growth, maturity, and decline phases as asserted by Vernon (1966). Life
cycles for many durable products are relatively short (Bayus 1994) which means new
product introductions are frequent and critical to the success of firms that produce them.
There is a notable amount of survey research comparing the effectiveness of various
forecasting methods on new products (Armstrong 2001a). Regardless of any accuracy
measured in backward-looking terms, a practicable forecasting model must be robust
enough to manage inconsistencies from real-world data inputs and at the same time be
capable of yielding actionable outputs. Selecting from models in and of itself can be a
difficult task, that is presently approached in a very sophisticated manner; Ching-Chin et
al (2010) note that "no standard procedure for new product sales forecasting currently
exists," and go on to suggest advanced techniques like the use of rule-based learning
algorithms to aid forecasters in selecting a forecasting model.
2.1
FORECASTING MODELS AND METHODS
A wide range of sales forecasting models are available, but the most relevant to this
research are those that posit an application to new product introductions and include
context for the product life cycle. Armstrong's (2001a) compilation of articles on the topic
is a frequently-cited authority on the range of options available. Two large categories of
forecasting methods exist: judgment methods - which can further be divided into expert,
survey and hybrids thereof - and purely statistical econometric forecasting.
13
2.1.1 Expert Judgment Methods
Judgment methods rely on human intuition to predict the eventual time and magnitude of
the product peak. One of the most basic categories of judgment forecasting is referring to
the opinions of experts (Armstrong 2001a). Expert opinions may be aggregated, using the
Delphi technique (Rowe 2001), which provides a structured framework for collecting
experts' opinion while avoiding common sources of bias. In some cases, expert opinions
have been found to best quantitative methods in forecasting new products (Basu 1977).
Experts' forecasts can be "bootstrapped", wherein the most accurate forecasts amongst
experts are decomposed into independent numerical coefficients (Armstrong 2001b).
Although this is an innovative way to infer quantitative causality from judgment-derived
data, because of the large degree of extrapolation and inference, it is better suited to crosssectional research than time series forecasts like those used in new product forecasting
(Armstrong 2001b).
Another expert method is expert system forecasting, which is the most recommended
process for new product forecasting (Collopy 2001). Rather than bootstrapping expert
forecasts for implicit understanding, expert system methods seek to identify and measure
the explicit underlying forces in an expert forecast in order to form complete system
models with quantitative variables that can be adjusted for different forecasting situations.
A salient example of an expert system forecasting component is the Bass model (1969)
that provides the foundational technology diffusion model that estimates the whole-market
adoption of a new product based on coefficients of innovation and imitation in the market
space. In an extension, Norton and Bass (1987) go on to apply this to the high technology
14
space by overlaying a substitution scheme for successive generations of similar products
rather than looking at adoption as a single mode.
Further extensions to product diffusion model such as Qin (2012) look at the marketplace
as a stochastic system, providing methods to estimate and control for uncertainty. Chien et
al (2010) go even further with a stochastic approach, attempting to create a comprehensive
decision-making framework based on stochastic diffusion theory that provides detailed
forecasts for capital costs, pricing, and demand planning.
2.1.2 Hybrid and Survey Judgment Methods
Judgment methods may also involve surveys, as in conjoint analysis (Wittink 2001),
where consumers are given sample attributes of products and asked to rank their interest
or willingness to pay, which is then extrapolated to establish sales figures based on market
shares. This sort of analysis is most effective in stable markets, where both producer and
consumer understanding of the markets is high.
Hybrid quantitative-judgment forecast methods exist, such as rule-based forecasting
(Armstrong 2001a), a hybrid method that is apt at using quantitative sales history, domain
knowledge, and empirical research to develop rules including thresholds, breakouts and
casual force implications to form and adjust any manner of quantitative forecast.
2.1.3 Econometric Methods
Econometric forecasting methods focus on the use of statistics and casual variables
derived directly from observed data, rather than experts. Econometric forecasting largely
relies on regression models, which are relatively straightforward to compute - the greater
challenge in econometric forecasting being the methodical selection and testing of
15
variables (Armstrong 2001a). Typically, the most robust and accurate econometric models
are those that are relatively simple (often linear regressions with just a few variables), and
validated through a range of statistical tests that identify issues like misspecification
(Allen 2001).
A recent example of econometric forecasting in practice is Tanaka (2010), who uses
regression analysis to forecast expected results using prior generations of the same or
similar products as an allegory, with positive results, although this example is from a
slower-moving, stable-market situation. In volatile markets where features are changing
rapidly and producers and consumers have limited information about the market, the
accuracy of econometric forecasting will be lower as past products don't correlate well
with new products (Allen 2001).
16
3
Methods
As mentioned earlier, the primary research question is whether a difference in demand
trends exists in between stable and volatile products. We suggested 4 quantitative
characteristics of a demand trend line: skew - whether peak sales volume is reached earlier
or later in the life cycle, modality - whether products experience one or more peaks in
demand during the product life cycle, rate ofgrowth/decline, how fast demand for the
product ramps up and down during the life cycle, and variance in demand for the product.
