International Journal of Management, Knowledge and Learning, 9(2), 185–205
Preventing Corporate Turnarounds
through an Early Warning System
Ramon Oehninger
ZHAW School of Management and Law, Switzerland
Michael J. Kendzia
ZHAW School of Management and Law, Switzerland
Felix Scherrer
ZHAW School of Management and Law, Switzerland
Bankruptcy proceedings of companies have been all but new phenomena in
the business world. Latest cases, encompassing Toys R Us, Fred’s, and Sears
in the US as well as Thomas Cook and Air Italy in Europe, demonstrate that
managers often fail to run their businesses properly. As an alternative in such
a case, managers could prevent potential downfalls through knowledge of a
successful turnaround management. Learning about the implementation of
an early warning system (EWS) might help avoid corporate turnarounds in the
first place. Hence, it is crucial to offer managers a pragmatic and solution-
oriented approach. That being said, the authors design a specific EWS that
might contribute to bypassing corporate turnarounds at an early stage. By
doing so, the article aims at disseminating information on better EWS for
public corporations.
Keywords: early warning system, turnaround management, financial ratios
Introduction
Over the last years, Fred’s, Toys R Us, Forever 21, Diesel USA and other
retailers in the US filed for bankruptcy. One of those filing for Chapter 11
was Sears, a more than a century old US retailer. Other such examples, like
Thomas Cook and Air Italy in Europe show that not only retailers but also
other company types face the risk of vanishing into thin air.
In earlier times in Europe, individuals who went bankrupt, often experi-
enced a form of humiliation. For example, in the Italian city of Padua, the law
foresaw that the individuals concerned had to appear in the palace of jus-
tice, in front of a rock of shame, to publicly declare bankruptcy. Nowadays,
however, bankruptcy poses a too little social stigma, according to Sullivan et
al. (2006). Therefore, it is not surprising that failures in corporations have
been documented widely in the literature, as shown by Hofer (1980), Cole-
man (2004), and Lymbersky (2011), and concern not only private but also
public organisations, as pointed out by Boyne (2004, 2006), and Cornforth
and Paton (2004).
www.issbs.si/press/ISSN/2232-5697/9_185-205.pdf
186 Ramon Oehninger, Michael J. Kendzia, and Felix Scherrer
No company is entirely immune against economic distress and there
seemingly is an aura of inevitability concerning eventual organisational fail-
ure. In many cases, as Slatter and Lovett (1999), Collard (2002), Faul-
haber and Grabow (2009), Müller (2013), and Pepels (2015b) indicate, this
stems from ignoring the signs of looming crises. In this context, early warn-
ing signals are frequently misinterpreted or hushed because of the unwill-
ingness of the management to admit that their strategy is failing. Far too
often it is only intervened when the liquidity and the continued existence
of the company is already threatened, as displayed by Slatter and Lovett
(1999).
Knowledge can be considered as a competitive advantage (Grant, 1997),
as well as a substantial resource of companies (Polanyi, 1966). And yet its
impact on innovation to tackle entrepreneurial challenges cannot be over-
estimated (Nonaka, 1994; Seidler-de Alwis and Hartmann, 2008). However,
creating new knowledge constitutes a dynamic interplay between individuals
and organisations, encompassing a high degree of context, such as specific
time and space (Nonaka et al., 2000), that might lead to a journey ‘from
being to becoming’ (Prigogine, 1980).
As history shows, management often underestimates the strategic
change in the wake of business turnarounds. Trying to avoid learning the
wrong lessons from history, the authors seek to reveal key factors that can
lead to firms escaping from a potential failure (Wild, 2010). To protect the
management from corporate failures better, the work presents a system,
which might prevent corporate turnarounds in the first place. Hence, the
authors test the most prominently discussed crisis recognition tools on
several case studies to create an early warning system (EWS), enabling
companies to detect potential symptoms at an early stage.
Literature Review
While there has been a large body of research in view of corporate
turnarounds, such as from Schendel et al. (1976), Schendel and Patton
(1976), Bibeault (1998), Vinten et al. (2005), the subject of early crisis
recognition, despite its practical significance, has not been a major fo-
cus of researchers in the past. The focus was put more on establishing
a general approach and techniques for turnarounds, as noted by Slat-
ter and Lovett (1999), and Platt (1998), or addressing the topic from a
more specific standpoint. Slatter et al. (2006) concentrated on leadership
during turnarounds, whereas Driendl (2012) looked at turnarounds form
a stock market perspective and set out how profitable some turnaround
stocks might become. Others dealt with identification of distinct turnaround
strategies, as, for example, for small firms by Boyle and Desai (1991),
restaurants, as displayed by Chathoth et al. (2006), hospitals, shown by
International Journal of Management, Knowledge and Learning
Preventing Corporate Turnarounds through an Early Warning System 187
Hidden crisis
Crisis denial
Disintegration of
organisation begins
Organisational collapse
Recovery Failure
Complacency: signals completely
overlooked
Crisis explained away: belief that it will
disappear, no need for action
Some action taken but need for action
underestimated
Inability to take action
Tu
rn
ar
ou
nd
si
tu
at
io
n
Figure 1 Four Stages of Crisis Development, according to Slatter and Lovett (1999)
Langabeer (2008), or industrial business units, as pointed out through
Hambrick and Schecter (1983).
