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Financial productivity issues related to assortment diversity and supply chain merchandise replenishment strategies

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Purpose – The purpose of this study is to examine the ability of supply chain merchandise replenishment strategies to minimize merchandise plan errors when assortments are diverse. Design/methodology/approach – Sourcing Simulator 2.1, a computer simulation of the merchandising process, was used. Sourcing Simulator generated a total data set of 4,320 and determined financial outcomes of the merchandising processes based on multiple scenario inputs. Findings – The impact of supply chain merchandise replenishment strategies on merchandising performance outcomes significantly differed, depending on assortment diversity and merchandise plan errors. The ability of supply chain merchandise replenishment strategies was limited in minimizing problems inherent in diverse assortments and over-volume errors. Research limitations/implications – Sourcing Simulator does not necessarily simulate merchandising processes and performance in real retail stores but principles developed through simulation can be applied in retail stores. Future research based on real information is suggested for additional realistic understanding. Practical implications – The study suggests that apparel and retail firms should develop both up-front assortment planning and replenishment strategies, considering the level of assortment diversity. Originality/value – Based on Behavioral Theory of the Apparel Firm, the study contributes to understanding the importance of merchandising functions in an apparel firm. In addition, the study illuminates assortment diversity as an important factor of merchandise planning, especially when apparel and retail firms plan replenishment strategies to minimize merchandise plan errors.
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Financial productivity issues
related to assortment diversity
and supply chain merchandise
replenishment strategies
Ui-Jeen Yu
Department of Family and Consumer Sciences, Illinois State University,
Normal, Illinois, USA, and
Grace I. Kunz
Iowa State University, Ames, Iowa, USA
Abstract
Purpose The purpose of this study is to examine the ability of supply chain merchandise
replenishment strategies to minimize merchandise plan errors when assortments are diverse.
Design/methodology/approach Sourcing Simulator 2.1, a computer simulation of the
merchandising process, was used. Sourcing Simulator generated a total data set of 4,320 and
determined financial outcomes of the merchandising processes based on multiple scenario inputs.
Findings – The impact of supply chain merchandise replenishment strategies on merchandising
performance outcomes significantly differed, depending on assortment diversity and merchandise
plan errors. The ability of supply chain merchandise replenishment strategies was limited in
minimizing problems inherent in diverse assortments and over-volume errors.
Research limitations/implications Sourcing Simulator does not necessarily simulate
merchandising processes and performance in real retail stores but principles developed through
simulation can be applied in retail stores. Future research based on real information is suggested for
additional realistic understanding.
Practical implications The study suggests that apparel and retail firms should develop both
up-front assortment planning and replenishment strategies, considering the level of assortment diversity.
Originality/value – Based on Behavioral Theory of the Apparel Firm, the study contributes to
understanding the importance of merchandising functions in an apparel firm. In addition, the study
illuminates assortment diversity as an important factor of merchandise planning, especially when
apparel and retail firms plan replenishment strategies to minimize merchandise plan errors.
Keywords Supply chain management, Performance measures, Clothing
Paper type Research paper
Introduction
A primary goal of merchandise planning is offering balanced assortments. A balanced
assortment of merchandise offered is achieved when the merchandise plan satisfies
both diversified consumer demands and financial productivity at the retail store level.
A balanced assortment results in adequate variety to attract target consumers,
adequate inventory to prevent stockouts, minimum inventory investment, and
maximum gross margin return on inventory (Kunz, 2005). Planning and developing
balanced assortments is an ongoing merchandising challenge because it is nearly
impossible to achieve all of the criteria due to merchandise plan errors and limitations
of supply chain replenishment systems.
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1361-2026.htm
JFMM
14,3
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Received February 2009
Revised July 2009
Accepted November 2009
Journal of Fashion Marketing and
Management
Vol. 14 No. 3, 2010
pp. 486-500
qEmerald Group Publishing Limited
1361-2026
DOI 10.1108/13612021011061906
Merchandise plan errors cause increased prices and markdowns, high levels of
stockouts, excess inventory, and lower gross margins mainly resulting from demand
uncertainty and inaccurate forecasting (Hunter et al., 1993; Fisher et al., 1994;
Abernathy et al., 1999). To reduce merchandise plan errors and implement more
balanced assortment plans to the stock-keeping unit level at the store level, quick
response (QR) merchandise replenishment systems, now commonly known as supply
chain merchandise replenishment systems have been adopted among apparel
manufacturers and retailers. This study uses the term of supply chain merchandise
replenishment systems instead of QR merchandise replenishment systems, because
supply chain encompasses “all the activities involved in delivering a product from raw
material through the customer”, which include planning, sourcing, making, and
delivering in quick response systems (Lummus and Vokurka, 1999, p. 11).
Previous research supports that supply chain merchandise replenishment systems
reduce the financial impact of merchandise plan errors (Hunter et al., 1996; King and
Hunter, 1997). To compensate for merchandise plan errors, supply chain merchandise
replenishment systems employ demand re-estimation and multiple deliveries to adjust
merchandise volume and assortment during the selling period (Hunter et al., 1996; Lin,
1996; Kunz, 2005). Therefore, supply chain merchandise replenishment systems
minimize stockouts and markdowns as well as improve inventory turns and service
level.
However, the ability of supply chain merchandise replenishment systems to
minimize merchandise plan errors has not been fully explored when assortments are
diverse as they commonly are with fashion, seasonal merchandise. Little research has
addressed how assortment and merchandise replenishment plans can affect
merchandising performance outcomes under the high risks of merchandise plan
errors with diverse assortments.
First, the purpose of this study is to use computer simulation of merchandising
process to examine the financial impact of the relationships of assortment diversity,
merchandise plan errors, and supply chain merchandise replenishment systems on
merchandising performance measures. This study contributes to understanding
front-end merchandise plans for assortment diversity and back-end supply chain
merchandise replenishment systems which crucially influence balanced assortments
and merchandising financial success. Second, this study provides a foundation for
additional studies which will examine merchandising process and financial outcomes
using actual retail store data. Furthermore, the proposed study may assist apparel and
retail firms to establish priorities related to developing more balanced assortment
plans as well as more accurate merchandise replenishment strategies, while coping
with merchandise plan errors.
Conceptual framework
The conceptual framework of this study is based on a Behavioral Theory of the
Apparel Firm as described by Kunz (2005). This model presents a context for the
merchandising process with a supply chain replenishment system in the rapidly
changing nature of apparel industry (Kunz, 2005). The focus of the firm’s organization
and decision-making is on the target market. The central focus of decision-making is to
satisfy customer demands and a firm’s profit goals based on interaction and
integration among functional constituencies. Especially important here is the
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487
merchandising constituency, which plays an essential role in formulating product
lines, managing the merchandise plan, and implementing supply chain merchandise
replenish strategies, for the firm’s primary source of revenue.