The following sections describe quantitative hypotheses, tests, and metrics to for each
characteristic. As a case study, we computed each of these statistics using shipment data
from a partner firm, the results of which will be discussed in the following chapter,
Results.
Measures of
Dependent Variable
Independent
Variable
Exogenous
Effects
Length of
Product Life
Cycl+
........
Dependent
Variable
Trend Of Product
Trend Of Product
HI Skewness of
It-----------Shipm ents
Rate of Change in
Product
+
Characteristics
Consu------er
-Shipments
Consumer
Volati Iity of
,
H2 Modalityof
Market
Understanding&of
Unertadig f
aret
+
H3 Variance of
Shares
Product Category
-----------------+ Shipm entsfac cle Lifecycle
-- - -- ----- . ....
Rate of Entry/Exit
From Market
H4 Rate of
- --Growth/Decline
in Shipments
:+
Producers'
Expected Rate of
Risk/Return
Proxy for Dependent
Variable
SimnsDmn
Simns
Throughout
ughout
Growth/
Decline by Phase
Figure 3-1: Research Design
17
... J
Dmn
Throughout
ughout
3.1
PRODUCT SHIPMENT DATA PREPARATION
To begin, sample shipment data must be obtained to use as a proxy for demand data.
Because of this implied relationship, we avoid cases products for which demand is known
to exceed supply. Sample products must be identified as belonging to stable or volatileshare markets, and as the tests use will be sensitive to sample size, the use of more
products will yield higher statistical significance. We will generally test hypothesized
features of volatile market products, using stable market products as a base.
3.1.1 Segmentation
For rate ofgrowth/decline, we are interested in the performance over the life cycle, for
instance: does variance decrease over time for new products or remain consistent? To
enable such an assessment, we developed a quantitative segmentation method based on the
general logic proposed in Vernon's (1966) theory to segment the product data into the four
phases: introduction, growth, maturity, and decline:
" The first bound, between the 'introduction' and 'growth' phases, is defined where
the slope of the pattern first transitions from positive accelerating to positive linear.
" The second bound, between the 'growth' and 'maturity' phases, is defined at the
peak of the first mode.
" The third bound, between the 'maturity' and 'decline' phases, is defined where the
slope transitions from negative decelerating to negative linear, working backward
from the end of the time series.
Because of the inherent noise in weekly and monthly shipment data, rate of
growth/decline was smoothed on an exponential moving average basis, (as detailed in the
section 3.1.2 - Measuring Rate of Growth/Decline). We were aware that if modality turned
18
out to be multi-modal or uniform, it would confound these results, so we were careful to
track the positive-trending phases from the left and negative-trending phases from the
right. With the higher rate of variation inherent in volatile products, we knew there may be
a need to adjust the smoothing to achieve reasonable bounds for the first and last phases.
We also expected that some demand spikes and dips might be so large that we would need
to ignore a single-month reversal in slope, so the final criteria was that any trend reversal
signaling a bound between segments had to be two months in length.
3.1.2 De-Trending
Shipments over the entire life cycle of a product are necessarily non-stationary in nature,
as they move from zero, to a peak, and back to zero rather than varying around the mean.
In order to calculate metrics that involve variance, de-trending of the data was required to
remove the influence of the non-stationary trend. To de-trend, we calculated exponential
moving average (or EMA, as detailed in section 3.2.2 - MeasuringRate of
Growth/Decline), and took the difference from the original trend. The monthly de-trended
residual DTM for each monthly shipment SHM and EMAM is thus:
(1)
DTM = SLM
19
-
EMAM
1
3
5
7
9
11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43
Month
Original
-De-Trended
Figure 3-2 - Example of De-Trending Effect
3.2
EVALUATING SHIPMENT BEHAVIOR
With the four characteristics clearly defined, metrics needed to be chosen to test for
significance. Each metric was selected based on assumptions about the dataset, as noted
below. Each of the metrics below were calculated for each product, and aggregated for
both stable and volatile product groups. Segmentation would be used for rate of
growth/declineand variance since we were interested in their change over time, as
opposed to skew and modality, which are features of the overall product life cycle
distribution. Data was summed to the monthly level unless otherwise noted - given the
short lifespan of these products, monthly bins provided a significant number of samples
without excess variation.
20
3.2.1 Measuring Skew
We expected volatile products to have a skew that is more positive than stable products,
exhibiting a peak in demand that occurs earlier in the product life cycle due to the faster
proliferation of these technologies:
HA: The difference between volatile group and stable group skew is positive
Ho: The difference between volatile group and stable group skew is negative or 0
Because of this, we chose the nonparametricskew method to evaluate skew. This statistic
is particularly resilient for evaluating d because it is capable of evaluating the direction of
the skew, and is unaffected by scale shift that might happen across the skew - an
important feature due to the supposed non-linear growth pattern proposed in the classic
product life-cycle theory. Given a sample mean ?, sample median Md, and sample
standard deviation s, the nonparametric skew statistic for a sample series x is defined as:
(1)
-
Sk =
Md
S
A formal statistical test would help to underscore the significance of the results. However,
undertaking a case study with a firm will involve a small sample of products, from a large
population about which we can make few assumptions about the nature of the distribution.
