Task: As Research Project is part of a postgraduate course it is also required that students employ and develop their research knowledge and skills in an applied fashion. The Research Project must involve the identification, generation, or collation of relevant primary or secondary data and the ability to analyze them in a meaningful and critical manner. Approach: Data was taken from a working organization to resolve the issue regarding the space optimization of the warehouse which results into losses to the company. Findings: Ada boosting algorithm works best for identification of the blowout products beforehand which would help warehouse manager to apply strategies on the products which would take time to sell. So that, the losses associated to such products can be avoided. Tools: Python programming, Excel visualizations, Overleaf latex

1. Mechanizing Optimization of Warehouses by
Implementation of Machine Learning
Methodologies and Pricing Policies
MSc Research Project
Data Analytics
Shrikant Samarth
Student ID: x18129137
School of Computing
National College of Ireland
Supervisor: Paul Liard

2. National College of Ireland
Project Submission Sheet
School of Computing
Student Name: Shrikant Samarth
Student ID: x18129137
Programme: Data Analytics
Year: 2019-2020
Module: MSc Research Project
Supervisor: Paul Liard
Submission Due Date: 11/08/2019
Project Title: Mechanizing Optimization of Warehouses by Implementation
of Machine Learning Methodologies and Pricing Policies
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3. Mechanizing Optimization of Warehouses by
Implementation of Machine Learning Methodologies
and Pricing Policies
Shrikant Samarth
x18129137
Abstract
In this fast-evolving world, demand and supply should behave complimentarily
and the warehouse plays a vital role in connecting the two aspects. It is important
to manage warehouses by providing better space optimization solutions to main-
tain the inflow of the markets trending products to the depot. When this factor is
turned a blind eye on, it gives rise to the stagnancy of products in the warehouse
thus incurring losses to the warehouse manager. Heuristic and data analytics have
been a major turn over factors to increase the efficiency of any warehouse storage.
This research paper uncovers the subject of space optimization of warehouse and
has a great potential to revolutionize the way machine learning can be implemented
to increase the warehouses flexibility. By the implementation of linear regression
models and the application of ensemble algorithms, there has been an outstanding
competitive comparison of evaluation metric scores to predict the best performing
algorithm. After conducting the final evaluation with different evaluation metrics
like R2, mean absolute error, etc., gradient boosting emerged as the best performing
algorithm among all other participants implemented in this research work. This can
assist the warehouse manager to predict the sale-rate and calculate the blow-out
period of the electronic products with better precision than heuristic analytics.
Keywords: E-commerce, Warehouse Optimization, Space Optimization, En-
hancement, Machine Learning, Pricing strategy, Blowout products
1 Introduction
The E-commerce industry is the most vital and versatile industry when it comes to
dealing with products online. It has the capability to attract customer attention even
if the market for products is not strong. E-commerce is a shift from a specific way of
thinking which has an equal effect on marketers and customers (Bhat et al.; 2016). It
has a whitewash change over the traditional way of dealing with commerce. The vari-
ous types of e-commerce business relationships are business-to-business (ex: Amazon
business, Alibaba, etc.), business-to-consumer (ex: sellers on Walmart, etc.), consumer-
to-business (ex: Google Adsense, etc.) and consumer-to-consumer (ex: eBay, etc.). The
focus of this paper is a business-to-business area where Klodawski et al. (2017) has rightly
pointed out the issues warehouse manager can face that would eventually result in over-
stocking. Based on factors like the market trend, sale forecast, fast-moving products,
1

4. etc., the warehouse manager orders the series of products. But not all products get sold
and remaining products becomes stagnant. Klodawski et al.’s (2017) analysis research
is purely heuristic-based which is a grand success against the small scale warehouse in-
dustry. Whereas, the application of this analytics on a greater scale gives rise to revenue
loss to the manager and product-stagnation which is termed as blow-out. Thus, it be-
comes very important to understand the trend of the products to be ordered which takes
into consideration not only the heuristic configurations but also some categorical values
that have an equal and significant outcome. This is easily facilitated with the help of
data analytical implementation in the field of the warehouse industry.
1.1 Motivation
Being a part of the warehouse industry for more than 5 years, it has been understood that
there is a level of complexity to superintend product vacancy in the warehouse. Auto-
mation can be the next possible solution which will reduce manual errors and increase
the efficiency of the warehouse. Automation, even if proven to be more delivery ori-
ented than manual efforts, must be implemented with certain protocols to maximize pro-
ductivity. Baker and Halim (2007) has explained about short-comings for non-productive
automation implementation in warehouse management. Their findings indicate that an
important reason for automation is to adjust growth with effective costing and service im-
provement. There is an evident risk of discrepancy in service level failures, cost-ineffective
and flexibility concerns. This can only be satisfied with the implementation of machine
learning strategies. It is easy to implement and understand the working model of tradi-
tional warehouse management, where the warehouse manager applies a level of previous
experience and orders the number of products that would be recurring in the warehouse
and volume will be used in an optimum way. But there is a great disadvantage in work-
ing with larger warehousing when there is a need to deal with tonnes of a wide range
of products, resulting in mismanagement of entities and monetary losses (Laudon and
Laudon; 2015). To deal with these issues, Reyes et al. (2019) directed the analysts to
face operations like quantity and volume of products, demand uncertainty and rapid cus-
tomer service. Ai-min and Jia (2011) explained how optimization can be achieved with
the assistance of genetic algorithm, replication of optimism with the help of decimal en-
coding and weight addition method to evaluate fitness function and mutation. Artificial
Intelligence can also play a vital role to understand different aspects of warehouse man-
agement. Kartal et al. (2016) has specifically mentioned about the application of machine
learning and artificial intelligence algorithms to study the patterns for stocked products
in a repository. Knoll et al. (2016) studied the inbound logistics operations and imple-
mented machine learning algorithms to predict different tactical strategies which are thus
very difficult to manage through heuristics. Machine learning has the capability to go
beyond the way to understand the sequence and pattern in the data and provide immense
suggestions that are valuable in the scenarios of the live market. By understanding the
benefits of machine learning strategies, this research work pedals in the direction of stock
management for finding the time period patterns of blowout products, that would help
warehouse manager to get an idea of stock clearance before products go out of trend and
such products are no longer a concern of any warehouse.
2

