# Pivoting Retail Supply Chain with Deep Generative Techniques: Taxonomy,
Survey and Insights

Yuan Wang<EMAIL_ADDRESS>Lokesh Kumar Sambasivan  Mingang Fu  Prakhar
Mehrotra

###### Abstract

Generative AI applications, such as ChatGPT or DALL-E, have shown the world
their impressive capabilities in generating human-like text or image. Diving
deeper, the science stakeholder for those AI applications are Deep Generative
Models, a.k.a DGMs, which are designed to learn the underlying distribution of
the data and generate new data points that are statistically similar to the
original dataset. One critical question is raised: how can we leverage DGMs
into morden retail supply chain realm? To address this question, this paper
expects to provide a comprehensive review of DGMs and discuss their existing
and potential usecases in retail supply chain, by (1) providing a taxonomy and
overview of state-of-the-art DGMs and their variants, (2) reviewing existing
DGM applications in retail supply chain from a end-to-end view of point, and
(3) discussing insights and potential directions on how DGMs can be further
utilized on solving retail supply chain problems.

[inst1]organization=Global Technology, Wal-mart,addressline=860 W California
Ave, city=Sunnyvale, postcode=94086, state=California, country=United States

[inst2]organization=Global Technology, Wal-mart,addressline=Cessna Business
Park, Kadubeesanahalli, ORR, city=Bengaluru, postcode=560087, state=Karnataka,
country=India

##  1 Introduction

The rapid advancement of artificial intelligence, specifically the blossoming
of generative models, has ushered in a new era for diverse fields,
fundamentally altering the way we perceive, interact, and innovate with
technology. Tools like ChatGPT (OpenAI, 2023) and DALL-E (Reddy et al., 2021)
shows the world their incredible power of generating contents such as text or
image.

The impressive capability to generate human-like text, to understand context,
and to even create artistic content was nothing short of revolutionary and
brought people attention to the science benethes these AI tools — Deep
Generative Models (DGMs). Practitioners are taking actions to attempt
leveraging DGMs to pivot their areas as diverse as healthcare (Smith et al.,
2023), finance (Noguer i Alonso and Pacheco Aznar, 2023), entertainment, and
more, in a storming way.

The retail supply chain is a key component in modern economy, ensuring the
smooth flow of goods from origin to end-users. In general, it can be segmented
into three distinct phases: purchase, logistics, and sell (Ge et al., 2019;
Ayers and Odegaard, 2017). The purchase phase entails the strategic
acquisition of goods from vendors, a process driven by demand forecasting and
procurement policies. Logistics, the second phase, involves the transportation
of these goods from vendors to distribution centers (DCs), and subsequently
from DCs to retail outlets. This step is vital in maintaining the efficiency
and effectiveness of the supply chain. The final phase, sell, encompasses the
actual sale and subsequent delivery of products to customers, completing the
cycle from supplier to consumer.

Due to the complexity of the modern retail supply chain, a host of challenges
arise in each phase, motivating researchers and practitioners to seek
innovative solutions. For example, forecasting demand accurately is a long-
existing challenge in the purchase phase, often fraught with the
unpredictability of consumer behavior and market conditions (Abolghasemi et
al., 2020). This challenge is compounded by the necessity to model and
simulate the supply chain to anticipate potential disruptions and bottlenecks.
The complexity of creating accurate models arises from the numerous variables
and stochastic elements inherent in supply chain processes, requiring advanced
tools and methodologies (Aamer et al., 2020; Babai et al., 2022). For another
instance, the optimality gap —the divergence between the most efficient
theoretical solution and the real-world execution—poses a persistent issue.
This gap can be attributed to various factors, including but not limited to,
logistical stochasticity, supply chain end-to-end visibility, and
computational NP-hardness (Chen, 2017). These discrepancies often lead to
inefficiencies that ripple throughout the supply chain, manifesting in delayed
deliveries, increased carrying costs, and diminished service levels (Qu et
al., 2020; Caro et al., 2020) and therefore it’s always impactful and valuable
to develop advanced methods to improve the optimality. As a result, the
ability to adapt and optimize the end-to-end process is not just a competitive
advantage but a survival imperative in the volatile landscape of global
commerce.

With the blossoming of generative AI, some researchers have attempted to
provide a survey or review of existing DGMs being used in supply chain
problems. Ooi et al provided a comprehensive discussion on potentials of using
DGMs accross disciplines including generative AI in retail (Ooi et al., 2023).
This work is timely and insightful, however, it’s more of a high-level
discussion of the potentials of DGMs in many areas’ supply chain without
focusing on retail supply chain. Similar survey can also be found in reference
(Sankaran and Holmes, 2023). Article (Wamba et al., 2023) is attempting to
address how DGMs applications such as ChatGPT may be a game changer in 21st
centenry supply chain excellence. This paper is focusing on specific
applications of DGMs, mainly language model, instead of discussing leveraging
techniques and methods of DGMs in retail supply chain. Reference (Hosseinnia
Shavaki and Ebrahimi Ghahnavieh, 2023) aims to provide a comprehensive vision
by reviewing 43 papers on the applications of Deep Learning methods to supply
chain management, trends, perspectives, and potential research gaps. Although
it contains relevant insights on leveraging deep learning in supply chain, it
is not exclusively focused on DGMs or retail supply chain. Reference (Oliveira
and Pereira, 2023) is a timely survey which briefly explains recent
developments in DGMs, including Energy-based models, Variational Autoencoders,
Generative Adversarial Networks, and Autoregressive models. It also reviews
recent applications of these techniques to supply chain, although it does not
discuss supply chain application in a end-to-end way. This survey is a good
starting point for practitioners to understand the landscape of DGMs, however,
it’s not focused on retail supply chain and lacks insights on how DGMs can be
applied to solve specific problems in retail supply chain.

