European Journal of Operational Research xxx (2013) xxx–xxx
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European Journal of Operational Research
journal homepage: www.elsevier.com/locate/ejor
Decision Support
Recent developments on Reactivity: Theoretical conceptualization
and empirical verification
Pietro De Giovanni a,⇑, Alfio Cariola b, Mariacarmela Passarelli b
a
b
Department of Information Systems, Logistics and Innovation, Vrije Universiteit Amsterdam, de Boelelaan 1105, Room 15A-381081 HV, Amsterdam, The Netherlands
Department of Business Administration, University of Calabria, Ponte Pietro Bucci, Cubo 3c 5 Piano, Arcavacata di Rende, Cosenza, Italy
a r t i c l e
i n f o
Article history:
Received 1 February 2012
Accepted 19 June 2013
Available online xxxx
Keywords:
Reactivity
Performance
Managerial practice
Unexpected demand
Empirical analysis
a b s t r a c t
This paper seeks to enrich the literature of operations and supply chain management through the development of the concept of Reactivity and the introduction of related performance indicators. Reactivity
explains the capability to perform operationally and economically under unexpected conditions. A qualitative investigation has aimed to identify useful managerial practices to be adopted to properly perform
Reactivity, while an empirical analysis has tested the relevance of each practice as well as the economic
benefits that Reactivity provides. The findings suggest that managers and practitioners should develop a
Reactivity orientation because it benefits firms’ economic performance when an unexpected event
occurs; in addition, several recommended managerial practices should be undertaken to ensure its correct implementation.
Ó 2013 Elsevier B.V. All rights reserved.
1. Introduction
Recent theoretical research has developed specific firm orientations such as Agile, Lean, and Leagile (Faisal, Banwet, & Shankar,
2006), and their operationalization has introduced novelties in
the operations and supply chain management literature that open
up new and appealing research directions. Nevertheless, these concepts do not deal with an important issue in business: unexpected
demand. Unexpected demand refers to a specific kind of demand
that is not anticipatable or predictable and for which the application of all forecasting methods is totally ineffective (Naish, 1993).
The existing concepts address volatile demand, which includes a
predictable variance; in contrast, unexpected demand incorporates
an abnormal variance due to completely unknown events (e.g., the
terror attacks of September 11, 2001). In this regard, organizations
frequently overlook a managerial orientation that facilitates performing under unexpected events and properly addressing such
events. We develop the concept of Reactivity, whose main novelty
consists of contemplating the unexpected demand.
The preliminary review of the literature revealed that, beyond
unexpectedness, the issue of performance has also received little
attention in the recent developments on Agile, Lean, and Leagile
orientations. When applying those concepts, firms cannot define
the boundaries of their orientations. For example, an Agile firm
cannot measure its agility because the literature is missing a specific performance indicator. Thus, we developed the Reactivity in⇑ Corresponding author. Tel.: +31 205986146.
E-mail address: [email protected] (P. De Giovanni).
dex, an indicator of operational performance that allows for
identifying how reactive a firm is, the sources of low Reactivity,
the managerial practices to be adopted to perform with Reactivity,
and the relationships between firms’ Reactivity and their economic
performance. Because unexpected demand is not at all anticipatable, firms are not prepared to face it. Several inefficiencies emerge
in terms of cost, time, and quality because satisfying an unexpected demand can be operationally ineffective and time consuming, while quality cannot be guaranteed. In this sense, the
Reactivity index along with other performance indicators is a useful tool enabling firms to monitor their operational performance
when an unexpected demand materializes.
Although the economic and operational benefits obtainable
through the adoption of Agile, Lean, and Leagile orientations are
clearly stated in the literature (Faisal et al., 2006), empirical analysis confirming these theoretical statements and relationships is
lacking. Similarly, the literature identifies the managerial practices
that must be implemented to reach a specific firm’s orientation
(Christopher, 2000), but scant empirical research provides robust
proof of the relationships. Therefore, we conducted an empirical
analysis including both a qualitative and a quantitative investigation to identify the managerial practices to be implemented to promote a reactive orientation, the implications in terms of cost, time,
and quality performance, and the economic gains that result from
firms’ Reactivity. The empirical analysis supported our construct
operationalization.
The current research is organized as follows. First, we explore
the existing literature on Reactivity. Then, we theoretically
compare Reactivity with Agile, Lean, and Leagile to explain their
0377-2217/$ - see front matter Ó 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.ejor.2013.06.030
Please cite this article in press as: De Giovanni, P., et al. Recent developments on Reactivity: Theoretical conceptualization and empirical verification. European Journal of Operational Research (2013), http://dx.doi.org/10.1016/j.ejor.2013.06.030
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P. De Giovanni et al. / European Journal of Operational Research xxx (2013) xxx–xxx
differences and to highlight our main theoretical contributions. A
global indicator of Reactivity (i.e., the Reactivity index) and three
partial indicators allow for measuring firms’ Reactivity. In addition,
an empirical analysis explores the managerial practices that a firm
must adopt to perform with Reactivity as well as the economic
benefits obtainable through its implementation. Finally, conclusions are drawn from the findings.
2. Recent developments on Reactivity: literature review
The term Reactivity has been used with different meanings in
several, heterogeneous contexts. Cannon (1932) introduced Reactivity as an individual ‘‘fight-or-flight’’ capacity response to stress:
After facing a situation, an individual reacts by attacking (fight) or
by escaping (flight). Fraser, Sumar, and Sumar (2000), writing
about negative reactions in consuming food, explained human
reactions as physical nuisances and malaises that require reactive
care.
Applied to the operations and supply chain management fields,
Reactivity is a firm’s capability to operate in an unstable environment (Kiefer, 2000). Huang, Trappey, and Yao (2006) associated
Reactivity with the agent’s intelligence. After perceiving its environment, the agent reacts in a timely and appropriate manner.
Nakhla (1995) linked Reactivity to the rapidity of decision making:
A wide range of products and high demand instability require reactive scheduling; postponement is an appropriate strategy for
reaching this target. Cigolini, Cozzi, and Perona (2004) incorporated Reactivity into the context of networking redesign by presenting 3M’s case. The firm is reactive in reallocating its
production capacity within different geographic contexts, from
Europe to Asia and from North America to Latin America.
Reactivity requires collaborative practices and partnerships in
the logistics channel (Bonet & Paché, 2005). Paché (1998) did not
introduce the definition of Reactivity or its measurements; instead,
he presented Reactivity as a necessary attribute for third-party
logistics in grocery distribution to provide reliability and quality
service. Hardaker and Ahmed (1995) studied similarities and differences between European and Japanese approaches in computer-integrated manufacturing (CIM), indicating that Japanese
firms are proactive whereas Europeans are reactive. Feraud, 1998
linked Reactivity to the competitive advantage obtainable from
the integration between logistics management and information
systems because both departments perform at 100% service levels.
Andraski (1994) highlighted the cost and time benefits that a continuous replenishment program offers, as information sharing
drives success. Nevertheless, firms and supply chains should only
provide what the customer believes is important, thus reacting
efficiently and in a timely manner. This calls for strong integration
between logistics and information systems to adopt a customeroriented attitude.