The merchandising constituency could minimize demand uncertainty and
merchandise plan errors by selectively or fully implementing supply chain systems.
The merchandising constituency is responsible for minimizing the difference between
customer demand and merchandise plan, as well as managing the financial
productivity of diverse assortments at the volume per stock keeping unit level.
Therefore, based on the BTAF model, this study gives an in-depth understanding of
the merchandising decision-making and activities related to assortments and supply
chain merchandise replenishments to achieve balanced assortments and minimized
merchandise plan errors.
Literature review
Assortment diversity and financial productivity
The most traditionally used terms of assortment dimensions are breadth and depth
(Rupe and Kunz, 1998). Breadth and depth are one-dimensional and consider only one
aspect of the assortment. These terms may indicate the number of stock keeping units
(SKUs) or number of brands in an assortment but never both dimensions at once.
Studies of merchandise assortments using Sourcing Simulator, a computer simulation
of the merchandising process, resulted in more multi-dimensional and financially
relevant concepts and terms for assortment dimensions. Rupe and Kunz (1998)
introduced assortment dimensions using a volume per stock keeping unit for an
assortment (VSA), which is the average number of units allocated for each SKU in a
given assortment. They also identified assortment diversity as the range of
relationships between assortment volume and the number of stock-keeping units in an
assortment (VSA). Assortment diversity is determined by assortment volume divided
by the total number of SKUs in the same categorized assortment. The smaller the VSA,
the more diverse the assortment; and the larger the VSA, the more focused the
assortment.
Rupe and Kunz (1998) found that more diverse assortments are less financially
productive than focused assortments at the individual retail store level. Given the same
level of mark-up, as VSA decreases (assortment becomes more diverse), the percent
gross margin (%GM) decreases. Since a diverse assortment has fewer units per SKU on
the average than a focused assortment, the chances of not having the right SKUs are
greater with diverse assortments. Therefore, lost sales increase, particularly with a
VSA of less than five at the retail store level. As lost sales increase, fewer purchases are
made, leaving more merchandise unsold at the end of the selling period. Leftover
merchandise at the end of the selling period is marked down, resulting in less total
revenue and lower gross margins. Consequently, diverse assortments with VASs of
less than 5 cannot generate the same financial productivity as focused assortments
with VSAs of more than 10 at the individual retail store level.
Rupe and Kunz (1998) also proposed an Assortment Diversity Index (ADI) as a
predictor of the impact of assortment diversity on financial productivity. The ADI
provides guidelines to support effective assortment planning. The key VSA
breakpoints in the ADI are 2, 5, 10, and 20, which have a significant impact on
%GM of over 3 percent, 2 percent to 3 percent, 1 percent to 2 percent, and less than
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1 percent, respectively. As assortment diversity increases, its impact on %GM becomes
greater because of increased lost sales and markdowns as well as potential assortment
error (Kunz and Rupe, 1999).
Previous research related to assortment diversity that provided the foundation of
the assortment diversity index (Lee and Kunz, 2001; Rupe and Kunz, 1998) simulated
no merchandise plan error and single delivery with 100 percent initial stock, a process
similar to traditional delivery of fashion merchandise. Thus, the financial productivity
issues of assortment diversity and merchandise plan errors are worthy of investigation
in the context of supply chain merchandise replenishment strategies to fill the gap of
the literature.
Supply chain merchandise replenishment and merchandise plan errors
Appropriate merchandise replenishment contributes to balanced assortments and
presents merchandise at appropriate times to meet customer needs in individual stores.
Merchandise replenishment is the process of moving stock from suppliers to the retail
sales floor.
Supply chain merchandise replenishment determines the number of deliveries, the
quantity of initial stock, number of additional deliveries, and the timing of initial and
additional deliveries. In contrast, traditional merchandise replenishment strategies
were not likely to calculate in-season merchandise replenishment because most
merchandise was ordered and received ahead of the selling period.
Previous research examined differences between QR (supply chain) and traditional
merchandise replenishment strategies, advocating the superiority of QR (supply chain)
merchandise replenishment strategies (Abernathy et al., 1999; Lowson et al., 1999).
Many researchers supported that QR merchandise replenishment strategies were
effective to reduce excess inventory and lost sales, resulting from merchandise plan
errors (Hunter et al., 1992, 1996; Lowson et al. 1999; Nuttle et al., 2000a, b; Lee and
Kincade, 2002; Al-Zubaidi and Tyler, 2004; Birtwistle et al., 2006). [We are using the
term “QR” throughout this section because that was the term used by the researchers
in their publications but we regard the terms as interchangeable in current use.]
Merchandise plan errors are the difference between customer demand and
merchandise plan, consisting of volume errors and assortment errors. Volume error
exists in the difference between actual demand volume and planned volume, while
assortment error exists in the differences of assortment factors (style, size, and color)
between planned and actual demand. Over-plan error means the assortment had more
customers than planned merchandise and under-plan error means the assortment had
fewer customers than planned merchandise (Kunz, 2005).
Previous research found that both volume and assortment plan errors could be
minimized by using QR systems, including demand re-estimation and reorder/multiple
delivery strategies (Fisher et al., 1994; Hunter et al., 1992). QR systems maximized
profit through revision of the original merchandise plan based on POS data; and
minimized stockouts and markdowns as well as improved inventory turns and service
levels. Thus, QR systems could solve problems caused by demand uncertainty and
merchandise plan errors.
However, supply chain merchandise replenishment strategies have been mostly
evaluated by focusing on back-end merchandise aspects such as initial stocks,
reorders, volume errors, lead times, or selling seasons, and less focused on front-end
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merchandise aspects such as diversity of assortments and assortment plan errors.
Little has been investigated on how supply chain merchandise replenishment
strategies, mainly determined by number of deliveries and percentage of initial stock,
can cope with merchandise plan errors when front-end merchandise aspects such as
assortment diversity are involved in merchandise planning at the individual store
level. Thus, this study investigates the effect of supply chain merchandise
replenishment strategies on merchandise performance when front-end assortment
planning and merchandise plan errors are considered.
Objectives and hypotheses
The purposes of this study are:
(1) to examine whether the effects of supply chain merchandise replenishment
strategies on merchandise performance measures differ depending on
assortment diversity and merchandise plan errors; and to
(2) investigate the potential ability of supply chain merchandise replenishment
strategies to cope with market uncertainty and negative outcomes due to
diverse assortments and merchandise plan errors.
Based on the BTAF model and the previous studies regarding assortment diversity,
merchandise plan errors, and supply chain merchandise replenishment strategies, two
hypotheses were generated.
H1. (a) Volume per SKU for an assortment (VSA); (b) Multiple deliveries in the
combination of number of deliveries (ND) and percentage of initial stock (PIS);
and (c) merchandise plan errors consisting of volume error (VE), and
assortment error (AE) significantly relate to the merchandising performance
measures.
