Causal Learning to Discover Supply Chain Vulnerability

Ying Zhao, Jacob Jones and Douglas MacKinnon

Naval Postgraduate School, Monterey, CA, U.S.A.

Keywords:

Causal Learning, Counterfactual Analysis, Cause and Effect, Supply Chain Vulnerability, Associations,

Correlations, Lexical Link Analysis, Data Mining.

Abstract:

This paper illustrates a methodology of causal learning using pair-wise associations discovered from data.

Taking advantage of a U.S. Department of Defense supply chain use case, this causal learning approach was

substantiated and demonstrated in the application of discovering supply chain vulnerabilities. By integrating

lexical link analysis, a data mining tool used to discover relationships in speciﬁc vocabularies or lexical terms

with pair-wise causal learning, supply chain vulnerabilities were recognized. Evaluation of results from this

methodology reveals supply chain opportunities, while exposing weaknesses to develop a more responsive and

efﬁcient supply chain system.

1 INTRODUCTION

U.S. Department of Defense (DoD) supply chains are

large, complex, and deal with unpredictable demand

signals from a variety of sources. DoD supply chains

account for over 100,000 suppliers, 2,000 control-

ling systems, 19 maintenance depots, 25 distribution

depots, and over 30,000 customer sites, and manag-

ing an inventory of $92.6 billion in 2015 (Haraburda,

2016). Private sector supply chains focus on proﬁt

margins and growth potential. However, the DoD’s

goal is to ensure that readiness of equipment and per-

sonnel are maintained at a rate sufﬁcient to engage

enemy combatants in both peace and wartime (Wil-

hite et al., 2014). Keeping the warﬁghters at a high

state of readiness is the main objective and a ma-

jor factor in the design of the supply chains, while

cost is a constraining factor professionals must man-

age and minimize it (Haraburda, 2016; Wilhite et al.,

2014). Therefore, military supply chains measure

themselves using two metrics: response time, which

is how quickly the force can be equipped with the re-

quired item; and effectiveness, ensuring the right sup-

ply asset is given at the right time (Jones, 2018).

2 IMPORTANCE OF CAUSAL

LEARNING

A unique aspect to military supply chains is that they

support complex weapon systems. They involve cut-

ting edge technology throughout the long life cycles

of the weapon systems and must be ready for com-

bat at any given time. To achieve the highest readi-

ness level, DoD supply chain organizations have to

constantly reinvent themselves to improve the sup-

ply chains and logistic processes by utilizing new

technology, process, and concepts, such as big data

mining, machine learning, and artiﬁcial intelligence.

While applying the new technologies, decision mak-

ers are interested in discovering and understanding

the reasons and causes from data which can address

the gaps and vulnerabilities of the current systems,

and where and how to make necessary improvements.

This calls for systematic causal learning to discover

supply chain vulnerabilities. For example, lengthy

supply delivery time could be a vulnerability. To

strengthen supply chains, causes for those vulnerabil-

ities must be discovered.

3 DATA DESCRIPTION

This project analyzed MV-22 Osprey supply chain

data, a multi-mission, tiltrotor military aircraft with

both vertical takeoff and landing, and short takeoff

and landing capabilities. It is designed to combine

the functionality of a conventional helicopter with the

long-range, high-speed cruise performance of a tur-

boprop aircraft, necessary to conduct sea basing and

expeditionary operations (V-22, 2018).

To assess the MV-22 Osprey supply chain, this re-

Zhao, Y., Jones, J. and MacKinnon, D.

Causal Learning to Discover Supply Chain Vulnerability.

DOI: 10.5220/0008070503050309

In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019), pages 305-309

ISBN: 978-989-758-382-7

305

search used the Aircraft Maintenance/Supply Readi-

ness Report (AMSRR) as the primary document iden-

tifying supply needs. The key data elements are listed

as follows:

• Parts: Discrete high priority components. To as-

sess available parts critical to aircraft readiness

and combat capability, the research delved into

Not Mission Capable Supply (NMCS) and Partial

Mission Capable Supply (PMCS) project codes.

Our intent was to discover which individual parts

most affected readiness while being accessed in

the supply chain.

• Project Code: To measure criticality of a part,

a project code is given to the component based

on the impact that part has to mission capability.

Project codes 706 (NMCS) and 707 (PMCS) were

examples of project codes analyzed.