To our best knowledge, there is no other existing survey or review paper that
is focusing on the existing and potential applications of DGMs in retail
supply chain. This paper is to fill this gap by providing a comprehensive
review of both techniques and business usecases of DGMs in retail supply
chain. The contributions of this paper can be summarized as follows:

  * 1.

This paper provides a taxonomy and overview of state-of-the-art DGMs and their
variants.

  * 2.

This paper reviewes existing DGM applications in retail supply chain from a
end-to-end view of point.

  * 3.

This paper discusses insights and potential directions on how DGMs can be
further utilized on solving retail supply chain problems.

The rest of this paper is organized as follows: Section 2 provides a brief
overview of DGMs with taxonomy and introduces serverl popular models and their
variants. Section 3 discusses the applications of DGMs in the purchase phase.
Section 4 discusses the applications of DGMs in the logistics phase. Section 5
discusses the applications of DGMs in the sell phase. And finally, Section 6
provides a summary of previous three sections and concludes the paper.

##  2 Deep Generative Models Overview

Generative modeling palys an increasingly important role in the realm of
machine learning, especially when the task revolves around understanding the
intricate patterns and structures within data. At its core, a generative model
seeks to capture the inherent distribution of the data it is trained on,
subsequently leveraging this learned distribution to produce or generate novel
data samples that are statistically similar to the original dataset
(Harshvardhan et al., 2020). Such models do not just replicate or mimic the
input data; instead, they endeavor to unlock the underlying probabilistic
mechanisms that might have led to the data to simulate and produce new
instances of it.

Contrasting this approach is discriminative modeling. While generative models
aim to represent the joint probability distribution of the input and output
data, discriminative models focus primarily on the conditional probability of
the output given the input (Minka, 2005). Essentially, discriminative models
are more concerned with distinguishing between different data classes or
categories and making decisions based on these distinctions. For example, in a
binary classification task, a discriminative model would focus on determining
the boundary that separates the two classes, whereas a generative model would
first seek to understand the distribution of each class individually.

Delving deeper into the landscape of generative modeling, the concept of deep
generative models (DGMs) is obtaining higher and higher visibilities, with the
recent developments of large language models (LLMs). These are a subset of
generative models that leverage the deep learning architectures, such as deep
neural networks, to capture and reproduce more complex data distributions. The
depth in DGMs, afforded by multiple layers of nonlinear processing units,
enables them to model intricate patterns and structures in high-dimensional
data, often outperforming their shallow counterparts (Xu et al., 2015). Their
impressive potential and applicability of generativing complex data like
images or text, have made DGMs a focal point of contemporary machine learning
research and applications.

There are many ways to categorize DGMs according to the their underlying
mechanisms, principles of the models and data modality. For example, based on
data modality,DGMs can be categorized into image generative models,text
generative models, audio generative models, and multi-modal generative models.
For another instance, based the network architectures, DGMs can be calssified
into forward neural network models, recurrent neural network models,
convolutional nerual network, and attention-based nerual networks etc.

A most common categorization of DGMs is based on learning paradigm, with which
DGMs are calssified into two major categories — explicit density models and
implicity density models (Oussidi and Elhassouny, 2018; Turhan and Bilge,
2018; Regenwetter et al., 2022).

Explicit Density Models (EDMs) specify a functional form for the data
distribution (probability density function) to be evaluated explicitly. These
models are designed to offers direct insights into the data distribution
allowing for exact likelihood calculation, data density estimation, etc. On
the other hand, Implicit Density Models (IDMs) do not provide a closed-form
expression for the data distribution but can generate samples from it. These
are designed to generate new data points that should follow the target
distribution without explicitly evaluating the probability of a given data
point and to be less restricted by the choice of functional form for the
distribution.

According to the different way of estimating the probability likelihood, the
EDMs can be further divided into two groups — tractable density models and
approximate density models. IDMs can also be splitted into Adversarial-based
and Score-based models based on their different generating processing. Figure
1 shows the taxonomy of deep generative models.

In the following sections, we will briefly introduce serverl popular models
(and their variants) in each category to provide readers a high level
understanding of their mechanisms.

forked edges, for tree=rounded corners, align = center,font=,edge=thick,
where=level ()¡2drawfor tree=folder,grow’=east, where=level ()==2draw,
where=level ()==1calign=edge midpoint [Deep Generative Models [Explicit
Density Models [Tractable Density
Models [Autoregressive] [Flow-based] ] [Approximate Density
Models [VAEs] [Deep EBMs] ] ] [Implicit Density Models [Adversarial-Based
Models [GANs] ] [Score-Based
Models [DDPM] ] ] ]

Figure 1: Taxonomy of Deep Generative Models

###  2.1 Explicit Density Models

####  2.1.1 Tractable Density Models

Tractable Density Models (TDMs) are a class of models that can evaluate the
probability density function (PDF) of given data points. This is achieved by
specifying a functional form $p(x)$ for the data distribution, allowing for
exact likelihood calculation, data density estimation, etc. The generation of
a new data point is to sample a new data point $x_{new}$ from the distribution
$p(x)$, where maximum likelihood estimation is tractable.

The tractability of these models is a key advantage, enabling them to offer
direct insights into the data distribution. However, this tractability comes
at the cost of flexibility, as the choice of functional form for the
distribution can be restrictive.

There are two major types of TDMs, namely autoregressive models and flow-based
models. Autoregressive models use parameterized functions to generate data
points sequentially, given all previous ones. The probability of a data point
$x$ is given by the product of the conditional probabilities of each element
$x_{i}$ in the data point, conditioned on all previous elements $x_{<i}$, that
$p(x)=\prod_{i=1}^{n}p(x_{i}|x_{<i})$, which is known as the chain rule of
probability (Bond-Taylor et al., 2021).

Flow-based models are designed to learn feature representation (latent
variables) with tractable likelihoods. The key idea of flow-based models is to
use invertible transformations to map a simple distribution to a complex
distribution, allowing for exact likelihood calculation
$p_{\theta}(\mathbf{x})=p(\mathbf{z})\left|\det\left(J_{f_{\theta}}(\mathbf{x})\right)\right|$,
where $\mathbf{z}$ is latent variable. Please refer literatrue (Kobyzev et
al., 2020) for more details on flow-based models. The following sections will
provide more details on autoregressive models.

