Data Analysis of Shipment for Textiles and Apparel from Logistics Warehouse to Store Considering Disposal Risk - MDPI
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sustainability
Article
Data Analysis of Shipment for Textiles and Apparel
from Logistics Warehouse to Store Considering
Disposal Risk
Rina Tanaka 1 , Aya Ishigaki 1, * , Tomomichi Suzuki 1 , Masato Hamada 2 and Wataru Kawai 2
1 Industrial Administration, Tokyo University of Science, Noda, Chiba 278-8510, Japan;
7418519@ed.tus.ac.jp (R.T.); szk@rs.tus.ac.jp (T.S.)
2 Data-Chef Co., Ltd., Koto-ku, Tokyo 135-0004, Japan; m.hamada@data-chef.co.jp (M.H.);
w.kawai@data-chef.co.jp (W.K.)
* Correspondence: ishigaki@rs.noda.tus.ac.jp; Tel.: +81-4-7124-1501
Received: 24 November 2018; Accepted: 2 January 2019; Published: 7 January 2019
Abstract: Given the rapid diversification of products in the textile and apparel industry, manufacturers
face significant new challenges in production. The life cycle of apparel products has contracted and is
now, generally, a several-week season, during which time a majority of products are supposed to be
sold. Products that do not sell well may be sold at a price lower than the fixed price, and products
that do not sell at all within the sales period may eventually become forced disposal. This creates
long-term management and environmental problems. In practice, shipping personnel determine
when to ship products to stores after reviewing product sales information. However, they may not
schedule or structure these shipments properly because they cannot effectively monitor sales for a
large number of products. In this paper, shipment is considered to reduce the risk of product disposal
on the premise of selling at a fixed price. Although shipment quantities are determined by various
factors, we only consider the change in inventory at the logistics warehouse, since it is difficult to
incorporate all factors into the analysis. From cluster analysis, it is found that shipping personnel
should recognize a policy to sell products gradually over time. Furthermore, to reduce the risk of
disposal, we forecast the inventory from conditional probability and are able to extract products out
of a standard grouping using past data.
Keywords: apparel products; supply chain management; quick response; clustering; forecasting;
sale at fixed price
1. Introduction
Environmental problems such as global warming and water shortages along with the expansion
of social disparities from globalization not only remain unsolved in the world, they continue to
escalate. Thus, the need for companies to play significant roles in shaping sustainable societies is
also increasing [1]. A society can be considered sustainable from three perspectives—environmental,
societal, and economic [2]; these are often referred to as the triple bottom line or 3BL [3]. Companies
must consider the impact of their business activities on the 3BL and establish long-term corporate
strategies to minimize adverse impacts in any of these dimensions. By employing this perspective
in supply chain management, in particular, companies are more likely to achieve sustainability [4],
which in turn can enhance a company’s competitive advantage [5]. Even in the textile and apparel
industry—which is subject to the vagaries of fashion and, thus, characterized by short product life
cycles—companies are focusing more on sustainability as globalization progresses [6]. In this industry,
the emphasis in sustainable supply chain management is placed on effective risk management.
Sustainability 2019, 11, 259; doi:10.3390/su11010259 www.mdpi.com/journal/sustainabilitySustainability 2019, 11, 259 2 of 14
In a textile and apparel supply chain, a company’s factories manufacture products according
to a designated production plan. Afterwards, via logistics warehouses, the company ships finished
products to stores. At the start of a sales period, a quantity of products is made available at stores based
on the production plan. At the end of the sales period, it is hoped that inventories of stores and the
logistics warehouse will have harmonized with the production plan as well as with customer demand.
In practice, however, customer demand often deviates from what is anticipated in the production
plan, and a surplus or shortage results. This is because of the long lead times before a sales period [7]
and because apparel products are seasonal, that is, usually only one season. Further, in the textile
and apparel industry, the logistics warehouses serve as stock points, and therefore, decisions about
shipments to logistics warehouses are crucial [8].
In recent years, as customer needs and preferences have proliferated, textile and apparel
products have been made available in an ever-widening range of colors and sizes [9]. Moreover,
an increasing number of manufacturers also sell more general apparel products—such as bags, shoes,
and accessories—in addition to clothes [10]. Because of these trends, leading to modifications in the
design of apparel products every season, demand uncertainty in the industry is large compared to
that for industries with more stable products, such as office supplies, or furniture. In other words,
every season there are new apparel products with very short life cycles [9]. Therefore, it is difficult to
forecast when and which products will be sold at any given time [11].
Many factors are at play in textile and apparel supply chains. When shipping products from
the production plants to the logistics warehouse, timing and quantities are determined in advance.
On the other hand, when shipping from the logistics warehouse to stores, timing and quantities can
be modified in accordance with customer demand by taking into account actual sales information.
The earlier that shipments are sent from the logistics warehouse, the higher is the possibility that
inventory at the stores will become excessive, such that unsold products will eventually be returned to
the logistics warehouse and discarded. Inventory space in a store may also be quite limited, resulting
in the need to return or dispose of excess product. In contrast, when shipments from the logistics
warehouse are delayed, stores can experience shortages and, as a result, lose opportunities for sales.
In addition, because of long lead times before sales seasons, trends may have already have shifted
by the time the season arrives, with the result that product designs will have become unsuitable
for selling.
Shipping personnel are responsible for controlling inventory by determining shipments (timing
and quantities) from the logistics warehouse to stores. Normally, these personnel rely on signals from
stores when deciding if additional shipments are necessary [12]. Because of the range of products and
frequency of shipments, however, it is difficult for them to efficiently and accurately track all sales
information. These shortcomings can result in excess product, the proper disposal of which has become
a significant environmental problem. The textile and apparel industry is the second largest industry in
the world; as such, it can have an enormous impact on the global environment [13], particularly since
waste is generated at every stage of in the apparel manufacturing process [14].
