estimating handling time in retail store in supply chain · most retailers, store handling...
TRANSCRIPT
A
Summary of Thesis
On
Estimating Handling Time in Retail Store in Supply Chain
Submitted in fulfillment
for the award of degree of
DOCTOR OF PHILOSOPHY
In
Mechanical Engineering
Submitted By:
Er. Niraj Gupta
Name of Supervisor
Prof. (Dr.) M.I. Khan
INTEGRAL UNIVERSITY, LUCKNOW
April, 2011
ii
ABSTRACT
Retailing consists of all activities involved in selling goods and services to
consumers for their personal, family or household use. Retailing is one of
the pillars of the economy in India and accounts for 35% of GDP. In today’s
global environment, many retailers are focusing on reducing costs as a
means of achieving operational excellence. Majority of the operational costs
are handling costs. Handling operations are costly and labor intensive.
In this research we have developed a model which not only describes the
handling process but also estimates the Total Handling Time per product
unit in the hyper store when replenishment of an item is from the backroom.
Total handling time includes traveling time of product from backroom to
shelf in the hyper store and the time needed to stack the product on the hyper
store shelf.
To test the model, empirical data on the handling operation was collected at
a hyper store using a stop watch. The data were collected for the entire set of
product groups available at hyper store. Data was analyzed using multiple
regression through version 17 of SPSS software.
The values of R, R2, t-test, F-test, examination of residual, value of VIF and
iii
tolerance indicate that our model is valid. The assumptions made for the
regression are met. Thus, it can be said that the model proposed in this work
for a sample can be accurately applied to the population of interest. Finally,
cross-validation of the model has also been carried out using Stein’s
equation and data splitting.
1
CHAPTER 1
INTRODUCTION
Retailing consists of all activities involved in selling goods and services to
consumers for their personal, family or household use. It covers sales of
goods ranging from automobiles to apparel and food products and services
ranging from hair cutting to air travel and computer education. Sales of
goods to intermediaries who resell to retailers or sales to manufacturers are
not considered a retail activity.
Retailing is one of the pillars of the economy in India and accounts for 35%
of GDP [1]. Over 12 million outlets operate in the country and only 4% of
them being larger than 500 sq ft. in size. The retail industry is divided into
organized and unorganized sectors. Organized retailing refers to trading
activities undertaken by licensed retailers, that is, those who are registered
for sales tax, income tax etc. These include the corporate backed
hypermarkets and retail chains, and also the privately owned large retail
businesses. Unorganized retailing, on the other hand, refers to the traditional
formats of low cost retailing, for example, the local kirana shop, owner
manned general stores, convenience stores, hand cart and pavement vendors,
etc. In India, a shopkeeper of such kind of shops is usually known as
„dukandar‟. Most Indian shopping takes place in open markets and million
of independently grocery shops called „kirana‟. Organized retail such as
supermarkets accounts for just 4% of the market as of 2008 [9] . Regulations
prevent most foreign investment in retailing. Moreover, over thirty
regulations such as “signboard licenses” and “anti-hoarding measures” may
have to be compiled before a store can open doors. There are taxes for
moving goods to states from states and even within states. The organized
retail market is growing at 35 percent annually while growth for unorganized
retail sector is pegged at only 6 percent.
2
In recent years, however, advances in computing capabilities and
information technologies, hyper-competition in the retail industry,
emergence of multiple retail formats and distribution channels, an ever
increasing trend towards a globally dispersed retail network have made the
retailer realize that to survive in the competitive market they need to give
due emphasis on reducing costs as a means of achieving operational
excellence. Since we focus on operational costs, total shelf space and
assortment are assumed to be known. The operational logistical costs made
in the part of supply chain that includes retailers‟ warehouse and the stores
are presented below [2]:
1. Handling in warehouse = 29%
2. Transportation = 22%
3. Inventory in store = 7%
4. Handling in store = 38%
5. Inventory in warehouse = 5%
It can be seen that the majority of the operational costs are the handling
costs. Handling operations are costly and labor intensive. In order to
minimize store operating expenses, one has to optimize order processing,
inventory, transportation, shelf space and handling cost. Presently, research
in retail operation does not focus together these issues in calculating the total
operational costs. They consider these issues separately [3, 4, 5, and 7].
