[2]M. F. Mridha

1]Department of Industrial Engineering and Management, Khulna University of
Engineering and Technology (KUET), Khulna, 9203, Bangladesh

2]Department of Computer Science, American International University-
Bangladesh, Dhaka, 1229, Bangladesh

3]Department of Computer Science and Engineering, The University of Aizu,
Aizuwakamatsu, 965-8580, Japan

4]Institute of Business Administration, University of Dhaka, Dhaka, Bangladesh

# Big Data – Supply Chain Management Framework for Forecasting: Data
Preprocessing and Machine Learning Techniques

Md Abrar Jahin<EMAIL_ADDRESS>Md Sakib Hossain Shovon
<EMAIL_ADDRESS>Jungpil Shin<EMAIL_ADDRESS>Istiyaque Ahmed
Ridoy<EMAIL_ADDRESS><EMAIL_ADDRESS>[  [  [  [

###### Abstract

This article systematically identifies and comparatively analyzes state-of-
the-art supply chain (SC) forecasting strategies and technologies within a
specific timeframe, encompassing a comprehensive review of 152 papers spanning
from 1969 to 2023. A novel framework has been proposed incorporating Big Data
Analytics in SC Management (problem identification, data sources, exploratory
data analysis, machine-learning model training, hyperparameter tuning,
performance evaluation, and optimization), forecasting effects on human
workforce, inventory, and overall SC. Initially, the need to collect data
according to SC strategy and how to collect them has been discussed. The
article discusses the need for different types of forecasting according to the
period or SC objective. The SC KPIs and the error-measurement systems have
been recommended to optimize the top-performing model. The adverse effects of
phantom inventory on forecasting and the dependence of managerial decisions on
the SC KPIs for determining model performance parameters and improving
operations management, transparency, and planning efficiency have been
illustrated. The cyclic connection within the framework introduces
preprocessing optimization based on the post-process KPIs, optimizing the
overall control process (inventory management, workforce determination, cost,
production & capacity planning). The contribution of this research lies in the
standard SC process framework proposal, recommended forecasting data analysis,
forecasting effects on SC performance, machine learning algorithms
optimization followed, and in shedding light on future research.

###### keywords:

Data analysis, Decision making, Demand forecasting, Hyperparameter tuning,
Literature review, Supply chain performance

##  1 Introduction

In the dynamic landscape of supply chain management (SCM), the relentless
pursuit of efficiency and adaptability has driven a continuous evolution in
forecasting strategies and technologies. This article embarks on a systematic
exploration, aiming to identify and analyze the state-of-the-art in supply
chain (SC) forecasting, ultimately proposing a novel framework that integrates
the power of big data analytics (BDA) into SCM. The increasing complexity and
interconnectedness of global SCs have underscored the need for sophisticated
forecasting strategies. Traditional approaches are being reevaluated in the
wake of technological advancements, leading to a paradigm shift in how we
perceive and optimize SC forecasting. The integration of BDA emerges as a
transformative force, promising enhanced predictive capabilities and a
holistic framework that spans problem identification, data sourcing,
exploratory data analysis, machine learning (ML) model training,
hyperparameter tuning, performance evaluation, and optimization.

The SC has evolved sufficiently over the past years to discover new methods
and techniques for solving SCM problems. The SC can develop its configuration
based on its control, coordination, and management [89]. The advent of big
data (BD) brings one such change. Like other fields, BD can be utilized to
improve decision-making reprocesses and alter business models through multiple
resources, tools, and applications [136]. Therefore, SC and BD usages are
connected to help one another. Although the concepts for SCM are already well-
developed, it is possible to improve further; recent research on enhancing
efficiency through collaboration [117], usage of RFID and intelligent goods
[62] are two examples of such innovations that improved SCM processes. Newer
technologies are further enabling the discovery of innovative strategies for
solving SC problems. BDA is one such disruptive innovation. Although BD has
been present for a long time, the approaches to making sense of BD are
comparatively new, and such systems have not been wholly integrated into other
branches of knowledge [148]. We identified the absence of data usage and
relevant processes in SC directly as a major problem that needs to be
addressed.

BD has similarly grown popular over the years. After academic and technical
publications first mentioned such technological developments, it drew the
attention of various people, including literary scholars, corporate leaders,
and government officials [59]. The most recognizable feature of BD is probably
its size or the amount of data stored. The distinctive features of greater
data variety, high velocity in collection and analysis, the necessity to
navigate veracity challenges, and the inherent value growth with increasing
data analysis [42] set the stage for a new era in SCM. Simply having access to
BD is not helpful; data analytics is a must to create value or gather
information out of the enormous collection of data. Where analytical methods
are applied to BD, it is called BDA. While BDA has vast applications, its role
in improving the SC process is notable.

The motivation behind this research stems from the recognition that the
traditional forecasting paradigms may not be equipped to address the
intricacies of modern SC dynamics. Phantom inventory, varying time horizons,
and diverse SCM objectives necessitate a more adaptive and data-driven
approach. By introducing a comprehensive framework, this article seeks to
bridge the gap between traditional forecasting methods and the demands of
contemporary SC environments.

###  1.1 Research gaps

We found potential research gaps combining the preprocessing for ML
forecasting, control process (SC processes where BDA is helpful), and post-
process (for the evaluation of the forecasting model). Although there is
fragmented research on these particular topics (“SC forecasting model
performance,” “the application of BDA on SC,” or “ML forecasting techniques
with BDA implementation,” or “BD driven SC performance evaluation”), the
necessity to form a cyclic connection among these three processes led us to
the development of this article. This paper identifies a critical gap in
current SCM practices — the underutilization of data and relevant processes -
and positions BDA as a powerful solution. The motivation behind this study
lies in addressing the pivotal challenge of harnessing the full potential of
BD within SCM, recognizing it not just as a technological evolution but as a
strategic imperative for future competitiveness.

###  1.2 Research objectives

The primary objective of this article is to shed light on the potential BDA
investigations on SCM studies, what significant contributions BDA has made to
the efficient use of ML forecasting in SC processes, what preprocessing and
post-processing SC forecasting techniques have been robustly developed so far,
and are currently in use. Mainly, the forecasting techniques in an SC setting
have been discussed from the perspective of BDA. This research aims to
continually enhance the performance of a forecasting model incorporating a
sustainable circular BDA-SCM framework that can drive future research using
business intelligence and value theory as theoretical approaches. Systematic
literature review (SLR) was used to perform this research, which is an
approach used to locate, evaluate, and interpret what relevant research has
been done on a specific issue or topic by sketching out and analyzing the
current intellectual landscape [129]. The review paper attempts to combine the
applications of BDA and ML forecasting in SCM by seeking the solutions to the
following research questions (RQs) for guiding the study’s development to
accomplish its overall objective:

  * •

RQ1: What are the efficient steps to formulate an ML Forecasting model to
predict the SC factors?

  * •

RQ2: How can the forecasting, SC decision-making, and performance measurement
processes be connected, tracked, and optimized in cyclic order?

  * •

RQ3: How can forecasting affect SC performance, and which ML forecasting
models are relevant to SC forecasting?

The scope of this research extends beyond the technical intricacies of
forecasting algorithms. It delves into the broader implications of forecasting
on the human workforce, inventory management, and the overall performance of
the SC. By addressing the adverse effects of phantom inventory and emphasizing
the dependency of managerial decisions on SC key performance indicators
(KPIs), this research contributes to the overarching goal of improving
operations management, transparency, and planning efficiency. The novelty of
this paper lies in several key aspects:

  * •

The introduction of a comprehensive BDA-SCM framework that provides a holistic
view of SC forecasting and highlights interconnections between processes,
offering a novel perspective.

  * •

The integration of ML techniques within SCM for forecasting purposes presents
novel approaches to enhance accuracy and effectiveness.

  * •

Addressing the issue of phantom inventory, providing insights and potential
solutions to improve inventory management practices and forecasting precision.

  * •

Exploring the connection between accurate forecasting and SC performance,
offering a novel perspective on leveraging forecasting models for
optimization.

  * •

Conducting a comprehensive survey of 152 papers spanning several decades,
providing a unique and valuable contribution to the field and consolidating a
vast body of literature.

Including papers from such a broad timeframe allows for identifying trends,
shifts in methodologies, and key milestones in the field. This comprehensive
survey sets this paper apart from other SLR review papers that have not
undertaken such an extensive examination of the literature. The insights
gained from this extensive survey enhance the robustness and reliability of
the conclusions drawn in the paper.

The forthcoming sections will comprehensively explore the “Research
procedures,” and the proposed cyclic connection embedded within the framework
will be elucidated in “BDA-SCM framework”. Moving forward, “Pre-process”
section addresses critical components, including the imperative need for
strategic data collection aligned with SC objectives, methodologies for data
preprocessing, feature engineering (FE), exploratory data analysis, and the
classification of forecasting types based on distinct time horizons. The
“Control-process” section delves into optimizing preprocessing methodologies.
This optimization, rooted in post-process KPIs, aims to elevate overall
control processes, encompassing inventory management, workforce determination,
cost optimization, and production and capacity planning. The discussion on SC
KPIs and error-measurement systems for model optimization unfolds in the
“Post-process” section. This section aims to provide insights into refining
forecasting models for superior performance. In the “Challenges” section,
attention is directed toward acknowledging and addressing technological
obstacles encountered during the extensive review of pertinent articles. This
section highlights and discusses the challenges inherent in navigating the
technological landscape within the scope of this research. In the “Practical
Implications” section, we delve into actionable insights for SC practitioners,
detailing the implementation of the proposed BDA-SCM framework in real-world
scenarios and outlining the substantial benefits they can expect. By
incorporating these novel aspects, the paper contributes to the existing body
of knowledge in the field of BDA-SCM framework for forecasting, offering new
insights, methodologies, and recommendations for future research and practical
implementation.

##  2 Research procedures

###  2.1 Planning the review

In this article, the BDA-SCM cyclic framework was initially developed to
incorporate pre-process, control process, and post-process phases. Each phase
was illustrated utilizing the most relevant selected works of literature. For
forecasting purposes, pre-process recommendations include a step-by-step
approach to forecasting and BDA best practices to facilitate comprehensive
demand forecasting considering state-of-the-art technologies and relevant
research. In the control process, how SC factors and forecasting affect
workforce efficiency have been discussed. The post-process portion of the
managerial decision-making process explains how managers use the KPI and
optimization of the forecasting model to choose the appropriate metrics and
insights.