In sum, the disposal of excess product within the apparel industry is an increasing threat to the
environment [15]. To minimize the quantity of unsold product, manufacturers should more carefully
consider customer demand when making production decisions. In this way, they can fulfill their role
in helping to build a sustainable society, at least from an environmental perspective. From an economic
perspective, manufacturers face another related challenge: anticipating a disposal problem, they may
significantly lower the price of products to try to sell more product and, hence, mitigate the challenge
of disposal. An increase in the number of unsold products at the end of a sales cycle, then, would lead
to an increase in the number of products whose price was discounted by the company. If this cycle
were to continue, profits would inevitably decline and sustaining the business over the long term
would become more difficult. To prevent problems such as this in the economic dimension, companies
would need to increase or at least maintain the number of products sold at a fixed price.Sustainability 2019, 11, 259 3 of 14
On the premise of selling at a fixed price, we consider the timing of shipping as a method to
reduce waste risk due to unsold inventory in this study. Initially, the sales scenario of the products
that the shipping personnel need to monitor is restricted. Next, in consideration of the need to sell
these products at a fixed price, personnel forecast the inventory ratio in the sales period and consider
shipping timing in order to reduce the disposal risk.
2. Literature Review
Unsustainable and unregulated production and consumption are contributing to the degradation
of the global environment [16]. Proper monitoring and analysis in the textile and apparel supply chain
is necessary to minimize its environmental impact [17]. A literature review on the apparel supply
chain is as follows.
The sustainability of the textile and apparel supply chain has two aspects: environment
sustainability such as reduction of unsold products and economic sustainability that improves return
on investment (ROI) [18]. With environment sustainability, manufacturers are required to manage
resource procurement, inventory, waste treatment, and transport efficiently. On the other hand,
with economic sustainability, manufacturers are required to increase the number of products sold
at regular price and have large sales. Caniato et al. investigated large and small companies on the
problem of environment sustainability in fashion supply chain [19]. As a result, large companies tend
to focus more on improving products and processes, and small and medium companies tend to rebuild
supply chains. Choi and Chiu proposed the mean-downside-risk (MDR) and mean-variance (MV)
newsvendor model considering both environment sustainability and economic sustainability [20].
Brun and Castelli explored specific factors or product characteristics that promote competition in
the fashion industry [21]. Čiarnienė and Vienažindienė identified key features of the modern fashion
industry and developed a time efficient supply chain model [22]. Mehrjoo and Pasek described the
structure of the apparel supply chain using system dynamics and quantitatively evaluated its risk [23].
Patil et al. developed a mathematical model to evaluate procurement, shipping, and markdown plans
for new short-term life cycle products considering discounts associated with large-scale purchase and
bulk transport [24].
In order for the shipping personnel to recognize when to send additional shipments, they rely
on signals that indicate when products have been sold at stores. This system is known as Quick
Response (QR), and it has demand-driven characteristics. Quick Response reduces lead time and
allows shippers to quickly respond to market needs [25]. It was first implemented in the US apparel
industry in the middle 1980s as a strategy for global response [26] and, since then, its use has spread to
other industries. It was designed to be an inventory management strategy that was based on sharing
information among supply chain agents [27]. By using a QR strategy, lead time is shorter because the
shipping personnel can better forecast customer demand and subsequently modify the production
plan [25]. In basic QR, a retailer sends point of sale (POS) data to its supplier, who then uses this
information to improve demand forecasting [28]. Tsukagoshi et al. proposed using a QR inventory
management strategy of introducing a monitoring period prior to the normal sales period in cases for
which replenishment lead time is short compared to the sales period. They found that this approach
reduced total shortage costs [29]. Tanaka et al. analyzed shipping data on the premise of selling
at a fixed price [8]. They considered the risk of running out of stock but not the risk of disposal of
unsold products.
In the apparel market, sales forecasting plays an increasingly important role in decision support
systems for market competition and globalization [30]. Thomassey explained specific constraints and
needs related to supply chain and sales forecasts in the textile apparel industry [31]. In a large set of
SKUs and two consecutive sales seasons, Mostard et al. compared the accuracy of three quantitative
forecasting methods based on advance demand information [11]. Fan et al. proposed a prediction
model combining the Bass/Norton model and the sentiment analysis method [32].Sustainability 2019, 11, 259 4 of 14
Sustainability 2019, 11 FOR PEER REVIEW 4
3. Status of Products
3. Status
In general, of Products
a manufacturer stores new products in one logistics warehouse for each supply chain.
Sustainability 2019, 11 FOR PEER REVIEW 4
Also, a range of products
In general, can bestores
a manufacturer shippednewfrom oneinlogistics
products warehouse
one logistics warehouse tofor
multiple stores,
each supply but the
chain.
3. Status
Also,
specific products of Products
a range of products
and can shipped
quantities be shipped from one
depend on thelogistics
uniquewarehouse
properties to multiple
of stores, stores,
such but the
as clientele,
specific products
sales floorInspace, and and quantities shipped
geographic location depend on
or products the
climate. in unique
If one properties
onelogistics
attempts of stores,
to consider such
all theas clientele,
characteristics
general, a manufacturer stores new warehouse for each supply chain.
salesinfloor
of stores space, and geographic location or climate. If one For
attempts to consider all the characteristics
Also, adetail,
rangethe scope
of products of can
the data becomes
be shipped fromvery
onelarge. this reason,
logistics warehouse towe analyze
multiple the composition
stores, but the
of stores in detail, the scope of the data becomes very large. For this reason, we analyze the
specific from
of shipping products
oneand quantities
logistics shipped depend
warehouse on the unique
to one store. Hence,properties of stores,
in this paper, the such as clientele,
textile and apparel
composition of shipping from one logistics warehouse to one store. Hence, in this paper, the textile
supply sales floorisspace,
chain and geographic
considered as shown location
in theorFigure
climate.1.IfThis
one attempts
supply to consider
chain all themultiple
includes characteristics
factories,
and apparel supply chain is considered as shown in the Figure 1. This supply chain includes multiple
of storeswarehouse,
one logistics in detail, the onescope
store,ofand
the many
data becomes
customers. veryProducts
large. Forarrive
this reason,
in the we analyze
logistics the
warehouse
factories, one logistics warehouse, one store, and many customers. Products arrive in the logistics
composition of shipping from one logistics warehouse to one store. Hence, in this paper, the textile
from warehouse
multiple factories, and then
from multiple the shipping
factories, and then personnel
the shipping ship products
personnel that
ship have arrived
products that havein arrived
the logistics
and apparel supply chain is considered as shown in the Figure 1. This supply chain includes multiple
warehouse to one store.
in the logistics warehouse to one store.
factories, one logistics warehouse, one store, and many customers. Products arrive in the logistics
warehouse from multiple factories, and then the shipping personnel ship products that have arrived
in the logistics warehouse to one store.