Also, in these models the handling time and its related costs are not
considered explicitly. This research focuses exclusively on the estimation of
handling time in store when replenishment is from back room. In our case
handling time includes traveling time of product from backroom to shelf in
the hyper store and time needed to stack the product on the hyper store shelf.
3
CHAPTER 2
OBJECT & SCOPE OF THE PRESENT WORK
A critical review of the available literature ends on the following facts. For
most retailers, store handling operations are not only labor intensive but also
very costly. Empirical study by Saghir and Jonson [6] suggest that 75% of
the handling time in the retail chain occur in the store. This shows the need
for a model which can estimate the handling time in the store when
replenishment process for the items on the shelves starts from back room. In
our case handling time includes traveling time of product from backroom to
shelf in the hyper store and time needed to stack the product on the hyper
store shelf. The objective of the work can be listed as follows:
To develop a model which can adequately describe the handling
process and estimate the total handling time per product unit in the
store when replenishment of an item is from the back room.
Collect data to test the model.
Analyze data using multiple regressions and estimate the equation for
total handling time.
Check assumptions, Test for significance, examine the residual and
check the multi-collinearity in order to validate the model.
Cross-validate the model using Stein‟s equation and data splitting.
4
CHAPTER 3
MODEL DEVELOPMENT
3.1 BACKGROUND FOR ESTIMATING HANDLING TIME
As per Van Zelst et al. [8], handling costs in the stores in the two retail
chains investigated are equal to around 60 million dollar per year. In another
empirical study by Saghir and Jonson [6], they have found that 75% of the
handling time in the retail chain occurs in the store. This shows the need for
a model which adequately describes the handling process and estimate the
handling time in the store. We have modeled the handling activity of one of
the oldest retail store in India known as Spencer‟s hyper.
3.2 MODEL DEVELOPMENT
In the Spencer‟s hyper store the replenishment process for the items on the
shelves starts from back room (warehouse). Items arrive from the regional
distribution center and local suppliers in the backroom based on a reduced
set of underlying factors , given a specific inventory replenishment rule,
assortment, shelf space and package. Since, the shelves are organized in the
store into different product categories and handled by store workers
individually. Handling operation in the backroom and store consists of the
following steps:
a) Receiving and storing of incoming items from regional distribution center
and local suppliers in the back room.
b) Issuing and loading of items in the material handling equipment like jack
and trolley
c) Handling of items during transportation from back room to shelves in the
hyper store.
5
d) Handling of items during shelf stacking.
Operations (a) and (b) are handled by backroom persons and operations (c)
and (d) are handled by the hyper store workers.
In our analysis, handling operation during replenishment will include
operations in steps (c) and (d) which consists of the following activities:
1) Move the deliveries near the shelf from back room using Jack or
trolley.
2) Grab and unpack the case pack.
3) Search for the assigned location in the shelf.
4) Travel to the shelf.
5) Check the shelf life of inventory on the shelf.
6) Prepare the location on shelf for stacking
7) Put the new inventory on the shelf.
8) Put the old inventory back on the shelf.
Replenishment process in the store is a continuous process which depends
upon the stocking policy of the store. Estimation of handling time in stores
will consist of total traveling time , TTT (operation(c )) and total stacking
time, TST (operation (d )).
THT=TTT+TST (3.1)
Total traveling time will depend upon the distance between back room and
the shelf location in the store i.e. variable F , pace of worker, efficiency of
material handling equipment and crowd in the store which may slow down
the movement if traffic is high. Efficiency of material handling is given
irrespective of worker and we have no control over the crowd in the store.