There are a few popular autoregressive models including Fully Visible Sigmoid
Belief Networks (FVSBNs) (Gan et al., 2015), Neural Autoregressive
Distribution Estimator (NADE) (Uria et al., 2016), and Masked Autoencoder for
Distribution Estimation (MADE) (Khajenezhad et al., 2020), PixelCNN (Van den
Oord et al., 2016), PixelRNN (Van Den Oord et al., 2016), and etc. Each model
has its own advantages and disadvantages. For example, FVSBNs are easy to
train and sample from, but they are limited to modeling low-dimensional data.
NADE is a simple model that can be trained efficiently, but it is limited to
modeling low-dimensional data. MADE is a more complex model that can be
trained efficiently, but it is limited to modeling low-dimensional data.
PixelCNN and PixelRNN are more complex models that can be trained efficiently,
but they are limited to modeling low-dimensional data.

An important development in this category is the attention-based models, which
uses self-attention mechanism to capture the long-range dependencies in the
data, enabling the method to select the previous steps that are more relevant
for the generation processing of the current step.

When the self-attention mechanism is used with an encoder-decoder nerual
network structure, an innovative and impactful architect is developed up with
the name of Transformers (Vaswani et al., 2017). In Transformers, self-
attention mechanism is used to enbale parallelization of the generation
process and detect relations between tokens. This is a key efficiency and
effectiveness advantage of Transformers over RNNs, which are inherently
sequential and cannot be parallelized.

A host of generative variants of Transformers are widely used in the field of
natural language processing (NLP) tasks. For example, Bidirectional Encoder
Representations from Transformers (BERT) (Devlin et al., 2018) is an encoder-
only Transformers that randomly maskes certain tokens in the input to avoid
seeing other tokens and uses bidirectional self-attention to learn contextual
representations of words, called embedding.

Another well known variants is called Generative Pre-trained Transformer (GPT)
(Radford et al., 2018), which is a decoder-only Transformers that uses
unidirectional self-attention to autoregressively generate the output sequence
by predicting the next token given the previous tokens. ChatGPT, a chat tool
being able to generate human-like text, is built on its 3.5 and 4th version of
GPT, trained on a massive dataset with as much as 175 billion parameters from
12 transformer layers and 12 attention head.

####  2.1.2 Approximate Density Models

Some other explicit density models often start with an explicit distribution
(a prior), but exact inference on this distribution is intractable due to the
complexity of the model and data. Thus, they rely on approximate methods to
infer the distribution and are called approximate density models (ADMs).

Variational Autoencoders (VAEs) (Kingma and Welling, 2013) are a class of
generative models that use variational inference to approximate the
intractable posterior distribution of the latent variables.

VAEs follows an encoder-decoder structure, where the encoder is used to infer
the latent variables from the input data and the decoder is used to generate
the output data from the latent variables. During the training process,
instead of defining the density function in a tractable way as
$p(x)=\prod_{i=1}^{n}p(x_{i}|x_{1},\ldots,x_{i-1})$ in TDMs, VAEs defines the
density function in an intractable way $p_{\theta}(x)=\int
p_{\theta}(z)p_{\theta}(x|z)dz$, where $z$ is the latent variable. Then VAEs
are trained to maximize the evidence lower bound (ELBO) of the data, which is
used to approximate the intractable posterior distribution of the latent
variables.

VAEs has its own obvious advantages and disadvantages. On one side, an unique
characteristic of VAEs is to encode input data points as a distribution
instead of a single point, making the generation process more robust and
regularized. However, on the other hand, VAEs tend to be fickle and unstable,
as small changes in input data can signficantly change the output (Dosovitskiy
and Brox, 2016).

Overall, VAEs is a popular tool for generating data, especially for images.
But recent developments of other DGMs such as adversarial-based or diffusion-
based models have made VAEs less popular in the field of image generation.

Another popular ADM is called Deep Energy-Based Models (EBMs) based on the
concept of the energy function (LeCun et al., 2006). In Deep EBMs, the
probability density function $p(x)$ can be expressed by an genergy function
$E(x)$ that $p(x)={e^{-E(x)}}/{\int_{\tilde{x}\in X}e^{-E(\tilde{x})}}$. The
learned genergy function can associate realistic points with lower values and
unrealistic points with higher values.

The key challenge of training EBMs is to handle the intractable normalization
constant in the denominator of the density function. To address this
challenge, EBMs use Markov Chain Monte Carlo (MCMC) methods to generate
samples from the mdoel distribution, estimate the gradient of the energy
function (Song and Kingma, 2021). For more details and optimize the
contrastive divergence (Carreira-Perpinan and Hinton, 2005).

There are serveral popular EBMs inlcuding Boltzmann machine (Hinton and
Sejnowski, 1983), Restricted Boltzmann machine (RBM) (Hinton, 2002), and Deep
Boltzmann Machines (DBMs) (Salakhutdinov and Hinton, 2009). The advantages and
disadvantages of EBMs are obvious. EBMs have fewer parameters and more
flexible generation process whereas MCMC methods such as random walk and Gibbs
sampling makes the training inefficient when applied to high dimensional data.

So far, we introduced four explicit density models, which define the
probability density function in a explicit way. The tractability of density
function makes them be divided to two categories, namely tractable density
models and approximate density models. The summarized density function of
these models are as follows:

  * 1.

Autoregressive models

$\displaystyle p_{\theta}(x)=\prod_{i=1}^{n}p_{\theta}(x_{i}|x_{<i})$ (1)

where $x$ is the data point and the density function is tractable.

  * 2.

Flow-based models

$\displaystyle
p_{\theta}(\mathbf{x})=p(\mathbf{z})\left|\det\left(J_{f_{\theta}}(\mathbf{x})\right)\right|$
(2)

where $\mathbf{z}$ is the latent variable and the density function is
tractable.