Based on analysis of previous studies, the characteristics and
meaning of Reactivity can be summarized as
– Appropriateness, immediateness, and opportunity to respond to
a change.
– Time compression.
– Capacity to face the environment’s volatility and instability as
well as address internal disturbance.
– Development, production, and distribution of products where
and when customers need them.
Based on the literature review and the emerged features, we
have attempted to develop the concept of Reactivity, which will
take a position in the literature among the Agile, Lean, and Leagile
orientations.
3. Reactivity against Agile, Lean, and Leagile
The key point in differentiating Reactivity from other constructs
in the literature is demand unexpectedness. Christopher (2000)
linked volatility with the variance of demand experienced; however, unexpectedness is more than that. Unexpectedness derives
from abnormal variance associated with an event that is not at
all predictable (Caron & Higgins, 1974). When an abnormal variance materializes, the demand is unexpected. Confounding anticipated and unexpected shocks generate serious mistakes. It is the
difference between unpredictability and unexpectability that
makes Agile and Reactivity different constructs.
From a theoretical point of view, Reactivity is the natural evolution of agility. Schonberger and Knod (1997) and Naylor, Naim, and
Berry (1999) introduced agility as the use of market knowledge
and virtual corporations to exploit profitable opportunities in a
volatile marketplace. Dowlatshahi and Cao (2006) defined an Agile
manufacturing system as a manufacturing paradigm that focuses
on smaller scale, modular production facilities; meanwhile, Agile
operations can deal with turbulent and changing environments.
In contrast, Reactivity allows for high performance in the context
of demand unexpectedness linked to completely unknown events.
Deadly and destructive events, such as the September 11, 2001,
terror attacks, the Indian Ocean tsunami in 2004, Hurricane Katrina
in 2005, and the earthquake in Haiti in 2010, have a drastic impact
on customer demand, resulting in an abnormal variance that cannot be managed through firms’ agility and/or Leanness. Reactivity
elevates the capability to perform under unexpected demand.
Therefore, reactive firms can deal with unknown circumstances,
abnormal sales variances, and completely unknown events. In this
sense, Reactivity can also be considered a useful managerial practice in specific business situations (e.g., a competitor introduces a
new, radical innovation, starts a heavy promotional campaign, or
produces a completely non-conforming batch of products).
The constructs reported in the literature are not consistent with
the characteristics of Reactivity. Christopher and Towill (2001) compared the Agile and Lean concepts, both of which focus on customer
responsiveness. However, the Lean concept emphasizes efficiency
by removing all waste in the operational process (Van Hoek, Harrison, & Christopher, 2001) and resolving any trade-offs based on
physical assets, labor, capital, and land; the agility concept instead
highlights a fast response to changing customer demand and provides proper solutions to any trade-offs based on time, information,
and knowledge (Van der Vorst, ban Dijk, & Beulens, 2001, Yusuf,
Gunasekaran, Adeleye, & Sivayoganathan, 2004). These two definitions were used to develop the concept of Leagile (Faisal et al.,
2006), which combines the cost advantages of Lean and the time
compression benefits of Agile, thereby enabling cost-effectiveness
in the upstream chain and high service levels in a volatile marketplace in the downstream chain (Faisal et al., 2006).
Although this stream in the literature appears to be rich in concepts, it lacks an orientation that deals with unexpectedness. Any
organization develops its own ability to sense, perceive, and anticipate changes in the business environment. However, Naish (1993)
clearly proved that the last statement is only valid when demand
shocks can be anticipated and predicted. If not, the demand is
unexpected; consequently, any forecasting tool is useless or, at
least, inadequate. Unexpected events change the management of
business, imposing rapid and effective changes that Agile, Lean,
and Leagile firms cannot make.
In this regard, we have proposed development of the concept of
Reactivity, which will overcome the limitations of previous concepts and fill a hole in the literature. Reactivity represents the ability to satisfy customers and perform in terms of cost, time, and
quality when an unexpected event occurs.
Please cite this article in press as: De Giovanni, P., et al. Recent developments on Reactivity: Theoretical conceptualization and empirical verification. European Journal of Operational Research (2013), http://dx.doi.org/10.1016/j.ejor.2013.06.030
P. De Giovanni et al. / European Journal of Operational Research xxx (2013) xxx–xxx
4. Reactivity and performance indicators
The current research seeks to provide theoretical development
of the notion of Reactivity as well as specific performance indicators related to the satisfaction of unexpected demand. This aim derives from limitations that emerged from the literature. As
mentioned earlier, although several articles have clearly introduced the concepts of Agile, Lean, and Leagile, to our knowledge
no research has proposed specific performance indicators to measure a firm’s capability. For example, if a firm wishes to measure
how Agile it is, the literature does not offer a specific performance
indicator for doing so. Therefore, together with theoretical developments, we seek to introduce a global indicator of Reactivity –
the Reactivity index – as well as partial indicators linked to cost,
quality, and time (i.e., Reactivity on cost, Reactivity on quality,
Reactivity on time). By simultaneously employing global and partial indicators of Reactivity, a firm can be reactive and succeed in
satisfying unexpected demand. This introduces novelties in the literature because of the lack of ad hoc indicators to measure how
successfully a firm can perform such a specific capability. An interesting evidence is given by Gong (2008), who stressed this idea and
proposed an indicator of economic performance to measure supply
chain flexibility. That indicator comprises labor flexibility, machine
flexibility, routing flexibility, and information technology supporting the decision-making process.
First, partial Reactivity involves a performance of cost. The satisfaction of an unexpected order is not always economically convenient. However, firms must activate atypical processes such as
rescheduling, searching for supplementary suppliers and employees, and providing alternative logistical service and auxiliary controls (Ho & Carter, 1994). Those processes may make the
satisfaction of an unexpected demand really inefficient. Therefore,
we state that:
‘‘A reactive firm produces at the standard production cost even
when unexpected demand occurs.’’
In Fig. 1, we characterize the indicator Reactivity on cost (ROC) to
measure firms’ capabilities in producing under unexpected conditions at a cost equal to the standard production cost. Thus, this index measures the gap between the standard production cost and
the production cost under unexpected events: When the latter is
higher, the costs to satisfy unexpected demand are higher than
standard costs, thereby making production inefficient under unexpected situations. As displayed in Fig. 1, the cost for unexpected
demand should be at least equal to the standard cost. Consequently, the best value for Reactivity on cost is 100%, which means
that a firm works at standard cost for both forecasted and unexpected demands.
Reactivity also accounts for performance with respect to time in
an unexpected setting. When an unexpected order comes in, delays
may occur in procurement, production, and distribution. Therefore,
we state that:
‘‘A reactive firm produces and supplies on time even when
unexpected demand occurs.’’
We have used the indicator Reactivity on time (ROT) to measure
the capacity to satisfy unexpected demand on time. As reported in
Fig. 1, ROT is given by the ratio between the unexpected demand
satisfied on time and the total unexpected demand. Its best value
is 100%, which means that unexpected demand is entirely satisfied
on time and thus the system works reactively with respect to time.
