Any INFORMS Journal IJDS-0001-1922.65

Forecasting Automotive Supply Chain Disruption with Heterogeneous Time Series

Bach Viet Do

Ford GDIA, 22001 Michigan Ave Dearborn MI 48124 USA<EMAIL_ADDRESS>
Xingyu Li

Ford GDIA, 22001 Michigan Ave Dearborn MI 48124 USA<EMAIL_ADDRESS>
Chaoye Pan

Ford GDIA, 22001 Michigan Ave Dearborn MI 48124 USA<EMAIL_ADDRESS>
Oleg Gusikhin Ford GDIA, 22001 Michigan Ave Dearborn MI 48124 USA,
<EMAIL_ADDRESS>
Operational disruptions can significantly impact a company’s performance.
Ford, with its 37 plants globally, uses 17 billion parts annually to
manufacture six million cars and trucks. With up to ten tiers of suppliers
between the company and raw materials, any extended disruption in this supply
chain can cause substantial financial losses. Therefore, the ability to
forecast and identify such disruptions early is crucial for maintaining
seamless operations. In this study, we demonstrate how we construct a dataset
consisting of many multivariate time series to forecast first-tier supply
chain disruptions, utilizing features related to capacity, inventory,
utilization, and processing, as outlined in the classical Factory Physics
framework. This dataset is technically challenging due to its vast scale of
over five hundred thousand time series. Furthermore, these time series, while
exhibiting certain similarities, also display heterogeneity within specific
subgroups. To address these challenges, we propose a novel methodology that
integrates an enhanced Attention Sequence to Sequence Deep Learning
architecture, using Neural Network Embeddings to model group effects, with a
Survival Analysis model. This model is designed to learn intricate
heterogeneous data patterns related to operational disruptions. Our model has
demonstrated a strong performance, achieving 0.85 precision and 0.8 recall
during the Quality Assurance (QA) phase across Ford’s five North American
plants. Additionally, to address the common criticism of Machine Learning
models as ”black boxes,” we show how the SHAP framework can be used to
generate feature importance from the proposed model’s predictions. It offers
valuable insights that can lead to actionable strategies and highlights the
potential of advanced machine learning for managing and mitigating supply
chain risks in the automotive industry.

Supply Chain Disruption; Supply Chain Resilience; Supply Chain Deep Learning;
Survival Analysis; Sequence-to-Sequence

##  1 Introduction

Ford maintains a complex supply chain and operational network, operating 37
plants globally, utilizing 17 billion parts annually to manufacture six
million cars and trucks. The company has up to 10 tiers of suppliers between
itself and raw materials. An extended disruption anywhere within this
extensive supply chain can inflict a substantial financial impact on the
company. In the literature, scholars and practitioners generally agree that
disruption negatively impacts the company [31, 12, 26]. However, there is less
agreement on classifying and forecasting such disruption [18, 37, 40, 34].
Understanding the different sources and risks of disruption is critical
because we can make informed decisions on which disruptions warrant mitigation
investment [32]. Supply chain disruptions can stem from many sources, such as
global pandemics, natural disasters, geopolitical risks, terrorist attacks,
environmental hazards, volatile fuel prices, rising labor costs, currency
fluctuations, counterfeit parts and products, delivery delays, market changes,
and supplier performance issues.

In the supply chain literature, efforts have been dedicated to modeling the
propagation of disruption effects through supply chain networks. One of the
early studies by [15] analyzed the propagation of disruption effects through a
simplified supply chain. [30] evaluated response strategies to minimize
service level impacts in a multi-echelon network during random-duration
disruptions. [24] examined the interaction between supplier and buyer response
strategies under random-duration disruptions. Simchi-Levi et al. [32, 33]
introduced the Time-To-Recover (TTR) metric to quantify the financial impact
of disruptions on the entire supply chain, measured by the Risk Exposure Index
(REI). This index enables companies to rank their direct and indirect
suppliers, identifying the ”weak links” in their supply chains. Additionally,
[33] proposed the Time-To-Survive (TTS) concept, which defines the maximum
duration the entire supply chain can generally function before the ripple
effects of a disruption impact performance. These concepts—TTR, REI, and
TTS—have been implemented at Ford Motor Company to manage supply chain risks
[33].