• Status Code: Supply status codes were evaluated

to understand the condition of the components and

supply chain robustness. A “BA” status code, i.e.

items that were being processed for release and

shipment and an “AS” status code, those parts in

shipping status, were the codes emphasized dur-

ing this research. Initial codes of “BA” and “AS”,

when they ﬁrst appeared on the AMSRR, were po-

tential indications of availability.

• Response Time: This ﬁgure was calculated using

the ﬁrst date the supply document was published

to the AMSRR and the last known estimated de-

livery date annotated.

• Routing Location: Routing Identiﬁcation Codes

were analyzed to assess which supply nodes

sourced a part and which locations were used as

a part transited the supply chain.

For example, if the response time for NMCS or

PMCS component was longer than the average, it po-

tentially shows a vulnerability. Causes for such vul-

nerabilities need to be uncovered to bolster the supply

chain and aircraft readiness.

4 LEXICAL LINK ANALYSIS

(LLA)

The data mining tool used for this research was Lex-

ical Link Analysis (LLA) which is unsupervised ma-

chine learning method and describes the characteris-

tics of a complex system using a list of attributes or

features with speciﬁc vocabularies or lexical terms.

Because the potentially vast number of lexical terms

from big data, the model can be viewed as a deep

model for big data. For example, we can describe a

system using word pairs or bi-grams as lexical terms

extracted from text data. LLA automatically discov-

ers word pairs, and displays them as word pair net-

works.

Figure 1 shows an example of such a word net-

work discovered from data. “Clean energy” and “re-

newable energy” are two bi-gram word pairs. For a

text document, words are represented as nodes and

word pairs as the links between nodes. A word center

(e.g., “energy” in Figure 1) is formed around a word

node connected with a list of other words to form

more word pairs with the center word “energy.”

5 PAIR-WISE CAUSAL

LEARNING

Bi-grams allow LLA to be extended to numerical or

categorical data. For example, using structured data,

such as attributes from databases, we discretize nu-

meric attributes and categorize their values to word-

like features. The word pair model can further be

extended to a context-concept-cluster model (Zhao

et al., 2015). A context can represent a location, a

time point, or an object (e.g. ﬁle name) shared across

data sources. For example, in “information assur-

ance”, “information” is the context, “assurance” is the

concept.

In this paper, we want to show that the bi-gram

generated by LLA can also be a form of causal learn-

ing. The bi-gram contextual associations relate to the

three layers causal hierarchy (Pearl, 2018; Mackenzie

and Pearl, 2018) of association, intervention, counter-

Figure 1: An example of lexical link analysis.

KDIR 2019 - 11th International Conference on Knowledge Discovery and Information Retrieval

306

factual, as well as a few other key elements of causal

learning as detailed in the following sections.

5.1 Association

The common consensus is that data-driven analysis or

data mining can discover initial statistical correlations

and associations from big data. Human analysts need

to validate the correlations to make them causal. As-

sociations are extracted from historical data and then

cross-validated using validation data sets. The associ-

ations and correlations are further validated by human

domain experts (Jones, 2018). Figure 2 shows con-

ceptually how the associations and correlations were

discovered by LLA in the use case. For example, we

discretized the total delivery days (response time) into

two categories (e.g. two effects):

1. E= total delivery days > average (e.g. 15 days for

the MV-22 Osprey)

2. Not E=total delivery days <= 15. We found

E is associated with a few possible factors

in terms of conditional probability P(E|C1 :

Pro jectCodeX ), P(E|C2 : RoutingLocationY ),

P(E|C3 : SmallQuantityOrder), and P(E|C4 :

PartNumberZ) as shown in Figure 2. This is

different from a traditional Bayesian network as

shown in Figure 3. A node in a Bayesian net-

work needs to compute the conditional proba-

bility P(E|C1,C2,C3,C4) based on all its parent

nodes. Conversely, LLA only computes the pair-

wise conditional probabilities. This allows us to

reason simply to remove the associations that are

not causal.

Figure 2: Pair-wise associations and correlations discovered

by LLA.