###  2.2 Conducting the review

####  2.2.1 Search strategy

This SLR aimed to provide a comprehensive and objective evaluation of the
existing research until 2023 on BDA-SCM, including an investigation and
analysis of various SC forecasting problems and BDA innovations, strategies,
and techniques. Major academic databases, including Google Scholar and Science
Direct, were searched to minimize bias and ensure the inclusion of a broad
range of relevant sources and content. Only English articles published in
peer-reviewed journals in the fields of Computer Science, Business, Management
and Accounting, Engineering, and Decision Sciences were included. Figure 1
shows the PRISMA flow diagram for the systematic review process, which
includes the number of articles identified, screened, and included in the
analysis. A combination of keywords and subject headings related to the topic
of interest was used to develop the search strategy. The search strings were
limited to the title, abstract, and keywords fields and included the following
terms:

  * •

(“Data Analytics” OR “Big Data” OR “Data Analysis”) AND (“Supply Chain
Management”) AND (“Forecasting”)

  * •

(“Data Preprocessing” OR “Data Wrangling” OR “Supply Chain Data Analysis”)

  * •

(“Supply Chain Forecasting” OR “Demand Forecasting”)

  * •

(“Warehouse” OR “Inventory”) AND (“Workforce” OR “Human”) AND (“Forecasting”)

  * •

(“Supply Chain Performance” OR “Supply Chain KPI” OR “Supply Chain
Monitoring”)

  * •

(“Forecasting KPI” OR “Forecasting Error Measurement” OR “Forecasting
Performance”)

  * •

(“Forecasting Model” OR “Time-series Forecasting”)

####  2.2.2 Selection strategy

The relevance of each publication was assessed to ensure that the selected
papers were empirically sound and conceptually relevant to BDA-SCM-related
research advances. Articles were considered more relevant if the search terms
appeared in the title, abstract, keywords, and throughout the text. The
identified papers were critically analyzed, particularly regarding the
relevant sections that mentioned BDA-SCM. This approach drew from relevant
views on SCM-forecasting challenges and BDA techniques and helped to achieve
the research review goals. The remaining articles were then assessed to verify
that they provided the necessary research perspective and empirical data to
meet the review’s objectives. Finally, to ensure that the selected articles
aligned with the review goals, we conducted a rigorous alignment process,
comparing the articles to the research review objectives. Only articles that
met all of the selection criteria were included in the final review.

Figure 1: PRISMA flow diagram illustrating the article selection process for
the SLR on BDA-SCM forecasting.

##  3 BDA-SCM framework

The proposed BDA-SCM framework, depicted in Figures 2, 3, and 4, establishes a
cyclic connection that facilitates continuous improvement in SC forecasting.
This cyclic process seamlessly integrates three essential stages: Pre-process,
Control-process, and Post-process, fostering a dynamic relationship that
optimizes SC operations iteratively. Figure 2 mainly consists of the use and
cyclic flow of data in SC. It only includes the SC parts where BDA may be
involved. Figure 3 complements Figure 2 by mentioning the methods for
cleaning, exploring, and analyzing data properly. It includes FE techniques to
select only the most relevant and unique features from which ML algorithms can
learn efficiently. Finally, Figure 4 is a proposed method for data splitting,
model training, hyperparameter optimization, cross-validation, testing, and
evaluating errors to perfect the forecasting methods mentioned in Figure 2.

In the Pre-process stage, the focus is on ensuring accurate and relevant data
aligned with SC objectives. The cyclic nature of this stage involves a
continuous feedback loop. For example, after training an initial ML
forecasting model, the performance is evaluated using real-time data. Any
discrepancies or deviations from expected outcomes trigger a revisit to the
Pre-process stage. This might involve reassessing data collection methods,
exploring new data sources, or refining the preprocessing steps to enhance the
quality of input data. The Control-process stage benefits from the cyclic
connection, encompassing decision-making areas like production planning,
workforce determination, and inventory management. Suppose a decision made
based on forecasted data results in suboptimal outcomes. In that case, this
feedback loops back to the Pre-process stage. The system may reevaluate the
forecasting model’s inputs, incorporating real-time data to enhance decision-
making accuracy in subsequent cycles. In the Post-process stage, the cyclic
connection enables continuous performance improvement. After the initial model
predictions, performance metrics are analyzed, and any deviations from
expected results trigger a reevaluation of the forecasting model. This
feedback loop, integrated into the Post-process stage, ensures that the model
evolves over time, adapting to changing SC dynamics and improving its
predictive capabilities.

Consider a scenario in demand forecasting where the initial ML model predicts
a surge in demand for a particular product. If the actual demand deviates from
the forecast, the cyclic connection triggers a reassessment in the Pre-process
stage. Analysts may explore new data sources, refine data preprocessing
methods, or adjust FE techniques to capture changing demand patterns more
accurately. In inventory management, the Control-process stage involves
decisions on stock levels based on forecasted demand. If the actual inventory
levels deviate significantly from the forecast, the cyclic connection prompts
a revisit to the Pre-process stage. This may involve refining the
preprocessing of inventory data, incorporating real-time data on SC
disruptions, or adjusting the forecasting model to enhance inventory
optimization. In production planning, the decision-making process relies on
accurate product demand forecasts. If the actual production output falls short
or exceeds the forecasted demand, the cyclic connection triggers a
reassessment in the Pre-process stage. This may involve refining data
collection methods, exploring new features relevant to production efficiency,
or adjusting the forecasting model to better align with dynamic production
needs.

Figure 2: Big data analytics in supply chain processes (pre-process, control-
process, post-process). Figure 3: Data preprocessing, feature engineering,
exploratory data analysis, and data reduction. Figure 4: Machine learning
model training, hyperparameter optimization, and model evaluation.

##  4 Pre-process

###  4.1 Identifying business problems

At the outset, the type of data that needs to be collected, stored, analyzed,
and interpreted is selected based on SC strategies. [143] categorized SC
strategies based on risk and impact, such as robustness for low-impact high-
risk, agility for low-risk high-impact, rigidity for low-impact low-risk, and
resilience for high-impact high-risk decisions. Varying levels of
responsiveness and efficiency can adopt the strategies. Responsiveness has
been a critical factor in gaining a competitive advantage, and it depends on
the deviations in demands and a company’s capability to respond to such
deviations. An increase in responsiveness decreases efficiency, and vice versa
[93]. The responsiveness level affects product volume, order fulfillment rate,
workforce, manufacturing capacity, warehouse capacity, transportation
carriers, product mix, supplier’s product mix, inbound and outbound logistics,
etc. [128]. Therefore, whether data needs to be collected should be decided
based on responsiveness, as different data sets are required to boost the SC
efficiency and responsiveness by allocating them. Furthermore, the frequency
of data analyses would also depend on the responsiveness level of the company.
In short, the factors that may dictate the sort of forecasting required for a
business include the context of forecasting, the types of data available, the
required level of accuracy, the length of the forecasting period, the time
available for each forecast, and the value addition made through the forecast
[20].

###  4.2 Identifying data sources

Once the data that needs to be gathered is selected, identifying the sources
is essential. Determining variables is required for timely forecasts to bring
helpful information [19]. Moreover, a conclusion may not be based on a single
type of data; the initial conclusion can be validated based on multiple data
types. [133] mentioned 56 different data sources for four main SCM levers,
procurement, warehouse operations, marketing, and transportation, as
leveraging various data sources allows finding actionable insights quickly;
some of the more relevant data sources have been listed below:

  1. 1.

Transportation

  2. 2.

Barcode systems

  3. 3.

Demand chain

  4. 4.

CRM Transaction data

  5. 5.

BOMs

  6. 6.

Customer surveys

  7. 7.

Blogs and news

  8. 8.

Demand Forecasts

  9. 9.

Procurement

  10. 10.

Delivery times and terms

  11. 11.

Invoice data

  12. 12.

ERP Transaction data

  13. 13.

GPS-enabled BD telematics

  14. 14.

Product reviews

  15. 15.

Competitor pricing

  16. 16.

Inventory costs

  17. 17.

Customer Location and Channel

  18. 18.

Traffic density

  19. 19.

Email records

  20. 20.

Crowd-based Pickup and Delivery

  21. 21.

Equipment or asset data

  22. 22.

Intelligent Transport Systems

  23. 23.

EDI purchase orders

  24. 24.

Warehouse operations

  25. 25.

Logistics Network Topology

  26. 26.

In-transit Inventory

  27. 27.

SRM Transaction data

  28. 28.

Transportation Costs

  29. 29.

Warehouse Costs

  30. 30.

Pricing and margin data

  31. 31.

RFID

  32. 32.

Origination and destination (OND)

  33. 33.

Local and global events

  34. 34.

Supplier current capacity & customers

  35. 35.

Sales history

  36. 36.

Weather data

  37. 37.

SKU level

  38. 38.

Supplier financial performance information

  39. 39.

Raw material pricing volatility

  40. 40.

On-Shelf-Availability

  41. 41.

P2P (Procure-to-Pay)

  42. 42.

Product traceability & monitoring system

###  4.3 Data preprocessing and feature engineering (FE)

####  4.3.1 Duplicates removal

It is problematic to waste space and runtime with duplicate rows. Duplicate
rows create incoherence, and the ML model fails to learn new information.
Because of input mistakes, changes in some feature values (e.g., the
identifier value) may generate duplicate rows that will be deemed distinct by
the machine. It is easier to drop the duplicates or substitute them with
relevant values using data preprocessing libraries in Python and R languages.
Nevertheless, the main challenge is identifying factors on which the
duplicates should be removed. Of the number of methods invented to remove
duplicates, we review the following:

Bayesian: The Fellegi-Sunter-algorithm is the most commonly used model in
probabilistic approaches because of its Bayesian nature [37]. The Bayes
Decision Rule is a common approach [33]. A Bayesian inference difficulty may
develop when the probability density of a unique row differs from a duplicate
record, and the functions are known. Neural network (NN) algorithms are more
accurate without the Fellegi-Sunter-algorithm if the data are adequately
described or labeled [144].

Partitioning Methods: Clustering methods identify and drop duplicates
utilizing graph partitioning approaches [121]. However, [58] compared 12
clustering methods and found that the popular sophisticated algorithms
provided lower accuracy, first suggesting that Markov Clustering is a more
scalable, accurate, and efficient algorithm.

Aggregate fitting: CART [24] and SVM [68] aggregate fitting results for
various row features. SVM is highly memory efficient and works well with lots
of dimensions. However, it does not work well with large datasets or data with
overlapping classes. CART is intuitive and easily used. The problem is that
data is classified based on the sample and may not apply to larger datasets.

Others: Bootstrapping clusters [134] or hierarchical graph structures encode
the features as non-matchable binary features creating dual probability
densities rather than probabilistic distribution modeling for the inspected
quantities [106]. Bootstrapping clusters are used for unsupervised data.
Simple techniques have long been studied, such as utilizing distance
measurements to identify duplication [97]. Weighted transformations also occur
in literature [27]. Additional methods, like ranking the most same-type
weighted rows comparable to those provided, are also utilized to identify the
least duplicated rows [50].