Figure 1. 1.Conceptual
Figure Conceptual Diagram ofSupply
Diagram of SupplyChain
Chain Flow.
Flow.
Then, we consider
Then, we consider inventory
inventorychange
changein in our
our logistics warehouse.
logistics warehouse. Figure
Figure 2 shows
2 shows changes
changes in thein the
Figure 1. Conceptual Diagram of Supply Chain Flow.
percentages
percentages of the inventories
of the inventories ofofproducts
products in in an
an actual logisticswarehouse.
actual logistics warehouse. As shown
As shown in Figure
in Figure 2, 2,
there are
there are products products with inventory
with inventory ratios
ratios of zero
of zero in the logistics
inlogistics
the logistics warehouse
warehouse at nine weeks, while some
Then, we consider inventory change in our warehouse. Figureat nine weeks,
2 shows changes while
in thesome
products
products remain remainin in high
high proportions.Figure
proportions. Figure33 shows
shows aahistogram
histogram ofofthetheinventory
inventory ratios at the
ratios at ninth
the2,ninth
percentages of the inventories of products in an actual logistics warehouse. As shown in Figure
week.
week.there
Theare The
ratio ratio is less
is lesswith than
than 0.10 for most
0.10 forratios products,
mostofproducts, but there are still products with considerable
products inventory zero in the but there
logistics are still products
warehouse at nine weeks,with considerable
while some
inventory remaining. There are two reasons why large inventories for a product could persist. The
products
inventory remain in high
remaining. There proportions. Figure 3 shows
are two reasons why largea histogram of the inventory
inventories ratios atcould
for a product the ninthpersist.
first is overproduction, that is, a discrepancy between scheduled and actual sales of product. As a
week.
The first is The ratio is less than
overproduction, 0.10
that is,for most products,
a discrepancy but there
between are still products
scheduled and actual with sales
considerable
of product.
countermeasure, there is the method of discounting and selling inventory or selling it through
inventory remaining.
As a another
countermeasure, There
there are method
two reasons why large inventories for inventory
a product could persist. through
The
channel, such asisanthe of discounting
electronic commerce and selling
(EC) site. The second or selling
potential cause isit that
first is overproduction, that is, a discrepancy between scheduled and actual sales of product. As a
another channel,
shipments such
have as an
been electronic
timed poorly.commerce
The shipping (EC) site. Theare
personnel second
the onespotential cause is that
who determine shipments
the timing
countermeasure, there is the method of discounting and selling inventory or selling it through
have and
beenquantities
timed poorly. The shipping personnel are the ones who determine
of products to be shipped based on sales information. However, due to the challenge the timing and quantities
another channel, such as an electronic commerce (EC) site. The second potential cause is that
of products
of managing to beashipped
large numberbasedofon sales information.
products, personnel may However,
overlook adue signal
shipments have been timed poorly. The shipping personnel are the ones who determine the timing
to the
thatchallenge
a product has of managing
sold at a
largeits fixed price.
number of If the number
products, of unsold
personnel may fixed-price
overlook products
a signal increases,
that a there has
product
and quantities of products to be shipped based on sales information. However, due to the challenge
will sold
be anatincrease
its fixed inprice.
If theinventory
number
of managing
in unsold
of need of fixed-price
disposal at the
a large number of products,
end of the
products sales period.
increases,
personnel may there After
will be
overlook
considering
an increase
a signal
sustainability,
in inventory
that a product
firms
has soldinatneed
should
of disposal increase
atprice.
the end the number
of the of products
salesofperiod. sold at a fixed price as much as possible.
its fixed If the number unsold After considering
fixed-price productssustainability,
increases, therefirms should
will be increase
an increase in the
inventory 1.00
in need of disposal at price
the end
number of products sold at a fixed as of the sales
much period. After considering sustainability, firms
as possible.
0.90 the number of products sold at a fixed price as much as possible.
should increase
0.80
1.00
Ratio Ratio
0.70
0.90
0.60
0.80
Inventory
0.50
0.70 Product 1
0.40
0.60 Product 2
Inventory
0.30
0.50 Product 1
Product 3
0.20
0.40 Product 2
0.10
0.30
0.00 Product 3
0.20
0.10 0 1 2 3 4 5 6 7 8 9
0.00 Week
0 1 2
Figure 2.3 Example
4 of Inventory
5 6 Ratio
7 Change.
8 9
Week
Figure
Figure 2. 2. Exampleof
Example ofInventory
Inventory Ratio
RatioChange.
Change.Sustainability 2019, 11, 259 5 of 14
Sustainability 2019, 11 FOR PEER REVIEW 5
500
450
400
350
300
Frequency
250
200
150
100
50
0
0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00
Inventory Ratio
Figure3.3.Histogram
Figure Histogramof
ofInventory
InventoryRatio
Ratioin
inNinth
NinthWeek.
Week.
4. Methods
4. Methods
In this paper, we assume the supply chain as shown in the Figure 1. Since the sales cycle of the
In this paper, we assume the supply chain as shown in the Figure 1. Since the sales cycle of the
textile and apparel supply chain is one week [33], we perform our analysis using weekly data. The data
textile and apparel supply chain is one week [33], we perform our analysis using weekly data. The
used in this paper is collected in the logistics warehouse.
data used in this paper is collected in the logistics warehouse.
4.1. Assumptions, Parameters, and Variables
4.1. Assumptions, Parameters, and Variables
In this paper, the following assumptions, parameters, and variables are used. The parameters and
In this
variables are paper, the in
described following
subsequent assumptions,
sections. parameters, and variables are used. The parameters
and variables are described in subsequent sections.