Under these conditions, the best technique would be a resampling technique such as a
permutation test. We elected to use a simple nonparametric permutation test for the
difference between the means of both distributions. First a permuted resampling for each
group's sample xA and xB is undertaken by permuting the two sample sizes where n is the
sample size and r is a random variable between 1 and n.
21
(2)
P(n,r) =
n
(n - r)!
This is repeated numerous for a large number of permutations p (a large number like
10,000), and a P-value is calculated by taking the average of the differences of means for
all permutations p:
(3)
p
Alternatively, if the case were that a large, normally-distributed sample of products in the
population we could perform a two-sample T-test between mean skew of the volatile
products Xi and stable products X2 by calculating a two-sample independent T statistic for
unequal sample sizes and variances (2) and comparing the corresponding results to a
probability density table for the t distribution (P-values).
(4)
Sk 1 - Sk 2
3.2.2 Measuring Rate of Growth/Decline
As with skew, we expected the slope of volatile products shipment volume to grow faster
and fall off faster, making for the following statistical hypothesis for each segment:
HA: The difference between volatile group and stable group slope is positive
Ho: The difference between volatile group and stable group slope is negative or 0
22
Here a decision was required whether to use simple or exponential moving averages
(SMAs and EMAs). EMAs were chosen because inherently have less lag than SMAs - for
the segmenting exercise we were looking for a tool to identify breakouts from a trend, and
for volatile markets the switch. EMAs were calculated on a monthly basis for the raw
monthly series values xt and smoothing output values st, and a is an inputted smoothing
factor 0 < a < 1.
(5)
XO
SO
st =--a xt-1 +
-a)s=_1,t > 0
We would begin with a neutral EMA smoothing factor of .5, and would adjust it
downward if premature segments occurred during the segmentation exercise. EMAs
would be calculated for each product and product group's phase, and expressed as the
percentage change so as to remain proportionally correct for comparison and future
extrapolation:
(6)
St - St-1, t >
0
St-i
To test our hypothesis, we would perform the same permutation test for the two groups as
was conducted for skew in the prior section, 3.2.1 - MeasuringSkew.
23
3.2.3 Measuring Variance
Our general hypothesis for variance is that the volatile group would exhibit higher
variance in monthly shipment volume due to more volatility in demand for the product,
thus we would be exploring the difference between the two groups:
HA: The difference between volatile group and stable group variation is positive
Ho: The difference between volatile group and stable group variation is negative or 0
Because variance is sensitive to trends, we elected to use the de-trended dataset, so we
would be exploring the variation of the actual data set from its moving average. To
measure variation we chose coefficient of variation (CV) as the key metric, because it
allowed us to place products with different sales volumes on the same terms for
comparison. Given the sample mean k, and sample standard deviation s, the coefficient of
variation is calculated as:
(7)
CV=-
S
To test our hypothesis, we would perform the same permutation test for each segment in
the two groups as was conducted for skew in the prior section, 3.2.1 - MeasuringSkew.
3.2.4 Measuring Modality
Our general hypothesis was that stable market products would tend to center around a
single mode, where the shifting share in volatile markets would drive multiple modes into
the data series. Several published statistical tests exist asses the modality of a distribution.
Since the presumption is that the volatile group has more than one mode (rather than
24
exactly 2, or more), we elected a test one that would test both groups against a null
hypothesis of unimodality and detect any number of additional modes. Hartigan's dip test
for unimodality (Hartigan 1986), which essentially tests for the presence of convexity,
satisfied these conditions well. Using a statistical package (Maechler 2013), we can easily
test each product the following hypothesis:
HA: The product's shipments are not unimodal, i.e. multi-modal
Ho: The product's shipments are unimodal
The dip-test incorporates its own probability density, with P-values being the primary
output, so we can compare the average P-values for each product group.
25
4
RESULTS
A partner firm interested in the consequences provided sample products to test our
methods. Eight products were provided from markets with stable market shares, and four
products were provided from markets with volatile shares. Despite these small sample
sizes, all tests and statistics were completed as planned in the prior chapter. In all cases,
the data provided by the partner firm proved complete and robust enough to perform valid
statistical analysis. Given the limitations of the study, our primary criteria for empirically
confirming a hypothesis was directional corrections on the comparison of the mean for
given statistics. Under rigid evaluation for statistical significance, some tests had relatively
low confidence levels, the implications of which will be discussed at length in the final
section.
Table 4-1: Summary of Results
Expectation for
Volatile Products
Greater Right Skew
Empirical
Result
Confirned
Statistical
Significance
Medium
Rate of Growth/Decline
Greater Rate
Confinmed
Low
Variance
Modality
Greater Variance
Multi-Modal
Confirned
Inconclusive
Low
None
Measure
Skew
4.1
SEGMENTATION
Using the second-derivative segmentation method described in the prior chapter, we were
able to successfully establish segment lengths for most products. As we noted in the
methods, we adjusted the smoothing coefficient to .4, which yielded demand lines with
clear segment signals that were unaffected by short-term trend reversals when using the
logic that we would ignore a single-month change.