5. 1.2 Research Question
”Can machine learning improve the prediction of blowout time better than legacy heuristic
methods to attain warehouse space optimization?”
1.3 Research Objective
This research paper is a complete answer to the following set objectives for the research
question:
• Objective 1: Data Collection from a working organization.
• Objective 2: Understanding the data through exploratory data analysis.
• Objective 3: Performing pre-processing techniques on the collected data
• Objective 4: Implement linear regression and ensemble models.
• Objective 5: Conduct data evaluation on the algorithms to select the best per-
forming model.
• Objective 6: Calculate the blowout time period using the best performing model.
• Objective 7: Execute dynamic pricing strategies to attain warehouse space op-
timization.
2 Literature Review
In the last two decades, logistic warehouse services have turned out to be significant
player for the various businesses and the supply chain industries. New difficulties arose
due to the advancement of the technology. So, to make a proper decision it is important
to have knowledge of how decision to be made and what are the key parameters affecting
the warehouse performance. To ensure inventory management under space restrictions,
it is important to improve the decision making, storage allocation and scheduling the
warehouse operations. Krauth et al.’s (2005) introduces a framework that clusters a
performance estimation of various streams. Based on their empirical validation, they re-
commend a list of performance indicators that could assist the warehouse management,
reexamining their operations and compelling them to think apart from cost minimiza-
tion. Effective decisions can only be made if the managers have an insight of these key
parameters of warehouse operations. One such key indicator discussed is inventory man-
agement which holds importance to make sure smooth warehouse operations. With the
introduction in the online shopping, ordering has changed coordination of the production
network that lead to a dramatic changes in the warehouse inventory management. Van den
Berg and Zijm’s (1999) explained about the relation between inventory control decisions,
product allocation and assignment problems by comparing various warehouse systems.
Thus, identified with the sophisticated class assignment approach, higher warehouse ser-
vice level and shorter response times could additionally help in saving more funds and
warehouse space. Kar´asek (2013) further examined the issues regarding the warehouse
layout which depends on the effective use of space. The real-world challenges such as
collision of vehicles and shared employees were studied and a technique was introduced
3

6. for achieving the optimization by utilizing the shop scheduling techniques combined with
Vehicle Routing Problem solving techniques. In the competitive e-commerce sector where
sellers assure in-time deliveries, there is a constant pressure of improving response time
and maintaining trending products in the warehouse. As a result, an enthusiasm for new,
complex arranging strategies is taking roots in this sector.
The ideal warehouse operation is achieved when customers get their order in due time
and when all the warehouse and logistic processes finish in the shortest possible time
with minimum utilization of cost and resources under dynamically changing conditions.
Kim (2018) studies different priority rules to improvise the delivery process based on the
order time. The decision which priority rule involves a trade-off amongst a numerous
performance attributes of the outcomes, for instance example, handling order volume,
service level and operational cost (Chen et al.; 2010). A popular technique to help
this type of decision is data envelopment analysis (Hackman et al.; 2001; De Koster
et al.; 2012). But in 2014, Mangano and De Marco’s (2014) uncovers that there is a
limited research done in the area of maintenance of logistic and operational warehouse
performance, hence there is a need for further optimization in this area. To deal with this
optimization, data scientific solutions prove to be a logical pick and have been illustrated
in the further sections.
2.1 Efficiency Improvement for Warehouse Optimization
In general, warehouse today is getting more complex as there is a constant pressure on
the warehouse manager to deliver the orders in time with minimum cost. Therefore,
variety of tools and performance of warehouse evaluation has also increased. The matrix
that are used for evaluating the performance for different scenarios is not clear (Staudt
et al.; 2015), for which warehouse manager have to perform regular analysis. Warehouses
generally aims towards reducing cost optimizing warehouse performance and customer
responsiveness. Estimating warehouse performance gives input about how the warehouse
performs compared with the requirements and with peers. Johnson and McGinnis (2010)
discusses that while evaluating warehouse performance technical criteria (that generates
output after utilizing resource) gives more clear picture than the financial criteria, as
warehouse most of the times does not generates revenues as there are many loses involves
regarding the stagnant products (blowout products) which have the slow movement than
predicted. On the other hand, Staudt et al.’s (2015) found that there are very few re-
search work done in cost related performance indicator than other operational perform-
ance indicators (i.e time, quality and productivity). Usually warehouses are integrated
part of large supply chains, traditional performance evaluation such as productivity, qual-
ity delivery are more applicable (Schmenner and Swink; 1998; Boyer and Lewis; 2002).
The researchers mainly focus on technical performance evaluation measurement, such as
order line pickup per hour/person, related products, shipment errors and special request
orders (Van Goor et al.; 2019). The issue with these indicators is that they are not inde-
pendent indicators and that each of them relies upon various input indicators (De Koster
and Balk; 2008).
These literature studies mostly focuses on technical aspects of the warehouse but
very few research work present on the warehouse Financial aspects which depends on the
warehouse product management. The above studies helps us to understand the direction
of research work for further improvisation in blowout product identification that could
help in maintaining financial goals of warehouse. The next section describes the various
4

