Consistency and Recovery in Retail Supply Chains

Published date01 March 2018
Date01 March 2018
Consistency and Recovery in Retail Supply Chains
Elliot Bendoly
, Nathan Craig
, and Nicole DeHoratius
The Ohio State University
University of Chicago
Practitioners and researchers describe inventory service level with metrics that communicate the likelihood of demand fulllment without
considering the ongoing capabilities of the supplier, for example, in-stock and ll rate. We develop a method for measuring inventory ser-
vice level that incorporates such supplier capabilities, namely consistency (the ability of a supplier to fulll orders repeatedly) and recovery (the
ability of a supplier to fulll orders after a lapse in service). Using data from two retail supply chains, we illustrate our approach. To demon-
strate the impact of consistency and recovery on supply chain performance, we model a retailer purchasing from competing suppliers with dif-
ferent levels of consistency and recovery. The model incorporates the retailers uncertainty about demand and the retailers uncertainty about its
suppliersservice levels. We characterize how the retailers orders and protability change with a suppliers delivery performance through
numerical experiments calibrated with eld data. We nd notable differences in market share across suppliers with similar traditional inventory
service level metrics but differences in consistency and recovery. Further, we observe that a retailer can increase its protability by determining
orders via consistency and recovery in lieu of common metrics like in-stock. Given the inuence of consistency and recovery on supply chain
outcomes, we discuss implications for practice and future research.
Keywords: product availability; inventory service level; supplier reliability; supplier performance; B2B
Supplier reliability affects numerous supply chain outcomes,
including supplier market shares, inventory at different levels of
a supply chain, and retailer cost and prices (Stank et al. 2003;
Dada et al. 2007; Federgruen and Yang 2009; Liu et al. 2009;
Davis-Sramek et al. 2010; Craig et al. 2016). Researchers typi-
cally model suppliers with imperfect product availability using
the type 1 and type 2 inventory service level metrics. Type 1 ser-
vice level, or in-stock, is the probability that a supplier will ll
all demand in a given period. Type 2 service level, or ll rate, is
the expected proportion of demand that a supplier will ll in a
given period (Nahmias 2008). The literature on supplier reliabil-
ity, however, offers a dynamic perspective on inventory service
level not captured by traditional metrics. Researchers and practi-
tioners often describe service level in terms of consistency, or
predictability (Dana and Petruzzi 2001; Swait and Erdem 2002;
Christopher 2005; Su and Zhang 2009; Solomon 2012). On the
other hand, the literature also highlights the importance of a sup-
ply chains ability to recover from service disruptions (Bakshi
and Kleindorfer 2007; Craighead et al. 2007; Shef2007; Turner
We extend prior research on retailers ordering from imperfect
suppliers by incorporating aspects of supplier performance identi-
ed in the literature, namely consistency (the ability of a supplier
to fulll orders repeatedly) and recovery (the ability of a supplier
to fulll orders after a lapse in service). We propose a stylized
model of supplier service level that captures both consistency
and recovery, and we develop a method for estimating consis-
tency and recovery using data commonly available within supply
chains. We demonstrate this method using data from a supplier
of consumer packaged goods (CPG) as well as a supplier of
apparel. Our results demonstrate the models ability to capture
distinctions in each suppliers performance, specically differ-
ences between the consistency and recovery rates for each sup-
plier. These differences reinforce the need to distinguish between
these service dimensions.
To further explore consistency and recovery, we model a retailer
purchasing identical products from two suppliers. The model incor-
porates both the retailers uncertainty aboutits demand and the retai-
lers uncertainty about its suppliersservice levels. The retailer
observes each suppliers delivery history, updates its beliefs about
each suppliers consistency and recovery,and then places orders that
minimize the expected market mediation costs (i.e., the expected
cost of overages and underages). The retailers beliefs may be infor-
malas in a buyers opinion of a particular supplieror formal
as in supplier scorecards (Duffy 2004). The model identies how
supply chain outcomesin particular, market share across suppliers
and retailer costvary with supplier performance.
To study the impact of consistency and recovery in practice,
we construct numerical experiments calibrated with empirical
data. The numerical experiments reveal the extent to which a
suppliers orders from a retailer depend on the suppliers consis-
tency and recovery. In particular, the numerical experiments
show that equivalent performances as measured by a traditional
inventory service level metric (in-stock) can result in materially
different market shares across two otherwise identical suppliers.
Further, the experiments show that a retailer can reduce its costs
substantially by placing orders based on consistency and recov-
ery rather than type 1 service level.
Our results suggest that metrics like consistency and recovery
that capture information about supply chain dynamics can be
useful to managers in retail supply chains. Suppliers that provide
service levels similar to their competitors according to common
metrics may nd they lag the competition in market share due to
differences in consistency and recovery. Further, retailers can
reduce the cost of supply uncertainty by tracking the consisten-
cies and recoveries of their suppliers.
Corresponding author:
Nathan Craig, Fisher College of Business, The Ohio State Univer-
sity, 644 Fisher Hall, 2100 Neil Avenue, Columbus, OH 43212,
USA; E-mail:
Journal of Business Logistics, 2018, 39(1): 2637 doi: 10.1111/jbl.12174
© Council of Supply Chain Management Professionals

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