Thus, the independent variables are hypothesized to have the following
influence on TTT,
6
(1) The higher the distance between backroom and shelf location , the
higher the TTT will be
(2) Fast worker in comparison to other worker will have less TTT.
The basic starting equation for TTT will be as follows:
TTT= a’+f’F (3.2)
Where,
a‟ = Constant
f‟ = Regression coefficient
F = Distance from backroom to shelf location in feets
TTT will remain the same whether there is one product or many product
units on the jack or trolley. Thus:
TTT/PU= a’+f’F (3.3)
Now, let us consider the second part of the equation 3.1 i.e. T.S.T (Total
Stacking Time). Shelf stacking represents the daily process of manually
refilling the shelf in the store with products from new deliveries. As with
most manual activities, such processes are often time consuming and costly.
Furthermore, unless clear and reliable work standards are implemented, such
activities may well suffer from a lot of variations which will negatively
affect the overall store performance . Shelf stacking process in the store is
seen as the reverse of the order picking process at the warehouse [8]. For
estimating the TST this work closely follows the methodology presented by
Van Zelst et al.[8]. Let us go to the details of the activities (2-8) of shelf
stacking process. Activities (2) and (4) will depend on the number of case
packs (CP) filled. Activity (3) and (6) are done only once for each stock
keeping unit (SKU) and will be independent of the number of CPs or PUs.
All stacking activities are handled by store worker, so TST will also depend
7
on the pace of the workers (not every employee works equally fast). Thus,
TST will depend on the number of CPs, PUs and the pace of the worker.
The independent variables are hypothesized to have the following influence
on the TST:
1) The higher the number of PUs to be filled, the higher the TST will be.
2) The higher the number of CPs, the higher the TST will be
3) Fast worker in comparison to other worker will have less TST.
The basic starting equation for TST will be as follows:
TST=a’’+b’’PU+c’’CP (3.4)
Where, PU = CP (Q) ; ( Q = Case pack size )
Since, we are interested in calculating TST per product unit, we rearrange
the basic equation by dividing the TST by PU and substituting PU= CP (Q).
The revised model is :
TST/PU=b’’+a’’/CP(Q)+c’’/Q (3.5)
Since, THT/PU=TTT/PU + TST/PU (3.6)
Combining equation 3.3 and 3.5 and rewriting in general form, we get:
THT/PU=a+b/CP(Q)+c/Q+fF (3.7)
Since, TST and TTT both depend on the pace of the worker i.e. not every
employee works equally fast. Consequently, (n-1 ) dummies for store worker
are added to equation 3.7 denoted as Dwi , (i= 1,………., n-1)
Where,
n = the number of store worker considered and
Dwi =1, if store worker „i‟ is selected and „0‟ otherwise.
8
Thus, general regression equation for THT/PU will be (with error term „e‟)
n-1
THT/PU=a+b/CP(Q)+c/Q+fF + ∑di Dwi +e (3.8)
i =1
Or
n-1
THT/PU = a + bK2+ cK1+ fF + ∑di Dwi +e
i =1
Where,
1) a = constant of regression equation
2) b, c, di , f = Partial regression coefficient
3) K1 = 1/Q
4) K2 = 1/CP(Q) = 1/PU
3.3 EXPERIMENTAL DESIGN
In the experiment, the data has been collected for handling activity during
replenishment process from the backroom to shelf location. The retail store
is categorized as hyper store. „Hyper‟ are mega stores, which combine a
supermarket with a department store. In the experiment, for each product
unit, total traveling time and total stacking time were measured using a stop
watch.
3.4 DATA COLLECTION
Empirical data on the handling operation was collected at a hyper store using
stop watch. But in our case the total number of observations recorded is 201.