  * 3.

Variational Autoencoders (VAEs)

$\displaystyle p_{\theta}(\mathbf{x})=\int
p(\mathbf{z})p_{\theta}(\mathbf{x}|\mathbf{z})d\mathbf{z}$ (3)

where $\mathbf{z}$ is the latent variable and the density function is
intractable. To handle the intractability and train the model, ELBO of the
data is used to approximate the posterior distribution.

  * 4.

Deep Energy-Based Models (EBMs)

$\displaystyle
p_{\theta}(\mathbf{x})=\frac{e^{-E_{\theta}(\mathbf{x})}}{\int_{\tilde{\mathbf{x}}\in\mathbf{X}}e^{-E_{\theta}(\tilde{\mathbf{x}})}}$
(4)

where $\mathbf{x}$ is the data point and $E_{\theta}(\mathbf{x})$ is the
energy function. The density function is intractable and MCMC methods are used
to train the model.

###  2.2 Implicit Density

Although explicit DGMs shows strong capabilities in many applications, their
limitations are also obvious. They either strongly restrict the model
architecture to ensure a tractable normalizing constant for likelihood
computation or must use surrogate objectives for maximum likelihood
approximation. In this section, we will review two ways to circumvent the
limitations of explicit DGMs, that are adversarial-based models and score-
based models respectively.

####  2.2.1 Adversarial-Based Models

Instead of explicitly defining the probability density function, adversarial-
based models (ABMs) use a generator and a discriminator to implicitly
discovery the data distribution in an adversarial manner. The generator is to
create fake data points as close to the true distribution as possible, whereas
the discriminator is to distinguish between real and fake data points. The
training process is to train the generator and discriminator simultaneously,
with the generator trying to fool the discriminator and the discriminator
trying to distinguish between real and fake data points. A well trained ABM is
to have a generator create data points that are indistinguishable between real
and fake to the discriminator. A generative adversarial network (GAN) refers
to a class of ABMs that uses a neural network as the generator and another
neural network as the discriminator.

Literatrues (Gui et al., 2021; Creswell et al., 2018) provide a comprehensive
review of GANs and summarized different variants of GANs designed for
different purposes. For example, with different type of nerual network, there
are Deep Convolutional GANs (DCGAN) (Radford et al., 2015), Wasserstein GANs
(WGAN) (Arjovsky et al., 2017), and etc. With different training tricks, there
are Least Squares GANs (LSGAN) (Mao et al., 2017), Energy-based GANs (EBGAN)
(Zhao et al., 2016), and etc. With different architectures, there are
Conditional GANs (CGAN) (Mirza and Osindero, 2014), Auxiliary Classifier GANs
(ACGAN) (Odena et al., 2017), and etc. With different objectives, there are
CycleGANs (Zhu et al., 2017), StarGANs (Choi et al., 2020), and etc. With
different applications, there are Text2Image GANs (Reed et al., 2016),
Image2Image GANs (Isola et al., 2017), and etc.

Although all above ABMs showed their impressive performance in different
applications, their advantages and disadvantages are also obvious in common.

On the advantage side, (1) GANs can paralleliz the generation process, which
is impossible for autoregressive or VAEs, (2) the generation design is more
general with fewer restrictions and is more flexible to various tasks (3) GANs
are subjectively thought of be able to produce better quality generations, e.g
higher resolution images, than other DGMs (Goodfellow, 2016).

On the other side, GANs are often accused of serveral disadvantages: (1) The
adversarial nature of GAN makes them difficult and unstable to train. The non-
cooperation mechanism cannot guarantee convergence and makes the equilibrium
between the generator and discriminator objectives hard to be achieved (2)
Another issue is called mode collapse, where the generator produces only a
limited set of samples, getting stuck on local optima, and fails to capture
the full distribution of the training data (Thanh-Tung and Tran, 2020). (3)
GANs are also known to be sensitive to hyperparameters, which can be difficult
to tune (Bond-Taylor et al., 2021).

####  2.2.2 Score-Based Models

Another way to circumvent the limitations of explicit DGMs is to use score-
based models, which is to model the gradient of the log probability density
function, also known as score function, directly with respect to the data and
guide the model in generating data that is likely under the learned data
distribution (Song et al., 2020c). The score-based models do not have to have
a tractable normalizing constant, and can be directly learned by score
matching (Vincent, 2011).

Unlike EBMs which are trained to maximize the log likelihood of the data with
intractable denominator
$\int_{\tilde{\mathbf{x}}\in\mathbf{X}}e^{-E_{\theta}(\tilde{\mathbf{x}})}$ in
their density function, the score-based models use score function instead of
density function to sidestep the intractability. The score function of a
distribution is defined as $\nabla_{\mathbf{x}}\log p(\mathbf{x})$ and the
score-based model is to learn a function such that
$s(\mathbf{x})\approx\nabla_{\mathbf{x}}\log p(\mathbf{x})$. Similar to
explicit models, the score-based models can be trained by minimizing the
Fisher divergence between the data and model scroes, but without worrying the
intractability. A family of training techniques minimizing Fisher divergence
are called score matching (Song et al., 2020b; Vincent, 2011). Once the score-
based model is trained, an iterative procedure called Langevin dynamics is
used to generate sample from it (Parisi, 1981; Grenander and Miller, 1994),
which utilize MCMC procedure to generate samples using only the score
function. Please refer paper (Song et al., 2020c) for more details.

Denoising Diffusion Probabilistic Models (DDPMs) extend this idea by training
a sequence of models to reverse a process that gradually adds noise to the
data (Ho et al., 2020; Song et al., 2020a) to tranform data into a known prior
distribution. Then, the generation process is the the reverse process called
denoising to generate samples from the noise, using stochastic differential
equations (SDEs) (Song et al., 2020c). DDPMs have several advantages,
including the ability to generate high-quality samples and to perform exact
likelihood computation. However, they can be computationally intensive and may
require careful tuning of the noise schedule and training process. Please
refer paper (Ho et al., 2020; Song et al., 2020a) for more details, where they
also discussed the detailed connections between DDPMs and score-based models.