Finally, Reactivity encompasses performance with respect to
quality. Firms must produce high-quality products independently
of demand expectedness. Quality is always required regardless of
the nature of the product, the market served, the existing compe-
3
tition, or the nature of the supply chain relationships (De Giovanni,
2011). Nevertheless, unexpected events may imply that quality
standards are not always met and quality controls are somehow
skipped. Thus, we state that:
‘‘A reactive firm produces goods with standard quality even
when unexpected demand occurs.’’
To investigate quality in an unexpected setting, we have developed Reactivity on quality (ROQ). Its best value is 100%, which is
reached whenever the unexpected demand met on time lacks defects and/or nonconformities (Fig. 1).
Using the previous statements of partial Reactivity, we proposed the following definition of global Reactivity: A reactive firm
satisfies unexpected demand by performing in unanticipated circumstances as consistently in terms of cost, time, and quality as
it does in standard circumstances.
To measure the firm’s global Reactivity, we developed the Reactivity index, which summarizes the capacity of a firm to work under unexpected demand with the same performance in cost, time,
and quality as with expected demand. In addition, it represents a
useful measure for comparing firms’ performance. As displayed
in Fig. 1, the Reactivity index is the combination of ROC, ROQ,
and ROT. Its best value is 100%, which signifies that a firm is able
to entirely satisfy unexpected demand and that it operationally
performs in terms of cost, time, and quality just as it does under
standard conditions. Global and partial indicators of Reactivity allow for investigation of the trade-offs that an unexpected demand
entails. Under unexpected conditions, a change in demand may
easily lead to increasing production costs because the consequences of a given event are not included in the production schedule. A high demand for goods negatively affects quality controls,
which can be escaped to save resources even though quality control standards must always be met. Alterations in the quality system increase production costs (e.g., higher number of trials) and
lengthen lead times. Reducing lead times is possible when escaping some quality controls; doing this may reduce production costs,
but it will eventually create indirect costs (e.g., unsatisfied customers). In this regard, companies should actively react to resolve any
trade-off and aim to satisfy customers (Andraski, 1994). The structure of the Reactivity index and the partial indicators allow for the
identification of sources, causes, and trade-offs resolution.
The Reactivity index is a heterogeneous indicator formed by one
economic and two noneconomic indicators. When it carries values
lower than 100%, the decomposition displayed in Fig. 1 allows for
the identification of any non-Reactivity source.
5. Qualitative analysis
Because the literature does not contain a list of features that a
company should possess to be reactive, we conducted a qualitative
analysis through structured interviews to investigate (a) the main
pillars of Reactivity, (b) managers’ perceptions and willingness to
adopt this new managerial practice, and (c) the impact of Reactivity on competitive advantage and customer satisfaction. The qualitative analysis helped to model the construct and precisely
identify its differences with respect to existing concepts.
Three structured interviews were administrated to the managing directors of three companies belonging to the sectors of
cosmetics and beauty, elevators, and information and communication technology (ICT). The analysis investigated the real perceptions of Reactivity, the foundations of unexpected demand, as
well as the related performance, exploring additionally its drivers.
Appendix A reports the results of the interviews.
The qualitative analysis revealed that Reactivity is not well assessed or understood within organizations. When organizing their
Please cite this article in press as: De Giovanni, P., et al. Recent developments on Reactivity: Theoretical conceptualization and empirical verification. European Journal of Operational Research (2013), http://dx.doi.org/10.1016/j.ejor.2013.06.030
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P. De Giovanni et al. / European Journal of Operational Research xxx (2013) xxx–xxx
Reactivity Index =
Partial
Indicators of =>
Reactivity
Cost
x
ROC
x
Time
ROT
x
Quality
x
ROQ
Standard production cost
. x Unexpected demand delivered on time x Unexp. demand delivered without defects
Marginal cost for unexp. demand
Total unex pected demand
Unexpected demand delivered on time
Fig. 1. Reactivity index.
business, firms do not think about their Reactivity. Moreover, its
conceptualization is close to performance with respect to time.
Any firm must satisfy the demand on time to boost customer satisfaction and loyalty as well as competitive advantage. Thus, the
interview investigated two novelties for Reactivity: unexpected
demand and related performance.
When discussing performance, the managers voiced doubts
about their ability to adequately perform under unexpected conditions. Although high standards of cost, quality, and time with
unexpected demand are difficult to achieve, reactive firms achieve
all of them simultaneously. The interviewer proposed the definition of Reactivity as the capacity to satisfy unexpected demand
by performing to cost, quality, and time standards simultaneously.
All agreed with this theoretical definition. It emerged as an ‘‘ambitious’’ target since unexpected demand generates innumerable difficulties and unpredictable changes. To date, firms have
emphasized the performance time when considering unexpected
demand. Cost and quality do not appear to be directly linked to
assumptions and feelings about Reactivity. Nevertheless, when
introducing the performance of cost and quality, interviewees recognized their importance in defining Reactivity. They cannot be
disregarded in its operationalization. The wish to satisfy customers
is always the driving motivation. If it is true that the customer is
the main driver, firms obtain higher competitive advantage only
by adopting a broad perspective that embraces customer satisfaction as well as firms’ performance under unexpected demand.
Finally, the qualitative analysis revealed the managerial practices to be adopted to enhance Reactivity. The interviewer proposed a list of possible operational practices as the most suitable
to explain Reactivity, including the following:
pected demand. Thus, we hypothesized that centralized logistics
has a larger impact on Reactivity than outsourcing.
5.3. Integrated information system (IS)
Integrated information systems allow for managing interconnections among companies, consenting data and information
transfers, data generation and processing time, database integration, accurate and timelier information flow, and responsiveness
(Gunasekarana & Ngai, 2004). Through the implementation of integrated information systems, firms reduce the negative impact of
the bullwhip effect, lowering the risks and increasing performance
(Yu, Yan, & Cheng, 2001). Because of the high initial investment of
time and costs and the characteristic of irreversibility, implementation of integrated information systems is critical. Thus, we
hypothesized that higher IS results in higher firm Reactivity.
5.4. Available capacity (AC)
This managerial practice emphasizes the presence of available
capacity necessary to satisfy unexpected demand. If a firm possesses unexploited capacity, the possibility of satisfying unexpected demand increases. Nevertheless, the presence of
unsaturated capacity implies an over-production structure, which
is not exploited entirely and requires higher initial investments.
More frequently, firms outsource a part of their production commitments when their own capacity is not sufficient. Therefore,
we hypothesized that the higher the available production capacity,
the higher the firms’ Reactivity.
5.5. Worker availability (WA)
5.1. Standard components (SCs)
SCs explain the practice of using standard and interchangeable
parts and components. They are modules, platforms, and interfaces, which are extremely important for characterizing a reactive
orientation (Gunasekarana & Ngai, 2009). The higher the use of
standard components, the higher the firms’ Reactivity.