These studies are based on the assumption that disruption events can be
identified well in advance. In the era of Big Data, the rise of Machine
Learning and Artificial Intelligence offers opportunities for predicting
disruptive events with high degrees of accuracy. However, the literature on
utilizing Machine Learning for supply chain risk management remains sparse.
[7] explored the potential of leveraging big data sources related to supply
chains and proposed a Supply Chain Risk Management (SCRM) framework to detect
emergent risks. [11] recognized the predictive capabilities of incorporating a
significant data analytical component into a generic SCRM framework.
Nevertheless, these works are theoretical and lack real-world application or
implementation of the proposed frameworks and models. More concretely, [42]
used a Support Vector Machine classifier to identify disruptions based on the
economic performance of firms within the supply chain, collecting public
financial data for these firms before, during, and after supply chain
disruptions. [2] analyzed historical data from an Original Equipment
Manufacturer (OEM) that produced complex engineering assets to predict order
delays using machine learning models, including Random Forest, Support Vector
Machine, Logistic Regression, and Linear Regression. In their study, the
Random Forest Classifier achieved the best performance. Although both studies
identify potential suppliers with a high risk of disruptions, they do not
provide estimates for the time until these disruption events occur. Such
estimates are crucial for integrating this research into the broader framework
of Supply Chain Risk Management.

As a leading automobile manufacturer, Ford can request and accumulate an
extensive repository of proprietary data on suppliers’ capacity and
performance, providing a potent source for highly accurate predictive
capabilities. Moreover, Deep Learning has recently emerged as preeminent
models in Artificial Intelligence, driven by a decade of rigorous research.
Consequently, many state-of-the-art Machine Learning models are now based on
Deep Learning.

Our contribution in this work is to advance the research on applying Big Data
to forecast supply chain disruptions. We demonstrate the construction of a
complex dataset comprising many multivariate time series that track the
arrangements between Ford and our first-tier suppliers for transporting
critical vehicle parts to Ford’s manufacturing plants. We meticulously select
data features that reflect capacity, inventory, utilization, and process
time—key aspects in the classical Factory Physics (see [14]). Furthermore, we
propose an AI model that integrates an enhanced Sequence to Sequence with
Attention Deep Learning architecture with a parametric survival analysis
likelihood model. This enhanced architecture is designed to model the
heterogeneity in the data, driven by various combinations of plants, sites,
and vehicle parts. Our work introduces an important improvement to the
Seq2Surv model proposed by [22], developed for generic time series exhibiting
similar underlying statistical patterns despite random variations. In
contrast, the disruption behaviors in our data can differ markedly, even
contradictorily, based on the specific values of suppliers, plants, and parts.
While originally developed for supply chain time series data, our methodology
can be generalized to any dataset comprising multivariate time series with
inherent heterogeneity. Lastly, we illustrate how to use the SHAP (SHapley
Additive exPlanations, [27]) framework to calculate feature importance for
each of the model’s predictions. This explainable AI technique provides
valuable insights that can inform actionable strategies for our business
partners.

Our model relies on the Survival Analysis framework to model observed time-to-
disruption data. Survival Analysis, a branch of Statistics, focuses on the
study of time-to-event data. This field encompasses a variety of applications,
such as predicting the survival of cancer patients [39], customer churn [38],
mechanical system failure [35], credit scoring [6], and reliability and
manufacturing problems [22]. The strength of Survival Analysis lies in its
interpretability, flexibility, and ability to handle censored data. However,
one of its notable weaknesses is predictive accuracy. To enhance predictive
performance, numerous studies have extended classical Survival Analysis using
Neural Networks.