Data was collected for the following variables:
Q = Case pack size
CP = Number of case packs
PU = Number of product units
F = Distance from backroom to shelf location in feets
9
Dw1 = 1 , If worker 1 is present otherwise “0”
Dw2 = 1 , If worker 2 is present otherwise “0”
TTT = Total traveling time from backroom to shelf location in
seconds
TST = Total stacking time in seconds
K1 = 1/Q
K2 = 1/CP(Q) = 1/PU
THT/PU = Total handling time per product unit in seconds.
The data were collected for the entire set of product groups available at
hyper store.
10
CHAPTER 4
DATA ANALYSIS
4.1 INTRODUCTION
The advent of the program like SPSS provides the unique opportunity to
apply statistics at a conceptual level without getting too bogged down in
equations. In the following paragraph we will discuss how to interpret the
output, create our multiple regression model and look at the assumptions
necessary to generalize our model. We will finish the chapter by cross
validation of the model.
4.2 INTERPRETING MULTIPLE REGRESSION
4.2.1 DESCRIPTIVES
The output described in this section is produced using the options in the
Linear Regression : Statistics dialog box. To begin with, if you select the
descriptive option, SPSS will produce the following Table 4.1.
Table 4.1: Descriptives
S.no. Variable Mean Standard Deviation
1 THT/PU 206.611 1.1009E2
2 F 114.9 25.09
3 Dw1 0.51 0.782
4 Dw2 0.53 0.50
5 K1 0.1402 0.252
6 K2 0.1149 0.240
11
This Table 4.1 tells us the mean and standard deviation of each variable in
our data set, so we know that the mean of dependent variable THT_per_PU
was 206.611. This Table 4.1 is not necessary for interpreting the regression
model, but it is a useful summary of the data.
4.2.2 MODEL SUMMARY
The next section of output describes the overall model. So it tells us whether
the model is successful in predicting THT_per_PU.
Table 4.2: Model Summaryb
Model R R- Square Adjusted R-
Square
Std. Error of
the Estimate
1 .892a .796 .765 80.52058157
a. Predictors: F, Dw1, Dw2, K1, K2
b. Dependent Variable: Total Handling Time_per_PU
In Table 4.2 the column labeled R, shows the value of the multiple
correlation coefficient between the observed values of THT_per_PU and the
values of THT_per_PU predicted by the multiple regression model. Its large
value (R=.892) indicates a strong relationship. The next column gives us a
value of R2
, which we already know is a measure of how much of the
variability in the THT_per_PU is accounted for by the predictors. Output
tells us that 79.6% of total variation in THT_per_PU is explained by five
independent variables.
The adjusted R2
gives us some idea about how well our model generalizes
and ideally we would like its value to be the same, or very close to, the value
of R2 . In our case the difference is small (infact the difference between the
12
values is .796-.765= .031 or 3.1%) . This shrinkage means that if the model
were derived from the population rather than a sample it would account from
approximately 3.1% less variance in the outcome. The last column gives us
the standard error of the estimate which is a measure of dispersion for
multiple regression.
4.2.3 MODEL PARAMETERS
So far we have looked at summary statistics telling us whether or not the
model has improved our ability to predict the outcome variable. The next
part of the output is concerned with the parameters of the model. Table 4.3
shows the model parameters.
Table 4.3: Model Parameters
Variable B Standard
Error
Beta t Sig
Constant 5.606 2.9364 - 1.91 0.024
F 1.211 0.235 0.276 5.153 0.000
Dw1 38.942 9.318 0.276 4.179 0.000
Dw2 35.014 14.65 0.159 2.390 0.018
K1 -88.465 76.129 -0.203 -1.162 0.047
K2 310.78 79.862 0.678 3.892 0.000
The first part of the Table 4.3 gives us estimates for regression coefficient
(b-values) and these values indicate the individual contribution of each
predictor to the model.