Organisational crises usually develop according to the four-stage model
by Slatter and Lovett (1999), as shown in Figure 1. According to it, the in-
ternal recognition that a company is in trouble usually comes as a surprise,
despite the crisis being triggered long before. Schendel et al. (1976) define
a turnaround situation as a low-probability and longer lasting, high-impact
situation leading to a decline in performance. However, challenges faced
are often ill defined and lack a single solution path, as note by Mumford et
al. (2000).
In general, a turnaround situation occurs as soon as corporate survival
is doubtful. Once such a state is apparent, corrective measures must be
taken to salvage the company from bankruptcy, as Slatter and Lovett (1999)
point out. As soon as an organisation regresses into a turnaround situa-
tion, long-term strategic planning recedes into the background, and ad-hoc
management measures are employed to try to turn the situation around.
Therefore, turnarounds pose stressful situations for a corporation, as Finkin
(1987), Faulhaber and Grabow (2009), and Marti (2013) show, owing to ad-
hoc corrective measures, which usually divert personnel from routine tasks.
Additionally, highlighted by Lenahan (1999), the use of external resources
often escalates the already considerable costs of the event and many com-
panies fail to execute a successful turnaround and go bankrupt.
Turnaround management includes all activities undertaken by an organi-
Volume 9, Issue 2, 2020
188 Ramon Oehninger, Michael J. Kendzia, and Felix Scherrer
Turnaround situation
S
ev
er
ity
Time
Strategy crisis
Earnings crisis
Liquidity crisis
Insolvency
Recovery
Figure 2 Emergence and Recognition of Organisational Crises, as shown by Bichkoff
and Eilenberger (2004), Evertz and Krystek (2014), and Müller (2013)
sation in an existence-threatening state, to avert impending insolvency and
return to sustainable profitability, as Buschmann (2006), Driendl (2012),
and Pepels (2015a) note.
Building on this model, research generally distinguishes between three
types of organisational crises of increasing severity, as illustrated in Figure
2. A strategy crisis is less serious than an earnings or liquidity crisis and
thus accompanied by less obvious signs. The longer a crisis remains un-
controlled, the more severe it gets and the more obvious the warning signs
become. Far too often, crises are not acknowledged until the liquidity of the
company is endangered (Müller, 2013). This is highlighted by the arrows in
Figure 2, as light arrows shows the order of recognition and dark arrows the
order of emergence.
Since the crisis types continuously progress over time, the importance
of early recognition can be highlighted. Furthermore, it challenges the no-
tion indicated by Platt (1998) and Pepels (2015a), where long-term strategic
planning is not considered the highest priority during a corporate turnaround
and the concerns about the future and longevity of the company are re-
placed by the immediate objective of saving the company from insolvency.
Such a focus reinforces the application of methods such as cost-cutting,
asset reduction or downsizing is, which often only lead to short-lived stabil-
isation of the business, as Slatter et al. (2006) shows.
Consequently, researchers have recognised the pitfalls to this being
the sole measure of turnaround success and directed the attention to-
wards the sustainability of such endeavours, like authors, such as Barker
International Journal of Management, Knowledge and Learning
Preventing Corporate Turnarounds through an Early Warning System 189
and Duhaime (1997), Bickhoff and Eilenberger (2004), and Faulhaber and
Grabow (2009), suggest.
Given the availability and relevance in business failure prediction, finan-
cial ratios are frequently used as crisis indicators, as Platt (1998), Situm
(2013), and Zopounidis and Doumpos (1999) note. Further, discriminant
analyses (Altman, 1968) and logistic regressions (Situm, 2013) both also
building on multiple financial ratios, have commonly been used to evaluate
financial health. The main caveat of the application of ratio-based analy-
ses is that financial statements could be subject to creative accounting,
and thus may provide a distorted picture of the company accounts and the
severity of the crisis. The risk can partly be mitigated by relying on data
from annually prepared statements, as they are audited and provide a more
truthful view (Plattt, 1998). Additionally, there is a high possibility that there
is a time lag between the internal occurrence and external recognition of
the crisis as most audited financial reports are published annually (Slatter
& Lovett, 1999).
Methodology
The broad EWS created for the analysis of the case studies consists of
specific ratio analyses, Altman’s (1968) discriminate model and Doumpos
and Zopounidis’ (1999) logistic regression. Appendix shows the formulas of
how the different EWS scores are calculated. The framework is applied to
several case studies and is tested on its predictive abilities. The cases have
been selected based on the presence of a crisis, the availability of financial
information, as well as the inclusion of various industries. The investigated
timespan has also been subject to the availability of financial information.
The analysis will consist of a thorough evaluation of the specific companies’
financial performance over various fiscal periods. The results are displayed
in a radar chart, where the changes in each measurement can be traced
and interpreted.