####  4.3.2 Dealing with categorical features

One-hot, ordinal, Helmert, polynomial, and binary encoders are outperformed
with a 95% accuracy by Sum, and Backward Difference encoders are preferred for
prediction jobs [102]. [87] presented a generic Information-based encoder that
transforms mixed-type features into numeric ones while maintaining the
dataset’s original dimension, with better accuracy than One-Hot and Feature-
Hashing. [47] demonstrated that Ordinal-encoder (straightforward and
convenient to execute but incorporates a sequence of features) outperformed
Hashing (introduces limited features and moderately ignores the feature
sequence); One-hot-encoding generates a massive number of features and forces
the use of a very simplified regression analysis. To train residual features
from time categorical variables derived from variable time stamps, a DeepGB
neural network with embedding layers may be used, which are necessary to learn
multiple time series at once to encode categorical features in a lower
dimension or by embedding their IDs and retrieve helpful information [73].

####  4.3.3 Data scaling

Normalization is valuable when using ANN, clustering techniques, or
classification software. The learning phase may be accelerated by normalizing
the data features in tanning faces for backpropagation NN methods.

Min-max normalization: The scaling of $b$ values of a numerical feature $F$ to
a defined range represented by $[\text{new-min}_{F},\text{new-max}_{F}]$ is
termed min-max normalization. To acquire the new value, the following equation
is applied in $b$ to produce a changed value $b^{\prime}$:

$\small b^{\prime}=\frac{{(b-min_{F})}}{{max_{F}-min_{F}}}\cdot(new-
max_{F}-new-min_{F})+new-min_{F}$ (1)

where $max_{F}$ and $min_{F}$ mean the maximum and minimum feature values,
respectively. In normalization, $[new-min_{F},new-max_{F}]=[0,1]$ or $[-1,1]$
are the usual intervals [46].

Datasets prepared for use with distance-based learning methods commonly use
this normalization technique. The features having a significant
$max_{F}-min_{F}$ difference will be prevented from dominating the distance
computation by applying a normalization to rescale the data to the same value
ranges, and it will not be able to distort the learning process by assigning
the older features much weight. It is also known to help ANNs learn faster by
allowing the weights to converge more quickly.

Z-score normalization: Min-max normalization is not practicable if the minimum
and maximum values are not provided. Even when these values are known, the
existence of outliers might cause the min-max normalization to be skewed by
clustering the values and restricting the computational accuracy available to
represent them.

$b^{\prime}=\frac{(b-\bar{x})}{s_{x}}$ (2)

where $\bar{x}$ is the sample mean.

$\bar{x}=\frac{1}{n}\sum_{i=1}^{n}b_{i}$ (3)

Moreover, $s_{x}$ is the mean absolute deviation of $x$ [56].

$s_{x}=\frac{1}{n}\sum_{i=1}^{n}(b_{i}-\bar{x})$ (4)

Decimal scaling normalization: Normalising the numerical feature values by
relocating the decimal-point by 10th power divisions so that the
$\text{$highest$ $absolute$ $value$}<1$ after transformation is a simple
method for minimizing the absolute feature values.

$b^{\prime}=\frac{b}{10^{k}}$ (5)

where $k$ is an integer (the lowest), such that $new-max_{F}<1$.

####  4.3.4 Data transformation

Data transformation can create new features, also known as changing features,
where mathematical formulae derived from business models or pure mathematical
formulae are used to integrate the raw input features. Linear, quadratic,
polynomial, non-polynomial, rank, and Box-Cox transformations are a few of the
different existing transformation techniques.

Normalizations may not be sufficient in research experiments, and full
automation to fit the data and optimize the resulting model. Combining the
data embedded in several features may be advantageous in some circumstances.
Linear transformation based on simple algebraic operations is a basic approach
that may be utilized for this goal. A quadratic transformation can occur when
a newly introduced feature is formed using the expressions in quadratic form.
Using the fundamental features of the dataset, quadratic modifications can
assist us in uncovering information that is not directly there. Transformation
approximation using polynomials could be implemented by brute force
exploration with one unit at a time when no expert assistance can tell us
which transformation and features to employ. The transformation of the rank
approach is recommended for identical training and test data or a complete
dataset for DA and cluster analysis model development [107].

Nonparametric approaches using rank transformation are not recommended to be
introduced into traditional statistics courses because it inhibits how widely
the nonparametric technique may be used, which is unnecessary. Another
misperception is that the nonparametric technique is utilized chiefly for
hypothesis testing. This entirely obscures the superior theoretical and
conceptual flexibility of many nonparametric methods.

[123] studied the limited sample aspects of the estimated parameters using the
Box-Cox transformation. Under the premise of approximating normalcy, the
technique worked well. The outputs were utterly impartial for forecasting, and
their differences were surprisingly small. Asymptotic variances and stability
features of Box-Cox estimates in the linear model were examined by [14]. In
the case of unknown transformation parameters, linear regression models with
minor to intermediate error variances showed much higher asymptotic variances
than known ones. Furthermore, they observed that Box-Cox approaches perform
inconsistently in models with minor to intermediate residual variance.

####  4.3.5 Filtering extreme outliers

The most often recommended approach in the literature of outlier
identification and repair is via filtering.

Outlier detection: Statistical methods for detecting outliers include box
plots, scatter plots, z-scores, and IQR (Interquartile Range) scores. Normal
distribution empirical relations should be followed for outliers where the
values are $<\mu$ \- $3\sigma$ or $>\mu+3\sigma$ for normal distribution,
where $\sigma$ and $\mu$ are the standard deviation and mean of a particular
feature. IQR proximity rule should be used in which outliers are $<(Q1$ –
$1.5\times IQR)$ or $>(Q1+1.5\times IQR)$ for skewed distribution. For other
distributions, a percentile-based approach should be used in which values that
are distant from the 99 percentile and $<1$ percentile are regarded as
outliers.

Outlier Treatment: Various techniques can be employed to address outliers
within a dataset. Trimming, the first method, involves the removal of
outliers, but it is generally not recommended due to potential information
loss. As the second approach, capping identifies outliers based on a
predefined threshold, either greater or less than the established limit. The
number of outliers in the dataset influences the determination of this capping
threshold. Alternatively, outliers may be treated similarly to missing values
(MVs). Lastly, Outlier Removal Clustering (ORC), a modification of K-Means
Clustering, eliminates outliers in iterative loops. ORC effectively removes
outliers from clusters, and careful parameter adjustments are essential as the
dataset influences model precision. Importantly, ORC ensures that the
computation of centroids remains unbiased, particularly when dealing with
distant locations from the k-clusters.

####  4.3.6 Dealing with missing values (MVs)

In SC Data Analysis, one of the preprocessing techniques, Imputation, is
adopted to overcome the drawbacks of MVs. The most straightforward approach to
drop the rows having MVs is if a comparatively small fraction of observations
is present and the analysis of all rows is not substantially skewed in
interpretation [83]. [7] showed that MVs are generally connected with three
sorts of issues:

  1. 1.

Inefficiency

  2. 2.

Difficulties in managing and interpreting data

  3. 3.

Skewness because of discrepancies between perfect and missing data.

When it comes to MV therapy, there are generally three options [36]:

  1. 1.

First, eliminate all instances that have MVs in their features. Thus, removing
features with higher-than-normal MV levels falls within this area.

  2. 2.

When estimating the model parameters for a whole dataset, another way is to
employ maximum likelihood processes, using the obtained model parameters for
imputation via sampling.

  3. 3.

Finally, MV imputation is a group of processes focused on substituting
predicted MVs for existing ones. Most of the time, the features in a data set
are interdependent. As a result, MVs may be calculated by identifying
correlations among features.

Common approaches: To keep the MVs unchanged, known as Do Not Impute (DNI), is
the most straightforward approach where if the baseline MVs strategies are
available, the algorithm must employ them. When many rows include MVs and
using DNI would lead to an irrelevant, inaccurate, and small dataset, then MVs
are commonly substituted by the universal-most-frequent feature value for
nominal features and the universal mean value for quantitative features [49].
[9] showed another process utilizing Hot Deck that partitions the complete
dataset into clusters, links each row with a cluster, and fills up the MVs,
where any complete row from the cluster can be utilized. The imputation of
Cold Deck is identical to the hot deck, except the dataset cannot be the
existing dataset. They demonstrated that the MVs imputation based on the KNN
might beat the internal techniques assessing C4.5 and CN2 to handle MVs and
exceed the imputation method of mean or mode, which is widely intended to
treat MVs.

Maximum likelihood imputation methods: Assume for $n$ independent rows
$(i=1,\ldots,n)$, there are $k$ variables $(y_{i}1,y_{i}2,\ldots,y_{i}k)$ with
no missing data. The maximum likelihood function is:

$\small L=\prod_{i=1}^{n}f_{i}(y_{i1},y_{i2},\ldots,y_{ik};\theta)$ (6)

Assume that $y_{1}$ and $y_{2}$ have MVs that fulfill the Missing at Random
(MAR)-assumption for a specific row $i$. The combined probability for that
observation is the chance of witnessing the remaining features, $y_{i3}$
through $y_{ik}$. If $y_{1}$ and $y_{2}$ are two discrete features, this is
the aforementioned combined probability multiplied by all potential values of
the two features with MVs:

$\small
f_{i}^{*}(y_{i3},\ldots,y_{ik};\theta)=\prod_{y_{1}}\prod_{y_{2}}f_{i}(y_{i1},\ldots,y_{ik};\theta)$
(7)

For continuous MVs,

$\small
f_{i}^{*}(y_{i3},\ldots,y_{ik};\theta)=\int_{y_{1}}\int_{y_{2}}(y_{i1},y_{i2},\ldots,y_{ik})\,dy_{2}\,dy_{1}$
(8)

The multiplication of probabilities for all the rows is the overall
likelihood. If there are $q$ rows with full data and $p-q$ rows with MVs on
$y_{1}$ and $y_{2}$ features, then the ML function becomes:

$\small
L=\prod_{i=1}^{q}f_{i}(y_{i1},y_{i2},\ldots,y_{ik};\theta)\prod_{i=q+1}^{p}f_{i}^{*}(y_{i3},\ldots,y_{ik};\theta)$
(9)

[43] narrowed down the following imputation options using non-parametric
statistical testing:

  * •

Row elimination (IM) and no imputation (DNI) methods are outperformed by
imputation techniques that fill in the MVs.

  * •

No single-size/generic imputation method works for all regressors or
classifiers.

CMC and EC methods are proposed that yield a lower noise ratio for Wilson and
balance the mean MI difference. The proposed imputation approaches focused on
classification techniques, including Rule Induction Learning Models: FKMI,
Black Boxes Methods: EC, and Lazy Learning (LL) models: MC.

####  4.3.7 Binning

In this method, a continuous variable is converted into a group of intervals.
Each interval can then be treated as a ‘bin,’ with the option of enforcing an
order dependent on the data’s subsequent processing. While smoothing, each
bin’s min and max values are calculated as bin borders. Then, for each value,
the nearest border value is substituted. Typically, the smoothing effect
increases with bin width. If the bin widths are identical, binning may be used
as a discretization method by substituting mean or median for bin value. It is
possible to create hierarchical ideas by iterating over this procedure
indefinitely. It’s unsupervised since class labels are not used, and the user
specifies bin numbers.