26
Due to the limited time window for the product selection, some products did not have
complete life cycle data, so some inferences were made based on expected production life
data given by the partner firm. For instance, if we had 28 months' worth of data and a
clear signals for phases 1-3, and the product was expected to be 36 months, we inferred
that final phase would be 8 months long. This was taken into account for the proportion of
the decline phase, however, it was not weighed in any future statistics
Table 4-2: Descriptive Statistics and Segment Lengths
Stable Group
Monthly Shiipmnnts (Units)
Product # of Mos
Producti
32
Product3
36
Product4
10
Product7
Product8
24
15
ProductO
44
Productl1
35
Product12
59
Mean
Median
Stdv
524,768
456,729
1,437,278
300,215
123,259
1,094,463
1,523,081
195,965
534,656
499,380
1,668,976
216,031
138,559
745,540
1,013,593
85,324
215,445
185,608
721,014
246,869
63,032
1,076,313
1,229,610
217,952
Mean
Median
Stdv
93,740
2,950,793
178,195
117,167
86,700
2,040,000
128,141
58,925
67,154
2,262,407
123,488
128,161
Seginent Length (Months)
Intro Growth Mat. Decline
5
13
6
3
9
8
9
8
2
3
4
3
6
5
4
5
11
5
14
15
7
11
14
8
10
17
14
36
Volatile Group
Product # of Mos
Product2
14
Product5
28
Product6
Product9
24
41
Intro Growth Mat. Decline
2
2
3
3
6
7
3
16
2
5
9
7
9
10
16
7
For segment proportion, we found very little difference between stable and products. The
figure below shows the proportions of each phase relative to the life cycle, along with an
estimated mean value for a future 3-year product. Although the volatile group showed
slightly higher deviation, given that the means were so close for practical purposes (the
same for intro and growth phases), we felt it was not necessary to perform any statistical
tests for the two groups.
27
Table 4-3: Relative Length of Product Segments
Stable Group
Product
Intro
Producti
6%
14%
31%
39%
Product3
Product4
Product7
8%
11%
13%
36%
17%
13%
14%
42%
29%
46%
Product8
17%
25%
Productl0
11%
18%
32%
39%
Product 1
11%
26%
23%
40%
Product12
8%
14%
17%
61%
11%
4
0.31
20%
7
0.40
24%
9
0.31
44%
16
0.19
Growth Maturity Decline
Statistics
Mean
Exp: 3-Yr Product
CV
Volatile Group
Product
Intro
Product2
Product5
Product6
Product9
14%
7%
8%
17%
21%
11%
21%
22%
43%
25%
29%
22%
21%
57%
42%
39%
Mean
12%
19%
30%
40%
Exp: 3-Yr Product
CV
4
0.41
7
0.29
11
0.31
14
0.37
Growth Maturity Decline
Statistics
4.2
SKEw
The skew test was conducted using the non-parametric (NP) skew statistic for the data
series. For this figure, positive values indicate right-hand skew, with where the peak in
28
demand occurs earlierin the life cycle, and negative values indicate left-hand skew. We
hypothesized that volatile products showed positive skew due to rapid growth, and the
data reflected this: mean and median were .35 and .40 for the volatile group, as opposed to
.09 and .14 for the stable group, respectively.
The permutation test returned P-values of .15, representing a 85% level of confidence in
the results. Given that this is a small sample of real-world data, we believe this represents
a result that is directionally correct. With a CV below .5 for the volatile group, the mean
and median have a moderate level of robustness in scale for extrapolation. By contrast, the
stable group had a much wider range, from -0.32 to 0.51, with a mean skew near 0.
Table 4-4: Skew Statistics
Stable Group
Product
Product7
Product12
Productll
Product3
Productl
ProductIO
Product4
Product8
Volatile Group
NP Skew
Product NP Skew
-0.32
-0.24
-0.23
-0.05
0.33
0.34
0.41
0.51
Product9
Product2
Product6
Product5
0.45
0.41
0.40
0.15
0.09
0.14
Mean
0.35
0.40
0.14
0.39
Statistics
Mean
Median
Median
Stdv
CV
Hypothesis Testing - Permutation
P-Value
0.15
29
RATE OF GROWTH/DECLINE
The trend in growth/decline was calculated using the % change of each month's
exponential moving average (EMA), for each of the segments. On average, the volatile
group showed more exponential growth in the introduction phase (13 1% vs 117%) and in
the growth phase (33% vs 23%), as well as a faster fall off from the peak in the maturity
phase (-11% vs -6%).
Table 4-5: Average % Rate of Growth/Decline (EMA) - Stable Products
Stable Group
Product
Intro
Growth
Productl
164%
44%
Product3
67%
12%
Product4
152%
27%
Product7
Product8
Productl0
88%
121%
Productl1
Product12
Maturity
-1%
0%
De cline
44%
11%
15%
-9%
-13%
-9%
-13%
99%
126%
9%
25%
-13%
-6%
-13%
-10%
117%
4
23%
7
-6%
9
-12%
16
0.30
0.61
0.81
0.11
Statistics
Average
Avg Months
CV 1
Absolute Values Are Taken for Decline Phases
The volatile group showed more variance around this result in all phases except maturity
-
4.3
we noted that in some cases the fall-off in the maturity phase was gradual, whereas in
other cases it appeared to be more of a declining step function change. Given the variation
present (CV's largely greater than or equal to .3), we would not recommend extrapolating
on these results, with the possible exception of decline phase for the stable group, all of
which had a smooth exponential decline that appeared to be fully planned in advance.