The figures are interpreted based on the changes in each measurement
and the predictive ability of different measurements is assessed. In doing
so, the final framework can be condensed to the few most decisive mea-
surements. The ratios used in subsequent analysis have been selected
based on their ability to predict crisis situations and their prominent use
in past research. The selected ratios were, with the exception of the quick
ratio and ROE, used by more than one third of the sources analysed. The
ROE has been specifically taken into consideration because of its ability to
illustrate the impact of debt on the generated return when compared with
the ROA (Leach, 2010). The quick ratio has been added to the analysis due
to its capability to quantify the impact of accumulation of inventory on a
firm’s ability to cover all its short-term liabilities (Leach, 2010).
Volume 9, Issue 2, 2020
190 Ramon Oehninger, Michael J. Kendzia, and Felix Scherrer
Table 1 Scale Determinants
(1) (2) Debt ratio
Lowest Highest Lowest Highest Lowest Highest
BlackBerry 0.447 5.027 –0.186 1.166 0.111 0.520
Bristol-Myers Squibb 2.460 14.188 0.059 0.550 0.478 0.665
General Electric 0.487 2.041 –0.001 0.058 0.774 0.835
Rolls-Royce 3.401 12.903 0.054 0.110 0.663 1.033
Current ratio Quick ratio ROA
Lowest Highest Lowest Highest Lowest Highest
BlackBerry 2.059 6.489 1.779 6.284 –77.8% 26.5%
Bristol-Myers Squibb 1.150 2.210 0.947 1.987 –5.4% 34.2%
General Electric 1.625 3.570 1.410 3.417 –7.3% 2.4%
Rolls-Royce 1.202 1.485 0.793 1.160 –15.8% 14.4%
ROE Z-score Logit model
Lowest Highest Lowest Highest Lowest Highest
BlackBerry –162.0% 32.9% –0.557 25.454 0.0% 100.0%
Bristol-Myers Squibb 14.4% 71.5% 3.152 9.469 90.3% 100.0%
General Electric –4.4% 12.2% 1.054 1.879 0.1% 15.9%
Rolls-Royce –228.0% 68.2% 0.212 1.850 0.0% 99.9%
Notes (1) working capital turnover, (2) cash flow to debt ratio.
This work uses Altman’s (1968) multiple discriminant analysis to predict
corporate turnaround situations. Due to the model discriminating between
bankrupt (score below 1.81) and non-bankrupt firms (score above 2.67),
negative changes in the Z value reflect underlying problems within the or-
ganisation and thus warn of imminent crisis situations. Negative changes
in the score can be seen as an early indicator for a potential crisis, even if
the score lies far above the grey area.
The logit model is also based on financial ratios, but, unlike Altman’s
(1968) Z-score, its foundation is a logistic regression and includes differ-
ent parameters, according to Situm (2013). Research shows that it is one
of the main alternatives to Altman’s (1968) discriminant analysis and over-
comes some of its limitations, as demonstrated by Doumpos and Zopouni-
dis (1999), and Situm (2013). According to the latter, the most prominent
advantage is that it indicates a company’s probability of financial health. In
brief, when a company achieves a score of 82 per cent, it shows an 82 per-
cept probability to be in a healthy financial situation. Were the score to drop
below 78 per cent, a firm is considered financially stricken (Situm, 2013).
Since all employed tools yield a different result that can only be inter-
preted on its specific scale, all figures need to be converted to a single
scale in order to be compared to one another. Therefore, this work first
determined the acceptable ranges for each ratio and converted those to
International Journal of Management, Knowledge and Learning
Preventing Corporate Turnarounds through an Early Warning System 191
a single 0 to 10 scale, where 0 is the lowest (worst) and 10 the highest
(best) score. The acceptable ranges are based on the highest and lowest
scores in the respective ratio across all case studies, which are adjusted if
the scores are deemed outliers, see Scale Determinants in Table 1.
In cases where the score exceeds the set range, they are fixed on either
lowest or highest range score respectively in order to facilitate interpreta-
tion of the results. To convert the figures to the radar scale, a conversion
function for each specific measurement was established, as displayed be-
low. The variable x1 stands for the initial ratio result, whereas x2 shows the
results on the radar scale. If the working capital turnover, for example, was
4.0 for any case study, the score shown on the ratio scale would be 5.8, as
given by its specific scale conversion function.
Formulas for Conversion to Radar Scale
Working capital turnover x2 =
x1 + 25
5
Cash flow to debt ratio x2 =
x1 + 0.5
0.2
Debt ratio x2 =
x1 −1.5
−0.15
Current ratio x2 = 2x1
Quick ratio x2 = 2x1
ROA x2 =
x1 + 20
6
ROE x2 =
x1 + 20
6
Z-score x2 = x1
Logit model x2 =
x1
10×100
Analysis
The findings of the analysis are presented in a coherent manner. The results
of the EWS model applications to what is deemed the relevant timespan for
early crisis recognition are illustrated, and the development of each mea-
surement score is assessed. To increase the understanding, the calculated
scores and their radar score equivalent have been given in squared brack-
ets [ratio value/radar score]. By applying the framework from the literature
review to case studies, the applicability of the different parameters in terms
of early crisis recognition is evaluated.