5.2. Centralized logistics (LG)
This factor highlights the critical role of logistics for promoting
a reactive orientation: Optimal materials, parts, final products, and
services management need an adequate network for logistics
implementation (Nyhuis & Vogel, 2006). LG is vital for firms due
to its direct impact on product value and performance. Despite
these key motivations, logistics is often managed via outsourcing,
which could result in a suboptimal practice for satisfying unex-
The availability of workers explains the capacity to rapidly
organize additional shifts and temporarily impose challenging
working standards. This capacity is crucial to properly satisfy
unexpected demand because the typical availability of workers is
not always sufficient. Management must spread the culture of
unexpectedness among employees so that workers will be available to work more if unexpected orders materialize. Therefore,
we hypothesized that the more workers are available, the higher
the firms’ Reactivity.
5.6. Supplier turnover (ST)
This factor indicates the stability of relationships among supply
chain members. If supplier turnover is high, firms need to rapidly
look for available new suppliers by activating new atypical processes whenever an unexpected demand occurs. Therefore, stable
relationships with suppliers would significantly contribute to
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P. De Giovanni et al. / European Journal of Operational Research xxx (2013) xxx–xxx
5.9. Product innovation (PI)
Table 1
Principal component analysis.
Component
Eigenvalues
% Of variance
1
2
3
2.252
0.453
0.295
75.079
15.089
9.833
Table 2
Construct validity and reliability.
Variables
Factors
Cronbach’s
Alpha
Alpha if the item is
deleted
AVEa
CRb
ROC
ROT
ROQ
0.876
0.895
0.830
0.832
0.755
0.723
0.814
0.751
0.901
a
Average variances extracted are computed as: (square of sum standardized
loadings)/[(square of sum of standardized loadings) + (sum of indicator measurement error)].
b
Composite reliabilities are computed as: (sum of the standardized loadings
squared)/[(sum of the standardized loadings squared) + (sum of indicator measurement error)].
Innovations could represent a serious barrier to adequately satisfying any unexpected order and to successful performance of
Reactivity. When a product is new, processes and activities are
not definitely established and demand is completely unpredictable, while parameters of cost, time, and quality are totally unstable. Thus, we hypothesized that the higher the product innovation,
the lower the firms’ Reactivity.
The findings of the qualitative analysis formed the basis for
developing a questionnaire (Appendix B) and carrying out an empirical verification in which two sets of hypotheses were tested. First,
we focused on the relationships between managerial practices and
Reactivity to determine which practices a firm has to adopt to become reactive. Thus, the following hypotheses were tested:
H1. The use of standard components positively influences
Reactivity.
H2. The implementation of a centralized logistics system positively
influences Reactivity.
H3. The implementation of an integrated information system positively influences Reactivity.
performance in terms of time, cost, and quality. Thus, we hypothesized that the higher the suppliers’ turnover, the lower the firms’
Reactivity.
H4. A large production capacity positively influences Reactivity.
H5. Workers’ availability positively influences Reactivity.
H6. A high supplier turnover negatively influences Reactivity.
5.7. Localization (LC)
A firm’s localization is extremely important for realizing coordination among suppliers to successfully and suitably serve the market (Torre & Gilly, 2000). Indeed, the right localization increases
the final customers’ value (Rahman, 2006) and becomes particularly relevant in providing a multitude of services (Hoek, 2000).
When firms are strategically located with respect to their suppliers
and customers, the chances of satisfying unexpected demand increase. Therefore, we hypothesized that the more closely located
a firm is to suppliers and customers, the higher its Reactivity is.
H7. A short distance between customers and suppliers positively
influences Reactivity.
H8. A higher customer impact positively influences Reactivity.
H9. Product innovations negatively influence Reactivity.
Furthermore, to provide a comprehensive analysis, we tested
H1–H9 using the partial indicators of Reactivity as dependent variables. Further, we tested the impact of Reactivity on economic performance to check the relevance of this managerial practice in real
economic terms. Therefore, we hypothesized that:
H10. Reactivity positively influences sales.
5.8. Importance of customers (IC)
H11. Reactivity positively influences return on investments.
This factor quantifies customers’ contribution to firms’ success.
Customers are not all equal in generating value, revenues, and
profits. Differences may emerge regarding the quantity purchased,
the status of new or old customers, and the potential future economic benefits obtainable. Firms may adopt this managerial practice to identify and classify their customers. Whenever the
customer is important to a firm, high motivations drive that customer’s satisfaction, regardless of the nature (expected or unexpected) of the demand. Consequently, we hypothesized that the
more important a customer is to the firm’s business, the higher
the Reactivity.
H12. Reactivity positively influences return on assets.
Finally, we also tested the impact of each partial indicator of
Reactivity on economic performance.
6. Quantitative analysis
6.1. Sample selection and data collection
To support our theoretical developments with more practical
and managerial insights, we developed an empirical analysis to
Table 3
Correlation matrix (n = 135 observations).
REA
Worker availability
Importance of the customer
Product innovation
Integrated information system
Localization
Centralized logistics
Supplier turnover
Standard components
Available capacity
REA
WA
IC
PI
IS
LC
LG
ST
SC
1.000
0.455
0.036
0.454
0.421
0.325
0.412
0.443
0.458
0.254
1.000
0.047
0.364
0.321
0.153
0.140
0.282
0.160
0.017
1.000
0.023
0.037
0.054
0.100
0.009
0.016
0.075
1.000
0.307
0.329
0.225
0.308
0.209
0.078
1.000
0.127
0.135
0.189
0.169
0.086
1.000
0.127
0.248
0.084
0.027
1.000
0.239
0.422
0.122
1.000
0.244
0.086
1.000
0.134
Please cite this article in press as: De Giovanni, P., et al. Recent developments on Reactivity: Theoretical conceptualization and empirical verification. European Journal of Operational Research (2013), http://dx.doi.org/10.1016/j.ejor.2013.06.030
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P. De Giovanni et al. / European Journal of Operational Research xxx (2013) xxx–xxx
identify the best practices that contribute to a Reactivity orientation as well as the economic benefits that Reactivity provides
through its global and partial effects. A questionnaire was
administered to 1400 firms’ representatives, who were contacted
by telephone and invited to participate in the study. Companies
were selected according to annual gross sales that for the year
2008 ranged from €1 million to €1 billion, with average annual
sales of €98 million. A total of 135 representatives agreed to participate, yielding a final 9.5% response rate, which is common in
this stream of research (De Giovanni & Vinzi, 2012). Responses
from the various sectors were distributed as housing (15%), paper and pulp (15%), wood and wood products (20%), ceramic
(5%), metal (10%), food (16%), textile (5%), electronic products
(10%), and other (4%). The vast majority answered directly by
telephone; others preferred to answer via mail or fax. The data
collection was completed in 2010 and information about phone
numbers, addresses, fax numbers, and financial indicators was
taken from the AIDA (Analisi Informatizzata delle Aziende) database. This database is managed by the Bureau van Dijk Electronic
Publishing (www.bvd.co.uk) and combines high-quality information with innovative software for searching and manipulating
data.
The respondent sample comprised purchasing managers (35%),
sales managers (25%), production managers (20%), and material
managers (15%), as well as other positions (5%).1
Table 4
Regression models based on Reactivity.