[8] were among the pioneers, extending Cox regression by replacing its linear
predictor with a single hidden layer multilayer perceptron (MLP). [17]
revisited this approach within the deep learning framework, introducing
DeepSurv, a model that outperformed traditional Cox models in terms of the
C-index (see [10]). Other similar works include SurvivalNet by [43] , which
fits Cox proportional models using Neural Networks and applies Bayesian
optimization on tuning hyperparameters. [44, 45] utilized Convolutional Neural
Networks instead of MLP in their work. [20] proposed an extension of the Cox
model where the proportionality constraint is relaxed, introducing an
alternative loss function that scales well for both proportional and non-
proportional cases. [22] further extended these methodologies by leveraging
Sequence-to-Sequence with Attention deep learning architecture for time series
survival analysis data.

For discrete-time survival problems, [21] applied neural networks to the
discrete-time likelihood for right-censored time-to-event data, parameterizing
the probability mass function. [9] adopted a similar likelihood approach,
parameterizing hazard rates with a neural network. [19], through simulation
studies and real-world data, found that hazard rate parameterization performed
slightly better. Building on this insight, the authors introduced PC-Hazard,
which parameterization the hazard rate for continuous survival time data by
discretizing the continuous time scale, assuming the continuous-time hazard is
piece-wise constant.

The remainder of the paper is organized as follows: Section 2 reviews the
relevant background and preliminaries. Section 3 offers a detailed explanation
of how to construct a dataset for forecasting supply chain disruptions by
selecting features that reflect key aspects of classical Factory Physics. In
Section 4, we present the Heterogeneous Sequence-to-Disruption AI model.
Section 5 evaluates the model’s performance and discusses methods for
interpreting the results using the SHAP framework. Finally, Section 6
concludes the paper.

##  2 Background

###  2.1 Survival Analysis

In this study, we aim to model the distribution of the time-to-disruption,
denoted as $T^{*}$. In practical scenarios, not all disruption times are
observable and are often subject to right censoring, wherein the observation
period concludes before the disruption event occurs. Let $C^{*}$ represent the
time to the censoring event. Formally, we define:

$\displaystyle T$ $\displaystyle=\min(T^{*},C^{*})$ $\displaystyle Y$
$\displaystyle=\mathbbm{1}(T^{*}\leq C^{*}).$

Here, $T$ is the observed time-to-disruption, and $Y$ is an indicator that
equals $1$ if the disruption event occurs within the observation window
$T^{*}\leq C^{*}$ and $0$ otherwise. This framework is fundamental in Survival
Analysis, a branch of statistics focusing on time-to-event data [16]. As
demonstrated in the next section, in our data, the time-to-disruption is
discrete, ranging from $1$ to $365$ days. The discrete disruption time can be
modeled using a parametric approach (see Chapter 10 in [25]).

Before we define the model, let’s go over some preliminaries. Consider a
random discrete variable $T^{*}$ with the discrete support
$\tau_{1}<\tau_{2}<\ldots<\tau_{j}<\ldots$, the cumulative distribution
function (CDF) is given by $F(t)=P(T^{*}\leq
t)=\sum_{i=0}^{t}\mathbb{P}(T^{*}=t)$, and the corresponding probability mass
function (PMF) is $f(t)=\mathbb{P}(T^{*}=t)$.

In Survival Analysis, it’s often more useful to discuss the survival function,
defined as $S(t)=1-F(t)=P(T^{*}>t)$, along with its associated hazard
function. For a discrete variable $T^{*}$ and two consecutive survival time
points $\tau_{j-1}<\tau_{j}$, the hazard function is denoted
$h(\tau_{j})=\mathbb{P}(T^{*}=\tau_{j}\;|\;T^{*}>\tau_{j-1})=\cfrac{f(\tau_{j})}{S(\tau_{j-1})}=\cfrac{S(\tau_{j-1})-S(\tau_{j})}{S(\tau_{j-1})}$.
As such,

$f(\tau_{j})=h(\tau_{j})S(\tau_{j-1})$ (1)
$S(\tau_{j})=[1-h(\tau_{j})]S(\tau_{j-1}).$ (2)