Score-based models have achieved impressive performance on many usecases,
especially in image generation (Song and Ermon, 2019, 2020; Ho et al., 2022)
(outperform GANs (Dhariwal and Nichol, 2021)), audio synthesis (Chen et al.,
2020; Kong et al., 2020; Popov et al., 2021) and audio generation (Cai et al.,
2020).

##  3 Applications in Purchase Phase

In this section, we will review existing applications of DGMs in the purchase
phase, such as deman forecasting, procurement policy optimization, and etc.
Some potential applications will also be discussed.

###  3.1 Demand Forecasting

A comprehensive survey provided a comprehensive review for using transformer
in time series forecasting (Wen et al., 2022). This paper proposed a new
taxonomy consisting of network design and application, summarized serverl
representative methods in each category and discussed their strengths and
limitations by experimental evaluation, showing performance improvements of
transformer over traditional methods. However, literatrue (Zeng et al., )
questions the validity of this line of research and presents experimental
results on the effectiveness of transformer models for time-series forecasting
on nine real-life datasets, claiming that transformer models are not as
effective as people expect.

Specifically in the context of retail supply chain, literatrue (Vallés-Pérez
et al., 2022) discusses leveraging transformer-based models to forecast
demand. It developes three alternatives to tackle the problem of forecast
sales at day/store/item level. The empirical results show that the proposed
models outperform the baseline models in terms of accuracy and efficiency, by
using sipme sequence to sequence architecture with minimal data processing
effort. In addition, it describes a training trick to make the model time
independent and general. Reference (Joshaghani et al., 2023) proposed an meta-
learning framework based on deep encoder-decoder nerual networks to forecast
demand for retail supply chain. The method is designed to automatically
extracts metafeatures from raw time series and exogenous variables, where
encoder-decoder temporal convolutional networks is to capture time series
features and encoder-decoder attention LSTM is adopted to capture higher
representation features of exogenous retail variables. Paper (Zhang et al.,
2023a) introduces transformer-based models to forecast intermittent demand and
tested empirically on a dataset with 925 intermittent demand items, compared
with two traditional methods such as Croston’s and the Syntetos–Boylan
approximation as well as several popular neural network architectures
including feedforward neural networks, recurrent neural networks, and long
short-term memory. The results show that Transformer performs very well
against other methods in a varity of settings. Similar usage of transformer in
other demand forecasting areas can also be found in finance (Gokhale et al.,
2023), energy (Koohfar et al., 2023), aviation (Wang et al., 2022a), tourism
(Bi et al., 2023).

###  3.2 Merchandising Planning

In retail, merchandising planning is to determine the product assortment,
pricing, and inventory levels for each product category to ensure that the
right products are available at the right time and right place to meet the
demand of customers. In general, its tasks consist of market analysis, product
selection, supplier selection, inventory planning and etc.

A key step of merchandising planning is the market research to understand the
market trends and customer needs: what they buy, when, where, why and how
often. The overall market analysis is huge project but DGMs can help on
serveral specific subtasks such as market research, trend analysis, and etc
that LLMs (please refer (Naveed et al., 2023) for a comprehensive review of
LLMs) can be used to help retailers to understand customer’s perception of
products and brands by analyzing the reviews and comments from customers. For
example, paper (Li et al., 2023c) explored and accessed if and when
appropriate adaptations of these models can substitute for market research
using human surveys, focusing on perception analysis, demonstrating LLMs is a
reliable augmentation or even substitute for human brand perception surveys. A
similar marketing analysis usecases are also seen from paper (Thida, 2023) for
job market and paper (Deng et al., 2023) for financial market.

Another key task of merchandising planning is to select suppliers, a process
of finding, evaluating, and engaging suppliers for acquiring goods and
services. DGMs can also help on this process. For example, supplier selection
in genearl is an optimization problem aiming to achieve the best trade-off
between cost, quality, and delivery stability etc. One typical challenge is
the high numbers of decision attributes and low numbers of data samples for
decision analysis. Paper (Lin et al., 2022a) proposed a dynamic supply chain
member selection algorithm based on conditional generative adversarial
networks (CGANs), which analyzes the index data of the suppliers’ relevant
factors.

###  3.3 Invertory Allocation

Inventory allocation is to determine the optimal assortment of products to be
stocked at each location to maximize the customer experience and subsequently
the profit.

To our best knowledge, we haven’t found any existing applications of DGMs in
inventory allocation and replenishment. However, this paper believes some
potential problems can be solved by DGMs. For example, in the retail context,
items are often sold in an associated way and the demand of one item is often
correlated with the demand of other items. One famous example is the demand of
beer is correlated with the demand of diapers. This correlation is called
cross-selling. Another key factor needs to be considered in inventory
allocation is called substitution effect, which is to consider the demand of
one item can be substituted by the demand of other items. Both cross-selling
and substitution effect requires to understand the correlations between items,
namely the joint distribution of the demand of all items.

Understanding the joint distributions for milliions of SKUs is not the trivial
task with traditional machine learning methods. This paper believes DGMs can
hopefully used in this task, as DMGs is well known for its capabilities of
modeling the joint distribution of multiple variables.

###  3.4 Replenishment Optimization

Inventory replenishment is to determine the optimal quantity of each product
to be ordered at each location to meet the demand, aimining to find the right
balance between the cost of holding inventory and the cost of not having
enough inventory to meet the demand.