####  4.3.8 Deep learning (DL) based FE

A multi-filter NN (MFNN) end-to-end model was developed for multivariate
financial time-series FE and classification-based forecasting utilizing DL
techniques [101]. Their proposal MFNN was 15.41% higher than the best result
(Logistic Regression) of traditional ML models and 22.41% higher than the
statistical approach (Linear Regression) in terms of returns.

###  4.4 Exploratory data analysis (EDA) & data reduction (DR)

In this process, the target or dependent column and independent features are
obtained. The DR, EDA, and clustering techniques reduce runtime and space
during the deep-learning modeling phase. DR can be employed to decrease the
size of a dataset while still keeping the data’s original integrity. In our
framework, we suggest performing Feature Selection and Feature Extraction
simultaneously after selecting the target column and finding redundant
features; then, Discretisation may be performed if necessary. Then, the
dataset will be ready for further analysis and model training.

####  4.4.1 Identifying redundant features

Feature Redundancy lengthens the modeling time of ML algorithms and leads to
model overfitting. Feature redundancy arises from the possibility of
derivation from another feature or set of features. The following techniques
may be adopted to handle redundancy:

Covariance and correlation: In statistics, covariance refers to the amount
that two features or factors change in tandem whose value lies in the
$(-\infty,+\infty)$ range. Positive covariance indicates they move in the same
direction. Negative covariance means that any features are greater than the
mean, and others are less than the mean, and vice-versa. Zero covariance means
features may be independent under a certain hypothesis. On the other hand,
correlation analysis is a widely used dimensionless measurement ranging from
$-1$ to $+1$ to discover redundancies in numerical features that evaluate and
quantify the relationship intensity. The features are positively correlated
for correlation values greater than zero (0); for zero, they are independent;
for less than zero, they are negatively correlated [46]. Covariance and
correlation are directly proportional to each other. In numeric feature
selection, correlation is better to use, as correlation analysis is scaled
[-1, 1], but the covariance range is indefinite $(-\infty,+\infty)$. We should
choose correlation for better interpretation. Changes in location, size, or
scale have no effect on correlation. However, both of them are limited to only
being able to identify linearity.

$\chi^{2}$ correlation: The $\chi^{2}$ (Chi-Square) test is often used when
dealing with nominal features and finite value sets. We can use the $\chi^{2}$
test to see whether there is any link between the values of two nominal
features, where a probability table with joint events is established. If
$\alpha$ (significance level) is less than the estimated one (or the
$\chi^{2}$-value (calculated) ¿ table value), the null hypothesis gets
discarded, and the two features can be said to be correlated statistically
[46]. SC Analysts must remember that the $\chi^{2}$-test does not tell much
about the strength of the relationship between two features. The
$\chi^{2}$-test offers advantages such as resilience regarding data
distribution, computational simplicity, extensive information produced from
the test, utilization for investigations where parametric criteria cannot be
satisfied, and scalability in processing data from two and multiple-group
research. The drawbacks are sample size constraints and difficulty in
comprehension when there are many (¿20) features.

####  4.4.2 Feature selection (FS)

The reasons for conducting FS may include removing unnecessary data, enhancing
forecasting accuracy, reducing data cost and model complexity, and improving
training efficiencies such as reductions in space needs and computational
costs [113]. FS approaches, despite their widespread use, have several
drawbacks:

  * •

Training data size significantly impacts the subsets produced by many FS
models (particularly those created using wrapper-based techniques). If the
training data is limited, then the feature subsets retrieved will be limited,
resulting in the loss of key variables.

  * •

Because the target feature is connected with many independent features, and
their removal would adversely influence learning accuracy, reducing high-
dimensional data to a limited range of features is not always possible.

  * •

When dealing with huge datasets, a reverse elimination approach takes too long
since the algorithm must make judgments based on enormous amounts of data in
the early stages.

  * •

In certain circumstances, FS results will still include significant important
features that may obstruct the use of complicated training strategies.

Leading methods: In order to create FS techniques by combining a feature
evaluation score and a cutting criterion, [3] recommended that functions based
on information principle produce better accuracy, not suggesting any universal
cutting condition. However, those independent of the metric perform best, and
outcomes differ across models. For each kind of model, wrapper techniques were
recommended to avoid this effect.

[16] investigated nine feature selectors running across 11 simulated datasets
to examine the methodologies in the context of a growing number of unnecessary
features, noise in the data, redundancy, correlation between attributes, and
the ratio of observations to features. ReliefF proved to be the best
alternative regardless of the specifics of the data, and it is a filter with a
cheap computational expense. Wrapper techniques have proven to be an
intriguing choice in specific disciplines if they can be used with the same
classifiers and consider the greater computing costs. Extensive theoretical
research has been conducted on the Relief and its variants, showing that they
are resilient, noise-resistant, and can decrease their space-time complexity
in parallel [109].

Since the emergence of rough sets in pattern recognition, several FS
techniques have based their criteria for assessing reductions and
approximations based on this idea [127]. Because total searches of substantial
datasets are impossible, stochastic methods based on meta-heuristics and
approximate assessment criteria have also been explored. [140] utilized
particle swarm optimization for this job. Features are discontinuous, making
it challenging to pick approximately set-based characteristics in the
literature. Rough set-based feature selection’s key drawback is the
constraining condition that all values be discrete, for which issue, a fuzzy
rough FS method (FRFS) was suggested [67].

When data is vast, messy, blended with categorical and numerical variables,
and may have dynamic effects requiring sophisticated models, the synthesis of
forecasting analytics in the form of ensembles can create a compressed sample
of non-redundant features [130]. There are four phases to the technique
suggested here: identifying relevant features, computing masking scores,
removing the masked factors, and generating residuals for progressive
modification. The Random Forest ensemble is considered in all four stages.

Two problems arose simultaneously with the growth of highly-dimensional data:
FS is essential in every training, and the accuracy and robustness of the FS
algorithms may be ignored. [110] discussed the FS reduction job introducing
the Quadratic Programming FS (QPFS), which utilizes the Nyströn-approximation-
matrix diagonalization method for large datasets. mRMR and ReliefF were
outperformed using Pearson’s correlation coefficient and MI. A local learning-
based approach may be beneficial when assessing many irrelevant attributes and
complicated data ranges [126]. The impacts of high-dimensional datasets may be
mitigated by pre-processing the feature ranking procedure to exclude class-
dependent density-based features [65]. To scale any method in significant data
issues demands cutting-edge distributed-computing frameworks like MapReduce
and Message Passing Interface (MPI) [151].

We can use supervised FS if the data has class labels; otherwise, unsupervised
FS is the best option. This approach generally maximizes clustering efficiency
or the FS based on correlation, feature dependency, and priority. The primary
premise is to eliminate features that bring almost no value beyond what is
already provided by the existing features in the system. [94] suggested using
feature dependency/similarity to reduce redundancy without needing a search
procedure. An information compression metric called the maximum information
compression index governs the clustering partitioning process, which uses
features as the measure of similarity. Forward orthogonal search (FOS) is
another unsupervised FS approach that aims to maximize the total reliance on
the data to find relevant features [141]. Without compromising performance in
clustering, the unsupervised FS used the Random Cluster Ensemble framework to
compress the set of features by roughly 1/100 of its initial dimensions [32].
When compared to well-known classifications, precision/recall analyses
revealed that feature weighting was highly successful in discovering the most
suitable clusters [96].

####  4.4.3 Feature extraction

Feature extraction accelerates the ML algorithm’s execution, optimizes raw
data quality, boosts the algorithm’s efficiency, and simplifies the
interpretation of the findings.

Principal component analysis (PCA): It aims to analyze a collection of
features’ variance-covariance patterns employing a few linear combinations and
seeks the optimal $k$ number of $n-dimensional$ orthogonal vectors for data
description, where $k\geq n$. Accounting for the most critical percentage of
the discrepancy in the original dataset, the principal component (the first
derived feature) is produced in decreasing order of contribution. Typically,
for containing $\geq$ 95% variance, just the top few principal components are
retained. PCA is beneficial when many independent variables correlate with one
another [70]. The principal component is quick and comprehensive and ensures a
solution is found for all datasets [44].

Factor analysis: The fundamental concept underlying component analysis
identifies a collection of influencing factors to restore the current features
through a series of linear adjustments on the components. It is a method that
finds out the range of factors along with their associated loadings, providing
the features as well as the mean of the features [70]. The factor models can
be solved by (1) the Maximum-likelihood method and (2) the Principal-component
method. Maximum likelihood presupposes actual data following a normal
distribution and is computationally costly. The comparative differences
between PCA and factor analysis are:

  1. 1.

Factor analysis, unlike PCA, implies a basic structure that connects the
factors to the empirical observations.

  2. 2.

A three-factor system is substantially different from a two-factor system in
factor analysis; however, in PCA, the two initial principal components stay
the same when employing a third component.

  3. 3.

PCA is simple and quick. There are several methods for doing the computations
in factor analyses, some of which are complex and tedious.

  4. 4.

Using a sequence of linear transformations, PCA attempts to spin the original
features’ axis. Again, factor analysis generates a new range of features to
demonstrate the observed covariances and correlations.

Multidimensional scaling (MDS): MDS may be used in SCM to estimate the map
depicting transportation distances between or within inventories using the
distance matrix. The result is skewed owing to the disparity between
calculated distances and the actual distances between inventories lying in a
straight line. The map is typically centered on the origin and expanded to
cover considerable distances. However, the answer may be found in any
rotation. Locally linear embedding (LLE): With LLE, local linear fits are used
to restore universal nonlinear configuration [111]. All points are a linear
weighted sum of their neighbors if adequate data is available. It is the basic
notion behind the manifold approximation algorithm. For the LLE algorithm, the
geometric principle is all that is required. LLE’s advantages are that local
minima are not involved in optimizations and have only two parameters. The
embedded space has a universal coordinate system and preserves the local
geometry of high-dimensional data. LLE also has several inherent shortcomings,
which are stated as follows:

  * •

LLE generates folds and nonhomogeneous warps when the dataset is small or the
points are irregularly measured.

  * •

Noise significantly affects LLE, which causes embedding derivation errors.

  * •

Short circuits may develop during the neighbors since the query typically uses
Euclidean distance.

  * •

Poor eigenproblems may arise.

  * •

If two high-dimensional space observations differ, LLE cannot assure that
their corresponding low-dimensional space instances also differ.

  * •

LLE’s embedding findings are extremely susceptible to its two system
parameters: the number of clusters of each instance and regularisation.

  * •

LLE presupposes that complete data exists on a unified surface and is
unsupervised, but that does not happen for multi-labeled classification tasks.

  * •

It is unclear how to assess the new sample data points because LLE does not
provide a parameterized function that reflects between high-dimensional space
and low-dimensional manifold.