13
From the number given in „B‟ column we can read the estimated equation
as:
THT_per_PU=5.606+1.211F+38.942Dw1+35.014Dw2-88.465K1+310.789K2 (4.1)
The b-values tell us about the relationship between THT_per_PU and each
predictor. If the value is positive we can tell that there is a positive
relationship between the predictor and the outcome, whereas a negative have
positive b-values except K1 indicating positive relationship. So, as variable
distance (F) increases, THT_per_PU increases. The b-values tell us more
than this though. They tell us regarding the degree each predictor affects the
outcome if the effects of all other predictors are held constant. For example,
for variable „F‟ the b-value is 1.211.This value indicates that as distance
increases by one unit , THT_per_PU increases by 1.211 units. This
interpretation is true only if the effects of all other predictors are held
constant.
Each of these b-values have an associated standard error indicating as to
what extent these values would vary across different samples, and these
standard errors are used to determine whether or not the b-values differs
significantly from zero. A t-statistics can be derived that tests whether a b-
value is significantly different from „0‟. In simple regression, a significant
value of „t‟ indicates that the slope of the regression line is significantly
different from horizontal. But in multiple regression, it is not so easy to
visualize what the value tells us. Well, it is easiest to conceptualize the t-
tests as measure of whether the predictors are making a significant
contribution to the model. Therefore, if the t-test associated with a b-value is
significant (If the value in the column labeled “sig” is less than .05(Level of
significance) then the predictor is making a significant contribution to the
model. The smaller the value of “sig” (and the larger the value of „t‟), the
14
greater the contribution of that predictor. From the magnitude of t-statistics
we can see that the predictor variable distance has the largest impact and
variable K1 has the smallest impact on THT_per_PU.
The b-values and their significance are important statistics to look at;
however, the standardized versions of the b-values are in many ways easier
to interpret (because they are not dependent on the units of measurement of
the variables). The standardized beta values are provided by SPSS (labeled
as beta, β) and they tell us the number of standard deviation that the outcome
will change as a result of one standard deviation change in the predictor. The
standardized beta values are all measured in standard deviation units and so
are directly comparable; these therfore provide a better insight into the
importance of a predictor in the model. The standardized beta values for „F‟
and Dw1 variable are identical indicating that both variables have a
comparable degree of importance in the model. To interpret these values
literally , we need to know the standard deviation of all of the variables and
these values can be found in Table 4.1.For example for variable distance,
„F‟, the standardized β =.276. This value indicates that as distance increases
by one standard deviation (25.09ft), THT_per_PU increase by .276 standard
deviations. The standard deviation for THT_per_PU is 110.09 and so this
constitute a change of 30.38 (.276 * 110.09). Therefore, for every 25.09 ft
more on distance, an extra 30.38 THT_per_PU (seconds) is increased. This
Interpretation is true only if other predictors are held constant.
Does THT_per_PU depend on independent variables. Is independent
variable significant? Our question leads to hypothesis of the form:
H0 : Bi = 0 (Null Hypothesis: Xi is not a significant explanatory variable)
H1 : Bi ≠ 0 (Alternate Hypothesis: Xi is a significant explanatory variable)
15
We will use the column headed “sig” to test whether Xi is a significant
explanatory variable. Entries in this column are probability values for the
two-tailed test of hypothesis. We only need to compare these probability
values with α, the significance level of the test, to determine whether Xi is a
significant explanatory variable for THT_per_PU. In our case α = 0.5 and
the value in the column is less than .05. Thus null hypothesis is rejected and
Xi is a significant explanatory variable.
4.2.4 INFERENCE ABOUT REGRESSION AS A WHOLE (F-TEST)
Given regression, it is natural to ask whether the value of R2
really indicate
that the independent variables explain THT_per_PU or might have happened
just by chance. Is the regression as a whole significant. Whether all the Xi
taken together significantly explains the variability observed in
THT_per_PU. Our question leads to hypothesis of the form:
H0 : B1 = B 2= B3 =….. = Bk = 0 (Y does not depend on Xis)
H1 : At least one Bi ≠ 0 (Y depends on at least one of Xis)
Table 4.4 below gives SPSS output for our problem.