Volume 9, Issue 2, 2020
192 Ramon Oehninger, Michael J. Kendzia, and Felix Scherrer
Figure 3
EWS BlackBerry
(based on data from BlackBerry,
https://global.blackberry.com)
10.00
8.00
6.00
4.00
2.00
Working capital turnover
Cash flow
to debt ratio
Debt ratio
Current ratio
Quick ratioROA
ROE
Z-Score
Logit Model
2008
2009
2010
2011
2012
2013
BlackBerry
Activity. The organisational efficiency, as measured by the working cap-
ital turnover, continuously increased from 2008 [3.00/5.60] to 2011
[5.16/6.03]. In the following year, there was a marginal decrease in the
ratio [5.03/6.02], followed by substantial decrease in 2013 [3.03/5.61].
The working capital turnover has been decreasing ever since, falling below
1 in 2018.
Coverage. The cash flow to debt ratio shows the first considerable decline
between 2008 [1.00/7.50] and 2009 [0.65/5.76]. In the following period,
the ratio nearly doubled [1.17/8.33], before beginning to decrease again
in 2011 [1.02/7.59]. In the 2012 fiscal year [0.80/6.51], a further decline
in the coverage ratio can be recognised. The negative trend continued, and
the ratio dropped by nearly 40 percept until 2013 [0.62/5.61]. In 2014,
the operating cash flow coverage became negative.
Leverage. The debt ratio changed only marginally over the assessed pe-
riod from 2008 [0.29/8.09] to 2013 [0.28/8.12]. The debt ratio initially
decreased until 2010 [0.25/8.30], showing an increasingly less risky fi-
nancial situation for BlackBerry. Beside a small spark in the ratio in 2011
[0.31/7.96], marking the highest level of debt for the recognition-relevant
time period, the debt level increased rather drastically thereafter.
Liquidity. The quick and current ratios improved from 2008 [Current:
2.36/4.72; Quick: 2.08/4.18] until 2010 [Current: 2.39/4.78; Quick: 2.13
/4.27] and began to drop in 2011 [Current: 2.06/4.13; Quick: 1.89/3.79].
Both ratios remained roughly on the same level for the rest of the times-
International Journal of Management, Knowledge and Learning
Preventing Corporate Turnarounds through an Early Warning System 193
pan. Overall, the liquidity ratios stayed above their respective limits, which
implies financial health for the whole recognition-relevant period.
Profitability. In the beginning, the ratios slightly declined from 2008 [ROA:
23.5/7.25; ROE: 32.9/8.70] to 2009 [ROA: 23.4/7.23; ROE: 32.2/8.70].
From there on, the ratios improved until 2011 [ROA: 26.5/7.75; ROE:
38.2/9.69], before plummeting by nearly 75 per cent in 2012 [ROA:
8.48/4.75; ROE: 11.5/5.25] and becoming negative in 2013 [ROA: –
4.91/2.52; ROE: –6.83/2.20]. BlackBerry continued to generate negative
profits until the 2018 fiscal period.
Discriminant and Logit Analysis. The logit score remained above 99 per
cent until 2013 [59.4%/5.94], where it fell notably, indicating the presence
of a turnaround situation. The score fell further to 0 percept in the following
fiscal year, which indicates a 100 percept probability that BlackBerry was
in financial troubles. In 2010 [12.0/10], the Z-score was still above the
upper limit of the radar scale, but decreased by more than 50 per cent in
2011 [5.13/5.13]. This worrying development continued, and the Z-value
fell further until 2013 [2.33/2.33] and even became negative in 2014.
Bristol-Myers Squibb
Activity. The working capital turnover of Bristol-Myers Squibb has im-
proved drastically between 2000 [4.35/5.87] and 2002 [10.3/7.06], and
the ratio more than doubled. In the subsequent year [4.11/5.82], the ratio
plummeted lower than the 2000 level and further decreased until 2005
[3.56/5.71]. Consequently, while the capital was managed increasingly
more efficiently until 2002, the notable drop in efficiency in 2003 indicates
serious underlying issues within the organisation.
Coverage. The cash flow to debt ratio decreased remarkably in the first
two years, from [0.55/5.27] in 2000 to [0.06/2.80] in 2002. This shows
that by 2002, at the lowest point of the year-end stock price development,
Bristol-Myers Squibb could only cover six per cent of its debt with its oper-
ating cash flow. The ratio then improved remarkably in 2003 [0.20/3.49],
before declining in 2004 [0.16/3.28] and 2005 [0.11/3.04].
Leverage. The debt ratio in the Bristol-Myers Squibb example shows a
substantial increase in the first five fiscal periods from [0.48/6.81] in 2000
to [0.66/5.57] in 2004. This indicates that an increasingly higher propor-
tion of the invested funds was raised from liabilities. In 2005 [0.60/5.99],
the ratio began to decrease again.