####  4.4.4 Cardinality reduction

The merging of two or more nominal or ordinal variables into a single unique
category is called cardinality reduction. It is challenging to manage nominal
features with a large number of groups. Converting high cardinal variables
into binary variables provides many new variables, mostly zeroes. However, if
utilized without conversion with models like Decision Tree that can accept
them, there are risks of model over-fitting. So, decreasing the number of
groups should be considered [44].

####  4.4.5 Discretization

The discretization method turns the numerical data into qualitative data,
i.e., quantitative features, into discontinuous or nominal features that
provide a non-overlapping segmentation of a linear system. Discretization can
decrease data since it converts data to a much smaller sub-ensemble of
discrete values from an enormous range of numerical values. Numerical features
should be discretized as real-world dataset features are generally continuous,
whereas most of the current ML algorithms can only be trained by utilizing
nominal features in categorical data [45].

Discretization generally involves four steps: (1) Continuous feature values to
be discretized need sorting, (2) identifying a breakpoint or nearby intervals
for joining, (3) dividing or combining continuous value ranges based on
specific criteria, and (4) stopping this process at definite value.

MVD and UCP are promising approaches that are not supervised and helpful to
apply to various ML issues other than the classification under adverse
circumstances. They generalized a subset of the top global discretizers based
on a compromise between UCPD, FUSInter, Distance, MDLP, and Chi2 as the ranges
and accuracy [45]. The possibility of utilizing multivariate discretization
features may be investigated since parallel computers are becoming strong.
Chi2 may delete redundant features, and Contrast or ID3 (dynamic
discretization methods) may be addressed to integrate discretization into a
learning process [84].

###  4.5 Forecasting

Forecasting can be called predicting or estimating a value from the future
[4]. Forecasting in a business-like SC performance is vital for suppliers that
do forecast more than those that do not [39]. Mainly three types of
forecasting are done based on the length of the forecast: operational
forecasting for short-term operational activities that range from hours to a
few weeks, tactical forecasting for a moderate duration to support tactical
planning that ranges from months to a few years, and strategic forecasting
which is aligned with long-term goals to make strategic decisions [80].
Furthermore, the frequency of a type of forecasting that is done is dependent
on the length of the forecast. Long-term forecasts are rarely done, whereas
operational forecasts may be required frequently. The different forecasts deal
with different uncertainties. Long-term forecasts deal with raw material cost
fluctuations, final product price changes, seasonal variations in demand, and
changes in production rate in the long term. In contrast, short-term
uncertainties are concerned with variations in daily processes, order
cancellations, random failures in production, etc. [52].

While forecasting is practical, forecasting correctly with more accuracy is
even more helpful. Demand forecasting that allows anticipating sales in the
forecasted period helps minimize overproduction and overstock [20].

####  4.5.1 Types

Although forecasting techniques have evolved, forecasting techniques may be
divided into three main categories: qualitative techniques, which deal with
qualitative data or information to forecast; time series analysis and
projection, which are related to historical data and patterns arising from
them; causal models, where along with the historical data, special events and
their relation with system elements are also considered [20]. The qualitative
technique is not much related to BD and data analytics; the other two are.
Even so, qualitative data can be used to adjust forecasting models toward
incredible accuracy. [77] displayed one such example: Qualitative data can be
used through fuzzy NNs combined with quantitative data for training the model.
Nevertheless, accurate forecasts cannot be done based on qualitative data
only. Time-series analysis is pretty straightforward, especially with the
recent advancement of statistical tools. However, the role of such forecasts
is to reduce errors in the forecast by minimizing the deviations at each
point. Therefore, they do not consider special occasions such as promotions
where sales are more remarkable than usual [26]. This flaw brings us to causal
models or models that consist of probabilities of forecasting accuracy, the
effect of outside interventions, and the interrelation of different types of
variables in the model [60].

With the evolution of knowledge, different techniques for forecasting have
emerged, and new classifications to understand them. [18] classified the
different techniques into two broad groups of Intuitive and Formalized methods
and divided Formalized methods further into Mathematical, System-structural,
Associated, and Advanced information methods.

####  4.5.2 Model fit and train

The dataset can be randomly split into the train, validation, and test sets
for unbiased evaluation with new data to evaluate predictive performance with
data different from training data. The best approach would be to split a
dataset by a date feature. The most recent samples can be utilized for
validation and testing. The primary concept is to choose a sample subset that
accurately reflects the model data.

Two factors determine the proportions of these three sets: the number of data
samples and training models. Some models require significant training data;
therefore, the model should be tuned for more extensive training sets in this
scenario. Models with fewer hyperparameters will be easier to validate and
tune, allowing a small validation set size. An extensive validation set will
benefit if the model contains more significant hyperparameters. There will be
no requirement for a validation set if the model has no hyper-parameters or is
challenging to adjust.

When using k-fold CV, the train-test dataset splitting is repeated for
k-times, with each new set being given a shot at becoming the hold-out set.
Time-series data cannot be used with k-fold CV directly since they believe
there is no connection between the rows and that they are all separate
instances. For time-series data, instances’ time horizon prevents arbitrarily
dividing them into clusters. Instead, data should be segmented, and the
chronological sequence of instances maintained. The term backtesting is used
in time-series forecasting to describe the technique of evaluating models
using past data. In meteorology, this is regarded as ’hindcasting’ rather than
’forecasting.’

####  4.5.3 Hyperparameter tuning

Optimizing performance requires tuning hyperparameters automatically by
Automated ML (AutoML). Hyperparameters are available in most ML systems.
Hyperparameter adjustment has the most influence in optimizing, regularising,
and architecting NNs. Common use cases of Automatic Hyperparameter
Optimization (HPO) include:

  * •

ML, specially AutoML, will require less manual effort

  * •

ML algorithms’ efficiency (by customizing them to the task at hand) has
improved, resulting in the new high state-of-the-art for significant ML
standards in research findings [122]

  * •

It enhances the opportunity to reproduce the ML process

  * •

It allows the fair comparison of methods with the same type of tuning.

One issue with HPO is that a particular configuration does not work well for
all datasets [75]. These days, optimizing hyperparameters above the default
parameters supplied by standard ML packages is increasingly acknowledged.

To assess a lower-cost optimization model, the authors proposed Bayesian
Optimization and Hyperband (BOHB) as an efficient, flexible, stable, and
parallelizable default HPO technique [38]. However, if all hyperparameters are
valid and just a few function evaluations are available, the (Spearmint)
Gaussian technique is recommended [122]. To solve restricted optimization
issues in vast areas, they suggested RandomForest-based Tree Parzen Estimator
(TPE) or Sequential Model-based Algorithm Configuration (SMAC) and Covariance
Matrix Adaptation - Evolution Strategy (CMA-ES). Genetic approaches were
initially used for adjusting two hyperparameters of RBF-SVM $C$ and $\mu$
faster than GridSearch for better forecasting accuracy [23]. CMA-ES was
initially utilized for the optimization of hyperparameters to optimize
hyperparameters of SVM $C$ and $\alpha$, (for all input sizes) the kernel
scale of length $l_{i}$, and the whole matrix of spin and scaling [40]. CMA-ES
has lately proved a perfect solution for Parallelized HPO, superior to current
Bayesian heuristics while optimizing 19 deep-NN hyperparameters on parallel 30
GPUs [88]. A Gaussian online approach incorporated EI to tune the SVM
hyperparameters, attaining factor 100 (regression, three hyperparameters) and
10 (classification, two hyperparameters) speedups against GridSearch [41]. A
robust, adaptable, and analogous combination of Hyperband and Bayesian
optimization was introduced that significantly surpassed both BlackBox and
Hyperband optimization for a broad variety of issues, along with SVM
adjustment, different types of NNs, and reinforced ML algorithms [103]. As
early as 2002, ancient ML models offered GridSearch for hyperparameter
optimization [69, 91]. PatternSearch and GDFS (Greedy Depth-First Search) were
the first dynamic optimization techniques for HPO, with GDFS outperforming
GridSearch. Particle Swarm Model Selection (PSMS) handles conditional
configuration space with a customized particle swarm optimizer. Modified
Ensembling was added to PSMS to prevent overfitting and integrate the better
methods from many generations [34]. In addition, to maximize pipeline
architecture and solely utilize Particle Swarm Optimization for every pipeline
hyperparameter, PSMS was modified to utilize a genetic optimization algorithm
[125]. For the hyperparameter adjustment of deep neural, [11] utilized
Bayesian optimization, outperforming random searching and manual. In addition,
TPE generated better output than a Gaussian approach considering the
mechanism. Random forest TPE and Bayesian optimization have also succeeded in
searching for combined neural and HPO [12]. We suggest a unique manual
approach that might be helpful in general cases:

  * •

If there are many hyperparameters, the CV score can be evaluated for the first
hyperparameter. After that, such a hyperparameter value should be selected to
avoid overfitting and lower accuracy. After setting that hyperparameter, the
next hyperparameter should be evaluated by iterating a similar process one by
one. The HPO algorithm should be chosen based on the hyperparameter type.

  * •

If there are less than or equal to two hyperparameters, the desired HPO
approach can be used directly.

####  4.5.4 Model evaluation

In our framework, we suggest model fitting and training on our analysis-ready
training data using default parameters and then move to the next step of
tuning the hyperparameters. If the model accuracy deteriorates, it is not the
feature’s fault; instead, we should focus on the HPO of the models. After HPO,
the top-performing models can be easily chosen based on the elimination
process, but the model-overfitting issue should be considered. The model can
be evaluated with the predicted sales against the actual sales after at least
one month in the initial operating period.

####  4.5.5 Top forecasting models

Table 1 provides a list of time-series demand forecasting models that have
been used in our reviewed literature. Table 2 provides a comprehensive
overview of the most recently proposed ML models in different forecasting
applications and the corresponding performance metrics evaluated in each
literature. Considering accuracy and precision in forecasting the future time-
series lags, the ARIMA model outperformed the AR (AutoRegressive), MA (moving
average), and SES (Simple Exponential Smoothing) models. The empirical
research reported that long short-term memory (LSTM) enhanced forecasting by
85% when evaluated by comparison to ARIMA (traditional-based model).
Furthermore, the number of epochs (training times) did not influence the
forecasting model’s performance, which showed genuinely random behavior [119].