H2. As volume error (VE) and assortment error (AE) change, the number of
deliveries (ND) and percentage of initial stock (PIS) significantly affect the
merchandise performance measures, depending on the levels of volume per
SKU for an assortment (VSA).
Method
Sourcing simulator
Sourcing Simulator 2.1, the computer simulation of the merchandising process, was used
for this research. Sourcing Simulator was evolved from the Apparel Retail Model (ARM)
developed at North Carolina State University, Raleigh, NC to simulate traditional and QR
sourcing strategies in the apparel retail process and evaluate the differences between the
two strategies (Nuttle et al., 1991; Poindexter, 1991). Since then, Sourcing Simulator, now
marketed by [TC]
2
, has been widely used as a valid research or education tool
(Al-Zubaidi and Tyler, 2004; Hunter et al., 1992, 1993, 1996; King and Hunter, 1997; Kunz
and Rupe, 1999; Lee and Kunz, 2001; Nuttle et al., 1991; Nuttle et al., 2000, b).
Sourcing Simulator simulates merchandising processes according to a scenario
created by a series of inputs and provides a financial analysis of the impact of the chosen
scenario. The inputs include customer demand for style, color, and size, a selling period,
an assortment plan, a pricing strategy, and a delivery strategy. The simulation tracks a
prescribed assortment of merchandise at the stock-keeping unit level throughout a
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selling period. The simulation generates customer shopping behavior data at a retail
store through the flow of purposive or browsing customers during a selling period
according to a Poisson statistical process (Nuttle et al., 1991; Poindexter, 1991). The
Poisson process is a stochastic process in which random events occur continuously and
independently in an interval of time (Barnett, 2004). That is, the Poisson process
describes time-varying occurrences of a specific random event in time. Random arrival of
customers over a time period is a well-known example of Poisson process.
In the simulation scenario, a shopper randomly arrives according to Poisson process
either as a purposive customer with a specific item in mind or as a browser. Customers
intending to purchase or browse are assigned a desired style, size, and color from
discrete probability distributions describing customer preferences. If the desired SKU
is in stock, the item is purchased. If it is out of stock, the customer may choose another
item, browse, or leave (King and Poindexter, 1991). The default scenario suggested 27
percent for probability of customers who alter their choice after a stock out and 73
percent for the probability of customer who leave after a stock out (King and
Poindexter, 1991). Upon completion of the simulation, Sourcing Simulator generates an
output for 46 variables (e.g. merchandising performance measures) analyzing the
success of the chosen scenario.
Simulation input data set and output
To evaluate the impact of merchandise assortments, multiple deliveries, and
merchandise plan errors on financial productivity, an assortment plan (e.g. VSA, total
number of SKUs), a delivery strategy (e.g. initial stock and the number of reorders), and
merchandise plan errors (e.g. consumer demand volume and SKU mix error) were
input. The other variables were held constant using the default settings to control
randomness and analyze output in a deterministic manner. Consequently, it was
possible to focus on output related to assortments, multiple deliveries, and
merchandise plan errors. The input data for the simulation is presented in Table I.
This scenario employed a plan of 1,000 units of jeans to sell over a 20-week period at
a single retail store. Assortments with VSAs of 2, 5, 10, and 20, as well as total number
of stock-keeping units of 500, 200, 100, and 50, respectively, were selected because
these points significantly affected financial productivity, identified by previous
research related to assortment diversity (Rupe and Kunz, 1998). Each VSA of 2, 5, 10,
and 20 was examined at each level or value of the following four variables, volume
error, assortment error, number of deliveries, and percentage of initial stock, which
were varied by increments of 25 percent (from 250 percent to 50 percent), 10 percent
(from 0/0/0 to 50/50/50), 2 (from 0 to 14), and 10 percent (from 50 percent to 100 percent),
respectively (see Table I).
Based on the 4,320 simulations, 21 merchanidise performance measures, generated
by Sourcing Simulator, were selected to identify which measures are significantly
associated with up-front assortment planning, merchandise replenishment systems,
and merchandise plan errors (see Table II). The performance measures were selectively
used, depending on critical success factors of retail merchandising. Percent gross
margin is commonly used to measure merchandising performance, but has limitations
in evaluating both profitability and inventory investment (Mattila et al., 2002). GMROI,
and GMROISL could be more effective measures to evaluate profitability along with
inventory investment and service level.
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Results
Factor analysis was used to reduce the 21 highly intercorrelated merchandise
performance measures, to three factors that were statistically uncorrelated. Principal
component factor analysis with orthogonal varimax rotation with Kaiser
Normalization was used. There were three factors with eigenvalues greater than 1.0
Input data Data manipulation
Buyer plan Product name Jeans
The number of weeks in the selling season 20 weeks
The planned number of units to sell
(Volume)
1,000
Volume per stock-keeping unit for an
assortment (VSA)
2, 5, 10, 20
Total number of Stock-Keeping Units (SKU): 500, 200, 100, 50
Number of styles *Number of colors *
Number of sizes
500 ¼5£10 £10
200 ¼4£5£10
100 ¼4£5£5
50 ¼2£5£5
The combinations of stock-keeping units Even distribution
Presumed seasonality Mid-peak seasonality
Consumer demand volume (% Volume
Error from plan)
250% to 50% (increments of 25%)
SKU mix error (% Assortment Error from
the buyers plan)
0/0/0 to 50/50/50 (increments of 10)
Consumer
demand
% Customers who alter their choice after a
stockout
27%
% Customers who leave after a stockout 73%
Actual seasonality Same as presumed seasonality
Cost data Initial wholesale cost $25.00
Replenishment wholesale cost $25.00
Retail price $50.00
Percent initial markup 50%
Jobbed off price $ 16
Ordering cost $25.00 (same as default)
Shipping fixed cost $100 (same as default)
% Inventory carrying cost 20% (same as default)
% Inventory handling cost 8% (same as default)
Markdowns
premiums
Price elasticity of demand 0.7
The number of markdown 1
Week occurs 18 week
% Markdown 25%
% Who look for alternative after stockout 50%
Price premium (additional markup) 0
Sourcing
strategy
Sourcing strategy Quick response
Initial stock 50% to 100% (increments of 10)
The number of reorders 0 to 14 (increments of 2)
Vendor reliability Perfect supplier
The number of simulation replications for a
data point
10
Table I.
Simulation input data for
testing H1 and H2
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(Table II). They accounted for 96.82 percent of the total variance. The first factor
explained 57.33 percent of the variance followed by 31.58 percent, and 7.91 percent of
the variance, respectively. The three factors were named as Sales and Profitability,
Inventory and Costs, and Service, shown in Table II. The three factor scores estimated
by Anderson-Rubin method were used as the dependent variables.