Liquidity. Since both liquidity ratios remained above 1 for the entire pe-
riod, they do not indicate grave liquidity issues. Both ratios decreased in
the first three years, from 2000 [Current: 1.74/3.49; Quick: 1.42/2.84]
to 2002 [Current: 1.21/2.43; Quick: 1.02/2.04]. Subsequently, the cur-
rent and quick ratio both increased until 2005 [Current: 1.78/3.57; Quick:
Volume 9, Issue 2, 2020
194 Ramon Oehninger, Michael J. Kendzia, and Felix Scherrer
Figure 4
EWS Bristol-Myers Squibb
(based on data from Bristol-Myers
Squibb, https://www.bms.com)
10.00
8.00
6.00
4.00
2.00
Working capital turnover
Cash flow
to debt ratio
Debt ratio
Current ratio
Quick ratioROA
ROE
Z-Score
Logit Model
2000
2001
2012
2003
2004
2005
1.48/2.97], with shortly decreasing in 2004 [Current: 1.50/3.01; Quick:
1.32/2.64].
Profitability. Overall, the pharmaceutical industry is extremely profitable,
which is shown by Bristol-Myers Squibb achieving a ROA of up to 34 per cent
and a ROE of over 70 per cent in 2009. During the assessed period, prof-
itability initially decreased drastically between 2000 [ROA: 23.3/7.22; ROE:
44.6/10] and 2001 [ROA: 9.34/4.89; ROE: 23.4/7.26]. In the following
two periods, Bristol-Myer Squibb achieved an increase in profitability until
2003 [ROA: 11.3/5.22; ROE: 31.7/8.62], before it dropped in 2004 [ROA:
7.85/4.64; ROE: 23.4/7.23] and rose again to similar heights in 2005
[ROA: 10.7/5.11; ROE: 26.8/7.79]. Overall, there is a distinct negative de-
velopment over the recognition timespan, which serves as an indicator for
the struggles Bristol-Myers Squibb faced during this period.
Discriminant and Logit Analysis. The Z-score shows the same pattern as
the profitability ratios, as it decreased drastically from 2000 [9.47/9.47]
to 2001 [4.42/4.42] and further declined in 2002 [3.22/3.22]. Thereafter,
the score improved until 2005 [3.52/3.52], which shows that a potential
crisis was tackled accordingly. The logit score never indicated the presence
of a turnaround situation, as the lowest point in 2004 [90.7%/9.07] re-
mained far above the 78 per cent limit. Hence, the company never faced
liquidity troubles during the organisational decay.
General Electric
Activity. The working capital turnover increased in the analysed times-
pan from 2014 [0.50/5.10] to 2018 [2.04/5.41]. The remarkable increase
International Journal of Management, Knowledge and Learning
Preventing Corporate Turnarounds through an Early Warning System 195
Figure 5
EWS General Electric
(based on data from General
Electric, https://www.ge.com)
10.00
8.00
6.00
4.00
2.00
Working capital turnover
Cash flow
to debt ratio
Debt ratio
Current ratio
Quick ratioROA
ROE
Z-Score
Logit Model
2014
2015
2016
2017
2018
shows that the working capital is progressively employed more efficiently,
despite the company suffering from a major stock price decline. The drastic
increase from 2014 [0.50/5.10] to 2015 [1.79/5.35] may have increased
management optimism regarding the future prospects of the firm, since it
correlated with the initial stock price improvement.
Coverage. The cash flow coverage was already extremely poor in 2014
[0.05/2.77], remained on the same level in 2015 [0.05/2.76], before it
became negative in 2016 [–0.0009/2.50]. In the following fiscal period in
2017 [0.04/2.68], it improved to around 4 per cent, only to plummet again
in 2018 [0.02/2.58]. Overall, at no point during the examined timespan
was the operating cash flow covering more than five per cent of the total
liabilities of General Electric.
Leverage. There was small net increase in the debt ratio from 2014
[0.79/4.74] to 2018 [0.83/4.45], which shows that the financial situation
of General Electric became riskier in the course of the corporate decay.
However, this is only reflected in a marginal increase and the ratio remained
roughly on the same level throughout the assessed period.
Liquidity. Both ratios decreased remarkably by more than 50 per cent
between 2014 [Current: 3.57/7.14; Quick: 3.42/6.83] and 2015 [Current:
1.63/3.25; Quick: 1.41/2.82]. In the following years, both ratios gradu-
ally increased again until the most recent fiscal year [Current: 1.96/3.91;
Quick: 1.65/3.29], without ever approaching similar values as in 2014.
Profitability. While the profitability initially increased until 2014 [ROA:
2.37/3.73; ROE 11.2/5.20], it plummeted to negative figures in 2015
[ROA: –1.18/3.14; ROE: –5.79/2.37]. However, the fact that the profitabil-
Volume 9, Issue 2, 2020
196 Ramon Oehninger, Michael J. Kendzia, and Felix Scherrer
ity recovered in 2016 [ROA: 2.34/3.72; ROE: 11.0/5.17] may have fos-
tered the internal belief that the fall in corporate performance was only
an evanescent issue. By again achieving negative profits in 2017 [ROA:
–1.6/3.07; ROE: –7.39/2.10] and further declining profitability in 2018
[ROA: –7.26/2.12; ROE: –43.6/0], the corporate crisis was likely to have
been acknowledged.