Table 1: List of Time-Series Demand Forecasting Models Used in Literature Review Model Name | Citation
---|---
ARIMA | [149]
LSTM |  [149], [82]
RBFNN | [149]
Winter models with SVM | [149]
Adaptive Network | [30]
Fuzzy reasoning strategy and ANN | [30]
GAD | [81]
SHEnSVM | [150]
Deep–Learning (DL) technique | [74]
SVR | [108]
RF |  [108], [95]
XGBoost |  [53], [108]
Ada Boost |  [118], [95]
Random Forest |  [118], [95], [55]
MLP |  [118], [152]
CNN–LSTM |  [99], [116]
GRU | [64]
EGD–SNet | [90]
LSTM, BiLSTM, GRU | [8]
Temporal Convolutional Network | [8]
Swish Activation | [99]
Shallow NN | [105]
DNN | [105]
Extreme Learning Machine (ELM) | [22]
Adaptive Neuro-Fuzzy Inference System (ANFIS) | [55]
SARIMAX | [55]
Prophet |  [55], [95]
RF–XGBoost–LR | [95]
RNN | [116]
GRU | [116]
XGBoost–LSTM | [142]
FB–Prophet | [76]
XGBoost–LightGBM | [147]
M–GAN–XGBoost | [139]
AUG–NN | [66]

For the demand forecasting procedure, [149] evaluated statistical models,
RBFNN (Radial Basis Function NNs), and winter models with SVM. According to
their conclusion, the efficiency of SVM outperforms other algorithms by about
a mean MAPE outcomes threshold of 7.7%. [30] demonstrated a new AI-utilized
forecasting approach evaluating a fuzzy reasoning strategy and ANN based on
the Adaptive Network to handle the demand containing inadequate knowledge.
During testing, they obtained MAPE 18% on average for some products. For
unpredictable customer demands, neural methods have provided a robust
forecasting strategy in a multi-level SC framework. A Greedy Aggregation
Decomposition (GAD) approach is a generic approach to self-development in a
discontinuous time-series forecasting method that considers double-based
causes of variation, addressing a practical discontinuous issue of forecasting
demand [81]. With a limited dataset, they outperformed SBA, Croston’s method,
TSB, MA-7, SES, MAPA, MA-3, ADIDA, iADIDA, and N-7 with a MAPE accuracy rate
of 5.9%. [150] offered the SHEnSVM (Selective & Heterogeneous Ensemble of
SVMs) model for sales forecasts. Individual SVMs were trained using samples
produced by the bootstrap method, and grid search created parameters, as
stated by the specified model. The optimum specific combo strategy was found
using a genetic algorithm. They claimed a 10% increment using the SVM
algorithm & a 64% on average enhancement in MAPE. The authors used beer data
from three product variants in their tests. [74] integrated DL technique, the
SVR algorithm, and best-performing time-series analytic models utilizing the
boosting ensemble approach to demand forecasting systems. Their DL
implementation in the new integration strategy (MAPE: 24.7%) lowers mean
forecasting error in the SC, outperforming both the conventional best-
performing forecasting model (MAPE: 42.4%) and the unique integration strategy
without DL (MAPE: 25.8%). XGBoost, ARIMA, and Snaive STL decomposition have
outperformed solo and hybrid models and the modeling mix and have provided the
best forecasting accuracy [53].

[82] used Facebook Prophet (FB-Prophet) and ANNs to forecast lithium mineral
resource prices in China. Quality and quantity of lithium data, network
architecture, and activation functions significantly impacted the performance
of an ANN forecasting model. Overfitting can occur when an ANN model is too
closely tailored to the training dataset, and regularization and early halting
strategies can enhance the model’s performance. The FB-Prophet model, which
uses a decomposable time-series model, can effectively forecast data with
fewer value matrices, handle missing values, and practice adjustments. [64]
created recurrent NN (RNN), LSTM, and gated recurrent unit (GRU) models to
forecast the demand for U.S. influenza vaccinations, with data from 1980 to
2011 serving as the training set and data from 2012 to 2020 serving as the
testing set. The prediction models may be scalable because there was no
overfitting between the expected and actual numbers. The error comparisons
demonstrated that GRU is more precise than LSTM and RNN in predicting
vaccination demand. Energy Generation and Demand Forecasting Search Net (EGD-
SNet), a framework that can anticipate energy production, demand, and
temperature across various areas, was reported in the study of [90]. Together
with the

The 10 most popular ML regressors in the EGD-SNet framework include 11
dimensionality reduction techniques and 13 alternative feature selection
algorithms. It employs a Particle Swarm Optimizer (PSO) to train regressors
intelligently by locating the best hyperparameters. Also, it can create an
end-to-end pipeline by selecting the right regressor, feature, and
dimensionality reduction methodologies to accurately anticipate energy
generation or demand for a specific geographical data set, depending on the
features of the data. [8] implemented many DL methods, including data
collecting, de-noising or pre-processing, feature extraction, and
classification stages. Two primary DL models determine feature extraction. The
first variation used three RNN structures: LSTM, BiLSTM, and GRU. The second
variant used the temporal convolutional network (TCN). They used SoftMax, RT,
RF, KNN, ANN, and SVM classifiers for an online dataset. TCN predicts
COVID-19-restricted shipping risk almost 100% accurately.

As the daily fish demand forecasting models for grocery merchants to reduce
food waste and enhance sustainable SCs, [92] investigated LSTM, Feedforward
NNs, Support Vector Regression, RF, and a Holt-Winters statistical model. The
findings showed that the LSTM model provided the best outcomes in terms of
root mean squared error (27.82), mean absolute error (20.63), and mean
positive error (17.86). [54] forecasted solar Global Horizontal Irradiance
using statistical and Deep Learning architectures, which aids grid management
and power distribution and highlights Pakistan’s solar power potential in
addressing global climate change. They employed SARIMAX, Prophet, LSTM,
convolutional NN (CNN), and ANN statistical approaches. Error measures like
$R^{2}$, MAE, MSE, and RMSE were used to evaluate each model’s performance.
They concluded that SARIMAX and Prophet are ideal for long-term forecasts,
whereas ANN, CNN, and LSTM are best for short-term forecasts. [118] found that
the optimum model for every participant in the SC across all three inventory
replenishment strategies is a stacked ensemble model consisting of XG Boost,
Ada Boost, and Random Forest. According to a methodology for comparing
forecasting methods developed by [120], the MLP method has a little edge over
the CNN, LSTM, and CNN-LSTM approaches. [135] utilized data from the last five
years to estimate demand for eight dairy products from five dairy production
facilities using a direct multistep prediction method. ARIMA works effectively
on a narrow subset of unpredictable series, whereas LSTMs excel at
anticipating seasonal patterns. It outperforms ARIMA for trends. Monthly data
decreased model training error.

For the purpose of forecasting daily energy use, [108] investigated the
effectiveness of three ML models, SVR, RF, and XGBoost; three deep learning
models, RNNs, LSTM, and GRU; and ARIMA. For both very short-term load
forecasting (VSTLF) and short-term load forecasting (STLF), the suggested
XGBoost models beat competing models; the ARIMA model did the worst. [10]
presented a smart platform for data-driven blood bank management that
forecasts blood demand and balances blood collection and distribution based on
optimal blood inventory management to avoid blood wastage and shortage. This
improves blood quality and quantity, increasing blood collection by 11% and
reducing blood waste by 20%. Balancing blood collection and distribution based
on good blood inventory management and arranging blood donation sessions to
avoid cancellations may lower inventory levels. [99] proposed a CNN-LSTM model
with Swish Activation to estimate a store’s supply based on prior sale. This
outperforms Rectified Linear Unit (ReLU), the most effective activation
function. They forecasted sales using Multilayer Perceptron, LSTM cells, and
CNNs. CNN-LSTM Model has a reduced RMSE, according to the experiment.
Pharmaceutical businesses can use Shallow NN and DNN demand forecasting models
for eight anatomical treatment chemical thematic drug groups [105]. Shallow NN
models performed well for five of eight medication categories, while the ARIMA
model performed best for the other three.

[22] introduced an extreme learning machine (ELM) model using the Harris Hawks
optimization (HHO) method to estimate e-commerce product demand. In
forecasting product demand for the next three months, the ELM-HHO model
outperformed the statistical ARIMA (7,1,0) model by 62.73%, the NN-based GRU
model by 40.73%, the LSTM model by 34.05%, the traditional non-optimized ELM
model with 100 hidden nodes by 27.16%, and the ELM-BO model by 11.63%. [55]
developed a novel ML forecasting approach by merging adaptive neuro-fuzzy
inference system (ANFIS) and time-series data features to forecast real-time
e-order arrivals in distribution hubs, helping third-party logistics providers
better manage hourly-based e-order arrival rates. ELM, GB, KNN, MLP, and DT
were five ML algorithms used by [152] to forecast demand in a business based
on Black Friday customer information. According to the results, MLP, ELM, GB,
KNN, and DT were the top algorithms in terms of MSE, while ELM, MLP, GB, DT,
and KNN had the greatest performances in terms of MAE. Moreover, ELM had a
higher $R^{2}$ value of 0.6365, whereas DT had a lower value (0.4877). [95]
compared RF, XGBoost, gradient boosting, AdaBoost, and ANN algorithms to a
hybrid (RF-XGBoost-LR) model for retail chain sales forecasting. A US retail
company’s weekly sales data was used to analyze estimates based on factors
like temperature and shop size. The hybrid RF-XGBoost-LR outperformed other
models in many criteria. RNN and LSTM were used by [116] to improve stock
price prediction. The memory cell, a computer that replaces artificial
neurons, is buried in the network. The study increased epochs and load sizes
to improve precision. Time and lot size boost prediction accuracy in this
work. The test data predicts the specified technique, which yields more
accurate outcomes. The proposed method forecasts stock markets more
accurately.

Table 2: The Most Recent (2022–2023) ML Models for Forecasting Applications in SCM Study |  ML Models |  Performance Metrics |  Application/Domain
---|---|---|---
[8] |  EGD-SNet Framework: 10 ML regressors |  Accuracy, RMSE, MAE |  Energy generation and demand forecasting
[92] |  LSTM, Feedforward NNs, Support Vector Regression, RF, Holt-Winters model |  RMSE, MAE, Mean positive error |  Daily fish demand forecasting for grocery merchants
[54] |  SARIMAX, Prophet, LSTM, CNN, ANN |  $R^{2}$, MAE, MSE, RMSE |  Solar Global Horizontal Irradiance forecasting
[118] |  Stacked ensemble model (XG Boost, Ada Boost, Random Forest) |  Not specified |  SC inventory replenishment
[120] |  MLP, CNN, LSTM, CNN–LSTM |  Not specified |  Forecasting method comparison
[135] |  ARIMA, LSTM |  Not specified |  Dairy product demand forecasting
[108] |  SVR, RF, XGBoost, RNNs, LSTM, GRU, ARIMA |  Not specified |  Daily energy use forecasting
[10] |  Smart platform utilizing ML models |  Not specified |  Blood bank management and demand forecasting
[99] |  CNN-LSTM model with Swish Activation |  RMSE |  Store supply forecasting based on prior sale
[105] |  Shallow NN, DNN, ARIMA |  Not specified |  Pharmaceutical demand forecasting
[22] |  Extreme Learning Machine (ELM) with Harris Hawks optimization (HHO) |  Not specified |  E-commerce product demand forecasting
[55] |  Adaptive Neuro-Fuzzy Inference System (ANFIS) |  Not specified |  Real-time e-order arrivals forecasting
[152] |  ELM, GB, KNN, MLP, DT |  MSE, MAE, $R^{2}$ |  Business demand forecasting based on Black Friday customer information
[95] |  RF, XGBoost, Gradient boosting, AdaBoost, ANN, Hybrid RF-XGBoost-LR |  Not specified |  Retail chain sales forecasting
[116] |  RNN, LSTM |  Not specified |  Stock price prediction

Based on the mentioned studies, we suggest considering the following recently
best-performing hybrid time-series demand forecasting ML models: 1. XGBoost-
LSTM [142] 2\. FB-Prophet [76] 3\. XGBoost-LightGBM [147] 4\. M-GAN-XGBoost
[139] 5\. SARIMA integrated AttConvLSTM, and FB-Prophet [138] 6\. AUG-NN [66].