On the other hand, we see that,

$\displaystyle\mathbb{P}(T=t,Y=y)$ $\displaystyle=\mathbb{P}(T=t,t\leq
C^{*})^{y}\mathbb{P}(T=t,t>C^{*})^{1-y}$
$\displaystyle=f(t)^{y}\mathbb{P}(C^{*}\geq
t)^{y}\mathbb{P}(T^{*}>t,C^{*}=t)^{1-y}$
$\displaystyle=f(t)^{y}\mathbb{P}(C^{*}\geq
t)^{y}\mathbb{P}(T^{*}>t)^{1-y}\mathbb{P}(C^{*}=t)^{1-y}$
$\displaystyle=f(t)^{y}S(t)^{1-y}\mathbb{P}(C^{*}\geq
t)^{y}\mathbb{P}(C^{*}=t)^{1-y}.$

The third equality is due to $T^{*}$ and $C^{*}$ are independent. Since random
variable $T$ is discrete, we can model the hazard function using a parametric
distribution with parameter $\theta$. We define $\kappa(t)$ as the index of
the discrete time point in the support, i.e., $t=\tau_{\kappa(t)}$. For a
sample of observations $(t_{1},y_{1}),(t_{2},y_{2}),\ldots,(t_{n},y_{n})$, the
log-likelihood function can be expressed as a sum of individual log-likelihood
contributions as follows using equations (1) and (2).

$\displaystyle l(\theta)$ $\displaystyle=\sum_{i=1}^{n}y_{i}\log
f_{\theta}(t_{i})+\sum_{i=1}^{n}(1-y_{i})\log
S_{\theta}(t_{i})+\sum_{i=1}^{n}\left[y_{i}\log P(C^{*}_{i}\geq
t_{i})+(1-y_{i})\log\mathbb{P}(C^{*}_{i}=t_{i})\right]$
$\displaystyle\propto\sum_{i=1}^{n}y_{i}\log
f_{\theta}(t_{i})+\sum_{i=1}^{n}(1-y_{i})\log S_{\theta}(t_{i})$
$\displaystyle=\sum_{i=1}^{n}y_{i}\log h_{\theta}(t_{i})+y_{i}\log
S(\tau_{\kappa(t_{i})-1})+\sum_{i=1}^{n}(1-y_{i})\log[1-h_{\theta}(t_{i})]+(1-y_{i})\log
S_{\theta}(\tau_{\kappa(t_{i})-1})$
$\displaystyle=\sum_{i=1}^{n}\left[y_{i}\log
h_{\theta}(t_{i})+(1-y_{i})\log[1-h_{\theta}(t_{i})]+S(t_{\kappa(t_{i})-1})\right].$

In the second equality, terms related to censoring times $C_{i}$ are omitted
from the log-likelihood function as they are constant and do not contain
parameter $\theta$. Utilizing equation 2 recursively in the final term of the
last equality above, we can finally write the log-likelihood of the sample
data as,

$l(\theta)=\sum_{i=1}^{n}\left[y_{i}\log
h_{\theta}(t_{i})+(1-y_{i})\log[1-h_{\theta}(t_{i})]+\sum_{j=1}^{\kappa(t_{i})-1}\log[1-h_{\theta}(\tau_{j})]\right].$
(3)

Observe that the equation 3 consists solely of the hazard function. To model
the observed data, we simply need to define the form of the hazard function.
Since the hazard function represents a probability in discrete form, its range
must lie between 0 and 1. One suitable choice is the logistic hazard function
(see [3]). Assume that the time-to-disruption is also conditioned on
covariates/features $X$,

$h(\tau_{k})=\cfrac{1}{1+\exp\left(-\theta_{k}^{T}X\right)},k=1,2,\ldots,$ (4)

where $\theta$ is the model parameter and the linear combination $\theta^{T}X$
reflects the linear relationship between the features and the model parameter.

###  2.2 Sequence-to-Sequence Architecture

The Seq2Seq model, introduced by [36] at Google, represents a pivotal
milestone in deep learning. This model processes a sequence of inputs to
generate a corresponding sequence of outputs. While initially developed to
address the challenges of machine translation, the Seq2Seq model has since
become a foundational framework for various natural language processing tasks
and has been adopted for time series data in other fields.

The core architecture of the Seq2Seq model is the encoder-decoder framework,
which is frequently implemented using Recurrent Neural Networks (RNNs, see
[29]). The encoder’s primary function is to convert the sequential input data
into hidden states and a context vector. The context vector, the aggregated
sum of these hidden states, encapsulates the information from the input
sequence into a fixed-size representation.