Discriminant and Logit Analysis. While the net profits were still increas-
ing between 2010 and 2014, the logit score was declining and showed a
0.07 per cent probability that General Electric was financially sound in 2014
[0.07%/0.01]. Although the score improved until 2016 [15.9%/1.59], the fi-
nancial situation only improved marginally with the logit score still indicating
imminent bankruptcy.
Furthermore, also the Z-score already showed the underlying troubles
within General Electric in 2014 [1.58/1.58]. The score continuously de-
creased, with the exception of a spike 2016 [1.88/1.88], and reached its
lowest point in 2018 [1.05/1.05].
Rolls-Royce
Activity. While the working capital turnover marginally improved between
2012 [5.07/6.01] and 2013 [5.11/6.01], it plunged in 2014 [3.92/5.78]
and further decreased in 2015 [3.48/5.70]. In the following fiscal period
[4.50/5.90], the working capital was employed more efficiently again. The
increase in the WC turnover possibly indicates that the previously existing
problems have been overcome.
Coverage. The cash flow to debt ratio showed and initial increase from
[0.10/3.02] in 2012 to [0.12/3.11] in 2013, before falling to [0.08/2.91]
in 2014. In the following two fiscal years, the coverage ratio further declined
until reaching [0.06/2.80] in 2016. Consequently, it can be recognised that
the cash flow coverage of the total liabilities progressively decreased over
the analysed timespan.
Leverage. The debt ratio continuously increased from [0.66/5.58] in
2012 to [0.71/5.25] in 2014, to [0.93/3.82] in 2016. This progressive
incline in the debt ratio implies that financial situation of Rolls-Royce has
become increasingly riskier over the course of the analysis, as the propor-
tion of assets financed by debt increased remarkably.
Liquidity. The liquidity ratios increased in the first four years from 2012
[Current: 1.33/2.67; Quick: 0.95/1.91] to 2015 [Current: 1.48/2.96;
Quick: 1.16/2.96], which implies that Rolls-Royce’s ability to cover its cur-
rent liabilities increased during this period. In 2016 [Current: 1.35/2.70;
Quick: 1.02/2.70], both ratios decreased and have been declining ever
since.
Profitability. 2012 [ROA: 12.7/5.44; ROE: 37.6/9.60] was an outlier
International Journal of Management, Knowledge and Learning
Preventing Corporate Turnarounds through an Early Warning System 197
Figure 6
EWS Rolls-Royce
(based on data from Rolls-Royce,
https://www.rolls-royce.com)
10.00
8.00
6.00
4.00
2.00
Working capital turnover
Cash flow
to debt ratio
Debt ratio
Current ratio
Quick ratioROA
ROE
Z-Score
Logit Model
2012
2013
2014
2015
2016
year regarding Rolls-Royce’s profitability as it skyrocketed. Subsequently,
the ratios decreased by more than 50 per cent in 2013 [ROA: 5.80/4.33;
ROE: 21.9/6.98] and further plummeted in 2014 [ROA: 0.21/3.38; ROE:
0.91/3.48]. A slight increase in 2015 [ROA: 0.38/3.40; ROE: 1.67/3.61]
was answered by negative profitability ratios in 2016 [ROA: –15.8/0.70;
ROE: –216/0].
In the following years, the ratios fluctuated immensely between high re-
turns in one period and negative returns in another. The annual statement
shows that the outlier year in 2012 could have been due to restructuring a
business unit, as noted by Rolls-Royce (2013).
Discriminant and Logit Analysis. The corporate decay was signalled by
a steadily declining Z-score from [1.83/1.83] in 2012 to [0.21/0.21] in
2016. The immense initial drop from 2012 [1.83/1.83] to 2013 [1.48/
1.48] classified the company as insolvent, according to Altman’s (1968)
definition. By nearly reaching 0, the discriminant analysis indicates that
Rolls-Royce was extremely close to organisational failure. The logit score
shows a similar reality as in other case studies. Subsequently to the Z-
score declining in 2013, the logit model indicated a 40.6 per cent chance
of financial health in 2014 [40.6%/4.06].
In the previous two years, the ratio only declined marginally from 2012
[99.9%/9.99] to 2013 [94.5%/9.45]. After already indicating financial
troubles in 2014, the logit score further declined to 0 per cent in 2016
[0.00%/0].
Volume 9, Issue 2, 2020
198 Ramon Oehninger, Michael J. Kendzia, and Felix Scherrer
Results
BlackBerry
In the BlackBerry case, the crisis transitioned to an earnings crisis in 2012,
as the net income began to be affected. In the following year, the logit score
indicated a shift towards liquidity crisis. According to the theory, it can be
assumed that the crisis was recognised as soon as the first losses occurred
in 2013.