The key challenge is to find an optimal expection trade-off between inventory
holding cost and sales opportunity cost, given the variabilities of demand,
replenishment lead time, fulfullment rate etc. To address the stochasticity,
replenishment probelms are usually solved with two steps: (1) prediction of
demand and other stochastic variables; (2) optimization of replenishment
policy. Unlike this prediction-then-optimization structure, paper (Qi et al.,
2023) proposed a one-step end-to-end (E2E) framework that uses deep learning
models to output the suggested replenishment amount directly from input
features without any intermediate step. In its nerual network architecture, an
autoregressive multi-quantile RNN (MQRNN) is used to receives multiple time
series (demand or promotion series) and other fully connected layers are used
to generate optimal results. The experimental results show that the proposed
E2E framework outperforms the traditional methods in terms of inventory cost
and service level.

DGMs are also widely utilized together with deep reinforcement learning
techniques for inventory management problems, where DGMs naturally take the
role to learn the stochasticity of RL rewards. Paper (Sekar and Zeng, 2022)
provides an overview of reinforcement learning algorithms used in inventory
control, highlighting that value-based methods often utilize neural networks
(including DGMs) to approximate the optimal action-value functions and are
most fruitful when data is scarce or gathering new data is difficult. Study
(Andaz et al., 2023) discussed incorporating a deep generative model for the
arrivals process as part of the history replay, with which reinforcement
learning is applied to optimize the replenishment policy. By formulating the
problem as an exogenous decision process, this paper can obtain a reduction to
supervised learning. Literatrue (Dehaybe et al., 2023) discusses the growing
interest in deep reinforcement learning approaches to inventory control
problems, particularly those characterized by features and dynamics too
complex for classical dynamic or mathematical programming, also calling the
capabilities of nerual networks (including DGMs) to approximate the optimal
action-value functions. Paper (Park et al., 2023) focuses on adaptive
inventory replenishment using a structured reinforcement learning algorithm,
particularly effective when demand distributions are non-stationary and change
over time. To address the stochasticity, DGMs are widely utilized together
with deep reinforcement learning techniques. Please also refer papers (Deng
and Liu, 2021; Madeka et al., 2022) for readers interested in this topic.

##  4 Applications in Logistics Phase

In this section, we will review existing applications of DGMs in the logistics
phase, such as network planning, warehouse operations optimization,
transportation optimization, and transportation vehicle routing. Some
potential applications will also be discussed.

###  4.1 Discrete Event Simulation

Discrete Event Simulation (DES) is a powerful tool for modeling and analyzing
complex systems. It is a popular tool in supply chain management, especially
in the logistics phase. For example, DES is used to model and analyze the
operations of the logistics entities to find insightful solutions to improve
the efficiency and effectiveness of the supply chain. The key idea of DES is
to model the system operations as a set of discrete events and each event is
excuted at a specific point in time as th-occurance. The brief proces of DES
is as follows. Given a sequence of events ordered chronologically, the system
state is updated (proceeding the simulation clock) by executing the earliest
event. Then, new events are generated based on all previous executed events
and put into the event queue. The simulation process is repeated until the
system reaches the end condition. Finally, the output of DES is a sequence of
excuted events with their time to be excuted.

Intuitively, the event generating process is similar to the autoregressive
generation process of text, where the next token is generated based on all
previous tokens. This similarity inspires researchers to leverage DGMs to
model the event generating process in DES, given the fact that large retailers
always own a tons of event data that can be used to the training of DGMs.

Although, we haven’t found exact applications of using DGMs in DES, there are
some related work are very similar to this idea. For examples, a close
research track is called temporal point processes (TPPs). TPPs are a class of
stochastic models that describe the occurrence of events over time, given the
probability distribution of time intervals and types of future events
occurrence conditioned on the impacts of historical observations.

Following this topic, literatrue (Shchur et al., 2021) provided extensive
review on using deep learning techniques for TPPs. Futher,paper (Lin et al.,
2022b) designed a framework of generative neural temporal point process
(GNTPP) by employing the deep generative models as the probabilistic decoder
to approximate the target distribution of occurrence events. The paper also
conducted experiments to study the effectiveness of DGMs in TPPs, showing that
GNTPP can improve the predictive performance of TPPs, and demonstrates its
good fitting ability. GNTPP established two tasks: (1) historical encoder to
learn how sequential historical evnets (point time and type) will impact next
evnet, (2) probabilistic decoder is set up conditional probabily of the next
event with parameters obtained by historical encoder. Several DGMs can be
found in each part. For example, reviesed attentive model (Zuo et al., 2020;
Zhang et al., 2021) in historical encoder, VAEs (Pan et al., 2020) and GAN
(Xiao et al., 2017) in probabilistic decoder.

Although DES is not exactly the same as TPPs and has its unique challenges,
this paper holds an optimistic view on the potential of using DGMs in discrete
event simulation.

###  4.2 Vehicle Routing Optimization

Veihecle routing is a classic problem in logistics, which is to find the
optimal route and tour for a fleet of vehicles to serve a set of customers.
Many DGMs researchers treat the routing process as the generation of next
location given the current location, which is similar to the autoregressive
generation process of text. This similarity inspires researchers to leverage
DGMs to model the routing process.

There are tons of studies exploring the possibility of utilizing Transformer
to solve VRPs. The survey (Bogyrbayeva et al., 2022) provides an overview of
machine learning methods applied to solve vehicle routing problems, including
discussions on transformer models and attention mechanisms given their
prevalence in recent machine learning research. Specifically for DGMs, the
work (Kool et al., 2018) is an early study using a standard transformer to
encode the cities and the decoding is sequential with a query composed of the
first city, the last city in the partial tour and a global representation of
all cities. Training is carried out with reinforce and a deterministic
baseline. Later, The work (Bresson and Laurent, 2021) focused on the
architecture of leveraging Transformer architecture for Traveler Salesman
Problem (TSP) and observed that the Transformer architecture can be successful
to solve the TSP expanding the success of Transformer for natural language
process and computer vision. In addtion, reference (Peng et al., 2020)
introduces a dynamic attention model with a dynamic encoder-decoder
architecture, which could dynamically explore node features and exploit hidden
structural information effectively, focusing on vehicle routing problems. In
paper (Rabecq and Chevrier, 2022), a general deep reinforcement learning
framework is introduced to solve routing problems. An encoder based on an
improved Graph Attention Network (GAT) is used, which forms a graph-attention
model with a Transformer decoder. This paper employs two deep reinforcement
learning algorithms to train the model. An improved transformer model with
both multi-head attention mechanism and attention to attention mechanism (AOA)
is proposed in paper (Lei et al., 2022), where the model is applied to solve
the low-carbon multi-depot vehicle routing problem (MDVRP), demonstrating the
versatility of transformer models in addressing various specifications of the
routing problems. Paper (Tang et al., 2023) discusses the use of a Graph
Transformer combined with reinforcement learning to address the classic
combinatorial optimization problem. Article (Fellek et al., 2023) tackles the
Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) and the
Capacitated Pickup and Delivery Problem with Time Windows (CPDPTW) by
developing a deep learning algorithm with an Attention Encoder-Decoder
structure and a novel insertion heuristic for the feasibility check of CPDPTW
problem.