7. automation techniques utilize for optimizing warehouse performance in different areas of
warehouse management.
2.2 Automation Today in Warehouse Optimization
There are many research that work on different strategies to automate the various areas
of the warehouse for reducing the time and to organize the warehouse structure. Zunic
et al. (2018) worked on optimal product placement for an operational warehouse where
no optimization mechanism was implemented before. They developed algorithm that cal-
culates the grade of an item by considering current product placement and frequency of a
product. As a result, there was a 17.37% reduction in the average picking length for more
than 40 orders and the picking process consumes 50% of the overall total cost. Whereas,
in his earlier work, Zunic et al. (2017) uses a statistic based fitting algorithm for optimal
strategic method and quantitative product placement in the warehouse picking zone with
the help of a real-world case study. This study addresses the issue that if the product
is not present in the picking zone, the workers get dependent on the forklift to take
down the total palettes from the top racks which ultimately decreases the warehouse effi-
ciency. The result shows that items to be moved to the picking zone with average 91.73%
shifted median accuracy which indicates any improvement in this area substantially im-
proves the efficiency of the warehouse. Both papers worked on the effective item picking
strategy based on the distance, product placement, and its product’s order frequency,
but have not considered auto-replenishment products placement and storage assignment
problems.Chan and Chan’s (2011) research paper, worked on a real-world case study
and addresses this issue by implementing class-based storage on manual order picking for
multilevel racks distribution for a real-world case study. Based on travel distance and
order retrieval, time performance is measured. Results obtained in this paper show that
a combination of different factors has different performance indicators. As a future work
Chan and Chan (2011) suggests to work on products congestion problem; this congestion
can be avoided if products are identify that take long retrieval time from the warehouse
than expected. The stagnancy issue faced by Chan and Chan (2011) has been resolved
in this research paper by identifying and informing the warehouse manager about such
products beforehand so that necessary actions could be taken in the first place.
With the emergence of the e-commerce sector and the worldwide order flooded the
distribution center, made the order fulfillment job more complex. Thus, there was a
need for automation which was solved by utilizing the Kiva System for order fulfillment.
Amazon back in 2012, introduced a Kiva system in the e-commerce warehouses (Bogue;
2016). This robotic system utilizes hundreds of robots that move around the warehouse
for picking the product and make it ready for delivery. Now in recent years, for the
process of automation Tejesh and Neeraja’s (2018) paper addressed the issue of finding the
product in the warehouse as it required manual searching, thus in such cases warehouse
inventory management system which utilizes RFID comes to rescue. Tejesh and Neeraja
(2018) developed a warehouse inventory management system based on the Internet of
Things (i.e IOT) for tracking products with its timestamp for verifying products. All
the information was made available on a web page interface for users which can be used
dynamically for getting remote information.
All the mentioned research papers work towards optimization of the warehouse using
various automation techniques, but these papers lack the detection and maintenance
of effective warehouse inventory. These limitations and the benefits of machine learning
5

8. implementation are identified. In the next section, some of the research works are drafted
that have adopted machine learning techniques, and work in different areas to improve
warehouse performance.
2.3 Warehouse Management using Machine Learning
This section is about the overview and critical analysis of major research work that
uses supervised and unsupervised machine learning techniques in the different fields of a
warehouse.
2.3.1 Using Artificial Neural Network (ANN)
For any optimal warehouse, the priority for operations is to be updated and reorder
its warehouse inventory. There are various researches done for calculating reordering
points using heuristic methods. But calculating these reordering points of all products
using mathematical function is time consuming (Inprasit and Tanachutiwat; 2018); Thus,
Inprasit and Tanachutiwat’s (2018) implemented artificial neural network (ANN) for re-
ordering point determination and safety stock management. Various algorithms were used
for training, testing, building and for comparison of all products based on factors such as
lead time, demand, standard deviation (SD) of demand, SD lead time and service. As a
result, ANN gave the most accurate results with MSE 0.03 × [10−4
] and 0.999 adjusted
R2
value for overall data. Rezaei (2012) uses ANN along with clustering techniques for
prediction of safety stock inventory. In this paper, prediction model was developed with
the combination of clustering K-mean and multi-layer perception methods; moreover for
data reduction in input vector, sensitivity analysis was applied that improves the accur-
acy of prediction by ANN in identifying the safety stock. The above two papers worked
on the reordering point determination and for the prediction of safety stock, but even
after stock prediction, determination of product stagnancy can improve the gap after the
stock prediction and storage of new stock.
2.3.2 Using Genetic Algorithm
A unique approach was used by Nastasi et al. (2018) on an existing steel-making ware-
house for multi-objective optimization of storage strategies. For this purpose, three ge-
netic algorithms i.e Niched Pareto Genetic Algorithm, the Non-dominated Sorting Ge-
netic Algorithm II and the Strength Pareto Genetic Algorithm II were implemented;
along with these algorithms, traditional reordering and product allocation procedures
have also been implemented in this research work. The issue of warehouse optimization
was tested on every algorithm by exploring the simulation system1
which was presented
in the paper. Ai-min and Jia (2011) worked for pharmaceutical logistics center, mentions
the issue with such medical storage centres need special attention in slotting optimiz-
ation, as requirement of medicines change according to the season or the influence of
flue. Hence to deal with such issues Ai-min and Jia (2011) utilize MATLAB and genetic
algorithm to resolve multi objective optimization issues of pharmaceutical logistic center.
The overall results shows that the approach is effective but needs further improvement
for real world scenarios as this paper uses very few goods in this approach.
1
The approach to understand the simulation process is provided under the section Numerical Approach
in (Nastasi et al.; 2018)
6