Although the falling stock price may have already raised awareness of a
potential crisis in 2011, the consequent period of continuous growth hints
at optimism potentially still being the prevailing management rhetoric at the
time. The EWS framework clearly indicated the deteriorating situation two
years before the solvency of the firm became endangered, as the declining
Z-score in 2011 should have already caused serious concern among Black-
Berry’s top management. In the following year, the falling profitability, com-
bined with the decreasing coverage ratio, further underlined the increasing
severity of the symptoms.
Bristol-Myers Squibb
The company’s net income hardly declined over the period. Plus, the liquid-
ity was never endangered either. It appears that the crisis was recognised
in due time, since the strategy crisis never transitioned to the next crisis
stage. The application of the system reveals that the first concerning signs
became visible in 2001, when the Z-score decreased substantially. There-
fore, the framework hints at an imminent turnaround situation one year
before the crisis was arguably recognised due to the fall in the year-end
stock price.
However, it can be assumed that corrective measures were taken accord-
ingly, stopping the organisational decay as the earnings or liquidity situation
of Bristol-Myers Squibb never was affected. Moreover, the fact that most of
the scores improved in 2005 possibly indicates that the measures have
been successful. Without such action, the logit score would have arguably
decreased further in the following years, potentially indicating a turnaround
situation. As a result, this case serves as an example of how the early
recognition of a looming crisis can lead to a timely resolution.
General Electric
GE’s discriminant value only slightly surpassed the proposed lower limit of
1.81 in its outlier year in 2016. This short-lived improvement coincided with
the highest stock price for the complete timespan. The General Electric
case is somewhat an exception to the previously analysed cases, as the
logit score and Z-value have indicated a crisis situation since the beginning
International Journal of Management, Knowledge and Learning
Preventing Corporate Turnarounds through an Early Warning System 199
of the timeframe. Although the stock price was still achieving continuous
growth, the underlying issues within the corporation became increasingly
more visible.
A notable decrease in the liquidity, as well as the profitability ratios in
2015, further reinforced the existence of a corporate crisis even before
the stock price plummeted. While the company generated net losses in
2015, fiscal results were probably seen as negative outliers, due to the
subsequent increase in net revenues in 2016. This development is also
recognised on the stock market and reflected in an additional increase in
the stock price. Consequently, it can be argued that the existence of the
crisis was not acknowledged until 2018, when the net losses increased
more than threefold. Since the logit score was consistently indicating a
turnaround situation, liquidity crisis was apparent, and the application of
the model would not have predicted the crisis prior to affecting the solvency
of the firm. However, since it can be argued that the crisis was not acknowl-
edged until 2018, the EWS would have enabled the GE management to
introduce corrective measures at least four fiscal periods prior to the crisis
recognition.
Rolls-Royce
In this case, the declining discriminant and logit results show that the im-
minent crisis became increasingly severe and reached the liquidity crisis
stage in 2014. The decline in the logit score indicated this transition to the
next crisis stage already in the previous year. Furthermore, it could even be
argued, since the Z-score was never above 1.85 in any of the previous pe-
riod, that the symptoms of the looming crisis were already visible six fiscal
periods before the logit score indicated that Rolls-Royce’s solvency was en-
dangered. Consequently, the application of this model once again managed
to show the underlying organisational issues, while the stock market was
still largely optimistic.
Conclusion
To conclude, the application of the EWS yielded clear results regarding the
predictability of crisis situations. That said, based on the cases, the EWS
was able to predict looming organisational crises on average 3.25 years
prior to their occurrence. In many cases, crisis symptoms were already visi-
ble two or more years prior to either the liquidity being affected, or the crisis
situation being recognised.
In the Rolls-Royce example, the signs should already have caused se-
rious concerns six years prior to the crisis, whereas the General Electric
case showed that the crisis could have been recognised four years earlier.
In both cases the rising stock price indicated that the overall perception of
Volume 9, Issue 2, 2020
200 Ramon Oehninger, Michael J. Kendzia, and Felix Scherrer
the company was still positive during a period where red flags were already
visible.
The Bristol-Myers Squibb case reinforced the notion that crises are more
easily rectified if they are recognised earlier. In general, the case studies
have clearly shown that turnaround situations, as implied by a fall in the
logit score, are predictable. In nearly all of the cases, the symptoms were
evident before a turnaround situation was apparent. The first indicators of
the looming decay are often a decline in the Z-value, followed by negative
changes in the organisational profitability. The cash flow to debt ratio was
in some cases more, and in others less meaningful.
Overall, neither the activity, leverage nor liquidity ratios were able to
unambiguously indicate a negative trend development. Therefore, the pro-
posed conceptual framework can be condensed to the most indicative fac-
tors, namely the Z-score, coverage and profitability ratios that signalled the
looming crisis reliably. The logit score is further included in the final EWS
because of its importance in defining the existence of a turnaround situa-
tion. In condensing the framework, its application and interpretation can be
facilitated.