The potential capabilities of DGMs in VRPs is still in a strong debt. Paper
(Wu et al., 2022) pointed out that tranditional methods outperformed
transformer-based models for VRPs in 2021 Amazon Last Mile Routing Challenge
dataset (Merchán et al., 2022). The paper explained as the transformer-based
models require a large amount of data to train, which is not available in the
challenge.

###  4.3 Logistics Network optimization

In the retail supply chian, network optimization consists of multiple key
tasks, such as (1) facility location to determine optimal number, location,
and DC-store assignment of DCs to cover the demand of customers; (2)
transportation network design to determine the optimal transportation routes
and modes for moving loads from DCs to stores; (3) service area optimization
to determine the optimal catchment area of each store aiming to find a proper
balance between service level agreement and transportation cost.

Those problems are long existing and well studied in the literature that most
of the existing methods are based on mathematical programming. Neural networks
are also widely used for solving network planning problems, although they are
not exact DGMs.

For example, paper (Liu et al., 2023c) approximated the matching-based multi-
objective facility location problem as node and edge classfication or
prediction tasks on graphs. Then it used graph convolutional networks (GCNs)
to extract high-dimensional characteristic of grahs and learn optimal policies
to solve the problem instead of relying on heuristics methods. In fact, this
research track is within the scope of leveraging graph neural network (GNN) to
solve combinatorial optimization problems. Please refer literatrue (Joshi et
al., 2019a, b; Khalil et al., 2017) for readers interested in this topic.

Another widely studied problem in network planning is the transportation
routing planning. Its task is to plan and design the optimal routes and modes
between nodes in a transportation network, aiming at minimizing the
transportation cost while satisfying the Service Level Agreement (SLA). The
common methods are based on mixed integer programming, consisting of two
steps: (1) generating valid candidate routes and modes; (2) selecting the
optimal routes and modes to cover origantion-destination demands, a.k.a the
set covering problem (SCP). When the problem scale is growing, the problem can
be expolentially difficult to solve. To reduce the amount of candidate routes
and modes, column generation technique is often used. There are a set of
studies considering using neural network to approximate SCP and reduce the
computational complexity as well. For example, paper (Shafi et al., 2023)
proposed the Graph-SCP to accelerate solving SCP with GNNs. This paper cast an
instance of SCP as a graph and learn a graph neural network (GNN) to predict a
subgraph that contains the solution with offline training. The results showed
that Graph-SCP achieves between 30–70 percent reduction of input problem size
which leads to run times improvements of up to 25x, compared to the commercial
solvers. This research track also belongs to the scope of leveraging GNNs to
solve combinatorial optimization problems.

To our best knowledge, there is no exact studies directly using DGMs to solve
either facility location problem or routing planning problem. A comprehensive
reivew can be seen in (Wang and Tang, 2021), which discussd deep reinforcement
learning in network optimzation problems. However, this paper still holds an
optimistic view on the potential of using DGMs in network planning, such as
incooperating DGMs with reinforcement learning techniques for problems.

###  4.4 Estimated Time of Arrival

Providing customers or stores accurate time of delivery is of great importance
to their purchasing decision and post-purchase experiences. Estimated Time of
Arrival (ETA) is a well defined and widely studied problem and a variety of
prediction algorithms are utilized for vairous operations scenarios. For
example, literatrue (Al-Naim and Lytkin, 2021) reviewed and compared different
algorithms for ETA using geospatial transportation data. Study (Reich et al.,
2019) prodived the survey of ETA prediction methods in public transport
networks. In the context of retail supply chain, particularly in last mile
delivery page, there are some unique challenges.

First, the overall ETA is usally the sum of a sequence of correlated sub-ETAs.
For example, to fulfill the last mile delivery in the omnichannel retail, it
consists of multiple steps: picking, sorting, requesting drivers, loading,
transporting and delivering. These steps are often sequentially dependent that
variation in previous steps will affect distribution of later steps. For
instance, transportation time tends to be shorter if delaying in loading has
happened. This characteristic of sequentially dependency makes the ETA more
difficult to be accurate, as we are not only estimating the distribution in
each single step but also approximating their joint distributions. In this
case, autoregressive DGMs are straightforward to be applied to model the joint
distribution of multiple correlated sub-ETAs. Paper (Zhou et al., 2023)
proposed inductive graph Transformer algorithm for delivery time prediction,
which uses Temporal and Heterogeneous GCN to encode high-order semantic
information of orders into latent element embeddings and then applied a
customized transformer for the ETA predcition.

Other than the above studies, Sun et proposed a novel, brief and effective
framework mainly based on feed-forward network (FFN) for ETA, FFN with
Multifactor Attention (FMA-ETA), where a novel Multi-factor Attention
mechanism is utilized to deal with different category features and aggregate
the information purposefully (Sun et al., 2021). Similar applications of
attion mechanisms can be seen from paper (Reich et al., 2022) where a
combination of an attention mechanism with a dynamically changing recurrent
neural network (RNN)-based encoder library is used for public transport ETA
prediction.