30
Table 4-6: Average % Rate of Growth/Decline (EMA) - Volatile Products
Volatile Group
Product
Intro
Growth
Maturity
Decline
Product2
Product5
237%
95%
120%
72%
45%
11%
49%
26%
-6%
-13%
-19%
-2%
-14%
-7%
131%
4
33%
7
-11%
11
-9%
0.60
0.61
0.30
0.92
Product6
Product9
-13%
Statistics
Average
Avg Months
CVI
14
Absolute Values Are Taken for Decline Phases
Permutation tests revealed low to moderate statistical significance in the difference in the
middle phases, and no significance in the beginning and end phases, which implies that
the launch and end-of life trajectories of each product are similar.
Table 4-7: Hypothesis Test Results - Permutation
Hypothesis Testing - Permutation
P-Value
4.4
Intro
0.63
Growth
0.30
Maturity
0.18
De cline
0.42
VARIANCE
The first test for variance was whether the coefficient of variation (CV) of the volatile
group exceeded that of the stable product group, which was performed using an
31
independent two-sample t-test. We first calculated the mean and median of the volatile
group (.66 and .64 respectively), which came in higher than those in the stable group (.59
and .59).
The results of the test itself yielded P-values of .3, and thus we arrived at an empirical
conclusion that the volatile group is more variable, albeit with a very low level of
statistical confidence (see table below). As is the case with skew, this is a defensible
directional result given that this was a limited sample of real-world data, and the degrees
of freedom in the test were limited accordingly. With a CVs below .5 in both cases, the
results would be mildly useful for extrapolation.
We also noted that CVs were had a wide range in the distribution. So far, our analysis has
focused on the comparison of two ordinal categories (stable vs volatile), but these
statistics open the possibility of looking at stable vs volatile as a scalar coefficient.
32
Table 4-8: Coefficients of N ariation
Stable Group
Product
CV
Product3
0.57
Producti
Product4
Product8
Productl1
Product7
ProductO
Product12
0.65
0.65
0.62
0.54
0.77
0.42
0.46
Statistics
Mean
Median
Stdv
CV
0.59
0.59
0.11
0.19
Vol atile Group
Prod uct
CV
Prod uct6
0.67
Prod uct5
0.61
Prod uct2
0.87
Prod uct9
0.49
Mea
Medi an
Stdv
CV
0.66
0.64
0.16
0.24
Hypothesis Testing - Permutation
P-Value
4.5
0.32
MODALITY
Although some products' profiles visually appear to be multi-modal, it was not proven
under statistical testing; With the exception of product 11, no product showed anything
approaching a statistically significant non-unimodality, with very high P-values of .70-.99.
The empirical result is thus inconclusive; Product 1, for instance, exhibited a strong visual
bimodal trend, despite a very high P-value.
33
Table 4-8: Hartigan's Dip Test for Modality
Product
P-Value
Product2
0.97
Product2
Product3
Product4
Product5
Product6
0.80
0.73
0.86
0.95
0.71
Product7
0.90
Product8
0.98
Product9
Productl0
Product 1
1.00
0.77
0.18
Product12
0.99
,--Visua IMo-de Tre-nd_
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19
Month
Figure 4-1: Monthly Shipments, Product 1
34
5
DISCUSSION
We empirically concluded that volatile-market products behave differently than stablemarket products in the case. Due to limited sample size and the consequent low level of
statistical significance, these statistics would not make good estimators in a purely
statistical econometric forecast, however their directional significance is still important:
we generally found volatile products to have a right hand skew, greater rates of change,
more modes or uniformity in distribution, and greater variance when compared with table
products. In order to make the best use of these relationships, we will explore the
implications through the three supply chain lenses mentioned earlier: salesforecasting,
capacityplanning, and inventory management.
5.1
SEGMENTATION
With our segmentation criteria using the rate of growth, we found that the length of the
introductionphase was quite similar for all products; estimated to be 4 months for a 3-year
product. This in itself is an interesting result - if further research were to be undertaken,
firms might look for correlation amongst multiple products.
The growth phases were similar for both product groups. Generally, demand for volatile
products fell off faster in the maturity phase - which is consistent with behavior in a
volatile marketplace where new entrant substitutes are frequently introduced to the market.
The decline phase was similar in length for both product groups, indicating a firm-driven
decision about phasing out the product.
5.2
SKEw
All products displayed skew, but the volatile products showed a significant skew earlier
toward the launch date. This is consistent with our reasoning that these products tend to
35
drive exuberant early-adoption demand whereas stable-market products embody
technologies that are being adopted at a slower rate, with carefully planned adoption, i.e.
large corporate customers phasing a new generation of equipment.