The aforementioned explanations provide strong evidence that all turn-
arounds are preventable, as long as they are predictable. For this to hap-
pen, an EWS is essential to guarantee both more time and alternatives
to respond to the crisis. By applying an internal EWS, managers are en-
abled to identify challenges at an early stage. Thereby, the EWS allows the
management to initiate counteractions to prevent the company from a po-
tential bankruptcy filing. This might be, amongst a row of potential other
measures, the launch of an internal investigation in view of the underlying
issues causing the adverse effect on the company business.
Overall, it can be concluded that the created EWS is a step forward
regarding crisis recognition and can possibly serve as the foundation for a
more extensive framework in the future. Future studies should consider the
application of the model in a practical environment to confirm the framework
value in real-life situations. Further, they could take industry-specific aspects
into consideration to enhance the quality of each scale provided. Following
this, the illustration of the radar scales as well as its interpretation could
be optimised. Finally, future works on this topic should concentrate on the
integration of qualitative factors in the EWS framework. This, in turn, might
help to recognise a looming threat to the organisation even earlier and
potentially facilitate a stronger in-depth analysis regarding the reasons for
deterioration.
The nature of the analysis applied is subject to the validity of publicly
available financial statements. Since the annual reports only show the spe-
cific performance at the end of the fiscal years, the predictive abilities of
International Journal of Management, Knowledge and Learning
Preventing Corporate Turnarounds through an Early Warning System 201
the framework may be restricted. Furthermore, it highlights one of the EWS
weaknesses, which, as the case studies have revealed, points to the quick
deterioration of specific situation of the company taking place from one year
to another. The foregoing stands in opposition to the assumption of a rather
slow and gradual decline.
However, for company internal applications of the EWS, this becomes
less relevant as the monitoring of the scores can be performed on a more
regular basis. Therefore, internal use would potentially allow for an even ear-
lier crisis recognition, as the decline may appear less drastic if the scores
are evaluated biannually or quarterly.
Additionally, the framework does not indicate why the analysed scores
are changing or how the crisis could be overcome, due to the lack of
qualitative parameters. It only indicates that there are organisational prob-
lems apparent, but is subject to further analysis of financial statements,
macroeconomic- and company-specific factors in order to define the root
causes of the organisational decay. The framework does not account for the
prediction of crises due to unforeseeable events or fraudulent behaviour, as
this arguably should be part of a company risk management rather than cri-
sis prediction.
Lastly, all cases have been analysed retrospectively, which arguably
makes it easier to determine negative trends. Consequently, it could be
argued that while the developments seem obvious in past cases, the appli-
cation of the framework in real time would not yield similar results. There-
fore, the capabilities of the EWS, when applied in practice, still need to be
verified, but based on the clear results, a successful transition to corporate
application could be expected.
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Appendix 1: Formula Directory
Financial Ratios
Activity Ratio Working capital turnover =
Sales revenue
Net working capital
Coverage Ratio Cash flow to debt ratio =
Operating cash flow
Total debt
Leverage Ratio Debt ratio =
Total debt
Total assets
Liquidity Ratio Current ratio =
Current assets
Current liabilities
Liquidity Ratio Quick ratio =
Cash + market. sec. + receivables
Current liabilities
Profitability Ratio ROA =
Net income
Total assets
Profitability Ratio ROE =
Net income
Shareholder’s equity
Discriminant Analysis
Z = (1.2x1) + (1.4x2) + (3.3x3) + (0.66x4) + (1.0x5)
x1 =
Working capital
Total assets
x2 =
Retained earnings
Total assets
x3 =
EBIT
Total assets
x4 =
Market value of equity
Book value of debt
x5 =
Sales
Total assets
Logit Model
F =
1
1 + e−(13.7813−4.7252x1+52.9741x2−3.0594x3−14.558x4+0.1886x5)
x1 =
Gross profit
Total assets
x2 =
Net income
Total assets
x3 =
Quick assets
Current liabilities
x4 =
Total debt
Total assets
x5 =
Net worth
Net fixed assets
Other Formulas
Working capital = Current assets−Current liabilities
Total market value = Year end stock price×Shares outstanding
Ramon Oehninger is currently a master’s student at Copenhagen Business
School in Economics and Business Administration. He holds a bachelor’s de-
gree in International Management from Zurich University of Applied Sciences.
International Journal of Management, Knowledge and Learning
Preventing Corporate Turnarounds through an Early Warning System 205
During his studies, he specialized on the prevention of corporate turnarounds.
raoe19ab@student.cbs.dk
Michael J. Kendzia studied business administration and economics at Univer-
sity of Cologne and Warsaw School of Economics. He was a scholar at the
Institute of Labor Economics in Bonn. In his current position, he heads the
bachelor’s program in International Management at the Zurich University of
Applied Sciences. kend@zhaw.ch
Felix Scherrer holds an MSc in Engineering from ETH Zurich and an MBA from
INSEAD. He has spent 30 years in management positions in the medical
device industry as well as in corporate development functions. At the Zurich
University of Applied Sciences, he implemented the MSc in International Busi-
ness. felix@scherrer-mail.ch
Volume 9, Issue 2, 2020