9. 2.3.3 Using Regression Models
Faber et al. (2018) uncovers the fit between warehouse management structure and per-
formance. A hypothesis was developed and tested among 111 storage warehouses of
Belgium and Netherlands using linear regression model. To ensure the robustness of re-
gression results, bootstrapping method was employed which hardly changes the p-value
except for food products dummy variable section. The ordinary least squares (OLS) re-
gression model performs well and statistically valid for drawing conclusion. Faber et al.
(2018) mentions the results obtained from this model could be further used by warehouse
manager for selecting appropriate planning system. To check the performance of linear
regression models, Vastrad et al. (2013) applies methods lasso, ridge, elastic net which
uses penalty-based shrinkage to handle data set with more predictors than observations.
It has been found that, elastic-net and least angle regression (LARS) gives similar results
but lasso outperformed ridge regression in terms of R2
and RMSE.
Whereas, de Santis et al. (2017) focuses on identifying the material back order is-
sue prior to its occurrence, provides the business sufficient time to take actions that
could increase its overall performance. This study addresses, a predictive model for
class imbalance issue, where frequency of products which gets backorder is low compare
to products that do not. Initially, logistic regression (LOGIST) algorithm was used to
create a baseline score for this issue followed by classification tree (CART) for outlier ro-
bustness and feature selection. Machine learning techniques like Random forest, Gradient
Boost and Blagging (under bagging) were then applied. Result gives RF = .9441 (area
under curve) score by utilizing bagging ensemble, Gradient Boosting = 0.9481 (area under
curve) score. Practically, BLAG identifies 60% of positive class items and 20% products
that could become backorder. In the same year, Larco et al. (2017) uses linear regression
for managing the workers discomfort through warehouse storage assignment decisions.
The results suggests the 21% in terms of cycle time improvement in picking zone for the
process of warehouse optimization.These research works rightly point towards adapting
linear regression and ensemble techniques for this experiment.
From all above studies, it has been understood that there are very few research works
that implement warehouse space optimization by identifying products which could pos-
sible go out of trend. Moreover, the use of electronic warehouse data-set is very unique
and has not been implemented by any other project. The next section gives the idea of
effective dynamic pricing, which is important for this project once the blow out products
are identified.
2.4 Dynamic Pricing for Warehouse Maintenance
In any supply chain industry, effective pricing strategy is an important aspect to provide
products a good momentum in the market depending upon market scenarios especially for
the B2C electronics industry; where fast changing trends and competitive market affects
the sale of products. Minga et al. (2003) proposes a price setting algorithm which is an
application of dynamic price setting strategy for stock optimization of warehouse. The
proposed algorithm for demand sensitive model helps warehouse manager to maximize
the profit while decreasing the marginal cost with increase in quantity ordered. It further
mentions some websites which allows buyers to register and let seller to set a minimum
threshold. When enough buyers register on the website, the price drops a little above the
threshold which helps seller to make more money. The proposed strategy can be used in
this research work after finding the blow out products to sell those products effectively.
7

10. Whereas, Ancarani and Shankar (2004) proposes a hypothesis on how costs and price
dispersion compare among the traditional retailers and the multi-channel retailers, and
test through a statistical analysis. Price sensitive multidimensional recommended system
(PSRS) and collaborative filtering was used. This research shows how price setting affects
the business performance. This research can be used to sell products in bulk once blowout
products are identified. After thorough analysis of literature work done in various sector,
the detailed explanation of adopted methodology is drafted in the next (section 3) to
answer the research question mentioned in (section 1.2).
3 Research Methodology and Specification
After the literature is understood, it is important to chalk out the methodology of the
research. For this experiment, it is very important to understand the business perspect-
ive of data. This data is further modelled with different regression strategies and then
evaluated to achieve the ultimate result of finding the blow-out time. It turns out that
CRISP-DM is the best ideology to work with this process as all the process steps have
descriptions of typical phases explained in CRISP-DM guide. It also has an advantage-
ous capability to make large data mining projects cheap at cost, manageable and reliable
(Wirth and Hipp; 2000). This process is discussed in Figure 1 below,
Figure 1: Modified CRISP-DM Process Model, Data Source: Wirth and Hipp (2000)
8