5.2.1 Skew - Implications for Sales Forecasting
The primary implication of skew for sales forecasting is the fitting of sales expectations to
an estimated quarterly distribution. One way our findings on skew could be implemented
is to take a the results from a sample of volatile products and use regression to compute
the expected skew, either distributed by quarter, or normalized to a percentile for the life
of the distribution. By computing a regression, a confidence interval can also be
established, against which each quarter's results can be evaluated in a rule-based system: a
breakout from the established confidence interval would trigger subsequent quarters'
forecasts to be adjusted, taking the actual value as the new estimator.
Breakout
Prior Range Forecast --
&V
Actual Shipments - -- New Range Forecast
Figure 5-1: Conceptual Rule-Based Range Forecast
36
Additionally, skew is also interesting because it implies an elongated thin tail later in the
product's lifespan for volatile products. A critical decision must be made about when to
cease production of the product, with profitability in the later stages being a key
consideration given the high rate of obsolescence in volatile markets. The above method
can be useful in generating accurate expectations sooner in the process because it gives
and updated result for all quarters as each quarter moves on.
5.2.2 Skew - Implications for Capacity Planning and Inventory Management
The question of the long tail is also interesting from capacityplanningand inventory
management standpoints as it necessarily drives a decision about actively supporting the
tail in inefficient, small batches vs ceasing production earlier and generating a large stock
of legacy inventory. This trade-off, of lower production asset utilization vs. higher
inventory carrying might result in differing strategies for a firm producing volatile
products: If the relative cost of inventory is high, it might make sense to invest in multiple
production lines of different scales - some firms have lower-efficiency reserve lines used
for test lots, and low-volume production. If the relative cost of inventory is low, then it
might make sense to carry it while carefully discounting along with the rate of
obsolescence. In either stead, supporting a long tail requires creative thinking on behalf of
capacity planners and inventory managers, who must clearly consider represent the
relevant opportunity costs to the financial management of the firm.
5.3
RATE OF GROWTH/IDECLINE
Generally, volatile products had faster growth in the introduction phase. This was
expected, due to a twofold upward influence in a volatile market: the technology is
diffusing at a high rate, and the product is gaining market share (as opposed to quickly
37
shrinking to the benefit of its competitors). Stable-market products had longer, planned
ramps in growth.
Slopes for volatile products during the middle phases were confounded by multiple modes
and variation, making them effectively flat, with no conclusive peak in demand. By
contrast, traditional products displayed more clearly defined growth and maturity phases.
A limitation to these results is that amongst all of the metrics, slope is perhaps the most
heavily influenced by production decisions as opposed to 'natural' market forces: at the
beginning of the life cycle, growth is limited by available capacity - in a volatile market,
firms may initially allocate less than the full expected level of capacity to mitigate against
relatively high prospects of product failure. At the end of the life cycle, the phase-out
decision often involves the consideration of internal opportunity costs around
capitalization and inventory, rather than sizing production quantities to market demand.
5.3.1 Rate of Growth/Decline - Implications for Sales Forecasting
Much like skew, slope is an early indicator of a product's eventual sales volume. Simply
put, slope during the growth phase points in the direction of the peak demand, making for
another opportunity for quantitative rule-basedforecasting.Once segment lengths have
been established using the prior criteria, a sales volume can be predicted for the segment
based on the expected length and slope of the segment. While in our results, the slope of
the segment was logarithmic (% change each unit), if a different relationship is found, the
formulation could be altered to use whichever method has the best fit.
38
Estimated Segment Lengths
% Rate of Change
IEstimated0 < Dy < 1
Inflection Point
U Actual Sales
E Forecast Sales
Figure 5-2: Conceptual Slope & Segment-Based PLC Forecasting
5.4
VARIANCE
Volatile products led stable products in overall variance as expected. Weekly variance was
extremely high, with CV ranging exceeding 100% even for stable products. Pooled to
monthly totals, CVs came in ranges much more suitable for use as a demand signal for
inventory management activities, although for some volatile products, edge case variation
outside the month bound drove it significantly higher. All parties making use of the
monthly demand signal would do well to examine the sensitivity of varying the number of
days/weeks in a month (in our case, 4-5-4 per quarter) if CV seems sporadically high, and
seek an explanation - a seemingly innocuous repeating spike in demand variance may
yield misalignments in the supply chain.
While exploring the change in variance amongst the different product life cycle segments,
we discovered an interesting result: all products exhibited similar variation during the
introduction phase. The greatest difference in variation between the two product groups
came in the middle phases. Variance for high-share products tended to diminish around
39
the peak, while low share products exhibited similar variance to the earlier and later
phases.
5.4.1 Variance - Implications for Inventory Management
The value of monthly variation is a primary input for inventory managers seeking to set
cost-efficient levels of safety stocks throughout the supply chain. Setting an expectation
based on the life cycle stage is important technique: if stable products 'stabilize' during
the middle phases, inventory targets can be reduced for these periods, and many firms set
targets accordingly as a matter of policy. However, efforts to do the same for volatile
products may be fruitless if volatility remains constant throughout the life cycle, as was
the case with our volatile product group.