Assumptions:
Assumptions:
• Products are sold in the same period
• Products are sold in the same period
• The total arrival quantity of each product is known
• The total arrival quantity of each product is known
• Product movement among stores is not considered
• Product movement among stores is not considered
•• The Theperiod
periodininwhich
whichproducts
productsare aresold
soldatatthe
thefixed
fixedprice
priceisisnine
nineweeks
weeks
•• Since
Since products are sold up for nine weeks, the sales period isnine
products are sold up for nine weeks, the sales period is nineweeks
weeks
•• IfIfthe product did not sell for nine weeks, the product is discarded
the product did not sell for nine weeks, the product is discarded
Parameters and Variables:
Parameters and Variables:
N: number of products
N: number of products
T: sales period [weeks]
T: sales period [weeks]
t: elapsed periods [weeks] (t = 1, …, T)
t: elapsed
i: productperiods [weeks]
number (i =(t1,= …, . . , T)
1, . N)
i: product
di (t): number
shipping(iamount N)product i in week t
= 1, . . . ,of
di (t): Dshipping amountamount
i: total delivery of product i in week
of product i t
Di : total delivery amount of product
ui (t): shipping rate in week t of product i i
ui (t): K: numberrate
shipping of clusters
in week t of product i
K: number of clusters sales period
T’: investigated
I i ,t : inventory
T’: investigated salesratio
periodof product i in week t
Ii,t : inventory
X i : random ratio of product i in week t
variable representing the realized inventory ratio of product i
Xi : random variable representing the realized inventory ratio of product i
Yi : random
Yi : random
variable representing the predicted inventory ratio of product i
variable representing the predicted inventory ratio of product i
μ ′ : conditional
µ0 : conditional expectation
expectationSustainability 2019, 11 FOR PEER REVIEW 6
Sustainability 2019, 11, 259 6 of 14
μX : average of X i
μY : average
µ X : average of Xi Y
of i
µY : average of Yi
ρ: correlation coefficient
ρ: correlation coefficient
σ x : variance of X i
σx : variance of Xi
σ Y : variance
σY : variance of Yi
of i
Y
0
σ : variance in conditional probability
σ ′ : variance in conditional probability
4.2. Decision-Making Process for Shipping Personnel
4.2. Decision-Making Process for Shipping Personnel
Based on the above parameters and variables, Figure 4 shows the decision-making process
Based on the above parameters and variables, Figure 4 shows the decision-making process of
of shipping personnel. It has seven steps. This paper proposes methods for supporting shipping
shipping personnel. It has seven steps. This paper proposes methods for supporting shipping
personnel who are in charge of each decision-making process.
personnel who are in charge of each decision-making process.
Figure 4. Decision-Making Process for Shipping Personnel.
Figure 4. Decision-Making Process for Shipping Personnel.
These steps are for product i.
These steps are for product i.
Step 1. Product i arrives at the logistics warehouse from a plant, and its quantity is Di .
Step 1. Product i arrives at the logistics warehouse from a plant, and its quantity is Di.
Step
Step2.2. Shipping personnel
Shipping practice
personnel firstfirst
practice shipment
shipmentof product i based
of product on a sales
i based on apolicy which is
sales policy givenis
which
by given
manufactures and its quantity is
by manufactures and its quantity d i (1). is di (1).
Step 3. Shipping personnel recognize sales information
Step 3. Shipping personnel recognize sales information of product
of producti fori for
oneone
week.
week.
Step 4. Shipping personnel consider and decide whether to ship additional
Step 4. Shipping personnel consider and decide whether to ship additional product product during
during the next
the next
week. At this time, they refer not only to sales quantity of product i but also
week. At this time, they refer not only to sales quantity of product i but also to sales to sales information
of similar products
information sold last
of similar year and
products soldcurrent trends.
last year and current trends.
Step
Step5.5. Based
Basedon on
shipping personnel
shipping personnel decision,
decision,product i is shipped
product whose
i is shipped quantity
whose is di (t)
quantity is dat t-th
i (t) at t-th
week.
week.In the determination
In the determination of the
of quantity,
the quantity,the inventory
the inventory of theofstore
the and
storethe
and scale
theof the store
scale of the
arestore
takenareintotaken into consideration.
consideration. Then, if decide
Then, if personnel personnel not decide
to makenot to make anshipment,
an additional additional
shipment,
shipping shipping
quantity of the productofdithe
quantity (t) product
is zero. di (t) is zero.
Step6.6. If the
Step If the next
next week,
week, t + t1,+ is
1, end
is end of sales
of sales period
period T, go
T, go to Step
to Step 7. Otherwise,
7. Otherwise, shipping
shipping personnel
personnel
return
return to Step
to Step 3 and
3 and repeat
repeat fromfrom
StepStep
3 to3 Step
to Step 5 until
5 until they
they fulfill
fulfill StepStep
6. 6.
Step7.7. Shipping
Step Shipping personnel
personnel finish
finish their
their decision.
decision.Sustainability 2019, 11, 259 7 of 14
4.3. Product Data Extraction
Products in the apparel and textile industry come in many categories, colors, and sizes; thus,
Sustainability
the amount of 2019, 11 FOR product
available PEER REVIEW
data enormous. Further, customer demand is uncertain, and7sales
area affected by fashion and other industry trends that are in a state of constant flux. In light of these
4.3. Product Data Extraction
circumstances, the data that shipping personnel have access to at any given time is limited in scope.