H1 was supported as shown in Table III. The results tested by Pearson coefficient
correlations identified that volume per SKU for an assortment, multiple deliveries,
percentage of initial stock, volume error, and assortment error significantly related to
the three factors of merchandise performance measures, Sales and Profitability,
Inventory and Cost, and Service.
In particular, as volume per stock keeping unit for an assortment (VSA) decreased
(assortments became diverse), Inventory and Costs increased, and Service and Sales
and Profitability decreased (see Table III). According to the correlations with each
Factor 1 Factor 2 Factor 3
Sales and profitability Inventory and costs Service
% Sell through 0.97 Cost of goods 0.96 % Lost sales
% Gross margin potential 0.97 Handling cost 0.96 % Service level 0.93
% Liquidated 20.97 Total offerings 0.96 % In-stock 0.91
% Offering sold 0.97 Total revenue 0.76
GMROI *0.97 Average inventory
Adjusted gross margin 0.96 Inventory carrying cost 0.76
GMROISL *0.94
% Gross margin 0.94
Gross margin 0.92
Inventory turns 0.91
Sales revenue 0.76
Liquidated revenue 20.74
% of variance 57.33 31.58 7.91
Notes: % Lost sales: the percent of customers who left the store after encountering an out of stock
situation. Average inventory: the average number of units in the store each week. Total revenue: the
sum of the sales revenue and liquidation revenue. % Gross margin: the ratio of the gross margin to the
total revenue. Gross Margin Return on Inventory (GMROI): the gross margin divided by the average
inventory investment during the selling period. Gross Margin Return on Inventory with Service Level
(GMROISL): the GMROI times % In-Stock
Table II.
Factor loadings for the 21
merchandising
performance measures
VSA ND PIS VE AE SP IC S
Number of Deliveries (ND)
% Initial Stock (PIS) 20.11*
Volume Error (VE)
Assortment Error (AE)
Sales and Profitability (SP) 0.45 *0.35 *20.12 *0.67 *20.08 *
Inventory and Cost (IC) 20.33 *20.18 *0.18 *0.68 *0.08 *
Service (S) 0.30 *0.29 *0.34 *20.26 *0.06 *
Note: *p,0.01 (two-tailed)
Table III.
Pearson correlation of
VSA, ND, PIS, VE, and
AE with the three factors
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merchandise performance measure, VSA was very strongly and negatively associated
with liquidated inventory revenue and percent liquidated (-0.61 and -0.56) as well as
percent lost sales, cost of goods, and average inventory (-0.42, -0.41 and -0.53,
respectively).
As an assortment had fewer units per SKU on the average (more diverse
assortments), lost sales increased and frequent merchandise replenishment due to
greater possibility of stock outs and lost sales caused increases in average inventory
and cost of goods. As lost sales and average inventory increased due to diverse
assortments, liquidation of leftover merchandise at the end of the selling period
increased, resulting in higher liquidated revenue and lower gross margin, %GM,
GMROI, and GMROISL. This result shows that the negative financial impact of diverse
assortments was mainly due to increased percent liquidated, resulting from increased
lost sales, average inventory, and cost of goods.
H2 was partially supported by examining statistical main and interaction effects
(see Table IV). According to the analyses of variance for the three multiple regression
models, there were significant main effects and interaction effects on Sales and
Profitability, Inventory and Cost, and Service except the insignificant three-way
interaction effect of VSA, Initial Stock, and Assortment Error on Inventory and Cost
(Table IV). The significant main and interaction effects indicated that the relationships
between supply chain merchandise replenishment strategies and merchandise
performance measures were conditioned or moderated by assortment diversity as
well as merchandise plan errors.
Figure 1 illustrates that when assortments were diverse, supply chain
replenishment strategies with multiple deliveries and a 90 percent or less initial
stock resulted in lower %GM and GMROISL than traditional replenishment strategies
with single delivery and 100 percent initial stock. In turn, supply chain merchandise
replenishment strategies had a limited ability to compensate for diverse assortments
(VSA #5). However, supply chain merchandise replenishment strategies had a greater
ability to improve GMROISL than %GM, especially when assortments were focused
(Figure 1). Supply chain merchandise replenishment strategies are more likely to be
effective to reduce inventory investments and lost sales when assortments were not
less diverse (approximately, VSA .5) (Figure 1).
These results also supported that the impact of assortment diversity and supply
chain merchandise replenishment strategies on merchandise performance measures
differed depending on merchandise plan errors. Figure 2 illustrates that the three-way
interactions among VSA, Number of Deliveries, and %GM differed, depending on the
levels of Volume Errors and Assortment Errors. As the number of deliveries increased,
%GM generally increased. However, compared with no additional delivery with 100
percent initial stock, multiple deliveries were likely to deteriorate %GM when
assortments were very diverse, as well as when over-Volume Errors, more customers
than planned merchandise, occurred (Figure 2). This means that multiple deliveries
were more effective when assortments were focused and when under-Volume Error,
fewer customers than planned merchandise, occurred. With under-Volume Error (250
percent) and more focused assortments, multiple deliveries had greater %GM than
no-additional deliveries with 100 percent initial stock.
Regarding the three-way interactions with Assortment Errors (Figure 2), multiple
deliveries had a greater ability to improve %GM when assortments were more focused
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as well as when over-Assortment Errors occurred. In sum, multiple deliveries were
more effective in improving financial productivity when assortments were more
focused as well as when under-Volume Errors and over-Assortment Errors occurred.
Discussion and implications
The findings identified the significant main and interaction effects of assortment
diversity, supply chain merchandise replenishment, and merchandise plan errors on
merchandise performance measures. The ability of supply chain systems to
compensate for problems inherent to assortment diversity as well as merchandise
plan errors was limited. When assortments were diverse, supply chain merchandise
replenishment systems were less likely to reduce merchandise plan errors and
generated lower financial productivity than traditional merchandise replenishments
with single delivery and 100 percent initial stock. Supply chain merchandise
replenishment strategies have competitive advantages:
.when offering focused assortments rather than diverse assortments; and
.for minimizing Under-Volume Errors and Over-Assortment Errors rather than
Over-Volume Errors.