5.4.2 Variance - Implications for Capacity Planning
The key question for capacity planners in considering variance is, will production level
need to vary on a short-term basis to mitigate for variance in demand? If variance is
relatively low as in the case of the stable products in our group, capacity planners can
optimize for a relatively smooth, planned level of output. With low variability in output,
both labor and capital resources can be specialized in their respective tasks, avoiding some
additional amount of overhead cost. With little variation in output, all resource utilization
can remain high - ideal for both labor and capital-intensive industries.
For volatile products, whose variation is much higher, planners may need to reserve for
variable production rates in order to maintain profitability. Here again, the flexible
production system becomes a valuable asset. Firms operating in these sorts of markets
should take holistic view of their entire product line and construct flexible production lines
40
that take advantage of opportunities to pool the risk together. This may involve investing
in flexible equipment and training employees in multiple skills. If the product allows for
it, postponement strategies are effective. In making these decisions, capacity planners
should be mindful upstream and downstream actors in the supply chain, as adjusting
production volume on a short term basis may drive shocks in both directions.
5.4.3 Variance - Implications for Sales Forecasting
Sales forecasting is primarily concerned with longer-term, quarterly results. We believe
that for all practical purposes the change in sales between quarters is a trend, not random
variation. Yet the change in variance for the monthly signal may be of interest to
forecasters for stable products - our observation that the variance reduces in the middle of
the life cycle means it may be used as a signal that the product is entering or leaving the
middle phases.
5.5
MODALITY
Our observations for modality were inconclusive; stable products were certainly unimodal,
where volatile products had multiple modes, or a less well defined mode, with multiple
small peaks around a center. Uniform distributions were not encountered in either product
group - visually, each product had a recognizable central peak in its distribution over the
time horizon. We believe that the lack of clarity for volatile products is significant - as it
represents continuing volatility in a product's market share as a function of features/price
even at peak demand.
5.5.1 Modality - Implications for Capacity Planning
The relevance of modality to capacity planners is twofold: first, the existence multiple
modes means a 'dip' or downtime in the center of production will occur wherein fewer
41
resources are required. For labor planning, this may mean furloughs, and for capital
planning, the usefulness of flexible-output production will be useful
VOLATILITY AND CAPITAL-INTENSIVENESS
As discussed in the beginning of this paper, volatility presents a key challenge for capitalintensive industries like the manufacture of durable goods: in order to be profitable, firms
must make efficient utilization of their assets, and thus must carefully consider the
opportunity costs of capitalization. Large firms, from automobiles to semiconductors,
often make billion-dollar bets with their go-to-market decisions. Under normal
circumstances, it seems that firms taking this level of risk would naturally want to avoid
the lower margins and higher uncertainty of volatile products.
An interesting paradox is created - large, capable, capital intensive firms want to avoid
participating in competitive, volatile markets because they have low apparent economic
profits (via high opportunity costs of producing more stable profitable products). But in
doing so, they may be avoiding the markets where the highest rate of innovation occurs
-
5.6
another sort of opportunity cost. When the market demands innovation, eventually a space
is created wherein a disruptor can take advantage of their competitors' wariness. An
example of this is electric passenger cars in the US - despite researching the idea for
decades, the established automakers were hesitant to invest the billions required to
develop scaled production and maintenance infrastructure, leaving room for Tesla Motors
to capture growing customer value in that market.
42
5.7
ADDITIONAL LIMITATIONS & FUTURE RESEARCH
5.7.1 Product and Industry Choice
We tested ten products from one firm in one industry. Based on the complementary role
this firm plays with both upstream and downstream products, as well as its overall
prevalence, we believe our results are robust enough to extend to most durable goods, but
variation is expected, most of which depending on the product lifespan. Future research
might attempt to compare these statistics across multiple firms and industries in order to
assess which methods are best
5.7.2 Variation and Exogenous Effects
While we have found a good deal of significance, coefficients of variation for all products
remained high. The motivation is to overcome the real-world problem of finding
significance in a very noisy dataset in order to make marketing and production decisions.
There will be some periods in any product's life cycle where the noise will confound any
signal of a transition between phases. This is especially true for volatile products that
exhibit high levels of variation and multi-modal demand patterns.
Additionally, many exogenous factors might have influenced the shipment trend. No realworld market is perfectly competitive - in durable goods industries firms tend to enter
long-term relationships with upstream and downstream firms wherein pricing, quantity,
timing, and even product design is heavily coordinated. In such an environment, a single
large B2B customer may have enough purchasing power to determine the overall success
of a product, exemplifying the sort of uncertainty that defines volatile markets. Future
research might assess how the competitiveness of upstream and downstream firms thus
influence the level of volatility in a market.
43
Exogenous factors may also drive shocks into the dataset - production delays, bullwhips,
and macro events all influence sales. While these effects were qualitatively assessed to be
minimal for our products, we cannot rule out their significance. Shocks may also have a
'make it or break it effect': even if by coincidence, we can imagine how a shortage or
recall lasting just several might cause a product to miss its window of opportunity for
growth in the volatile environment. Future research might examine rates of recovery for
products that have experienced these effects.