Once the input sequence has been encoded, the decoder generates the output
sequence. It leverages the context vector and hidden states to produce the
output sequence. Operating in an autoregressive manner, the decoder generates
one unit of the output sequence at a time. This step-by-step generation
ensures that each subsequent unit is conditioned on the previously generated
units, thereby maintaining coherence in the output sequence.

A significant enhancement to the Seq2Seq model is integrating the Attention
mechanism proposed by [1]. This mechanism enables the model to dynamically
focus on different parts of the input sequence while generating each output
unit. Specifically, the context vector is computed as a weighted sum of the
encoder’s hidden states, with the weights reflecting the relevance of each
hidden state to the current decoding step. This dynamic focusing capability
significantly improves the model’s performance, particularly for tasks
involving long and complex input sequences.

###  2.3 Seq2Surv Model

Leveraging the power of Sequence-to-Sequence neural networks and the Attention
mechanism for modeling sequential data, [22] employs this advanced deep
learning architecture to handle time series data in the reliability and
manufacturing domain. This approach replaces the linear predictor in Cox
proportional hazard models (see Chapter 5 in [25]) and discrete time logistic
hazard parametric models (see equation 4) with learnable non-linear functions.
In this methodology, time series are treated as sequential input to the
Encoder, which [22] implemented using Bidirectional Gated Recurrent Units
(GRUs), as introduced by [5]. The GRU, similar to a Long Short-Term Memory
network (LSTM, see [13]) with its gating mechanisms for input and forgetting
features, lacks output gates, resulting in fewer parameters than LSTMs. The
Decoder in Seq2Surv then generates a sequence of survival probability
estimates for the entire lifetime, which is then used in the Survival
Analysis’ log-likelihood function to model the observed disruption times.

##  3 Model Performance & Explainability

###  3.1 Predictive Performance

Ford Plant | Precision | Recall | Normalized Confusion Matrix
---|---|---|---
True Positive | False Negative | False Positive | True Negative
Kansas City Assembly Plant | 0.85 | 0.82 | 0.23 | 0.05 | 0.04 | 0.68
Michigan Assembly Plant | 0.95 | 0.9 | 0.22 | 0.02 | 0.00 | 0.75
Dearborn Truck Plant | 0.95 | 0.81 | 0.15 | 0.04 | 0.01 | 0.8
Ohio Assembly Plant | 0.95 | 0.86 | 0.21 | 0.03 | 0.01 | 0.75
Kentucky Truck Plant | 0.85 | 0.81 | 0.25 | 0.06 | 0.04 | 065

Table 1: Quality Assurance (QA) performance of the model for five selected
plants in North America. Metrics were calculated using the definitions of
Adapted True Positive, False Negative, False Positive, and True Negative as
previously described. For brevity, ’Adapted’ is omitted in the column names.

In this section, we detail the approach for evaluating the proposed model at
Ford to ensure its quality. The trained model estimates the time until a
disruption event occurs related to the shipment of vehicle parts between a
supplier and a Ford plant. To assess the model’s performance, we modify and
adapt the standard classification metrics to fit our specific needs.

In binary classification, the two standard metrics for evaluation are
Precision and Recall (see [4]). Binary classification involves two labels:
positive and negative. For a given classifier, True Positives (TP) are the
instances where the classifier correctly identifies positives. True Negatives
(TN) are the instances where the classifier correctly identifies negatives.
False Positives are instances where the classifier incorrectly predicts
positives when the true label is negative. Conversely, False Negatives (FN)
are instances where the classifier incorrectly predicts negatives when the
true label is positive. Formally, Precision and Recall are defined as,

$\displaystyle\text{Precision}=\cfrac{\text{TP}}{\text{TP}+\text{FP}}$
$\displaystyle\text{Recall}=\cfrac{\text{TP}}{\text{TP}+\text{FN}}.$

Precision and recall range from $0$ to $1$. The closer these values are to
$1$, the better the classifier’s performance. Specifically, recall quantifies
the proportion of actual positive data points that the classifier successfully
identifies. Precision, on the other hand, measures the proportion of predicted
positive cases that are, in fact, positive. Informally, recall measures how
effectively a classifier identifies positive events (disruptions in this
context), while precision measures the frequency with which the classifier
avoids generating false alarms. These two metrics offer a comprehensive view
of a model’s classification performance.