Model 1
Constant
Standardized
components
Centralized logistics
Integrated information
system
Available capacity
Availability of workers
Localization
Importance of
customers
Product innovation
Supplier turnover
6.2. Construct reliability and validity
As seen in Fig. 1, REA is a new performance indicator whose
operationalization needs to be validated empirically. Therefore,
we ran a statistical analysis to verify the construct dimensionality, validity, and reliability. We ran a factor analysis to reduce
the dimensions of Reactivity and in particular to check whether
ROC, ROT, and ROQ belong to a specific underlying factor, that
is, REA. We conducted a principal component analysis using
Statistical Package for the Social Sciences (SPSS) 17.0 and
extrapolated three components (Table 1). This resulted in only
the first component having an initial eigenvalue higher than
1, while it explained 75.079% of the total variance. Therefore,
we concluded that ROC, ROT, and ROQ belong to a unique
dimension.
Then, we used the component values to verify construct reliability and validity. The reliability analysis included the Cronbach’s
alpha, which is generally used to establish the consistency and reliability of each factor. This index investigates the internal consistency of the scale’s reliability, its unidimensionality, and the
correlation between the items. As reported in Table 2, the alpha
shows good results, being higher than the 0.7 threshold generally
considered to be satisfactory. In addition, the ‘‘alpha if the item
is deleted’’ identifies the reliability changes when one factor is removed. It shows that no elimination can improve the construct
reliability.
The validity of a construct can be assessed through the average
variance extracted (AVE), which explains the squared sum of factor
loadings and the sum of measurement errors. According to Fornell
and Larcker (1981), values lower than 0.9 represent suitable construct validity. The results linked to our analysis show satisfactory
values (Table 2).
Finally, construct reliability can also be assessed through composite reliability, used to check the adequacy of indicators as
Model 2
a,**
Adjusted R2
WHT (F-stat)
Jarque–Bera test
81.415
(25.325)b
26.878**
(8.059)
[0.780c; 1.282d]
17.678* (7.793)
[0.778; 1.286]
18.621**
(6.619)
[0.833; 1.200]
7.187** (2.276)
[0.940; 1.064]
12.426**
(3.686)
[0.789; 1.267]
6.169* (2.882)
[0.863; 1.159]
1.580 (9.451)
Model 3
***
98.248
(17.181)
46.609***
(8.430)
[0.968; 1.034]
28.203***
(7.851)
[0.895; 1.117]
33.646***
(7.190)
[0.959; 1.043]
8.786***
(2.433)
[0.960; 1.041]
15.432***
(3.878)
[0.833; 1.201]
11.566***
(3.151)
[0.980; 1.021]
3.506 (10.174)
[0.975; 1.026]
7.666* (3.305)
[0.982; 1.018]
12.126***
(3.378)
[0.792; 1.262]
8.119**
(2.668)
[0.836; 1.196]
[0.709; 1.411]
6.104**
(2.515)
[0.805; 1.242]
0.543
1.044°°
1.606##
19.582***
(23.804)
0.380
1.207°°
1.850##
0.491
0.879°°
8.619
e
White’s heteroskedasticity test.
p-Value > 0.1.
° F-value > 0.1.
a
Unstandardized coefficient;
b
Standard error.
c
Tolerance.
d
VIF.
*
p-Value < 0.05.
**
p-Value < 0.01.
***
p-Value < 0.001.
##
p-Value > 0.05.
°°
F-value > 0.05.
#
appropriate representatives. The composite reliability ranges between 0 and 1, and in our analysis the index exceeded the proposed level of 0.70 (Hair et al., 2006).
To summarize, AVE and composite reliability, together with
Cronbach’s alpha, support the proposed construct.
6.3. Which managerial practices influence Reactivity?
We developed a regression model to identify the managerial
practices that contribute to forming a reactive orientation. The
dependent variable is the Reactivity index (REA), while the
independent measures consist of best practices identified in
the literature and reinforced by personal interview, as discussed
herein.
Table 3 shows low correlation among independent variables.
The general specification of the multiple regression model can
be expressed as follows:
Reactivity ¼ b0 þ b1 SC þ b2 LG þ b3 IS þ b4 AC þ b5 WA b6 ST
1
Five personal interviews were conducted before starting the survey; the
questionnaire was modified by clarifying unfamiliar words and eliminating those
that were redundant or ambiguous. These pre-tests helped in checking the content of
the questionnaire, as well as judging its validity and conformity for the study.
þ b7 LC þ b8 IC þ PIb9 þ e
where bi, with i = 1 9, represents the coefficients of each independent variable to be estimated and e is the error. Starting from this
Please cite this article in press as: De Giovanni, P., et al. Recent developments on Reactivity: Theoretical conceptualization and empirical verification. European Journal of Operational Research (2013), http://dx.doi.org/10.1016/j.ejor.2013.06.030
P. De Giovanni et al. / European Journal of Operational Research xxx (2013) xxx–xxx
model, we investigated the impact of those managerial practices on
firms’ Reactivity.
The regression analysis employed three models. In particular,
Model 1 included all the explanatory variables (managerial practices). However, a more precise consideration should be made
according to their (short- and long-term) nature. On one hand,
some managerial practices are more difficult, costly, and risky
to undertake because a firm must deal with those decisions for
long-term periods (e.g., localization). On the other hand, some
managerial practices influence the firms’ Reactivity in the short
run, and their adoption is not disruptive. Thus, Model 2 and
Model 3 focus on reversible and irreversible decisions, respectively, whose differentiation has been described by Malik, Routhbaum, and Smith (2010). In this study, irreversible decisions
refer to the implementation of a managerial practice that implies
high initial investments and whose decisions affect the company
in the long run, while stepping back from its implementation is
quite costly and complex. For each model, Table 4 reports the
estimated coefficients, the standard errors, as well as the tolerance and variance inflation factor (VIF). These last two indexes
were proposed with the intent to study the multicollinearity
among independent variables. The tolerance is 1 R2 for the
regression of one independent variable on all other independents, ignoring the dependent. For each independent variable,
tolerance is unique, and optimal values should be higher than
0.3 to indicate the absence of correlation between independent
variables. Tolerance provides information on standard errors:
The higher the multicollinearity, the lower the tolerance, the
higher the standard error. The VIF is the reciprocal of tolerance.
It shows how much the variance of each coefficient estimate is
being inflated by multicollinearity: The higher it is, the higher
the multicollinearity. High values of VIF and low values of
tolerance reveal severe multicollinearity effects. Table 4 reports
the White’s heteroskedasticity test, F-statistic, and Jarque–Bera
test. White’s test is a test of the null hypothesis of no heteroskedasticity against heteroskedasticity of some unknown general
form (White, 1980). It is a general test for model misspecification because the null hypothesis assumes that the errors are
both homoskedastic and independent of the regressors and that
the model specification is correct. A non-significant test statistic
implies that none of the three conditions is violated. The
F-statistic shows the significance of each regression model.