5.7.3 Validity of Judgment Methods
Because of the multitude of limitations in a purely quantitative assessment of demand
profiles, we would not recommend that any firm transition to making decisions on a
purely quantitative basis, whether it be econometric, or our notional PLC rule-based
algorithm, or otherwise. Judgment must still carry some weight - in fact, many of the
agreements and relationships that we listed as confounding factors actually enhance
forecast accuracy when the firm strives to fulfill its exact commitments (which can be
explained as transition from pure make-to-stock to partial make-to-order). When a firm
builds what it commits to suppliers and customers, and suppliers and customers do
likewise, a resilient supply chain is born! Further research into resilient supply chain
methods might further the understanding of when and how to best use judgment methods
for both marketing and production decisions. Of particular interest to us is the practice
sharing forecasts up and down the supply chain.
44
5.7.4 Sales Forecasting, Capacity Planning, and Inventory Management Strategies
Choosing these three lenses proved to be a very useful tool in decidingwhat to measure, a
key component of any firm's operational strategy. Each presents an opportunity for further
research.
To look into salesforecasting, a survey of firms with regards to volatile markets would be
an interesting way to assess overall effectiveness of different methods. Such a survey
could define common characteristics of various forecast methods - in terms of inputs,
calculations, and outputs - as well as ask how firms vary their forecasting based on the
perceived volatility of markets. Accuracy could also be assessed, both qualitatively and
quantitatively.
Our discussions on new products via the lens of capacityplanningbrought flexibility into
the forefront of new product theory. Future research might explore the trade-offs of level
of flexibility given a range of demand patterns, and how firms mitigate against volatility in
their production plans.
The lens of inventory management reminded us what a challenge it can be to manage a
high level of volatility in a new product market. Using our method, an expected level of
variance can be calculated for the entire lifespan, and traditional inventory theory would
have firms carry more inventory as a buffer to expected volatility, albeit at a high cost.
The practical counter to this additional cost is to invest in supply chain innovations like
postponement or use instruments like risk-sharing contracts to mitigate for the uncertainty.
But how do these well-established techniques perform with regard to new products?
45
5.8
CONCLUSION
This thesis accomplished both its motivational and research aims, in the specific sense of
empirical findings to support the proposed relationships in data, as well as in the general
sense that these results are consistent with intuitive thinking about the nature of volatile
markets.
The research question was whether demand patterns for volatile products had anything in
common, and whether they differed from traditional products. We answered 'yes' to both
questions, with significant correlation, using metrics that would allow a likewisemotivated firm to conduct the same sort of analysis.
The motivating question was whether firms may use this knowledge to make better
decisions about marketing and producing volatile products. This was done by looking
through the lenses of sales forecasting, capacity planning, and inventory management. The
high degree of significance in the research results allowed us to demonstrate sample
statistical forecasts, as well as propose heuristics for making decisions on production
flexibility, inventory management and phase out decisions.
In all of our discussions, we highlighted key-trade-offs to consider when deciding how to
market andproduceproducts in volatile markets. Many firms struggle to make datadriven decisions when metrics do not properly align to such trade-offs. In the case of
volatile markets, shortening life cycles and lead times leave little room for error, a
problem to which evolving from judgment-based methodology to an informed quantitative
method is an effective solution.
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GLOSSARY OF TERMS
Capacity Planning- The discipline or division(s) of a firm concerned with the construction of
facilities, hiring of labor and procurement of equipment in order to produce a product.
Decline Phase - The fourth and final phase of the product life cycle, where sales of the product
end.
Growth Phase - The second phase of the product life cycle, where the product fully expands into
its potential market on an order of magnitude that tends toward its peak demand.
Introduction Phase- The first phase of the product life cycle in which the initial market reaction
to the entry of the product occurs.
Maturity Phase- The third phase of the product life cycle, where the product reaches its full
extent into the market.
Modality - The degree to which demand for a product is centered around one or more peaks over
time.
Naive Forecasting- A forecasting methodology that where the pending period's demand is
predicted to be exactly the same as the last period's demand.
ProductLife Cycle - The entire life cycle of a product, from development, to production, to
legacy support and end of life. In this paper, we bound the life cycle to the lifetime of
product sales.
Rate of Growth/Decline- The rate at which demand for product(s) is increasing or decreasing
over time.
Sales Forecasting- The discipline or division(s) of a firm concerned with accuracy of forecasts
over large intervals - quarters and years; and the consequent long-range financial
performance of a product.
Skew - The tendency of the distribution of a product's sales over time to tend toward the
beginning or end of the life cycle.
Stable Market - A market where market shares vary gradually over time, as influenced by
gradual change in product features, widespread consumer understanding of products, and
accurate producer understanding of market segments. Risk is relatively low and entry and
exit are of low frequency.
Variance - The level of volatility in product demand that external to the life cycle trend.
Volatile Market - A market where shares vary rapidly over time, as influenced by rapid change
in features, and limited consumer and producer understanding of products and market
segments. Risk is higher and market entries and exits are frequent.
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