11. 3.1 Business Understanding
The most important thing is to understand the business for whom this product is targeted.
Due to the increase in customer demand, the business has to be more flexible and provide
a user-friendly solution as well as business-friendly. From the literature review, it has
been understood that there is an issue with the warehouse limited space and the stagnant
products make it worse as they take more time to sell than usual that which blocks revenue
and space resulted in losses to the warehouse management. This research work mainly
focuses on space optimization solution for warehouse manager, so that the manager does
not have to bear losses on the products that have worn out. Thus, to produce the blow
out time of the electronic products becomes the business persona and understanding of
this research work.
3.2 Data Understanding
Data collection is an important step as it will initiate the process of execution of the
planning. The data for the research should not be anything other than live data; so that,
it is easy to understand what issues the warehouse controllers are facing on a regular
basis. This data understanding section should be divided into the following 3 sections to
comprehend the data.
3.2.1 Data Collection
The data is collected from a live working organization to sustain the objective of assisting
the organizations where heuristics are applied to understand the blow-out duration of
products. The data has been arranged from a startup e-commerce organization Price
Save that mainly deals with electronic products. This data has mainly 4200 unique
products data across 3 months. This data has many parameters like cost price, selling
price, profit and an additional feature called Sale Rate which is mainly explained in the
data preparation phase.
3.2.2 Data Ethics
To maintain the confidentiality of the data, a prior consent letter was taken to use the
data for educational and research purposes. The collected data is only gathered keeping
in mind the business interest. The letter of consent is attached in the configuration
manual.
3.2.3 Exploratory Data Analysis
The collected data is summarized considering the values that have obtained in the form
of different entities like mean, standard deviation, etc.. The summary of some columns
is shown in the Figure 2 below,
9

12. Figure 2: Column Summary
The only purpose of this is to identify any anomalies in the data than can be stroked
from the data at an early stage.
1) Validation of Data Linearity: Scatter plot shown in the Figure 3, shows the
data distribution of the columns. That shows the column data is not linear.
Figure 3: Scatter-plot
2) Multicollinearity: Multi-collinearity comes into existence if we have features
that are strongly dependent on each other. In the dataset under consideration, the cost
price, profit and price are the strongly dependent features. To understand their inter-
dependence, the correlation matrix is been plotted for all these features,
Figure 4: Correlation Matrix
10

13. From the above correlation matrix, it has been found that the cost price, profit and
price columns are correlated. The profit column was calculated using the price (i.e selling
price) and the cost price. Thus, to increase the efficiency of model training, the feature
profit has been dropped and fitting of the model is performed by keeping cost price and
price features; as these features are very an important parameters for any product and
could be helpful in training the model.
3.3 Design Specification
One of the key aspect of any project is to understand from the logical and business point
of view. Thus, this section gives an insightful overview of the project and and the steps
involved in the project is shown in the Figure 5 using two tier architecture.
Figure 5: Project Flow
3.4 Data Preparation and Transformation
This phase has mainly dealt with the pre-processing of the data that is collected. As
it was earlier declared that Sale Rate has been additionally used as a new parameter,
its only use in the data is to calculate the blow out time for the products in heuristic
analytics. To create the additional column a master file was created assimilating per day
data of 90 days. Using the inventory and the number of the day when the stock gets
sold out is recorded, if there is no sale for 90 days of certain products; such products get
the priority for clearance and are given named as infinite blowout to distinguish from the
other products. The formula for calculating the Sale Rate is given below,
SaleRate =
Initial Inventory − Remaining Inventory
N
(1)
11

14. where, ’N’ is the number of the day when the stock gets sold out.
It is very important to pre-process the data before we use it fit the model as nature of
information and the data quality have direct influence over the capacity of model to be
adapted Nali´c and ˇSvraka (2018). Following factors have been taken into consideration
before the data modelling.
3.4.1 Managing NULL values
The data was validated if null values are presented in it. If those are present, it was
made sure that those values are replaced with ’default’ value that does not make any
significant change in the database but also does not misguide the models that are applied
for irrelevant outputs.
3.4.2 Handling missing values
Missing values cause major issues while applying machine learning algorithms to the
data, as it becomes prone to outliers which returns inaccurate results2
. This is handled
in the dataset by replacing the missing values with default values that do not cause much
variance to the dataset.
3.4.3 Dealing with categorical values
Categorical values are very discrete and thus not continuous. It is very important for
an algorithm to receive continuous values as it is not favourable to judge the outcome
through categorical values. There are two types of categorical values namely ordinal and
nominal. As the dataset is free of ordinal variables, only nominal variables have been
taken into consideration.
• One-Hot Encoding:
The dataset mainly has many categorical values which were required to improve the
accuracy of machine learning models. The data was converted into the ordinal type
and nominal type while feature engineering; whereas, categorical data was taken into
nominal and numerical data into ordinal values. One-hot encoding was performed on
these nominal data, here the dataset is divided into n unique columns where m is the
number of unique nominal columns. As there are 458 different categories combination
in the nominal dataset, additional 458 columns have been added in it. The data after
encoding is shown in the Figure 6,
Figure 6: One-hot encoding
2
understood how to manage the missing data -
https://towardsdatascience.com/introduction-to-data-preprocessing-in-machine-learning-a9fa83a5dc9d
12