##  5 Applications in Sell Phase

###  5.1 Customer Service and Engagement

Customer service and engagement is a key operation in sell phase to provide
customers with the best service and experience to increase the conversion
rate. With an intuitive understanding, customer service and engagement is a
process of communication between customers and retailers. The communication
can be in various forms, such as text, voice, image, video, and etc.
Tranditional customer service and engagement is often done by human in the
call center, which is time consuming and costly. With the development of DGMs,
especially LLMs, the customer service and engagement can be automated and
improved.

Utilizing LLMs for customer service is straightforward and natural, as LLMs
are well known for their capabilities of natural language generation. From the
engineering practice point of view, there are four major components for a LLM-
based automated customer service system: data collection and preprocessing;
knowledge embedding; model selection;system integration.

(1) Data collection and preprocessing is to collect and preprocess the data
for training or fine tuning the LLMs, including text, voice, image, video, and
etc. The data can be preprocessed by removing the sensitive information, such
as customer name, address, phone number, and etc. The data can also be
augmented by adding noise, such as typos, grammatical errors, and etc. (2)
Knowledge embedding is to convert the collected data or knowledge into vector
representations in the mathematical space. The embedding is to provide the
LLMs contextual information for the converstaion so that models can provide
meaningful response to user queries. Paper (Su et al., 2022) introduced a
instruction-finetuned text embedding method aiming at any task. (3) Model
selection is to select the appropriate LLM for the specific automation task.
This key idea is to find a proper balance between model performance and
inference cost (Chung et al., 2022). (4) And last step is to integrate the
selected model into the system and deploy it to the production environment.
Thanks to the rapid development of LLMs application, there are a lot of open
source projects and tools available for the integration and deployment, such
as langchain (Topsakal and Akinci, 2023) and gradio (Abid et al., 2019).

###  5.2 Search and Recommendation

Search and recommendation is to provide customers with the most relevant
products based on their search queries or browsing history. It is a one of
most critical tasks in the sell phase for helping customers find the products
they want and increase the conversion rate. Recent development of DGMs,
especially LLMs, will certainly have a huge impact and pivots the research
direction of search and recommendation. For example, the tranditional
interface is by key words, where customer input key words such as “XX phone”
or “YY laptop” and returns the most relevant products. In the era of LLMs, the
input can be comprehensive and conversational such as “I need a list of items
for my upcoming road trip”. This is a huge improvement and will certainly
improve the search accuracy and customer experience.

Paper (Li et al., 2023b) provides a timely survey and visionary discussion for
generative recommendation with LLMs. In this paper, it divides the generaive
recommendation into two major steps (1) ID creation (2) Generative
recommendation.

ID creation is to create an ID for each item such as a sequence of tokens that
can uniquely identify an entity. To implenment generative recommendation with
LLMs,the input (particularly user and item IDs) should be made into the right
format that is compatible with LLMs. The key idea is to use a small amount of
tokens to distinguish as well as effectivelya and accurately distinguish an
astronomical number of items. This process is similary to embedding the items
into a lower representation space. There are a few existing ways to create
IDs. For instance, Singular Value Decomposition acquires an items ID tokens
from its latent factors (Petrov and Macdonald, 2023). A noise-adding operation
can ensure that there are no identical item embeddings, and thus make each
item ID unique. Another way is called product quantization (PQ). Paper (Hou et
al., 2023) proposes VQ-Rec, a novel approach to learning Vector-Quantized item
representations for transferable sequential Recommenders based on the idea of
product quantization (Jegou et al., 2010). Other ID creation methods can be
seen from colloerative indexing (Hua et al., 2023).

After IDs are created, the next step is to generate recommendations with LLMs.
Given the prompt, the recommendation process is basiccally to generate outputs
(predicted item IDs) in an autoregressive way like natural language
generation. The recommendation process is often involved with leveraging LLMs
for following tasks such as rating predcition (Liu et al., 2023b), top-N
recommendation (Wang et al., 2022c; Zhang et al., 2023b), sequential
recommendation (Xu et al., 2023; Petrov and Macdonald, 2023), explainable
recommendation (Li et al., 2023a; Cui et al., 2022), review summarization (Liu
et al., 2023a; Wang et al., 2022b) and converstaional recommendation (Friedman
et al., 2023; Gao et al., 2023). For more information, please refer to paper
(Li et al., 2023b).

Overall, generative search and recommendation is a hot and promising research
direction and we will see more and more theoretical advancements as well as
practical applications in this area in a short future.

##  6 Discussion

This paper provides a comprehensive review of DGMs, and its existing and
potential applications in retail supply chain. We first introduced the
definition of DGMs, its taxonomy, and some popular models. Then, we reviewed
the existing applications of DGMs in the purchase phase, logistics phase, and
sell phase, while discussed our insights for their the potentials.

Overall, from the model capability perspective, all DGMs showed their
impressive potentials in the area of retail supply chain. We belive DGMs are
powerful tools for the following two scenarios:

  * 1.

For predictive tasks. DGMs showed superier performance in modeling the joint
distribution or sequential dependency of multiple variables, such as demand
forecast, ETA and so on.

  * 2.

For prescriptive tasks. DGMs showed greate potentials for stochastic
optimization, by incooperating with reinforcement learning techniques for
problems with sequential decisions, such as inventory replenishment, routing
planning and so on.

However, other than that, we haven’t seen much obvious advantages of DGMs over
traditional methods for the deterministic optimization problems, such as staff
scheduling optimization or network planning.

In addtion, some relative mature applications such LLMs have been showing its
pivotal role in the retail supply chain. For example, it’s increasingly
promising to utilize LLMs for customer service and engagement, search and
recommendation, and etc. We believe LLMs will continue to play a key role in
the retail supply chain in the near future.

DGMs are still in its early stage and there are a lot of challenges and
opportunities. We believe DGMs will have a huge impact on the retail supply
chain in the near future.

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