Table 4.4: ANOVA
Description Sum of
Squares
df Mean
Squares
F Sig
Regression 1160046.375 5 232009.275 35.785 0.000
Residual 1264294.991 195 6483.564
Total 2424341.366 200
This part of output includes the computed F ratio for the regression and is
called analysis of variance for regression.
16
The tabulated value of F at alpha=.05 and df 5 and 195 is equal to 3.02,
which is less than the calculated value 35.784. So the Null Hypothesis that
coefficients are equal to zero is rejected. Hence, regression as a whole is
highly significant. We can reach the same conclusion by noting that the
output “sig” is 0.000 because this probability value is less than our level of
significance α = 05. We conclude that regression as a whole is significant.
We can interpret these results as meaning that the model significantly
improved our ability to predict the outcome variable.
4.3 MULTICOLLINEARITY IN MULTIPLE REGRESSION
This condition refers to situation in which one or more of the predictors are
highly correlated with each other. Table 4.5 provides some measures of
whether there is collinearity in the data. Specifically, it provides the
Variance inflationary factor (VIF) and tolerance (with tolerance being 1
divided by the VIF) .
Table 4.5: Multicollinearity
S.no. Independent
Variable
VIF TOLERANCE
1 F 1.072 0.932
2 Dw1 1.637 0.610
3 Dw2 1.657 0.603
4 K1 1.1392 0.877
5 K2 1.1354 0.880
For our model the VIF values are all well below 10 and the tolerance
statistics all well above 0.2; therefore, we can safely conclude that there is
17
no collinearity within our data. To calculate the average VIF we simply add
the VIF values for each predictor and divide by the number of predictors.
Average VIF = 1.328
The average VIF is near to 1 and this confirms that collinearity is not a
problem for this model and our model is valid.
4.4 CHECKING ASSUMPTIONS
To draw conclusions about a population based on regression analysis done
on a sample, several assumptions must be true. In our case, all predictors are
of variable type, have non-zero variance, multicollinearity does not exist and
errors are distributed normally. Thus, the assumptions of regression are met.
4.5 WORKER 1 VS WORKER 2
In our analysis we have selected two workers for handling activities. The
following Table 4.7 gives the statistics associated with them.
Table 4.7: WORKER 1 VS WORKER 2
Sl Description Dw1=0, Dw2=1 Dw1=1, Dw2=0
1 No of cases 107 94
2 Mean THT/PU(sec.) 209.97 202.91
From the data given in the Table 4.7 we conclude that mean THT/PU
(202.91 sec.) is less when worker 1 was selected for handling activities.
Thus, worker 1 is more efficient than worker 2 in handling the product units.
18
4.6 CROSS VALIDATION OF MODEL
Even if we can‟t be confident that the model derived from our sample
accurately represents the entire population, there are ways in which we can
assess how well our model can predict the outcome in a different sample.
Once we have a regression model there are two main methods of cross
validation. They are data splitting and use of Stein‟s equation. The analysis
done using data splitting and Stein‟s equation shows that cross-validity of
the regression model is very good.
19
CHAPTER 5
RESULTS
RESULTS
The following results have been obtained by multiple regression analysis:
1) The larger value of multiple correlation coefficient (R=.892) between
observed and predicted value of THT_per_PU indicate a strong
relationship.
2) The value of coefficient of multiple determination (R2
= .796) tells us
that 79.6% of total variation in THT_per_PU are explained by five
independent variables.
3) The estimated equation is :
THT_per_PU=5.606+ 1.211F+38.942Dw1+35.014Dw2-88.465K1+310.789K2
4) The value in the column labeled “sig” in Table 4.3 is less than
.05 (level of significance) . Thus, the predictor is making significant
contribution to the model.