Therefore,Products in the apparel
when analyzing ourand
owntextile
data,industry
we dividecome thein many categories,
products colors, and
into several sizes; thus,
groups, the data
extract
amount of available product data enormous. Further, customer demand is uncertain, and sales area
from products likely to require shipping plan revision, and analyze them.
affected by fashion and other industry trends that are in a state of constant flux. In light of these
An example of the actual change in inventory in a logistics warehouse is shown in Table 1. There is
circumstances, the data that shipping personnel have access to at any given time is limited in scope.
a significant
Therefore, difference in product
when analyzing our quantities
own data, we even in the
divide the first week, into
products making it difficult
several groups, to see inventory
extract data
transitions of all products
from products likely toon one scale
require of inventory
shipping quantity.
plan revision, Therefore,
and analyze them.the shipping ratio is calculated
as the incoming
An example inventory in each
of the actual week
change di (t), normalized
in inventory in a logisticsbywarehouse
the total isarrival in Table 1.D
shown quantity i of each
There
is ai:significant difference in product quantities even in the first week, making it difficult to see
product
inventory transitions of all products on one scale ofdinventory (t) quantity. Therefore, the shipping ratio
ui ( t ) = i . (1)
is calculated as the incoming inventory in each weekD dii(t), normalized by the total arrival quantity Di
of each product i:
After calculating the shipping rate for each product, a k-means cluster analysis of ui (t) is conducted
for t = 1, . . . , 9, with a sample of N = 796 uin(order dito( t divide
)
i t) = . products into groups. Since the (1) number
of pieces of data is as large as N = 796, we decided Di to use nonhierarchical instead of hierarchical
clustering.After calculating
To calculate thethe shipping
distance rate forsamples,
between each product, a k-means
Euclidean cluster
distance analysis
is used. One of ui(t) is
disadvantage
of the k-means method is the dependence on the initial value. Therefore, some initial valuesthe
conducted for t = 1, …, 9, with a sample of N = 796 in order to divide products into groups. Since are set
numberanalysis
and cluster of piecesis of data is as and
performed, largeinitial
as N values
= 796, we are decided
chosen toto minimize
use nonhierarchical instead
the distance withinof the
hierarchical clustering. To calculate the distance between samples, Euclidean distance is used. One
average cluster. The results of the cluster analysis leads to patterns, here called sales policies (i.e.,
disadvantage of the k-means method is the dependence on the initial value. Therefore, some initial
policies involving selling by advertising products that will be recognized by customers vs. policies to
values are set and cluster analysis is performed, and initial values are chosen to minimize the distance
sell normally,
within theasaverage
standard products).
cluster. The results of the cluster analysis leads to patterns, here called sales
policies (i.e., policies involving selling by advertising products that will be recognized by customers
Table 1. Examples of Changes in Inventory.
vs. policies to sell normally, as standard products).
Change in Inventory in Each Week
Table 1. Examples of Changes in Inventory.
Product 0 2 1 3 4 5 6 7 8 9
Change in Inventory in Each Week
A 2884 291 266 240 198 154 123 80 44 36
Product 0 1 2 3 4 5 6 7 8 9
B 2078 1151 1038 347 221 171 122 0 0 0
AC 2884293 291 249 266248 240
220 198
220 154
220 123
191 11680 94 44 32 36
B 2078 1151 1038 347 221 171 122 0 0 0
C 293 249 248 220 220 220 191 116 94 32
The result of the k-means method suggested that it was reasonable to divide the analyzed data
into two groups, which
The result of thewere
k-meansthen roughly
method dividedthat
suggested into five reasonable
it was groups to toprovide
divide more detail. data
the analyzed Figure 5
shows into
thetwo groups,
average which
value ofwere then roughly
inventory ratios divided
in each into
week fiveforgroups to the
each of provide
five more detail.
clusters. Figure 5 on
Depending
shows thepercentages
the shipment average valueinofthe
inventory ratios
first week, in each
five weekcan
clusters forbeeach of the five
broadly clusters. Depending on
categorized.
the shipment percentages in the first week, five clusters can be broadly categorized.
1
0.9
0.8
0.7
Inventory Ratio
0.6
0.5
0.4
0.3
0.2
0.1
0
0 1 2 3 4 5 6 7 8 9
Week
1 2 3 4 5
Figure 5. Average
Figure 5. AverageInventory
InventoryRatios in Each
Ratios in EachWeek
Weekfor
forEach
Each Cluster.
Cluster.Sustainability 2019, 11, 259 8 of 14
For clusters 1 and 2, the shipment ratio in the first week is high. On the other hand, in clusters 3,
4, and 5, the shipment ratio of the first week is low. Investigating the characteristics of each cluster
further, products of clusters 1 and 2 represent the policy that a manufacturer wants to sell featured
products. Also, the trend of shipment ratios from the second to the ninth week shows that products
remaining in the logistics warehouse are gradually shipped until the end of the sale period. Therefore,
the shipping personnel will not revise the shipping plan later. On the contrary, products in clusters 3,
4, and 5 demonstrate a policy whereby the shipping personnel sell the product in stages, conditional
on the level of sales. Accordingly, products belonging to these classes are highly likely to have been
subject to revised shipping plans. In other words, shipping personnel should have recognized products
exhibiting this kind of sales information.
4.4. Forecasting Inventory Ratios
The cluster analysis identifies products that shipping personnel should recognize as undergoing
significant changes in inventory. However, the shipping personnel do not know the optimal time or
other factors about the shipment. Therefore, the reference lines obtained by cluster analysis (Figure 5)
predict the inventory ratio at certain weeks. Changes in inventory that deviate significantly from
the basic line are signals that should be transmitted to shipping personnel to highlight important
sales information.
In this paper, to predict how far the inventory transition will be from the basic line, the conditional
probability of the bivariate normal distribution is used for each product. In this way, it is possible to
predict the subsequent inventory transition when the change in inventory is recognized until a specific
week. The conditional expectation and variance in the conditional probability of the bivariate normal
distribution are obtained by the following Equations (2) and (3), respectively.
Ii,t − µ X
µ0 = µY + ρσY , (2)
σx
q
σ0 = σY 1 − ρ2 (≤ σY ). (3)
Based on the above conditional expectation and the conditional variance of the conditional
probability, a 95% confidence interval is generated, which is used as the range of prediction. However,
since the actual inventory ratio may be smaller than the inventory ratio considering 95% confidence
intervals for the conditional expectation obtained by the conditional probability of the bivariate normal
distribution, this paper proposes minimum values for each week. Since the inventory ratio cannot be
smaller than zero, it is considered to be the minimum. Hence, the upper and lower limits of the range
from the inventory ratio considering the 95% confidence interval and the actual inventory ratio are
as follows:
Upper limit : Min µ0 + 1.96σ0 , Ii,t ,
(4)
Lower limit : Max µ − 1.96σ0 , 0 .