5.2 Intervention

Intervention ranks higher than association in the hi-

erarchy which involves taking actions and generat-

ing new data. A typical question at this level would

be: What will happen if we increase the intensity

of an action? For example, instead of examining

P(X|M), one might further ensure M is actionable or

P(X|do(M)) (Mackenzie and Pearl, 2018) can be ex-

amined. The answers to the question are more than

just mining the existing data. The action needs to

generate new data as an effect of the intervention to

determine if the underlying action causes to the de-

sired effect, or to determine how sensitive the effect

is to the cause. The intervention can be modeled as a

“treatment.” Effect is the potential outcome compared

to the control situation in the causal learning litera-

ture. Sensitivity is the “dosage” concept associated

with a “treatment”.

5.3 Counterfactuals

A typical question asked is: “What if I had acted

differently?” or counterfactual reasoning as shown in

Figure 4. P(E|C) and P(E|NotC) are the counterfac-

tuals needed in the reasoning. Traditionally, the effect

is deﬁned as the outcome of a “treatment” for an en-

tity and for the same entity without the treatment, i.e.,

P(E|C) and P(E|NotC). However, since this causal

effect is impossible to directly observe for the same

entity, this is commonly referred to as the fundamen-

tal problem of causal inference (Gelman, 2018). The

potential-outcome or counterfactual-based model of

casual inference explores the idea of an entity-level

treatment effect, although it is unobservable as well,

Figure 3: Bayesian networks consider a conditional proba-

bility based all its parent nodes.

Causal Learning to Discover Supply Chain Vulnerability

307

it can be aggregated in various ways.

For example, the causal effect is typically mea-

sured using two randomized populations, one with

the “treatment” (or with C) and another one without

the “treatment” (Not C or control group). The two

populations are randomized to ensure they are similar

to each other (as if they were the same entity).

This is the Randomized Control Treatment (RCT)

theory, which is a standard practice found in social

sciences, drug development, clinic trials, and other

applications. With recent data-driven approaches

such as data mining and machine learning, people can

robustly estimate a local average treatment effect in

the region of overlap between treatment and control

populations, but inferences for averages, outside this

zone are sensitive to underlying machine learning al-

gorithms (Gelman, 2018). For instance, people have

moved to nonparametric models of machine learning

such as nearest neighbors (i.e., use the outcome of

the nearest neighbor of an entity as the surrogate for

the unobservable outcome of the same entity) and ran-

Figure 4: Comparison the four probability to remove non-

causal associations.

Figure 5: LLA and causal learning to discover supply chain

vulnerability.

dom forests (Wager and Athey, 2018) for better causal

learning since these methods can approximate the lo-

cal treatment and control populations close to an RCT

setting.

6 PUT THEM TOGETHER

Using LLA, we are able to

• First compare P(E|C), P(Not E|C), P(E|Not C),

and P(Not E|Not C). Choose the links that higher

than a predeﬁned threshold shown in Figure 4.

• Choose among P(E|C1) − P(E|Not C1),

P(E|C2) − P(E|Not C2), P(E|C3) −

P(E|Not C3), and P(E|C3) − P(E|Not C3)

that are higher than a predeﬁned threshold.

We can put causal learning elements together to

discover the links that impact the supply chain’s over-

all cost. For example, the cause for “longer than av-

erage total delivery time” might be a “small quan-

tity (C3)” or “part number Z (C4)” from which the

strong links point to E. Other factors are eliminated

because the links are below the thresholds. The com-

mon dilemma of causal learning for the network in

Figure 5 is that one can not decide which confounder

C3 or C4 causes E. However, by using LLA, we also

can discover a link between C3 and C4, i.e. P(C3|C4)

is greater than the threshold, therefore, C4 (part num-

ber Z) not C3 (small quantity) is the cause of E (de-

lay). Although “Not C4” or C2 also point to C3, but

they do not link to E, therefore, are not the causes for

7 CONCLUSION

This research developed a potentially promising

methodology, demonstrating that through the use of

LLA, both weak and strong connections can be iden-

tiﬁed among a myriad of variables. This level of

connection can generate associations suggesting the

strength and effect of the counterfacturals as they

are considered toward sensitivity analysis, providing

causal learning in the process using pair-wise asso-

ciations. This study was able to discover supply

chain vulnerabilities from the investigated and ana-

lyzed data.

ACKNOWLEDGEMENTS

Authors would like to thank the Naval Research Pro-

gram at the Naval Postgraduate School. The views

KDIR 2019 - 11th International Conference on Knowledge Discovery and Information Retrieval

308

and conclusions contained in this document are those

of the authors and should not be interpreted as rep-

resenting the ofﬁcial policies, either expressed or im-

plied of the U.S. Government.

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