5) From the F-test , we can conclude that regression as a whole is
significant.
6) For our model the VIF values are all well below 10 and the tolerance
statistics, all well, above 0.2; therefore, we can safely conclude that
there is no collinearity within our data and our model is valid.
7) The list of assumptions of regression is met. Thus, we can say that
the model we proposed for a sample can be accurately applied to the
population of interest.
20
CHAPTER 6
CONCLUSIONS AND FUTURE RESEARCH DIRECTIONS
6.1 CONCLUSIONS
The following conclusions can be drawn from this study:
1) The empirical findings of this study offer the practitioners the
opportunity for a better control of the overall logistical costs.
2) We have considered the replenishment process from the backroom,
which is a part of replenishment process in Indian environment
settings while it have not been considered in the earlier model [8].
3) The effect of the work pace of a worker shows that worker 1 is more
efficient than worker 2 . Also, less THT/PU for efficient worker
indicates that our hypothesis is valid. This result suggests that worker
training is an important aspect to reduce THT/PU.
4) F-statistics and other statistics considered indicate that independent
variables and regression as a whole is significant. Thus, our model is
valid.
5) Histogram of regression residual indicates that normality assumption
is not violated.
6) Collinearity test performed indicates no problem with regard to
multicollinearity for the estimated model.
6.2 FUTURE RESEARCH DIRECTIONS
Four research avenues emerge as important future research directions.
1) Since, we focussed on operational costs; total shelf space and the
assortment are assumed to be known. Assortment planning and shelf
21
space allocation are important issues in retail. So tradeoff between
shelf space, inventory costs etc. should also include handling costs in
future work.
2) Time lost due to interruption during transportation of an items from
backroom to shelf location due to movement of the crowd in the store
or helping the customer are not considered in the estimation of
THT/PU. This time loss could be considered in future work.
3) The effect of human resource variables such as employee turnover,
training and workload on handling inaccuracy also needs to be
examined.
4) In addition, the future researchers may examine the effect of the
execution of the handling operations on other financial or non-
financial measures of store performance.
We hope that the analysis and empirical study presented in this research will
provide the foundation for future research that will advance the state of the
art in retail supply chain management and provide significant additional
value for retailer‟s supply chain operations.
22
REFERENCES
1. Bajaj, C., Srivastava, N.V., Tuli, R., (2008), Retail Management,
11th Impression, Oxford University Press, New Delhi.
2. Broekmeulen,R., Van Donselaar, K., Fransoo, J., Van Woensel,T.,
(2006), “The Opportunity of Excess Shelf Space in Grocery Retail
Store”, Operations Research 49, 710-719.
3. Cachon, G., (2001), “ Managing a Retailer‟s Shelf Space , Inventory
and Transportation”, Manufacturing and Service Operation
Management ”, 3, 211-229.
4. Corstjens, M., and Doyle, P., (1981), “ A Model for optimizing
Retail Shelf Space Allocations”, Management Science ,27, 822-833.
5. Dreze,X., Hoch, S.J., and Purk, M.E., (1994), “ Shelf Management
and Space Elasticity”, Journal of Retailing, 70, 301-326.
6. Saghir, M., and Jonson, G., (2001), “ Packaging Handling
Evaluation Methods in the Grocery Retail Industry”, Packag
Technol Sci, 14, 21-29.
7. Urban,T.L.,(1998), “ An Inventory-Theoretic Approach to Product
Assortment and Shelf Space Allocation”, Journal of Retailing, 74(1),
15-35.
8. Van Zelst, S., et al. (2009), “ Logistics Drivers for Shelf Stacking in
Grocery Retail Stores; Potential for Efficiency Improvement”,
International Journal of Production Economics, v121, 620-632.
9. http://www.economist.com/displayStory.cfm?story_id=11465586,
Retailing in India : Unshackling the chain stores ,The Economist, 29
May 2008.