0
(5)
Here, we predict the inventory ratio of product A using information on all clusters of products to
be sold in stages, namely, clusters 3, 4, and 5, and for the cluster to which it belongs, 3. The prediction
for product A is shown below. The gray line of each figure is a line connecting the conditional
expectation values at each week calculated by the Equation (2). The blue line represents change of the
upper limit value in each week obtained from Equation (4). The orange line shows change of the lower
limit derived from Equation (5). Finally, in order to compare how the actual inventory ratio changes to
changes of gray, blue, orange lines, the change of the actual inventory ratio is indicated by yellow line.
Here, the calculation results are shown assuming that the sales period is T = 9. Therefore, based on the
assumption, the products will not be sold after nine weeks and will be discarded.Sustainability 2019, 11, 259 9 of 14
Sustainability 2019, 11 FOR PEER REVIEW 9
The shipment
The shipment of of the
the first
first week
week isis determined
determined by by the
the manufacturer’s
manufacturer’s sales sales policy,
policy, and
and shippers
shippers
who oversee shipping decisions begin shipping after the second week. Therefore,
who oversee shipping decisions begin shipping after the second week. Therefore, this paper predicts this paper predicts
inventory levels
inventory levels for
forall
allweeks,
weeks,conditional
conditional onon
thethe
realized level
realized of sales
level up toup
of sales anytogiven week. week.
any given Form
Figure 6a,b, since the shipping ratio at the third week is large, the range of prediction
Form Figure 6a,b, since the shipping ratio at the third week is large, the range of prediction after the after the fourth
week can
fourth weekbecan narrowed
be narrowedsignificantly.
significantly.Furthermore,
Furthermore,with thethe
with expected
expectedvalue
valueof of the conditional
the conditional
probability, which
probability, which isis the
the gray
gray line,
line, at
at the
the time
time when
when thethe inventory
inventory ratio
ratio up
up to
to the
the second
second week
week isis
known, the inventory ratio of the ninth week has already come close to the actual
known, the inventory ratio of the ninth week has already come close to the actual inventory ratio. inventory ratio.
Therefore,in
Therefore, inthe
thecase
caseofofproduct
productA, A,the
theinventory
inventoryratio
ratiothat
thatwill
will result
result inin disposal
disposal could
could bebe predicted
predicted at
at the
the second
second week.week. Shipping
Shipping personnel
personnel who know
who know this
this can canreduce
then then reduce the amount
the amount of product of disposal
product
disposal
by by considering
considering various countermeasures
various countermeasures and takingand taking action.
action.
1 1
0.8 0.8
Inventory Ratio
Inventory Ratio
0.6 0.6
0.4 0.4
0.2 0.2
0 0
0 2 4 6 8 0 2 4 6 8
Week Week
(a) (b)
1 1
0.8 0.8
Inventory Ratio
Inventory Ratio
0.6 0.6
0.4 0.4
0.2 0.2
0 0
0 2 4 6 8 0 2 4 6 8
Week Week
(c) (d)
Figure 6. The
Figure 6. The Expectation
Expectation for Product
Product A A (conditional
(conditional on data of clusters
clusters 3, 4,4, and
and 5).
5). Note:
Note: graph (a)
represents
representswhen
whenthe theactual
actualinventory
inventoryratio
ratiois is
known
known byby
thethe
second
secondweek, (b)(b)
week, is when it isitknown
is when is knownby the
by
third week, (c) is when it is known by the fourth week, and (d) is when it is known
the third week, (c) is when it is known by the fourth week, and (d) is when it is known by the fifth by the fifth week.
The gray
week. Theline
grayshows the conditional
line shows expectation,
the conditional the blue
expectation, theand orange
blue lines show
and orange linesthe
showupper and lower
the upper and
limits
lower of the range,
limits of the respectively, and theand
range, respectively, yellow the line shows
yellow linethe change
shows the in the actual
change in theinventory ratio of
actual inventory
product A.
ratio of product A.
In both Figures 6 and 7, the more weeks in which the inventory ratio is known, the narrower the
In both Figures 6 and 7, the more weeks in which the inventory ratio is known, the narrower
range of prediction. In the case of product A, the prediction range is narrowed significantly in the
the range of prediction. In the case of product A, the prediction range is narrowed significantly
third week. This is because the actual inventory ratio of product A is drastically reduced in the third
in the third week. This is because the actual inventory ratio of product A is drastically reduced
week. Moreover,
in the third week.when predicting
Moreover, whenwith only the
predicting cluster
with to which
only the clusterproduct
to which A product
belongs,Athe prediction
belongs,
range
the prediction range is narrowed as a whole. However, product A is shipped at a high rate inThen,
is narrowed as a whole. However, product A is shipped at a high rate in the third week.
the
the inventory
third week. ratio at the
Then, thethird week ratio
inventory is much smaller
at the third than
weekthe expected
is much inventory
smaller ratio
than the of the third
expected
week at the
inventory second
ratio week.
of the third The
weekactual
at thesales areweek.
second not known at that
The actual time,
sales are but
not shipping
known at more than the
that time,
expected
but shipping more than the expected value may result in more waste. Hence, excess shipping the
value may result in more waste. Hence, excess shipping can be reduced by shipping
product in the fourth
can be reduced week after
by shipping the receiving
product in more
the detailed
fourth weeksalesafter
information.
receivingInmoreotherdetailed
words, sales
since this
product is highly
information. likelywords,
In other to result in some
since this disposal,
product isit is possible
highly to reduce
likely extra
to result shipping
in some by lengthening
disposal, it is
possible to reduce extra shipping by lengthening the period for recognizing sales information.Sustainability 2019, 11, 259 10 of 14
Sustainability 2019, 11 FOR PEER REVIEW 10
As
the in the other
period method, when
for recognizing salesshipping at theAs
information. third week,
in the it method,
other is better when
to adjust the shipping
shipping at the third
amount
week, it of product
is better to A. For the
adjust example, product
shipping A isofshipped
amount productatA.proportions
For example,according
producttoAthe basic at
is shipped
line (gray line
proportions of Figureto6 the
according or Figure 7). (gray line of Figure 6 or Figure 7).