When selecting the primary top-forecasting model, it is recommended to
consider the best cross-validation (CV) score, minimum runtime, and space
consumption as criteria for evaluation.

##  5 Control-process

Actual processes do not always go as predicted. There are variabilities in
performances emerging from changing levels of efficiency. In ideal cases, the
workforce and existing capacity can achieve the goal as planned. However,
performance levels are inconsistent with humans [104] and vary with other
factors such as WIP inventory, machine utilization, product mix, and queueing
system [146]. Such fluctuations in efficiency cannot be predicted accurately.
Hence, they must be recognized in time, and appropriate measures to meet the
requirements must be taken. Responsive to the SC process’s randomness may
consist of three steps: capturing or recording data simultaneously with the SC
activity, comparing the recorded data with the standard, and adjusting
capacity to meet the short-term goal. Data can be used to determine the
optimal decision for the changing suppliers, changing price levels, the
competitiveness of the competitors, and monitoring performance during the
process [31]. Furthermore, during a discrepancy in planned and actual output
levels, the root cause can be identified using BDA [112]. [6] mentioned other
benefits of BD on workforce scheduling, production efficiency, employee
productivity, capacity utilization, flexibility, and lead time reduction.

###  5.1 Information flow

Forecasting decisions affect further SC planning. As such, information flows
across multiple phases of the SC process. Logistics superiority and better
stock level synchronization are possible through a flow of demand information
from downstream members to the upstream ones and the flow of production plan
and delivery information from the upstream members to the downstream ones
[132]. Like the three types of forecasting, there are three types of decisions
in SC: strategic, operational, and tactical [124]. Recent studies have shown
how the findings from one level can affect other decisions and limit the
number of options for subsequent decisions [131].

It is possible to attain efficiency through forecasting by properly allocating
resources in different areas such as workforce, capacity, inventory
management, etc. There are need-based, supply-based, and demand-based models
for forecasting and planning in such areas of SCM [114]. Each method requires
some sort of information flow. The forecasted amount can be used to estimate
the number of dependent inventory demands. The final product’s demand
indirectly affects the required workforce, capacity, warehouse planning, and
lower costs through optimization.

###  5.2 Production efficiency

Having real-time data on production boosts production efficiency. Firms can
manage order processing across SCs and companies while decreasing errors and
waste inside manufacturing facilities by incorporating real-time data into SC
operations [136]. This efficiency is further enhanced when data from suppliers
and distributors are available. Through close connections and sharing
information with SC partners, data-driven SCs may also affect manufacturing
and operations processes through increased efficiency in product development,
product design, quality improvement, and balance between capacity and demand
[115]. Additionally, data integration in the SC has been found to aid in
developing production strategies and the timely delivery of products and
services [28].

###  5.3 Employee productivity

In general, there is either an excess of the workforce or a shortage in the
production process; the question is how to reduce the inefficiency. Under
variable output requirements, workforce scheduling without data analysis
entails investing in cross-job training to enable workers to be more
productive and efficient in their work. However, this reduces performance as
time is spent on upskilling or reskilling.

Data-driven decision-making, or the forecasting of required outputs to
estimate the required workforce, is an excellent way to minimize such costs of
hiring and laying off by adequately scheduling the workforce [6]. Through
proper scheduling, the workforce from idle time can be shifted for workdays
requiring extra hours and thereby balanced. [100] showed that work pressure
could be balanced with reduced slack time and workforce through different
heuristic algorithms, with each algorithm performing well in different areas
of efficiency.

###  5.4 Inventory management

Reducing costs from inventory can cut the overall cost of the business.
Different models have been created to minimize costs and maximize profits that
aid with material planning mechanisms, stock-out predictions, inventory level
predictions, and many more [1]. Inventory costs can be lowered at sourcing,
transportation, and holding levels, optimizing inventory decisions [115].

###  5.5 Role of data by time frame

Although BDA can make SC processes efficient, it cannot be done with the same
forecast. Capacity planning or storage size falls under long-term strategic
decisions requiring long-term forecasts or aggregated short-term forecasts.
Contrarily, production plans may be short-term operational decisions requiring
short-term predictions. [2] stated how predictions could be derived from the
aggregation of short-term, disaggregation of long-term, or a co-integration of
both kinds of forecasts. Hence, on the one hand, separate forecasts can be
produced. Conversely, forecasts can be derived from other forecasts to
maintain relevance.

##  6 Post-process

###  6.1 SC performance

Once an SC process is completed, the performance needs to be reviewed to
identify the gaps in planning models. Performance measurement is defined as
quantifying actions across two fundamental dimensions: effectiveness and
efficiency [98]. Performance measurement is essential to control the output;
without it, no person or machine can be held liable for subpar performance,
and problems will be harder to identify and solve. Performance measurement
helps with information for management feedback, decision-making, monitoring
performance, diagnosing problems, motivating people, identifying potentials of
a decision, measuring success or failure, reviewing and adjusting business
strategies, specifying company goals, and much more [21]. [61] offered a
comprehensive methodology considering all three SC system stages, including
ERP-based SC performance. To comprehend whether network scanning and
embeddedness are linked to SC performance, Bernardes and Zsidisin (2008, 209)
studied the correlation between SCM strategy and network scanning and
embeddedness concepts.

Immediately after the tasks are completed, the performance data must be
recorded. For data collection, the performance metrics are first to be
identified, just as those found for business evaluations [15]. [51] mentioned
plan success, source optimization, production efficiency, delivery
performance, and customer support-relation and satisfaction, each having
multiple performance metrics under them. The performance level found afterward
can be of three types: below average, average, and above-average [5]. The
actions followed after such a finding are different in each case. When a
below-average performance is observed, managers can either look for anomalies
in the system or review whether the goals set were too high to achieve.

Conversely, an above-average performance requires rechecking the goals so that
optimization of resources is possible. In order to meet the goals-setting
theory stated by [86], these changes may be adjusted to suit. Operational
benefits such as performance monitoring, objective setting, management,
transparency, and planning functions can be improved with the assistance of
BDA and performance metrics derived from them through the use of predictive
KPIs, dashboards, and scorecards by the SC operational managers within the
organization [31].

Besides managerial decision-making, the performance data are crucial to
modifying existing forecasting models. Under a considerable deviation of
performance, the data received from this level needs to be sent back to the
forecasting stage for tweaking the forecasting model to higher perfection. The
performance metrics can thus act as an indicator of forecasting model errors.

###  6.2 Forecasting error measurement

Our proposed cyclic framework is evaluated against predicted sales when the
actual sales data is available or using the hold-out set. Nevertheless, the
hold-out set might not be perfect for real-world scenarios, so we encourage a
cyclic and continuous development process from real-sales data evaluation
insights. We encourage a cyclic and continuous development process from real-
sales data evaluation insights. A few evaluation metrics can be used for the
post-process evaluation to fine-tune the forecasting model in the
preprocessing phase. Assume test data with m periods, $t=1,\ldots,m$. The
difference between forecasted sales $f_{t}$ and actual sales $y_{t}$ at a
period $t$ can be referred to as the forecasting error $e_{t}=y_{t}-f_{t}$.

####  6.2.1 Mean absolute error

$\small MAE=\frac{1}{m}\sum_{t=1}^{m}|e_{t}|=mean(|e_{t}|)$ (10)

MAE is very straightforward and relatively simple to explain, and scale
dependence is its disadvantage.

####  6.2.2 Mean absolute percentage error

$\small MAPE=\frac{1}{m}\sum_{t=1}^{m}|\frac{e_{t}}{y_{t}}\times 100\\\ $ (11)

MAPE is perhaps the most often utilized error indicator for business
forecasting because of its comprehensiveness. However, despite the term
‘Percentage,’ the MAPE value may be higher than 100%. The rows equal to 0
causes problems since the fraction’s denominator cannot be filled in. MAPE is
an appropriate metric when dealing with intermittent demand. Asymmetry is its
major drawback as it penalizes overfitting more than underfitting, leading to
probable skewness.

####  6.2.3 Mean squared error

$\small MSE=\frac{1}{m}\sum_{t=1}^{m}{e_{t}}^{2}$ (12)

Compared to RMSE, MSE takes less runtime and is more flexible. However, we
might not interpret MSE as the actual sales because the error is squared.

####  6.2.4 Root mean squared error

$\small
RMSE=\sqrt{\frac{1}{m}\sum_{t=1}^{m}{e_{t}}^{2}}=\sqrt{mean(|{e_{t}}^{2}|)}$
(13)

Two consequences occur by performing the dual transformation in RMSE: more
weight is placed on more significant errors, and positive and negative errors
cannot cancel one another out since they are all transformed into positives.

####  6.2.5 Mean absolute scaled error

For non-seasonal time-series,

$\small
MASE=\frac{\frac{1}{J}\sum_{j}|e_{j}|}{\frac{1}{T-1}\sum_{t=2}^{T}|y_{t}-y_{(}t-1)|}$
(14)

For seasonal time-series,

$\small
MASE=\frac{\frac{1}{J}\sum_{j}|e_{j}|}{\frac{1}{T-m}\sum_{t=m+1}^{T}|y_{t}-y_{(}t-m)|}$
(15)

With MAE, outliers are protected; with RMSE, we are assured of an impartial
prediction. SC Analysts need to analyze MAE and see whether it results in a
significant bias; therefore, they should utilize RMSE. In situations when
there are many outliers in the dataset, MAE may help correct the skewed
prediction.

####  6.2.6 Tracking signal

The tracking signal is the way to verify if the current forecasting method is
correct. A tracking signal that changes according to the forecast bias shows
bias in the prediction model. It is often employed when the forecasting
model’s validity is questionable.

$\small\text{Algebraic sum of forecast error}=\sum_{t=1}^{m}|e_{t}|$ (16)
$\text{Tracking Signal}=\frac{\text{Algebraic sum of forecast
error}}{\text{Mean Absolute Error}}$ (17)

A rule of thumb holds that the technique employed for forecasting is accurate
when the tracking signal is within $-4$ to $+4$.

###  6.3 Phantom inventory

Research has shown that imprecise perpetual inventories (PIs) are
overestimated approximately 50% of the time; that is, PI displays higher stock
than that is present in the shop, called a phantom inventory. The most severe
issue in a phantom inventory is unavailability – the system considers it has
an adequate inventory and does not order a replenishment. The recognized
reasons for phantom inventory are [137]:

  * •

Stolen goods, defective products that are not reported

  * •

Cashier mistakes

  * •

A shop may get deliveries from the distribution center (products that should
have but were not received)

  * •

Returned goods that should update the system are sometimes wrong.