The Jarque–Bera test is used for testing whether the residuals
are normally distributed. This analysis uses E-views 5 and
SPSS 17.0.
6.3.1. Discussion of Model 1
All the explanatory variables (managerial practices) included
in Model 1 except for importance of customer are significant
and the VIF and tolerance yield suitable results. The model significance, the absence of heteroskedasticity, the satisfactory results of the normality residuals test, and the high R2 show a
good model fit. It is possible to identify two groups of practices.
The first group is composed of standardized components, centralized logistics, integrated information systems, and availability of
workers, each of which provides high contributions to the implementation of a reactive orientation. The second group is composed of localization, product innovation, supplier turnover, and
available capacity, which provide a moderate contribution to
firms’ Reactivity because their coefficients (average beta = 6.781)
are weaker than the first group practice coefficients (average
beta = 18.901).
Reactivity increases when the practice of standard components is
in use: Standardization, interfaces, components changeability, and
modular products increase firms’ Reactivity. Although several
firms externalize some activities that do not belong to their core
7
business, the empirical verification indicates that centralized
logistics should be pursued to implement a reactive orientation.
Centralized logistics leads to increased Reactivity; therefore, firms
should not outsource this managerial practice if they aim to properly satisfy unexpected demand.
Although the implementation of integrated information systems requires a huge investment of cost and time, it substantially improves firms’ Reactivity. Firms need to evaluate
different trade-offs. Integrated information systems are essential
for satisfying unexpected demand, even though unexpected
events materialize only sporadically. Because the failure to satisfy unexpected demand may imply indirect negative effects
(e.g., lower brand image), firms should opt for the implementation of integrated information systems even though it is expensive. The empirical analysis also highlights the positive
contribution that availability of workers supplies to Reactivity.
Thus, Reactivity can be implemented successfully when the culture of performing Reactivity is spread over the organization and
people are aware of its existence. Thus, a reactive orientation can
be successfully implemented when deep consensus exists among
employees.
The second group of managerial practices has a lower but still
positive impact on the global Reactivity. Available capacity belongs to this group; it provides a positive contribution to the
global Reactivity. When a firm establishes its production capacity, it should also evaluate the negative, indirect effects due to
failure to satisfy an unexpected demand. Installing higher capacity to satisfy a sporadic unexpected demand is risky and costly;
therefore, firms prefer outsourcing as part of their production
when their capacity is not sufficient. In addition, a trade-off exists regardless of whether satisfying the unexpected demand is
economically more convenient than installing addition production capacity. A similar discussion is valid for firms’ localization.
A localization that puts a company close to its suppliers and customers promotes global Reactivity. Nevertheless, this decision affects firms’ business over the long term; therefore, managers
should carefully evaluate its adoption. Finally, innovative product
and supplier turnover have a significant and negative impact on
Reactivity. In particular, the negative sign of product innovation
shows that the higher the product innovation, the lower the
Reactivity. Consequently, firms cannot properly perform with
Reactivity when a product is new. This seems quite obvious
since firms do not exploit any economies of scale and the market
is unpredictable in itself. Similarly, high supplier turnover is a
sign of unstable relationships with suppliers that lead to low
Reactivity. Firms can properly satisfy an unexpected demand
and perform with Reactivity only when supplier relationships
are well established.
6.3.2. Discussion of Models 2 and 3
In contrast to Model 1, Model 2 involves irreversible decisions
related to standard components, information system, and localization.
Similarly, reversible decisions refer to all managerial practices that
imply flexible options, medium-sized financial investments, and
decisions that affect the company’s management for short-/medium-term periods, while stepping back from its implementation
is not too costly or complex. Model 3 comprises all reversible decisions related to centralized logistics, available capacity, worker availability, importance of customers, innovative product, and supplier
turnover. Model 3 accounts for all the explanatory variables that
can change over time and hence influence and modify firm Reactivity in the short run.
For Model 3 all the explanatory variables are significant except
one, importance of customer. Nevertheless, the model is quite good,
with a high R2 and acceptable multicollinearity. Comparing Model
2 to Model 3, it appears that the influence of reversible managerial
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8
P. De Giovanni et al. / European Journal of Operational Research xxx (2013) xxx–xxx
practices on Reactivity is higher than that for irreversible practices.
Model 2 only explains 38% of the total variance, while Model 3 explains 49.1%.
In summary, managers can easily implement a reactive orientation through the centralization of their logistics network, looking
for outsourcing options to increase their production capacity, promoting Reactivity as an orientation to be undertaken from the
bottom to the top of the organization’s hierarchy, implementing
a few product innovations, and establishing strong relationships
with suppliers.
Note 1. The same empirical results obtained for Models 1–3
were achieved when we used the partial indicators of Reactivity
as dependent variables. This is appropriate because the Reactivity index consists of a combination of the partial indicators, ROC,
ROT, and ROQ. The results of the regression analysis for the
partial indicators of Reactivity are displayed in Table 3. We
concluded that standardized components, centralized logistics,
integrated information systems, and availability of workers have a
high and significant impact on both the global and partial
indicators of Reactivity; available capacity, localization, product
innovation, and supplier turnover have a significant but low
Table 5
Regression models based on partial Reactivity indicators.
Partial Reactivity
indicator
ROC
ROT
ROQ
Constant
78.512a,**
(23.311)b
24.673** (8.296)
84.847**
(26.271)
26.852**
(8.259)
[0.780; 1.282]
16.414*
(8.002)
[0.799; 1.303]
21. 165**
(6.996)
[0.865; 1.313]
6.879**
(2.316)
[0.933; 1.254]
11.886**
(3.309)
[0.851; 1.276]
6.757* (2.834)
[0.899; 1.245]
1.738 (9.825)
82.728**
(24.824)
26.878**
(8.169)
[0.843; 1.381]
17.219*
(7.381)
[0.731; 1.016]
18.745**
(6.313)
[0.901; 1.318]
7.269**
(2.143)
[0.968; 1.044]
13.609**
(2.985)
[0.658; 1.011]
6.071* (2.851)
[0.808; 1.078]
1.580 (10.383)
[0.788; 1.559]
6.145**
(2.767)
[0.799; 1. 452]
[0.965; 1.112]
8.102*
(3.552)
[0.603; 1.904]
8.118**
(2.743)
[0.867; 1.353]
[0.933; 1.137]
6.826*
(3.002)
[0.702; 1.372]
7.154**
(2.859)
[0.825; 1.522]
0.511
1.057°°
1.716##
0.587
1.182°°
1.881##
0.539
1.067°°
1.771##
Standardized
components
Centralized logistics
Integrated information
system
[0.754; 1.369]
19.829** (7.231)
Available capacity
[0.867; 1.198]
7.227** (2.686)
Availability of workers
[0.913; 1.088]
14.174** (3.265)
Localization
Importance of
customers
Product innovation
Supplier turnover
Adjusted R2
WHT (F-stat)
Jarque–Bera test
e
[0.802c; 1.224d]
18.651* (7.837)
[0.783; 1.311]
6.561* (2.522)
[0.809; 1.379]
1.665 (9.482)
[0.995; 1.215]
7.609* (3.138)
White’s heteroskedasticity test.
p-Value < 0.001.