Based on these findings, financial productivity issues are discussed here in relation to
assortment diversity, merchandise plan errors, and supply chain merchandise
replenishment strategies. First, the results of this study provide insight about the
influence of assortment diversity on financial productivity of merchandising
performance, considering merchandise plan errors. Previous research identified the
relationships between VSA and financial productivity, based on traditional delivery
with 100 percent initial stock and no merchandise plan error (Rupe and Kunz, 1998;
Dependent variables
Factor 1 Factor 2 Factor 3
Sales and profitability Inventory and cost Service
Independent
variables
Standardized
coefficients of
Beta t-value
Standardized
coefficients of
Beta t-value
Standardized
coefficients of
Beta t-value
VSA 1.00 28.31 ** 20.66 213.64 ** 1.00 15.83 **
ND 0.40 35.77 ** 20.28 218.37 ** 0.44 22.41 **
IS 0.05 4.35 ** 0.12 7.68 ** 0.53 26.78 **
VE 0.50 46.66 ** 0.79 54.36 ** 20.26 213.46 **
AE 20.03 23.11 *0.03 2.04 *0.04 2.05 *
VSA *ND 2.08 23.98 ** 0.16 5.45 ** 20.30 27.92 **
VSA *IS 20.47 213.05 ** 0.18 3.59 ** 20.54 28.40 **
VSA *ND *VE 0.12 9.04** 0.10 5.93 ** 0.25 11.03 **
VSA *ND *AE 20.04 22.29 *0.08 3.04 *0.13 4.05 **
VSA *IS *VE 0.11 7.27** 20.23 211.59 ** 20.23 29.09 **
VSA *IS *AE 20.05 22.61 0 *0.02 0.64 20.08 22.33 *
R-square 0.81 0.66 0.42
F-value
(df ¼11, 4308) 1708.01 ** 758.79 ** 281.47 ***
Notes: *p,0.05; ** p,0.01; ***
p,0.001 (two-tailed)
Table IV.
Multiple regressions for
three-way interactions on
the three factors
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Kunz and Rupe, 1999; Lee and Kunz, 2001). Traditional delivery with 100 percent initial
stock did not cope with demand uncertainty and inaccurate forecast error, and
inventory-related factors did not significantly change according to merchandise plan
errors. Thus, previous research attributed the financial impact of assortment diversity
primarily to lost sales in relation to stockouts. However, this study extends this and
suggests when merchandise plan errors exist and multiple deliveries are employed; the
financial impact of assortment diversity results in increased lost sales as well as
increased inventory and costs.
Second, volume per SKU for an assortment (VSA) has been given little attention as a
factor that influences the effects of supply chain merchandise replenishment strategies
on merchandising performance measures. This study illuminates VSA as an important
factor of merchandise planning, especially when apparel and retail firms plan
replenishment strategies to minimize merchandise plan errors. Both assortment
diversity and supply chain merchandise replenishment strategies are related to
inventory performance as well as in-stock service level.
Thus, effective assortment planning at the VSA level is necessary when apparel and
retail firms want to enhance balanced assortments with minimum inventory
investments through supply chain merchandise replenishment strategies. This study
suggests apparel and retail firms should develop both up-front assortment planning
and replenishment strategies considering the level of VSA at the individual store level.
Figure 1.
Two-way interaction
effects of assortment
diversity on Percent Gross
Margin (%GM) and Gross
Margin Return on
Inventory with Service
Level (GMROISL)
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Furthermore, the combinations of traditional and supply chain replenishment
strategies depending on assortment diversity are also suggested for financially sound
merchandising performance. For example, providing diverse assortments with
traditional replenishment strategy can build financially sound profitability and using
supply chain replenishment strategy for focused assortments can be effective in
reducing lost sales and inventory investments.
Third, previous research found merchandise plan errors can be reduced by
implementing QR systems (Hunter et al., 1992; Fisher et al., 1994; Abernathy et al., 1999).
However, this study found that supply chain merchandising replenishment strategies
were more effective in improving %GM in the cases of under-volume errors and
over-assortment error, especially when assortments are focused. Furthermore, the ability
of supply chain merchandise replenishment strategies was limited in the case of
over-volume errors, no matter the levels of VSA. This may be because reorders and
multiple deliveries based on inaccurate over-volume errors end up with excess inventory
and a high rate of liquidation of unsold inventory at the end of the selling period.
This study suggests that apparel and retail merchandisers should carefully forecast
merchandise plan errors based on sales history as well as current point of sales data
Figure 2.
Three-way interaction
effects among Volume per
Stock keeping unit for an
Assortment (VSA),
Number of Deliveries
(ND), Volume Error
(VE)/Assortment Error
(AE) on percent gross
margin
Financial
productivity
issues
497
because inaccurate forecasts about customer demand (merchandise plan errors) impact
merchandising performance through up-front assortment planning and replenishment
strategies. Merchandise plan errors can bring about undesirable effects, such as lost
sales and high unsold inventory, resulting in a negative impact on profitability (Mattila
et al., 2002; Myers et al., 2000). Thus, merchandise planning should consider different
replenishment strategies by level of assortment diversity, depending on estimated
merchandise plan errors. For example, diverse assortments with highly unpredictable
demand should be sourced in advance of the season with single delivery and 100
percent initial stock; but focused assortments with highly predictable under-volume
error and over-assortment error can be cost-effective with supply chain merchandise
replenishment strategy.
Fourth, the desirable financial productivity in relation to diverse assortments
depends on how much improvement in service level and inventory investments can be
achieved. To achieve balanced assortments which minimize forecast errors and excess
inventory investments, it becomes essential to develop an integrated supply chain
internally and externally that can supply diverse assortments with short lead time and
without any costs associated with unnecessary inventory. That is, only well-planned
integration of flexibility, agility, and accuracy in the entire supply chain from raw
materials to the ultimate consumer can make diverse assortments more profitable.
However, in the reality of apparel and retail business, most supply chains line up as a
set of fragmented parts rather than as an integrated implementation (Birtwistle et al.,
2006; Lowson et al., 1999). Thus, integrated supply chain management systems should
be further developed in a way capable of carrying diverse assortments with minimum
inventory and costs.
Limitations and future research
The findings of this study related to the merchandising issues mentioned are based
on the assumption that Sourcing Simulator is representative of the merchandising
process and can be applied to real-world apparel and retail businesses. However,
these findings from the Sourcing Simulator may not apply to all apparel and retail
situations because a few external merchandise planning factors were excluded in
this study. According to previous research, lead times, selling periods, and
seasonality significantly impact supply chain replenishment performance
(Al-Zubaidi and Tyler, 2004; Hunter et al., 1996). Other the internal merchandise
planning factors, such as pricing and mark up/down, also significantly affect the
financial productivity. In relation to pricing, diverse assortments can achieve the
same financial productivity of a less diverse assortment by increasing initial
markup (Lee, 1999) as long as customers are willing to pay a higher price. Thus, the
application of these findings to real apparel and retail situations needs to be
considered, depending on these business realities.
Future research opportunities related to this topic include identifying financial
issues associated with assortment diversity on the basis of the real stores’ data in the
apparel and retail industry. This could make it possible to empirically identify why
supply chain merchandise replenishment strategies for diverse assortments carry more
unsold inventory at the end of selling period. The influences of the fragmented supply
chain, frequent reorders based on inaccurate in-season re-estimation, long lead time,
short selling period, and other factors could be examined as factors resulting in an
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increase in inventory investment and costs. Studies such as this could provide apparel
and retail firms with a realistic understanding about the limited ability of supply chain
merchandise replenishment strategies for diverse assortments.