15. 3.5 Modelling
After the vast literature survey reviewed in section 2, it was seen that the identification of
blowout products can be done using the regression and ensemble methods. The objective
outlined is to predict continuous dependent variable Sale Rate to figure out blow-out
duration using the best performing algorithm from independent variables like category
and price etc. As the dependent variable is continuous in nature, which is the only reason
to use linear regression analysis over logistic regression. Fulfilling the same purpose, al-
gorithms such as LASSO, Ridge, Elastic Net, Gradient Boosting, Ada Boosting, Random
Forest Regressor have been modeled.
3.5.1 Linear Regression
1) LASSO (Least Absolute Shrinkage and Selection Operator):
LASSO is a linear regression analysis method that displays variable selection to increase
the accuracy of the prediction of statistics model. It uses the shrinkage method that
shrinks data values towards the center. LASSO is penalized for the sum of absolute
values of weights. The objective of the lasso is to acquire the subset while minimizing
the predictor error for a quantitative variable. It reduces model complexity and avoids
overfitting.
2) Ridge Regression:
Ridge regression estimates are usually little affected by small changes in the data on
which the fit regression is based. It goes one step ahead by penalizing the sum of squared
values of the weights. Thus the weights are more evenly distributed and have values
closer to zero. Ridge is a regression technique that is generally used to deal with multi-
collinearity in the dataset. Ridge regression model dismantles the multi-collinearity by
reducing the immensity of correlations in the data. Thus, it became part of this research
work.
3) Elastic Net:
One another type of linear regression model is an elastic net that can deal with the
penalties present in both Lasso and Ridge. It is a hybrid technique of Lasso and Ridge
where absolute penalty and square penalty both are inclusive and regulated with another
coefficient ration. It best works with scaled data i-e a data which is more than 100K
rows, thus it would be a good choice to test with it as it is an improvement to Lasso and
Ridge algorithms.
3.6 Ensemble Models
1) Gradient Boosting Regressor:
Boosting is generally a procedure to convert weak learners into strong learners. Gradi-
ent Boost trains model in an additive and sequential manner. It identifies the limitation
of weak learners by using gradients in the loss function, where loss function indicates
how coefficients of model fit the underlying data. Ke et al. (2017) popularized the use
of highly efficient Gradient Boosting decision tree by proposing two novel techniques
13

16. namely Gradient based One Sided Sampling (GOSS) and Exclusive Feature Bundling
(EFB). After viewing the astonishing performance of in training time cost of 0.28 secs
for one training iteration, this algorithm should best suit to the warehouse data.
2) Ada Boosting Regressor:
The AdaBoost algorithm AdaBoost regressor is a meta-estimator that starts by fitting
a regressor on the dataset and fits extra duplicates of the regressor on the same dataset,
the weight of those observations are balanced by the error of the present expectation. It
is implemented with equal weight given to each observation. Solomatine and Shrestha
(2004) compared the working of M5 model trees with AdaBoost.RT and found that it
performed better in most of the considered datasets. Researchers proved practically that
the AdaBoost algorithm gives better performance even in the presence of noise in the data
and is better than the individual machine with a confidence level higher than 99%. It has
a high capability to perform against the warehouse data considered all these presented
factors.
3) Random Forest Regressor:
Random forest is the bagging ensemble that operates by constructing a series of de-
cision trees during training and aime to reduce the variance by randomly selecting (which
de-correlates) trees from the dataset. They are an ensemble of different regression trees
and are utilized for nonlinear multiple regression.
All the above algorithms have been selected based on their performance in their re-
spective fields implemented which has been exceptional. The main motive of selecting
Linear regression and ensemble technique is because the data is very continuous and not
discrete.
3.6.1 Test Design
Before building the model, it is very important to understand what type of splitting
technique is best suited for the dataset. There are two main types of data splitting
namely the train-test split and train-validation-test split. There are various advantages
proven about the train-validation-test split over the train-test split. Guyon (1997) has
provided splitting measure by giving a relation of ratio of validation set size over training
set size with the minimizing validation error and error rate in training set. The data
split used in the warehouse dataset is 80% train, 10% validation and 10% test. After
this splitting is performed, the model is built, and evaluation is conducted on it. The
three best performing algorithms are subjected to tuning with the help of grid search
parameter
3.7 Evaluation
While preparing a model, it is important to consider how model generalizes an unseen
data while performing machine learning techniques. In this research, hold-out strategy is
used which is a simplest kind of cross validation and requires less computational power
than the k-fold cross validation. This method provides an unbiased prediction of learning
performance. In this method, the dataset has been divided into three subsets i.e Train,
test and validation. The model is trained on the validation subset and for selecting a best
14

17. performing model, it provides a test platform for fine tuning the model’s parameter. For
this research we have divided data into train 80%, test 10% and validation 10%. All the
models are first trained on validation and then with the best performing model predictions
are calculated on test set. While training the model in all evaluation techniques, the
performance of the models have been observed by iterating the data in batches and
models have been compared with these evaluations in testing. Moreover, in order to
further validate, Modelling has been done separately on products which are sold and
evaluation are checked, whereas for not sold products, best algorithm was tested to check
the R2 score. All the graphs are shown in below subsection.
3.7.1 R-Square Criterion
The coefficient of determination (i.e R2
) is a statistical technique of regression that de-
termines proportion of variance in dependent variable experienced by predictor variables.
R2
can vary between 0 and 1; where 1 means the model correctly fits the data. Formula
for R-square is given below:
(R2
) =
V ariance explained by the model
Total V ariance
(2)
Graphs obtained after modelling are shown below:
Figure 7: R2 score - full dataset
Figure 8: R2 score - soldout dataset
From the above Figure 7 and Figure 8, it is clear that GBM outperforms all other al-
gorithms for both modelling performed on full dataset as well as on soldout dataset.
3.7.2 Mean absolute error
As the data under use is continuous, measure of differences between the continuous vari-
able can be derived using mean absolute error. MAE shows how big an error we can
anticipate from the forecast on average. It is also an average distance each point and the
line of equality. The formula for MAE is described below,
15