#
p-Value > 0.1.
° F-value > 0.1.
a
Unstandardized coefficient.
b
Standard error.
c
Tolerance.
d
VIF.
*
p-Value < 0.05.
**
p-Value < 0.01.
##
p-Value > 0.05.
°°
F-value > 0.05.
impact on both the global and the partial indicators of Reactivity. Importance of customers has no significant impact on partial
Reactivity indicators. Finally, reversible managerial practices
have a higher significant impact on partial indicators of Reactivity than irreversible practices; thus, they allow for effective
and efficient satisfaction of unexpected demand in terms of cost,
time, and quality (see Table 5).
6.4. What Is the impact of Reactivity on economic performance?
The Reactivity index is a measure of operational performance
that shows how well firms perform with respect to indicators of
cost, quality, and time under unexpected demand. Nevertheless,
firms are always concerned with economic performance (De
Giovanni, 2012). The Reactivity index only reflects how firms
work and perform operatively when an unexpected order
arrives, while firms pay (even more) attention to economic
performance.
To test the effect of Reactivity on economic performance, we
conducted a further empirical analysis investigating whether performing with Reactivity leads to higher economic performance.
This analysis introduced another novelty in this stream of literature. Although several contributions propose that the Agile, Lean,
and Leagile constructs are appropriate means for improving economic performance, empirical research that supports these statements is still lacking. The current empirical investigation tested
the relationships between Reactivity and economic performance
to illustrate the economic benefits that a reactive orientation
supplies.
Using the same sample described earlier, a regression model
explored the influence of global Reactivity on a firm’s performance.
The Reactivity index was used as the independent variable,
while returns on investment, sales, and assets – namely, ROI,
ROS, and ROA, respectively – were the dependent variables.
Consequently, the empirical analysis consisted of the following
model (Model 4):
Performance ¼ b0 þ b1 Reactiv ity index þ e
Table 6 reports the results of the empirical analysis.
We ran three regression analyses in which the dependent
variable performance assumes the values of ROI, ROS, and ROA,
respectively.
The empirical analysis reported in Table 6 clearly illustrates
the economic benefits derived from Reactivity, which
significantly explains the performance of ROI, ROA, and ROS;
therefore, it represents a valid orientation in practical terms.
For all models, the adjusted R2 is quite satisfactory, showing
values always higher than 0.6. To reinforce the statement in
Note 1, Table 6 also displays the impact of partial indicators
of Reactivity on economic performance (Model 5). Consequently, the empirical analysis consisted of the following
additional model:
Performance ¼ b0 þ b1 ROQ þ b2 ROC þ b3 ROT þ e
Our results indicate that each partial indicator has a significant
coefficient, and the R2 performed is really close to the one
obtained when using the Reactivity index as an independent variable. Thus, these empirical results reinforce the statement earlier
presented.
7. Conclusion
This research theoretically operationalizes and practically applies the concept of Reactivity. The motivations for this study
Please cite this article in press as: De Giovanni, P., et al. Recent developments on Reactivity: Theoretical conceptualization and empirical verification. European Journal of Operational Research (2013), http://dx.doi.org/10.1016/j.ejor.2013.06.030
P. De Giovanni et al. / European Journal of Operational Research xxx (2013) xxx–xxx
9
Table 6
Regression models of performance and Reactivity indicators.
Dependent variable
ROI
ROA
ROA
Independent variable(s) and significance
Model 4
***
Model 5
Constant
ROQ
ROC
ROT
REA
Adjusted R2
WHTe (F-stat)
Jarque–Bera test
9669
Constant
ROQ
ROC
ROT
REA
Adjusted R2
WHT (F-stat)
Jarque–Bera test
Constant
ROQ
ROC
ROT
REA
5. 911***(0.452)
Adjusted R2
WHT (F-stat)
Jarque–Bera test
0.659
4.301
2.482##
(1.329)
0.358*** (0.024) [1.000; 1.000]
0.639
1.319°
2.226##
0.196***(0.008) [1.000; 1.000]
0.822
0.130°°
0.058##
3.272***(0.433)
43.268a,*** (3.523)b
0.255*** (0.04) [0.642c; 1.559d]
0.232*** (0.042) [0.669; 1.495]
0.164*** (0.029) [0.950; 1.053]
0.655
1.915°°
4.273##
25.608*** (1.267)
0.130*** (0.015) [0.605c; 1.652d]
0.152***(0.016) [0.629; 1.589]
0.093***(0.011) [0.937; 1.067]
0.815
1.833°°
1.984#
15.689***(1.183)
0.077*** (0.014 [0.605; 1.652]
0.100*** (0.015) [0.629; 1.589]
0.057*** (0.010) [0.937; 1.067]
0.122*** (0.008) [1.000; 1.000]
0.664
1.741°
4.015##
a
Standardized coefficient.
Standard error.
c
Tolerance.
d
VIF.
e
White’s heteroskedasticity test.
#
p-Value > 0.1.
##
p-Value > 0.05.
***
p-Value < 0.001.
°
F-value > 0.1.
°°
F-value > 0.05.
b
emerge from several gaps left by previous contributions on Agile,
Lean, and Leagile in the operations and supply chain management
literature. These concepts do not apply when demand is unexpected, which is when demand is subject to an abnormal variance
and linked to a completely unknown event. Firms and supply
chains need a new managerial orientation that allows for facing
unexpected events without underperforming operationally and
economically. When an unknown event such as the terror attack
of September 11, 2001, occurs, Agile, Lean, and Leagile firms cannot properly handle the abnormal demand changes. In an unstable world where catastrophic events and terror attacks are
frequent, firms must be able to promptly manage their business.
Inspired by these motivations, we have proposed a new concept,
Reactivity, and developed ad hoc indicators of performance.
The adoption of this managerial orientation allows firms to
adequately face the unexpected demand and always be
competitive.
The indicator of performance – the Reactivity index – allows for
measuring how reactive a firm is, and thus understanding how
well a firm performs with respect to its competitors when an unexpected order materializes. The Reactivity index supplies information on the global performance obtained. In addition, three
measures of operational performance – Reactivity on cost, Reactivity on time, and Reactivity on quality – allow for monitoring how
firms perform in terms of cost, time, and quality when an unexpected demand occurs.
A qualitative investigation revealed the relevance of Reactivity for managers and practitioners, highlighting the practices to
be put in place to become reactive. In addition, an empirical
analysis had a twofold purpose: On one hand, it identified the
managerial practices that a firm should implement to become
reactive; on the other hand, it empirically investigated the links
between the Reactivity orientation and firms’ economic performance to show the economic benefits that such an orientation
supplies. The empirical analysis revealed that a firm may easily
increase its Reactivity by (a) producing and using standard components, modules, and changeable parts, (b) implementing a centralized logistics network and thus disregarding any outsourcing
option, (c) adopting integrated information systems along its
supply chain, and (d) spreading the culture of being reactive
throughout the organization so that management finds wide
consensus among employees. Moderate contributions to firms’
Reactivity were supplied by other managerial practices, such as
(a) outsourcing a part of the production, (b) being as close as
possible to suppliers and customers, (c) introducing a small
number of product innovations, and (d) establishing long-term
relationships with suppliers. In contrast, Reactivity does not depend on the importance of customers. Customer satisfaction is
always relevant and stands independent of customers’ contribution to firms’ success.