References
Abernathy, F.H., Dunlop, J.T., Hammond, J.H. and Weil, D. (1999), A Stitch in Time: Lean
Retailing and the Transformation of Manufacturing – Lessons from the Apparel and
Textile Industries, Oxford University Press, New York, NY.
Al-Zubaidi, H. and Tyler, D. (2004), “A simulation model of quick response replenishment of
seasonal clothing”, International Journal of Retail & Distribution Management, Vol. 32
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Chichester.
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demand in an uncertain world”, Harvard Business Review, Vol. 72, May-June, pp. 83-93.
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Journal of Textile Institute, Vol. 33 No. 3, pp. 462-71.
Hunter, N.A., King, R.E. and Nuttle, H.L. (1996), “Evaluation of traditional and quick-response
retailing procedure by using stochastic simulation model”, Journal of Textile Insitute,
Vol. 87 No. 1, pp. 42-55.
Hunter, N.A., King, R.E., Nuttle, H.L.W. and Wilson, J. (1993), “North Carolina apparel pipeline
modeling project”, International Journal of Clothing Science and Technology, Vol. 5 Nos 3-4,
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King, R.E. and Hunter, N.A. (1997), “Quick response beats importing in retail sourcing analysis”,
Bobbin, Special Report, March.
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Kunz, G.I. (2005), Merchandising: Theory, Principles and Practice, Fairchild, New York, NY.
Kunz, G.I. and Rupe, D. (1999), “Volume per stock-keeping unit for an assortment:
a merchandising planning tool”, Journal of Fashion Marketing and Management, Vol. 3
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measures and markup“, unpublished Master’s thesis, Iowa State University, Ames, IA.
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contributions towards merchandising theory”, Journal of Fashion Marketing and
Management, Vol. 5 No. 4, pp. 303-12.
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manufacturers’ inventory performance”, Textile Institute, Vol. 93 No. 2, pp. 26-39.
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assortments“, unpublished Master’s thesis, Iowa State University, Ames, IA.
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Consumer Demand, John Wiley & Sons, New York, NY.
Financial
productivity
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Lummus, R. and Vokurka, R.J. (1999), “Defining supply chain management: a historical
perspective and practical guidelines”, Industrial Management & Data Systems, Vol. 99
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Mattila, H., King, R. and Ojala, N. (2002), “Retail performance measures for seasonal fashion”,
Journal of Fashion Marketing and Management, Vol. 6 No. 4, pp. 340-51.
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Nuttle, H.L.W., King, R.E. and Hunter, N.A. (1991), “A stochastic model of the apparel-retailing
process for seasonal apparel”, Textile Institute, Vol. 82 No. 2, pp. 247-58.
Nuttle, H.L.W., King, R.E., Fang, S.C., Wilson, J.R. and Hunger, N.A. (2000a), “Simulation
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Further reading
Kunz, G.I. (1995), “Behavioral theory of the apparel firm: a beginning”, Clothing and Textiles
Research Journal, Vol. 13 No. 4, pp. 252-61.
Kunz, G.I. (1998), Merchandising: Theory, Principles and Practice, Fairchild, New York, NY.
About the authors
Ui-Jeen Yu is an Assistant Professor of Apparel, Merchandising, and Design in the Department
of Family and Consumer Sciences at Illinois State University. Her current research focuses on
merchandise planning and analysis, global supply chain management, multi-channel retailing,
and consumer behavior toward e-commerce. Ui-Jeen Yu is the corresponding author and can be
contacted at: fcs@IllinoisState.edu
Grace I. Kunz is an Emeritus Associate Professor of Apparel, Educational Studies, and
Hospitality Management at Iowa State University. Her research focuses on merchandising,
apparel production management, global sourcing, and retailing technology implementation.
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... US apparel manufacturers and retailers face heterogeneous customer preferences and diversified customer demands for apparel products (Kunz, 2009). Intensive changes for product variety increased the importance of the role of assortment planning in relation to product lines (Kunz, 2009;Yu & Kunz, 2010). Assortment planning, the determination of the range of choices -style, size, and colour -offered at a particular time, is a primary merchandising function (Bohlinger, 1977;Glock & Kunz, 2005;Mason, Mayer, & Ezell, 1994). ...
... Assortment diversity is defined as the relationships between assortment volume and the number of SKUs in an assortment. Previous research supported the negative relationship between assortment diversity and percent gross margin (Kunz & Rupe, 1999;Lee & Kunz, 2001;Rupe & Kunz, 1998;Yu & Kunz, 2010). As assortments become diverse, financial profitability decreases, attributed primarily to lost sales in relation to * Email: uyu@ilstu.edu ...
... stockouts (Kunz, 2009;Kunz & Rupe, 1999). Yu and Kunz (2010) also found that when assortments are diverse, supply chain merchandise replenishment systems -previously known as quick response (QR) merchandise replenishment systems which is a preliminary foundation of supply chain systems -have limited ability to cope with demand uncertainty and minimising negative impacts of merchandise plan errors such as volume error and assortment error on financial productivity. ...
Article
Full-text available
for an assortment (VSA), was introduced as a guide for assortment planning, proposing a negative relationship of VSA to percent gross margin (%GM) by Kunz and Rupe, 1999. However, the functional relationship between the nature of assortments and potential financial performance has not been extensively explored when merchandise replenishment plans have been implemented to reduce merchandise plan errors. Thus, the purposes of this study were, 1) to identify how assortment planning and merchandise replenishment planning affect merchandising performance outcomes under the high risks of merchandise plan errors including both volume error and assortment error and, 2) to explore the interrelationships of merchandise performance measures, determined by internal merchandise planning factors (e.g., assortment diversity, multiple merchandise replenishment, and initial inventory) and external consumer demand factors (e.g., merchandise plan errors). Theoretical Frameworks and Hypotheses. The conceptual framework of this study is based on a Behavioral Theory of the Apparel Firm with a Quick Response Construct (BTAF/QR), proposed by Kunz (1998). This model explains the importance of a merchandising function in minimizing merchandise plan errors by implementing assortment and merchandise replenishment plans in the supply chain system. Based on the conceptual framework and a synthesis of previous literature, this study developed a conceptual model (See Figure 1). The model mainly hypothesized that both the internal merchandise planning factors and external consumer demand factors significantly influence merchandise performance outcomes. For instance, lower volume per SKU for an assortment (more diverse assortments) will tend to decrease profitability because diverse assortments will require higher average inventory investment and lost sales. (Insert Graphic 1 about here) Method. Sourcing Simulator, a computer program, is a well known research tool that has been used to simulate the financial result of merchandising plans by creating a series of planned inputs. Sourcing Simulator provides a financial analysis of the impact of the chosen strategies in the form of 21 merchandise performance measures. Of these performance measures, three measures—Average Inventory, % Lost Sales, and Gross Margin Return On Inventory with Service Level (GMROISL)—were selected as the most relevant to assortment planning and merchandise replenishment planning. GMROISL was chosen because it was a more effective measure to evaluate profitability along with inventory investment and service level.