Given that the model proposed in Section LABEL:model estimates the time until
a disruption, we first define a forecasting horizon, $\Delta$ (in days), and a
margin-of-error, $\epsilon$ (in days), to evaluate its performance. We
introduce the following definitions to adapt the concepts of True Positives,
False Positives, True Negatives, and False Negatives to our specific problem.
Given an observation time $t_{0}$, and the forecasting window $\Delta$ and
margin-of-error $\epsilon$, $t_{0}+\Delta$ represents a time point $\Delta$
days from $t_{0}$. Adapted True Positives (ATP) are defined as the number of
disruptions that occur exactly $\Delta$ days from $t_{0}$ and are estimated by
the model within $\epsilon$ days of $t_{0}+\Delta$. The difference between the
model’s estimated time and $t_{0}+\Delta$ must fall within the range
$[-\epsilon,\epsilon]$ days. Adapted False Positives (AFP) are instances where
the model predicts a disruption time exactly $\Delta$ days from $t_{0}$, but
no disruptions actually occur within $\epsilon$ days of $t_{0}+\Delta$.

Conversely, Adapted True Negatives (ATN) are instances where the model
estimates the disruption time to be more than $\Delta$ days from $t_{0}$, and
no disruptions occur between $t_{0}$ and $t_{0}+\Delta$. Adapted False
Negatives (AFN) are instances where the model predicts a disruption time
beyond $\Delta$ from $t_{0}$, while disruptions actually occur within $\Delta$
days from $t_{0}$.

With these definitions, Adapted Recall is computed as the ratio of Adapted
True Positives (ATP) to the total of ATP and Adapted False Negatives (AFN).
Likewise, Adapted Precision is the proportion of ATP relative to the sum of
ATP and Adapted False Positives (AFP). Additionally, we provide the Normalized
Confusion Matrix in Table 1 also known as the error matrix. This table offers
a detailed visualization of the four cases: True Positives, False Negatives,
False Positives, and True Negatives. The normalization is done by dividing
each case by the total number of cases.

Model on Data | Performance Metrics
---|---
Precision | Recall
Seq2Surv on Kansas Assembly Plant Data | 0.62 | 0.3
Seq2Surv on Kentucky Truck Plant | 0.52 | 0.21
Heterogeneous Seq2Surv on Kansas Assembly Plant Data | 0.85 | 0.82
Heterogeneous Seq2Surv on Kentucky Truck Plant | 0.85 | 0.81
Table 2: Model Performance Comparison of Seq2Surv and Heterogeneous Seq2Surv
on Kansas Assembly Plant and Kentucky Truck Plant Datasets. Heterogeneity
presents a critical challenge for Seq2Surv.

We conducted 20 iterations of Quality Assurance (QA) testing from August 1,
2023, to January 13, 2024 for the result in Table 1. For each observation
week, we trained the model on the cumulative data up to that week and used it
to predict disruptions in the near future. We computed Adapted Precision,
Adapted Recall, and the Normalized Confusion Matrix using definitions for
Adapted True Positives, True Negatives, False Positives, and False Negatives,
with parameters forecasting horizon $\Delta=28$ days and margin-of-error
$\epsilon=7$ days. The metrics in Table 1 are averaged over the 20 iterations.
The Quality Assurance (QA) evaluation was conducted at five selected Ford
manufacturing facilities in North America: Kansas City Assembly Plant,
Michigan Assembly Plant, Dearborn Truck Plant, Ohio Assembly Plant, and
Kentucky Truck Plant.