basic line
1 1
0.8 0.8
Inventory Ratio
Inventory Ratio
0.6 0.6
0.4 0.4
0.2 0.2
0 0
0 2 4 6 8 0 2 4 6 8
Week Week
(a) (b)
1 1
0.8 0.8
Inventory Ratio
Inventory Ratio
0.6 0.6
0.4 0.4
0.2 0.2
0 0
0 2 4 6 8 0 2 4 6 8
Week Week
(c) (d)
TheConditional
Figure 7. The ConditionalProbability
ProbabilityExpectation
Expectationofofthe theBivariate
Bivariate Normal
Normal Distribution
Distribution of of Product
Product A
A for Data of Cluster 3. Note: graph (a) represents when the actual inventory
for Data of Cluster 3. Note: graph (a) represents when the actual inventory ratio is known by ratio is known by the
second week,
second week, (b)
(b) is
is when
when itit is
is known
known by by the
the third
third week,
week, (c)(c) is
is when
when itit is
is known
known byby the
the fourth
fourth week,
week,
when it is
and (d) is when is known
known by by the
the 5th
5th week.
week. The
The gray
gray line
line shows
shows thethe conditional
conditional expectation,
expectation, the blue
and orange lines show the the upper
upper and
and lower
lower limits
limits of
of the
therange,
range,respectively,
respectively, and the yellow line shows
shows
the change in the actual inventory ratio of product A.
As aa second
As second example,
example, thethe prediction
prediction results
results for
for product
product B B are
are shown
shown in in Figures
Figures 88 and
and 9.
9. Product
Product
B is also sold in stages, and the detailed cluster number is 5. As with product
B is also sold in stages, and the detailed cluster number is 5. As with product A, we predict cases A, we predict cases
for
which the actual inventory ratio is known in the second week. The figures show that product B has B
for which the actual inventory ratio is known in the second week. The figures show that product a
has ainventory
high high inventory ratioateven
ratio even at theweek.
the ninth ninth Also,
week.inAlso, in the
the case of case
productof product B, the predicted
B, the predicted ninth
ninth week’s
week’s inventory
inventory ratio atratio
the at the second
second weekweek
does does not differ
not differ much muchfrom fromthethe prediction
prediction atatthe
thefifth
fifthweek.
week.
Therefore, some degree of prediction is possible in the second week. Furthermore,
Therefore, some degree of prediction is possible in the second week. Furthermore, as can be seen as can be seen
in
in Figure 9d, the inventory ratio at the sixth week is lower than the expected
Figure 9d, the inventory ratio at the sixth week is lower than the expected value, suggesting that value, suggesting that
shipments were
shipments were made
made to to avoid
avoid being
being left
left with
with aa large
large inventory
inventory in in the
thelogistics
logisticswarehouse.
warehouse.However,
However,
since the product is unlikely to sell much after being shipped in the
since the product is unlikely to sell much after being shipped in the sixth week, perhaps it sixth week, perhaps it should
should
have been shipped earlier to increase sales. By shipping in this manner, the product
have been shipped earlier to increase sales. By shipping in this manner, the product would have been would have been
placed in
placed in the
the store
store for
for aa longer
longer period, and opportunities
period, and opportunities to to sell
sell atat the
the fixed
fixed price
price would
would alsoalso have
have
increased. This would have reduced the amount of waste due
increased. This would have reduced the amount of waste due to unsold product. to unsold product.
When using the results of prediction, first, the shipping personnel would compare the
conditional expectation with the inventory ratio of the product at the present moment. This shows
how the inventory ratio of the product changes with respect to the average. Then, the shipping
personnel would plan the shipping for the next week. Finally, they would determine the amounts of
the shipment while also taking into account the sales policy.Sustainability 2019, 11, 259 11 of 14
Sustainability 2019, 11 FOR PEER REVIEW 11
1 1
0.8 0.8
Inventory Ratio
Inventory Ratio
0.6 0.6
0.4 0.4
0.2 0.2
0 0
0 2 4 6 8 0 2 4 6 8
Week Week
(a) (b)
1 1
0.8 0.8
Inventory Ratio
Inventory Ratio
0.6 0.6
0.4 0.4
0.2 0.2
0 0
0 2 4 6 8 0 2 4 6 8
Week Week
(c) (d)
Figure 8. TheFigure 8. The Expectation
Expectation of Product
of Product B (conditional on
B (conditional ondata
dataforfor
clusters 3, 4, and
clusters 5).and
3, 4, Note:5).
graph (a) graph (a)
Note:
represents when the actual inventory ratio is known by the second week, (b) is when it is known by
represents when the actual inventory ratio is known by the second week, (b) is when it is known by the
the third week, (c) is when it is known by the fourth week, and (d) is when it is known by the fifth
third week, (c) is
week. Thewhen it isshows
gray line known by the fourth
the conditional week,the
expectation, and
blue(d)
andisorange
whenlinesit isshow
known by the
the upper andfifth week.
The gray linelower shows the
limits of theconditional expectation,
range, respectively, the blue
and the yellow line and
shows orange lines
the change in show theinventory
the actual upper and lower
limits of theratio of product
range, B.
respectively, and the yellow line shows the change in the actual inventory ratio of
Sustainability 2019, 11 FOR PEER REVIEW 12
product B.
Also, the results of this prediction lead to different recommendations depending on the type of
product.
1 For example, in the case of a standard product, 1 manufacturers sell for a longer term, so if
the inventory runs out earlier than forecasted, it is possible to produce additional products to meet
0.8 0.8
customer demand. On the other hand, in the case of a product sold for only one season, when the
Inventory Ratio
Inventory Ratio
expected
0.6
value of the stock ratio at the ninth week is large,
0.6 this is a signal that products are not selling
well that should be heeded by shipping personnel.