... US apparel manufacturers and retailers face heterogeneous customer preferences and diversified customer demands for apparel products (Kunz, 2009). Intensive changes for product variety increased the importance of the role of assortment planning in relation to product lines (Kunz, 2009;Yu & Kunz, 2010). Assortment planning, the determination of the range of choices -style, size, and colour -offered at a particular time, is a primary merchandising function (Bohlinger, 1977;Glock & Kunz, 2005;Mason, Mayer, & Ezell, 1994). ...
... Assortment diversity is defined as the relationships between assortment volume and the number of SKUs in an assortment. Previous research supported the negative relationship between assortment diversity and percent gross margin (Kunz & Rupe, 1999;Lee & Kunz, 2001;Rupe & Kunz, 1998;Yu & Kunz, 2010). As assortments become diverse, financial profitability decreases, attributed primarily to lost sales in relation to * Email: uyu@ilstu.edu ...
... stockouts (Kunz, 2009;Kunz & Rupe, 1999). Yu and Kunz (2010) also found that when assortments are diverse, supply chain merchandise replenishment systems -previously known as quick response (QR) merchandise replenishment systems which is a preliminary foundation of supply chain systems -have limited ability to cope with demand uncertainty and minimising negative impacts of merchandise plan errors such as volume error and assortment error on financial productivity. ...
Article
Full-text available
The purpose of this study was to explore the interrelationships of selected merchandise performance measures as determined by merchandise plans and merchandise plan errors through development of stochastic computer simulation models. Utilising Sourcing Simulator, a merchandising analysis tool, a total of 4320 data were generated and analysed through path analysis. The findings indicate significant relationships among inventory management, service level, and profitability, which were critically influenced by the internal merchandise planning factors – assortment diversity and merchandise replenishment – and external consumer demand factors – merchandise plan errors. This study identified the significant role of volume per stock-keeping unit for an assortment (VSA) as a guideline of assortment diversity on inventory management, service level, and profitability. Well-integrated merchandise planning, considering the internal merchandise planning factors and the external consumer demand factors, is suggested for successful merchandise performance outcomes.
... 3.2 Merchandise plan errors between offshore and "Made-in-USA" domestic production strategies Previous studies proved well-integrated supply chain management through demand re-estimation during selling periods and reorder/multiple delivery strategies can minimize negative impacts of merchandise plan errors, due to customers' demand uncertainty, such as stockouts, lost sales, and markdowns of unsold inventories (Kunz, 2010;Yu and Kunz, 2010). In the real world of fashion industry, there are merchandise plan errors, which are defined as the difference between actual customer demand and merchandise plan. ...
... Sourcing decisions cannot be truly tested in the actual marketplace. Thus, simulation of sourcing decisions serves as an efficient decision support tool for merchandise planners and buyers to reduce uncertainty and adequately handle dynamism in production and distribution of fashion goods in retail supply chain environments (Terzi and Cavalieri, 2004;Yu and Kunz, 2010). ...
Article
Full-text available
Purpose The purpose of this paper is to examine merchandise performance-based financial productivity of offshore vs reshore sourcing scenarios for fashion/seasonal products with higher demand uncertainty, using computer simulation software. Design/methodology/approach Using Sourcing SimulatorTM, the researchers generated a data set of 530 simulations concerning merchandising performance measures for offshore and reshore sourcing scenarios. Analysis of covariance was conducted for data analysis. Findings Results show financial productivity differs, depending on a sourcing decision between offshore and reshore sourcing scenarios as well as on the levels of volume error and assortment error. The reshore sourcing scenario through “Made-in-USA” domestic production strategy can have a better profitability, including gross margin return on inventory with service level, in cases of under-volume error and over-assortment error, than the offshore sourcing scenario. Research limitations/implications Findings from this study are based on simulation data, which may have a gap between simulations and reality concerning the competitive advantages of “Made-in-USA” domestic production strategy. “Made-in-USA” domestic production strategy can be more agile and responsive to the uncertainty of markets and customer demands when the supply chain systems are well-integrated and fully implemented. Originality/value Results from this study contribute to fill the literature gap about differences of financial productivity between offshore and reshore sourcing scenarios for apparel manufacturers and retailers. This study also offers an insight of which decision response may be better to uncertain customer demands, while satisfying financial productivity.
... For example, shortening of the supply pipeline can be achieved by moving onshore production facilities, thus choosing local suppliers, by improving information exchange, and collaborative relationships between organizations along the SC (Ernst and Whinney, 1988;Al-Zubaidi and Tyler, 2004;Caridi et al., 2010). Merchandise assortment has been found to affect the ability of replenishment strategies in mitigating merchandise planning errors (Yu and Kunz, 2010). ...
... In order to achieve the aim of the research, a case study research has been conducted to describe the SC of the luxury Italian fashion companies and to identify links between variables related to organization, process, information and management choices. In fact, case studies are normally used to gain a more in-depth understanding of the research, often in an effort to answer "how" and "why" questions (Yin, 1984). ...
Purpose – The purpose of this paper is to investigate how luxury Italian fashion companies manage the replenishment process, and how they leverage supply chain (SC) to be able to match supply and demand of fashion products. Design/methodology/approach – Literature review was the first step performed; then, a case study research has been conducted in order to have a comprehensive view of the real context of luxury Italian fashion companies concerning merchandise planning and replenishment processes. After the sample was individuated, a questionnaire has guided the interviews and then data have been collected. Analysing data has concerned a primary case analysis and then cross-case patterns have been searched. Finally, several variables coherent to the aim of the study have been pinpointed and a framework has been designed. Findings – The paper provides a characterization of the luxury Italian fashion industry concerning merchandise planning constraints and the replenishment processes. To guarantee the flexibility required to match supply and demand when there is a high percentage of seasonal products in the collection, companies leverage on both downstream and upstream SC alignment. Originality/value – The enhancement of performance within the fashion SC is a topic not too much examined in depth, in particular referring to the luxury fashion companies and to the Italian context. Aligning upstream and downstream activities, information sharing between vendor and retailer and securing strategic alliances with the suppliers constitute important steps to reach flexibility and reactivity and to be in step with the market needs. The paper provides valuable insights to companies that are trying to decrease their lost sales and to increase their sell-out and customer service through a review of their SC processes.