18. Where, n= number of error and |xi − x| is absolute error
Figure 9: MAE - Full dataset
Figure 10: MAE - soldout dataset
All six models was evaluated on both datasets using mean absolute error and it has
been noticed that gradient boosting has lowest MAE for soldout dataset whereas, Ran-
domforest regressor gives the lowest MAE when evaluated on full dataset.
3.7.3 Mean squared error
Mean square error informs how close the data points to the regression line. It calculates
using the square of the distance from the regression line. The squaring removes all the
negative data points. MSE also gives importance to large differences. Smaller the MSE
closer to the line of best fit. The comparison of MSE for all algorithm is shown in the
below graph,
Figure 11: MSE - full dataset
16

19. Figure 12: MSE - soldout dataset
From the above Figure 11 and Figure 12, it can be seen that the RFR gives the lowest
error on full dataset, but GBM shows the least amount of error for the soldout dataset.
3.7.4 Median absolute error
The median absolute error is very robust to outliers. The loss function is calculated by
simply considering all the median of absolute differences between prediction and target.
The lowest difference shows more accuracy. The built model was evaluated using median
absolute error and algorithm comparison has been graphically plotted below,
Figure 13: Median absolute error - full dataset
Figure 14: Median absolute error - soldout dataset
From the above Figure 13 and Figure 14, RFR shows the lowest median absolute error
for full dataset while GBM has lowest error in soldout dataset.
3.7.5 Variance score
Variance score or explained variance is the measure of calculating how important a math-
ematical model is, in terms of variation in the given data set. It is a measure of discrep-
ancy in predicted to the actual model. Higher rates of explained variance shows stronger
association strength. The built model was evaluated and compared using variance score
and is shown below,
17

20. Figure 15: Variance score - full dataset
Figure 16: Variance score - soldout dataset
From the above comparison, RFR shows the strong association than rest of the al-
gorithms for full dataset but GBM shows betters result for soldout one.
After looking at all the evalutions on both the datasets, RFR shows lower error rate
than all other algorithms but the difference between train and test scores are wide for
most of the evaluations. If this condition is checked for remaining algorithms, GBM has
low error rate and lesser score difference which makes it best suited for application.
This GBM algorithm is further tested against unsold subset, to compare the perform-
ance and there is a reduction in test R2
score as compared to the full dataset, whereas
the mean squared error increased; the reason which can be anticipated here is the subset
contains noise i.e outliers of the the full dataset. The graphs for R2
and MAE is plotted
in the below Figure 17,
Figure 17: Full data vs unsold subset
3.8 Deployment
This section gives an overall idea about the implementation of the project as well as the
how the prediction of blowout time period and application of pricing strategies implemen-
ted for the objective of achieving the automation of the warehouse for space optimization.
18

21. 3.8.1 Implementation
For the objective of identifying the accurate blowout time period using different machine
learning algorithms, first, the data has been collected from an organization named ’Price
Save’ which is a startup company, following proper ethics. The consent letter has been
provided in the configuration manual of this project. The dataset contains 4200 unique
products per day for a period of 3 months. Initially, exploratory data analysis was
performed, which is explained in section in section 3.2. After understanding the data, pre-
processing techniques (explained in section 3.4) such as scaling one-hot encoding, feature
extraction, etc. were committed. The data was split into a train, test and validation
set (in 80%, 10%, 10% respectively) to maintain the integrity of the dataset and trained
with all six models. algorithm. The model was first trained on validation by iterating the
dataset in batches to check the performance of all models. Based on the R2
score best
model was further tuned for improving the scores. But, unfortunately, the model did
not find the best fine-tuning parameters and the R2
scores were inaccurate as compared
to the training validation ones. Hence, it has been decided to use models trained on
validation for further predicting the scores on the test data set. Now the dataset was
added with a new column ”Sold Status” which gives logical value on resultant product
exhaustion. This sold subset was trained against all the models performing all the similar
tasks as of full dataset and the results from both experiments were noted to check the
best performing algorithm. After the comparative evaluation, it has been identified that
the gradient boosting regressor gives the best performance in terms of R2
and using the
other metrics as explained in section 3.7. The already trained GBM model is subjected to
testing against unsold dataset to calculate the comparative error rate. The best algorithm
was further used to predict the blow out time-period for all products and how to apply
effective pricing strategies is explained in the following section 3.8.2 and section 3.8.3.
3.8.2 Prediction of Blowout Time-Period
This is the application of the machine learning strategy for the benefit of warehouse
industry. Identifying the blowout time period will not only saves the time of a warehouse
manager but also prevent warehouse from overstocking which ultimately helps in space
optimization of the warehouse. The best performing algorithm identified from the above
data analysis, the algorithm was further used to predict the sale-rate which is calculated
using the sales obtained per day. Based on the accuracy obtained from the testing the
sale rate, the accurate sale rate is obtained. The blowout day can be calculated using
formula,
Blowout Day n =
Initial Inventory
Predicted SaleRate
(3)
where, predicted sale rate - which is obtained using the best performing algorithm.
The results are shown in the output below after calculating blowout using best performing
algorithm.
y pred - is the predicted blowout using Gradient boosting algorithm and blowout test is
calculated using heuristic method.
It can been seen from the above blowout prediction output that the actual blowout
calculated through application of machine learning algorithm is far more accurate than the
heuristic ones. Thus after identifying the time required for every product get exhaust, a
19