To resolve the stock inconsistency, businesses may perform a bunch of tasks
[72]:

  * •

The supply of safety may be raised. The enhanced security inventory aims to
mitigate inventory problems by having ‘excess’ inventories at hand. RFID may
reduce the costs of storing this additional and redundant inventory.

  * •

The business may often conduct manual inventory numbers. Physical inventory
audits may interrupt storage, are extremely expensive, and differ in precision
– improved RFID precision may be an affordable option.

  * •

The business may construct a continuous decrease equivalent to the total
inventory loss that one believes takes place to balance the phantom inventory.
The issue is that the precise inventory loss is not known. The visibility
provided by RFID may be more accurate than conventional stock loss techniques.

  * •

The business may attempt to minimize mistakes by improving inventory
management, decreasing fraud, etc.

Inventory precision determines forecasting, procurement, and replenishment
quality, where inventory records are used as input. Inaccurate demand
forecasting due to phantom inventory (overstated PI) may be improved by
including RFID in the process [57].

##  7 Challenges

This section aims to provide a comprehensive overview of the challenges
encountered during the review of 152 articles from 1969 to 2023 in the field
of BDA-SCM for forecasting, with a specific focus on data preprocessing and ML
techniques. The challenges identified herein will serve as a valuable resource
for future researchers, enabling them to address and overcome these obstacles,
ultimately advancing the domain and contributing to its growth and
development.

###  7.1 Data quality and reliability

One of the critical challenges observed in the reviewed literature is the
issue of data quality and reliability. Many studies acknowledged the presence
of incomplete, inconsistent, and erroneous data within SC datasets. Future
research efforts should focus on developing robust data cleansing,
integration, and quality assurance techniques to enhance the reliability and
accuracy of the forecasting models.

###  7.2 Scalability and performance

With the exponential growth of data in SCM, scalability and performance have
become significant challenges. The reviewed articles often lacked details on
how their proposed techniques would scale up to handle large-scale datasets or
real-time processing requirements. Future researchers should explore scalable
algorithms, distributed computing frameworks, and parallel processing
techniques to ensure the effectiveness and efficiency of forecasting models.

###  7.3 Variety and complexity of data Sources

The diverse range of data sources, such as structured, unstructured, and semi-
structured data, presents challenges in data preprocessing and feature
extraction. The reviewed literature indicated limited exploration of
techniques for effectively handling data variety and complexity. Future
research should focus on developing innovative methods for integrating and
analyzing heterogeneous data sources to extract meaningful insights for
accurate forecasting.

###  7.4 Feature Engineering and Selection

Effective FE and to improve forecasting accuracy, including automated FS,
dimensionality reduction, and feature representation approaches to identify
the most relevant features for forecasting within the SC context. Future
researchers should investigate advanced FE techniques, including automated FS,
dimensionality reduction, and feature representation, to improve forecasting
accuracy.

###  7.5 Model interpretability and explainability

The black-box nature of some ML models limits their interpretability and
hampers decision-making processes. The surveyed literature revealed a lack of
emphasis on model interpretability, hindering the wider adoption of
forecasting techniques in SCM. Future research should focus on developing
transparent and interpretable models that provide explanations for their
predictions, enabling practitioners to understand and trust the results.

###  7.6 Real-time data processing and analysis

SCM requires real-time monitoring and decision-making capabilities. However,
the surveyed literature demonstrated a limited exploration of real-time data
processing and analysis techniques for forecasting purposes. Future research
efforts should concentrate on developing real-time forecasting frameworks that
leverage stream processing, online learning, and adaptive algorithms to handle
dynamic and time-sensitive SC scenarios.

###  7.7 Privacy and security concerns

Integrating big data in SCM raises concerns regarding data privacy and
security. The surveyed articles paid limited attention to these challenges,
and there is a lack of comprehensive approaches to ensure the privacy and
security of sensitive SC data. Future researchers should focus on developing
robust privacy-preserving and secure ML techniques to safeguard data while
maintaining the accuracy and efficiency of forecasting models.

###  7.8 Integration of domain knowledge

SCM involves complex domain-specific knowledge, including industry-specific
constraints, regulations, and contextual factors. The reviewed literature
showed a limited integration of such domain knowledge into the forecasting
frameworks. Future research should emphasize the incorporation of domain
expertise and contextual information to enhance the relevance and accuracy of
forecasting models within the SC domain.

###  7.9 Lack of benchmark datasets and evaluation metrics

The absence of standardized benchmark datasets and evaluation metrics hinders
the comparison and reproducibility of forecasting techniques. The reviewed
articles often utilized different datasets and evaluation metrics, making it
challenging to assess the performance of various models. Future researchers
should strive to establish benchmark datasets and evaluation protocols
specific to SC forecasting, enabling fair comparisons and facilitating
advancements in the field.

By overcoming these challenges through innovative techniques and
methodologies, researchers can contribute to the advancement of this field,
leading to more accurate, scalable, and interpretable forecasting models for
SCM.

##  8 Practical implications

The findings of this research offer substantial practical implications for SC
practitioners, providing actionable insights that can be effectively
implemented in real-world scenarios. The proposed BDA-SCM framework serves as
a strategic guide, and its practical application holds the potential for
significant benefits in enhancing overall SC operations.

SC practitioners can implement the BDA-SCM framework by initially aligning
data collection methodologies with specific SC objectives. This involves
adopting a systematic approach to gathering data directly relevant to the SC
ecosystem’s unique dynamics and challenges. By integrating the framework into
their operational processes, practitioners can leverage the power of BDA at
various stages, from problem identification to performance evaluation.

The implementation of the BDA-SCM framework promises several tangible benefits
for SC practitioners. Firstly, the framework enhances the accuracy of
forecasting models, providing practitioners with more reliable insights into
demand patterns, inventory needs, and workforce requirements. This, in turn,
enables optimized decision-making across various facets of SCM. Secondly, the
cyclic connection within the framework ensures adaptability to dynamic SC
conditions. SC practitioners can continuously refine and optimize their
forecasting models based on real-time data, thereby staying responsive to
changing market dynamics and mitigating potential disruptions. Furthermore,
the framework’s emphasis on KPIs and error-measurement systems enables
practitioners to evaluate and improve the performance of their forecasting
models systematically. This enhances operational transparency and contributes
to the overall efficiency and planning effectiveness of the SC.

In practical terms, the BDA-SCM framework supports inventory management by
providing accurate demand forecasts, aids in determining workforce needs,
optimizes cost factors, and facilitates efficient production and capacity
planning. By fostering a holistic approach to SCM, the framework equips
practitioners with a systematic and data-driven strategy to address the
intricacies of modern SC dynamics. In essence, the practical implementation of
the BDA-SCM framework empowers SC practitioners to navigate the complexities
of their operational environments with greater precision and foresight,
ultimately contributing to enhanced resilience, efficiency, and
competitiveness in the ever-evolving SCM landscape.

##  9 Conclusions

This systematic review diligently identified and compared state-of-the-art SC
forecasting strategies and technologies within the defined temporal scope,
conducting a comprehensive review of 152 papers spanning from 1969 to 2023.
This study has made significant strides in addressing the challenges inherent
in SC forecasting, offering cutting-edge technological solutions within the
context of a comprehensive BDA-SCM framework. The key findings and
contributions of this study can be summarized as follows:

  1. 1.

Pre-process: In the pre-processing stage of SC forecasting, the significance
of accurate data aligned with SC objectives was emphasized. The study provided
recommendations for SC analysts, including using EDA, FE, hyperparameter
tuning, and recent ML model training approaches to improve forecasting
accuracy. However, it is essential to note that further research is needed to
explore advanced techniques for data cleansing, integration, and quality
assurance to ensure reliable and high-quality input data.

  2. 2.

Control-process: The study discussed how BD could facilitate efficient
managerial decision-making in various areas of SCM, such as production &
capacity planning, workforce requirements, and inventory management.
Leveraging insights from forecasted data allows decision-makers to optimize SC
operations and resource allocation. However, future research should focus on
developing real-time decision support systems that can integrate and analyze
large-scale data streams to enable timely and effective decision-making.

  3. 3.

Post-process: The post-process section emphasized SC performance measurement
and the role of BDA in optimizing model predictions. By analyzing performance
metrics and leveraging BDA techniques, SC practitioners can identify areas for
improvement and refine their forecasting models accordingly. Future research
efforts should focus on developing comprehensive performance measurement
frameworks specific to SC forecasting, including quantitative and qualitative
metrics, to enable more accurate evaluation and comparison of forecasting
models. Additionally, the study addresses the accuracy of inventory records as
a crucial determinant for forecasting, procurement, and replenishment quality.
Mitigating inaccuracies resulting from phantom inventory is highlighted, with
the inclusion of RFID technology in inventory management processes as a viable
solution. Future research should explore advanced techniques and methodologies
to address phantom inventory, incorporating emerging technologies and
developing comprehensive inventory management and forecasting frameworks to
enhance overall SC performance.

This study has successfully addressed the research questions posed:

RQ1: The study has identified and outlined efficient steps to formulate an ML
forecasting model for predicting SC factors. Recommendations for accurate data
preprocessing, FE, hyperparameter tuning, and advanced ML model training
approaches have been provided to enhance the accuracy of SC forecasting
models.

RQ2: The study has emphasized the importance of connecting, tracking, and
optimizing the forecasting, SC decision-making, and performance measurement
processes in a cyclic order. The proposed BDA-SCM framework encompasses the
Pre-process, Control-process, and Post-process stages, providing guidance on
integrating these processes to optimize SC operations and resource allocation.

RQ3: The study has explored the impact of forecasting on SC performance and
identified relevant ML forecasting models for SC forecasting. The connection
between accurate forecasting and improved SC performance has been highlighted,
with recommendations for performance measurement and using BDA techniques to
optimize model predictions.

By successfully addressing these RQs, this study contributes to the
advancement of the field by providing insights into efficient ML modeling
steps, the integration of forecasting and SC decision-making processes, and
the relevance of ML forecasting models for SC forecasting. Future research
should build upon these findings to further enhance the understanding and
implementation of BDA in SCM.

While this systematic literature review (SLR) followed a rigorous and
objective evaluation approach, acknowledging its limitations is crucial. These
limitations include the availability of relevant literature, potential
publication bias, and the dynamic nature of the BDA-SCM field. Future research
endeavors should aim to address these gaps by conducting further empirical
studies, developing benchmark datasets, and exploring emerging technologies
and methodologies to advance the understanding and implementation of BDA in
SCM.

By considering and addressing the challenges and limitations outlined in this
study, future researchers can build upon its findings and contribute
meaningfully to the continued advancement of the BDA-SCM domain.

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