Finally, the empirical analysis supplied further information on
the impact of Reactivity on economic performance. We have
demonstrated the positive effect of Reactivity on ROI, ROS,
and ROA and concluded that the implementation of a Reactivity
orientation through the adoption of the previously mentioned
practices allows for the satisfaction of unexpected demand
as well as a significant improvement in firms’ economic
performance.
Please cite this article in press as: De Giovanni, P., et al. Recent developments on Reactivity: Theoretical conceptualization and empirical verification. European Journal of Operational Research (2013), http://dx.doi.org/10.1016/j.ejor.2013.06.030
10
P. De Giovanni et al. / European Journal of Operational Research xxx (2013) xxx–xxx
Appendix A. Structured interviews
Company 1
Part 1
Company 2
Company 3
What do you think about the I never thought about it
Reactivity of your firm?
before
Our firm is reactive because is We are always reactive. Our
able to satisfy customer order customers have to be always
in any situation
satisfied on time. We do not
have customer orders
undelivered. This is important
for increasing customer
loyalty and gaining
advantages against our
competitors
Do you agree if we associate Well, I feel the Reactivity as We generally do not face with Yes and no. Yes, because we
react satisfying the
unexpected demand. Our
our capacity to deliver on
Reactivity with the
unexpected demand
forecasting system works
time the demand. As an
satisfaction of unexpected
whenever we have to face
quite adequately and the
order arrives, it must be
demand?
delivered under the planned demand is quite stable as well. with. No, because we react
lead time. We do not mind if In case of unexpected, I think always independently if the
demand is unexpected or not.
the demand is forecasted or our systems are ready for
Customers are the kings. They
facing it, no problems
unexpected. It must be
must be always satisfied. This
always satisfied
is our policy
We know our demand, so we The expected demand is quite
What is the difference
The expected demand
known. We can face with. In
do not face this distinction.
between expected and
derives from our
the sense that we can forecast
The unexpected demand is
unexpected demand?
computational forecasting
not controllable, not forecasts it and we organize our
exploiting the information
activities according to. The
about it
shared along the supply
unexpected demand is not
chain. As our purpose is to
predictable. We face
optimize production and
constantly with. We are quite
delivery, we used to spend
able to face it. It requires high
huge amounts of money for
flexibility. That job is not easy;
forecasting as precise as
fortunately we have people
possible. The unexpected
and resources for dealing with
demand is not comprised in
our forecasting. It derives
from events like September
11, unexpected financial
changes, or occasions like that
implying strange behavior of
the demand that we are not
able to manage or to predict
It is not always the case. Our
When satisfying unexpected We try to do always our best, We do not face unexpected
ability to perform unexpected
demand, so I do not feel
but it is very difficult to
demand, is your firm
comfortable in answering to demand is not that much. As I
performing as when satisfying manage unexpected events
this question. But I am sure, in was saying previously, our
expected demand?
case of unexpected demand policy is to satisfy always the
our firm will respond
customers also when that job
satisfactorily
is not economically
convenient for us. Customer
satisfaction is more important
than economic performance
If we should face unexpected Our costs are modest in the
We try to perform
When satisfying the
demand, our firm will perform sense that they tend to
adequately all these
unexpected demand, how
increase. We try to manage
all these optimally
performance. We monitor
does your firm perform in
them but it is difficult under
constantly all of them. While
terms of cost, quality, and
unexpected environment.
the quality must be always
time?
Conversely, we always
satisfied, the performance of
optimize time and quality
cost and time are not. For
instance, we need to activate
new logistic process for
delivering the unexpected
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11
P. De Giovanni et al. / European Journal of Operational Research xxx (2013) xxx–xxx
Appendix A. (continued)
Company 1
Company 2
demand. This is not at all
planned for us. The cost
explodes while we cross our
fingers concerning the time.
We made some partnership
with some special logistic
operator. In extreme and
special case they are
contacted but they are terrible
expensive. Nevertheless, until
now, we are satisfied on how
they performed in terms of
time
Well, this is a nice definition It is a very ambitious target,
Do you agree if we try to
but we can get it
of Reactivity. I did not
formulate a definition of
reactive as the firm’s ability to consider before also the cost
and quality performance. In
satisfy unexpected demand
while performing with respect my mind I was thinking only
in terms of time. Effectively, it
to cost, time, and quality
is difficult to manage
simultaneously?
unexpected demand
performing adequately time,
cost, and quality
contemporarily. I think a
reactive firm should do it. But
I do not know if it exists
Part 2
As long as you satisfy the
unexpected demand, which of
these variables influences the
Reactivity of your firm?
– Information system
– Standardization
– Centralized logistics
– Available capacity
– Worker availability
– Supplier turnover
– Localization
– Importance of customers
– Innovation of product
– Continuous improvement
programs
– Supply quality
management
– Joint planning
– Customer service
management
– Supplier training
– Category management
– Returns management
– Order fulfillment
– Customer relationship
management
– Manufacturing flow
management
– Postponement
– Vendor management
inventory
– Other
Company 3
I think this definition is right.
A firm should perform
adequately cost, time, and
quality. We point out the
optimization of time and
quality as they are more
directly linked to customer
satisfaction. The minimization
of cost is more our internal
problem. If we can realize it, it
will be great. Otherwise, it is
ok as well; the importance is
to satisfy the customer
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
No
Yes
No
No
Yes
Yes
Yes
No
No
No
No
Yes
No
Yes
Yes
Yes
Yes
Yes
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
No
Please cite this article in press as: De Giovanni, P., et al. Recent developments on Reactivity: Theoretical conceptualization and empirical verification. European Journal of Operational Research (2013), http://dx.doi.org/10.1016/j.ejor.2013.06.030
12
P. De Giovanni et al. / European Journal of Operational Research xxx (2013) xxx–xxx
Appendix B. Survey questionnaire
Variables
Questions
Reactivity indexes
ROQ
ROT
ROC
We are able to make products to satisfy an unexpected demand without defects
We are able to deliver products to satisfy an unexpected demand on time
We are able to make products to satisfy an unexpected demand at the standard
production cost
Economic
Performance
ROI
Our return on investment is:
ROE
ROA
Our return on equity is:
Our return on assets is:
Standardized components
We make use of standard components over our production process
Centralized logistics
Integrated information
systems
Available capacity
Availability of workers
We directly manage our logistics network
We share information with our suppliers through integrated information systems
Managerial
Practices
Localization
Importance of customers
Product innovation
Supplier turnover
We have implemented an over-production capacity
Our workers are always available to prolong their working time to face an
unexpected order
We select our localization according to customers’ and suppliers’ positions
We classify our customers according to their importance for our business
We constantly promote product innovations
We establish short-term business relationships with our suppliers
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