22. Figure 18: Blowout prediction
warehouse manager can apply pricing strategies release these products from the warehouse
effectively. The pricing strategy is explained in the next section.
3.8.3 Application of Efficient Pricing Strategies for Warehouse Optimization
Application of pricing strategies is an additional layer of the project. After the predic-
tion of blow-out time period for each product using the best performing algorithm as
explained in the section 3.8.2. This part is important in terms of achieving the object-
ive of automating warehouse products by clearing-out with optimal pricing strategy. As
discussed in the section 2.4 of literature survey, a dynamic price setting strategy can
be used using the price setting algorithm as mentioned in Minga et al. (2003). If the
predicted blowout product have more quantity to be sold and have more time period as
predicted by the algorithm, then in such scenario profit could be further dropped up-to
a threshold limit until the product gets its momentum. When dealing with a wide range
of products, it is not possible to do pricing on every products and as such products are
prone to overstocking. Hence a method which is mentioned below can be utilized to do
pricing on all these products with ease and can focus on other warehouse products for
generating profit. The formula mentioned below, uses a strategy of dropping the price by
taking the percentage above cost price. For this method a minimum profit is defined first
and then using the trial and testing method, certain amount of profit percent dropped
every time until the product gets the sale momentum. This is a self invented and tested
method while working in the previous organization. The cost price consist of various
parameters i.e Cost of the product, shipping, warehouse fees (cost of warehouse), website
commission (ex. third party e-commerce website). The idea is explained below using the
example formula,
Example Formula:
Selling price = 1 +
15 ∗ (product cost + shipping + commission + warehouse fees)
100
(4)
This means, selling price = 15% above the cost price. i.e the selling price will give the
value to be set in order to take 15% above the cost price. Here just by changing the
percentage, a selling price can be obtained for each product. Hence the formula,
Sellingprice = 1 +
n ∗ (total cost price involved)
100
(5)
Where, ’n’ symbolize percent to be set. This way mass pricing can be done using the
master file for products which are slow moving (blowout products).
20

23. 4 Discussion
In this research work of application of machine learning strategies for optimization of
the warehouse space, it has been found that the machine learning methods perform
better in predicting the sale rate which helps this experiment in identifying the blow out
period. Machine learning algorithms such as elastic net, lasso, ridge, gradient boosting,
ada boosting, and random forest regressor were applied on ful dataset and evaluated on
different metrics. It was found that amongst all algorithms, gradient boosting algorithm
outperforms with R2
= 0.9197 and 0.177 mean squared error which is better than all other
algorithms, followed by ada boosting regressor with an R2
of 0.9064 and MAE = 0.2120.
This trained GBM model is tested against unsold subset to validate its performance
against the products that are unsold. The results show a reduction in the R2
as compared
to the R-squared obtained from full dataset. Moreover, for sold subset data all the
algorithms were again trained and tested. The results found to be improved as the noise
i.e unsold data which contains outliers was removed from the dataset. This study will
help the warehouse manager understand the pattern of his unsold products while re-
ordering them for the next iteration and apply dynamic pricing strategies to effectively
optimize the warehouse space. Blow-out time calculated with actual and that with the
implementation of GBM have unique values, some of the examples are plotted in the
Figure 19 below. At this moment, there won’t be huge differences noted in the blow out
durations, but in long run, the heuristics (i.e decisions based on experience/knowledge)
would not prove effective but GBM will always improve with an increased amount of
pattern data and products. Thus, the automation can be marked fruitful and executable
in case of warehouse space optimization.
Figure 19: Blowout ML vs actual blowout
5 Conclusion and Future work
A competitive comparison between heuristic analytics is conducted against different ma-
chine learning strategies and it can be concluded that that heuristics can proved very
effective for small scale organizations but to handle the amount of data generated by
huge organizations, only the machine learning strategies can set a stepping stone and
provide better results. From the experiment, it has been proved that the warehouse
21

24. space optimization can very well be achieved with the implementation of machine learn-
ing strategies with good amount of accuracy.
There is always a scope of improvement in the research work. The most considerate
one would be provision of factors like varied seasonal data, increased number of products
and additional domains for sale. These factors bring about increased quality pf data
which in turn would train the algorithms and bring about more accurate predictions of
products’ blow-out time. While conducting research, there are various studies done with
the help of applied deep learning viz. ANN, that provides higher accuracy. But regarding
this research, as there is inadequate amount of data, it is not exposed to seasonal data to
be able to successfully build the model and predict the accurate patterns. Nevertheless,
space optimization is achievable and this research paper adequately answers the research
object with great success.
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