0.4 0.4
In QR strategy in the logistics warehouse, product shipping quantities are decided at the time
when0.2shipping personnel receive sales information on 0.2each product during the monitoring period.
However, in this paper, we give a signal to shipping personnel that is based on the comparison of
previous
0 inventory transition for a similar product to the 0 current inventory transition for the current
0 2 4 6 8
product.0 Therefore,
2
even 4if there is
6 8
no arrival of sales information, shipping personnel can receive
Week Week
signals about shipping. In addition, this method is effective for all sales periods.
(a) (b)
1 1
0.8 0.8
Inventory Ratio
Inventory Ratio
0.6 0.6
0.4 0.4
0.2 0.2
0 0
0 2 4 6 8 0 2 4 6 8
Week Week
(c) (d)
Figure 9. Figure 9. The Conditional
The Conditional ProbabilityExpectation
Probability Expectation of of
thethe
Bivariate NormalNormal
Bivariate Distribution of Product Aof Product
Distribution
for Data of Cluster 5. Note: graph (a) represents when the actual inventory ratio is known by the
A for Data of Cluster 5. Note: graph (a) represents when the actual inventory ratio is known by the
second week, (b) is when it is known by the third week, (c) is when it is known by the fourth week,
second week, (b)
and (d) is is when
when it is it is known
known by the by
fifththe third
week. The week,
gray line(c)shows
is when it is known
the conditional by the the
expectation, fourth week,
and (d) is blue
whenanditorange
is known by the
lines show thefifth week.
upper Thelimits
and lower grayofline
the shows the conditional
range, respectively, and theexpectation,
yellow line the blue
and orange shows theshow
lines change in the
the actual
upper andinventory
lowerratio of product
limits A.
of the range, respectively, and the yellow line shows
the change in the actual inventory ratio of product A.
5. Conclusions
In this study, based on the premise of selling at fixed price, we tested an approach for reducing
waste from unsold products in the textile and apparel industry and, thereby, mitigating adverse
effects on the environment. The purpose of shipping personnel in the study is to recognize relevant
data about sales situations and make appropriate judgments (i.e., predictions) about whether to issue
alerts. The alerts in this paper are made from the result of data analysis to the product that shipping
personnel should watch sales situation carefully. Although there are many kinds of data that can beSustainability 2019, 11, 259 12 of 14
When using the results of prediction, first, the shipping personnel would compare the conditional
expectation with the inventory ratio of the product at the present moment. This shows how the
inventory ratio of the product changes with respect to the average. Then, the shipping personnel
would plan the shipping for the next week. Finally, they would determine the amounts of the shipment
while also taking into account the sales policy.
Also, the results of this prediction lead to different recommendations depending on the type of
product. For example, in the case of a standard product, manufacturers sell for a longer term, so if
the inventory runs out earlier than forecasted, it is possible to produce additional products to meet
customer demand. On the other hand, in the case of a product sold for only one season, when the
expected value of the stock ratio at the ninth week is large, this is a signal that products are not selling
well that should be heeded by shipping personnel.
In QR strategy in the logistics warehouse, product shipping quantities are decided at the time
when shipping personnel receive sales information on each product during the monitoring period.
However, in this paper, we give a signal to shipping personnel that is based on the comparison of
previous inventory transition for a similar product to the current inventory transition for the current
product. Therefore, even if there is no arrival of sales information, shipping personnel can receive
signals about shipping. In addition, this method is effective for all sales periods.
5. Conclusions
In this study, based on the premise of selling at fixed price, we tested an approach for reducing
waste from unsold products in the textile and apparel industry and, thereby, mitigating adverse effects
on the environment. The purpose of shipping personnel in the study is to recognize relevant data
about sales situations and make appropriate judgments (i.e., predictions) about whether to issue
alerts. The alerts in this paper are made from the result of data analysis to the product that shipping
personnel should watch sales situation carefully. Although there are many kinds of data that can
be analyzed in this particular industry, we chose to focus on logistics warehouse data, examining
stock points to assess changes in the warehouse inventory ratios. Since the total quantities of apparel
products varied considerably, the data was normalized in our study. Furthermore, because it was
difficult to monitor the status of each individual product, we categorized products into several groups
using cluster analysis. We found that the products whose sales information required the most careful
monitoring were products that were sold in stages. For these products, we also forecasted inventory
ratios using cluster results, and predictions showed that some products were more likely to remain
unsold. Given this, we considered the products whose shipment patterns (i.e., timing and quantities)
should be modified.
This study has practical value for shipping personnel. By using conditional probability in the
manner we have proposed, shipping personnel could accurately predict inventory ratios at the ninth
week from changes in the inventory ratios up to the third week. Therefore, at the third week, shipping
personnel could take remedial measures for products predicted to have excess inventory in the
logistics warehouse.
In light of the trade-off between the risk of disposal from unsold product and the risk of
opportunity loss from product shortage, future research should investigate optimal shipment timing
when disposal and shortage are simultaneously taken into consideration. Also, the optimal shipping
quantities should be obtained at the optimum shipment timing. In practice, however, the timing or
quantities of products shortage cannot be obtained as data. Therefore, under the assumption of a
product shortage situation, the risk of opportunity loss will be evaluated. In addition, future research
should verify the effectiveness of the proposed method by performing analyses in several different
scenarios. In sum, using past data as well as current sales data can allow for better detection of
market trends. Although the larger the amount of data, the easier it is to detect market trends in
past and present, to do so, many textile and apparel companies are needed to share and analyze data.
Apparel and textile companies can use our findings to improve the timing and quantity of shipments,Sustainability 2019, 11, 259 13 of 14
more effectively manage supply chains, and protect profits. In so doing, they can also reduce the
burden of disposal, minimize adverse effects on the environment, and contribute to the development
of more sustainable societies.
Author Contributions: R.T. performed the research and wrote the manuscript. A.I. coordinated and supervised
the overall research. T.S. assisted in developing mathematical methods. M.H. and W.K. acquired funds and
collected data. All authors read and approved the final manuscript.
Conflicts of Interest: The authors declare no conflict of interest.
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