The model demonstrated on average precision exceeding $0.85$ and a recall
surpassing $0.8$ across the five plants during the 20-week QA period.
Additionally, the normalized confusion matrix indicates that the combined
error rate of false positive and false negative errors was below $10$% on
average. Furthermore, we present performance comparison between Seq2Surv, as
described in [22], and our Heterogeneous Seq2Surv proposed in Section
LABEL:model, as shown in Table 2. The metrics provided are the averages
obtained from 20 iterations of QA testing. Our analysis reveals that the
heterogeneity in plant, supplier, and part groups poses significant challenges
for Seq2Surv. However, by incorporating heterogeneous behaviors in the data
into the model, our Heterogeneous Seq2Surv demonstrates substantially improved
performance. For a comparison between Seq2Surv and other models, please refer
to [22].

###  3.2 Explainable AI with SHAP

Figure 1: Utilizing the SHAP (SHapley Additive exPlanations) framework to
calculate feature importance for a specific prediction instance made by the
trained model.

The proposed model demonstrates desirable predictive performance, largely due
to the Sequence-to-Sequence Neural Network’s capability to capture complex
non-linear relationships. However, despite their effectiveness, Deep Learning
models often face criticism for their ”black-box” nature. The complexity of
these models frequently makes it difficult to understand their underlying
mechanisms or interpret internal embeddings and representations (see [41]).
This lack of transparency is a significant challenge in a multi-departmental
corporation like Ford, where it is essential to adequately communicate the
model’s behaviors and decision-making processes to provide actionable insights
to the Business department.

Explainable Artificial Intelligence (XAI) is an emerging field dedicated to
developing principles, frameworks, and tools to elucidate the reasoning behind
AI decisions and predictions (see [27]). A prominent technique in this area is
the Shapley value, which is based on cooperative game theory (see [23]).

Introduced by Lloyd Shapley in 1951, the Shapley value is a technique for
fairly allocating profits among players in a cooperative game, adhering to
four fairness axioms (see [28]). In machine learning, Shapley values can be
applied by treating features as players and the model’s behavior as the
profit. This approach allows us to quantify the impact of each feature. The
SHAP (SHapley Additive exPlanations) framework, popularized by [23], utilizes
Shapley values to explain individual predictions. Figure 1 demonstrates the
application of the SHAP framework to a specific prediction instance using the
model described in Section LABEL:model. The waterfall plot illustrates the
important factors influencing the model’s predicted time-to-disruption, such
as the progression of ‘’qt_behind_release‘’ over 13, 7, 4, and 3 days ago, and
the progression of ‘’production_usage‘’ over 27, 14, 12, 9, 8, 7, and 6 days
ago, among others. This information enables a deeper investigation into the
data, helping to uncover the potential reasons behind the model’s prediction
for this specific instance.

##  4 Conclusion

In this paper, we first outline the process of constructing a dataset of
multivariate time series for forecasting first-tier supply chain disruptions.
We carefully selected features representing key aspects of classical Factory
Physics, such as capacity, inventory, utilization, and processing.

The dataset, comprising over five hundred thousand individual time series,
presents significant technical challenges due to its complexity and scale.
Although these time series exhibit some commonalities, they also demonstrate
substantial heterogeneity within specific subgroups, making traditional
industrial and statistical models inadequate for capturing the intricate
dynamics and managing the extensive data volume. To address these challenges,
we propose a novel methodology that integrates an enhanced Attention Sequence-
to-Sequence Deep Learning architecture with Neural Network Embeddings to model
group effects, combined with a Survival Analysis model. This approach is
designed to effectively capture complex heterogeneous data patterns related to
operational disruptions. Our model has achieved strong performance, with at
least $85$% precision and $80$% recall during the Quality Assurance (QA) phase
across Ford’s five North American plants.

To mitigate the common critique of Machine Learning models as ”black boxes,”
we employed the SHAP (SHapley Additive exPlanations) framework to elucidate
feature importance in the model’s predictions. This technique provides
valuable insights that can guide actionable strategies for business partners.
Our work highlights the potential of advanced machine learning techniques to
manage and mitigate supply chain risks effectively within the automotive
industry.

## Acknowledgement

I would like to express my gratitude to Zhen Jia for assisting in the cleaning
and merging of multiple raw data sources into a single dataset for this work.
Additionally, Zhen Jia provided clarification on the field names and their
meanings in the raw data.

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