... The authors named as well Abbott and Palekar (2008) in dealing with store multiproduct problem with shelf availability and display-space constraints, and Pan et al. (2009) in defining the optimal replenishment level for retailers. In this context, Yu and Kunz (2010) examined, in a framework of assortment diversity, the capability of minimizing the merchandising errors. ...
Chapter
Purpose. This paper aims to define the overall Made in Italy perception within the on-line and off-line contexts. Particularly, authors attempt to consider three main aspects; the first one regards the key product categories linked to the Made in Italy production; the second aspect concerns the key characteristics linked to the Italy Country Image and the overall sentiment related to it. Finally, the research aims at identifying whether Italian brands enhance their Country of Origin (COO) image or not.
Chapter
With few historical data and quick response of the market, fast fashion apparel retailers should make decisions about replenishment policies and assortment strategies. Deciding the quantity to deliver for each point of sales, in term of quantity and assortment mixture, is one of the big retailers challenges, and keys of success. In this paper, our proposal is about a mathematical model, for fast fashion retail planning chain. Our model is a dynamic tool to make the loop on the assortment, replenishment and inventory quantities, to help decision makers delivering the right product in the right point of sales with the right quantity, by maximizing the profit. It constitutes a flexible tool, allowing retailer to add new items in the optimization process, or even to renew the product range regularly, for fast fashion retailers, who aim for just in time production models. The replenishment supply chain is fragmented into strategic, tactic and operational levels. Each level is modeled as an integer linear program. Looping is made from Head Quarters, through countries until stores. Chorological horizon is sub divided according to season collections, monthly and weekly basis. Our integer linear programs are developed and solved with IBM Cplex Optimizer. Model validation is established with random data instances, inspired from real case studies.
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For years agents believed that technology could have bridged the gap in fashion companies. This has not come true. Significant investments aimed at implementing complex systems have often failed. Indeed, they have not been able, in a simple, flexible and comprehensive way, to integrate all the processes that, by definition, are changeable and not only influenced by deterministic factors. Therefore, it proves necessary, in complex organizations, to promote those best practices and habits that support and enhance personal freedom, judgments and hypotheses. This is the process by which the retailer seeks to provide the right amount and quality of the right merchandise in the right store at the right time, while also seeking to meet the financial goals of the company. This project, developed by the collaboration between the Department of Industrial Engineering (University of Bologna) and K.Group, aims to show how financial planning of Merchandise Planning may be implemented in a major Italian Fashion Retail Company, presenting the preliminary plan to integrate it with the specific processes of Supply Chain Planning and Execution, hence highlighting achievements, methodology and technological resources in terms of: data management (normalization and load data), business intelligence (score carding, dashboards, reporting, analysis), predictive analytics (clustering, simulation), performance management (budgeting, planning and forecasting, profitability), workflow management and data integration.
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Discusses an ongoing programme, the overall purpose of which is to develop an integrated set of stochastic simulation models of firms in the textile, apparel and retail industries. The models are to be used primarily for research, education and industrial problem solving in the areas of plant and company operations with emphasis on Quick Response (QR) methodologies driven by Point-of-Sale (POS) data. A second objective is that of developing interactive management information systems using high-level decision-surface models (simulation metamodels) of expected system performance as a function of key input parameters.
Full-text of this article is not available in this e-prints service. This article was originally published in International Journal of Retail & Distribution Management, published by and copyright Emerald Publishing Group. Stochastic computer-simulation models have been constructed of the clothing supply chain and applied to retail inventory control. This quantifies the performance of quick response procedures for seasonal merchandise, thus creating an analytical tool. They are designed to investigate the effects of improved retailing and supply procedures on financial and other performance measures using two supply strategies: fixed quantity re-ordering and fixed interval re-ordering. These offer a wide range of options in experimentation. They permit an evaluation of both purchasing systems in relation to different quantities and lead-time scenarios. Experimental work with both models has shown that if the replenishment time exceeds two weeks, the potential for lost sales greatly increases. This provides a benchmark figure for assessing the responsiveness of clothing industry supply chains.
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In Part I of the series, we describe stochastic computer models that simulate operations in the spinning, knitting, weaving, dyeing and finishing, and cut/sew sectors of the textile industry. The models are scaled to represent a supply chain designed to feed a garment-manufacturing operation involving four or five plants, i.e. part of each plant's output is ‘dedicated’ while simultaneously providing yarns and fabrics to the industry at large. Each of the sector models is unique because of the very different types of processing technology employed. The models are linked by means of streams of fabric orders from the manufacturing plants that make a range of garment types requiring many different fabrics for Basic (year-round sales), Seasonal (two or three seasons per year), and Fashion (shelf lives of 8–12 weeks) goods in a broad range of colors. In addition to each plant's product ranges and order sizes and frequencies, particular attention is paid to the machine-scheduling algorithms, although the models are deliberately kept at a ‘high’ as opposed to a ‘shop-floor’ level. The purpose of this modeling is to allow senior management to answer broad questions about the plants' ability to operate in a Quick Response environment. The various model outputs reflect this, having a heavy emphasis on on-time shipments, back-order levels, and service levels. In Part II of the series, we shall present the QR-related operating results to date, a description of a master-scheduling procedure to orchestrate the operations of the supply chain, ideas on an improved scheduling method, and an account of the construction of neural-network decision surface models as a decision support tool. We also overview ongoing efforts in technology transfer and in using ‘fuzzy’ mathematics to model the vagueness and uncertainty inherent in the supply- chain decision-making environment. The research effort of which this is a part is ongoing. We present these results in the hope of encouraging others to help carry the investigations forward.
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Retail success can be defined as achieving high gross margins and customer service levels (i.e. being in-stock) with as little inventory as possible. Forecast accuracy, process lead-time, offshore/local sourcing mix and up-front/replenishment buying mix can have a significant impact on success in connection with sourcing seasonal products with a fashion content. Forecast accuracy depends on the characteristics of the product and supply lead-time. Lead-times are traditionally long and buying decisions are often made seven to eight months prior to the start of the selling season. Forecast errors lead to some of the items being liquidated at clearance prices while others stockout and lead to lost sales. As a result retailers often resort to higher mark-up prices with fashion products. However, typical retail performance measures such as service level, lost sales, product substitute percentage, gross margin, gross margin return on inventory, sell-through percentage and mark-down rate mask the source of the problems. In this paper, we discuss these performance measures and propose a new one. Additionally, case study analysis of a group of Finnish department stores is presented.
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Discusses an ongoing programme, the overall purpose of which is to develop an integrated set of stochastic simulation models of firms in the textile, apparel and retail industries. The models are to be used primarily for research, education and industrial problem solving in the areas of plant and company operations with emphasis on Quick Response (QR) methodologies driven by Point-of-Sale (POS) data. A second objective is that of developing interactive management information systems using high-level decision-surface models (simulation metamodels) of expected system performance as a function of key input parameters.