complexity science & adaptive supply networks

Complexity Science and Adaptive

Supply Networks:

One Answer to the Challenge

of Sea Enterprise

Major Kelly G. Dobson

Commandant of the Marine Corps National

Fellow

IBM Business Consulting Services

Complexity Science 1

Complexity Science and Adaptive Supply Networks:

One answer to the Challenge of Sea Enterprise

Major Kelly G. Dobson

Commandant of the Marine Corps’ National Fellow

IBM Business Consulting Services

Supply Chain and Operations Solutions

May 21, 2003


Abstract

The Challenge of Sea Enterprise calls upon the naval services to draw on

the lessons of the business world to make activities such as operating the supply

chain cheaper, more efficient, easier to use, and less manpower intensive.

Additionally, with an eye to future war fighting strategies, the naval services must

transition to anticipatory, more flexible logistics which leverage information and

provide needed support where and when it is most needed. Building upon both

evolutionary and revolutionary examples from the business world, the naval

services have the opportunity to leverage Agent-Based Modeling, aided by

dynamic tracking technologies, into a truly anticipatory, responsive, and adaptive

supply network. This network could not only answer the challenge of Sea

Enterprise, but also would adapt well to form the nucleus of the future joint supply

network.


Complexity Science and Adaptive Supply Networks:

One answer to the Challenge of Sea Enterprise

The Challenge of Sea Enterprise

“Among the critical challenges that we face today are finding and

allocating resources to recapitalize the Navy.” (40) These are the opening words

Admiral Clark used to describe Sea Enterprise, an essential element of Sea

Power 21. He went on to say that, “we will make our Navy’s business processes

more efficient to achieve enhanced warfighting effectiveness in the most cost-

effective manner.” (40) Admiral Clark then sums up the means and the goals of

Sea Enterprise: “Drawing on lessons from the business revolution, Sea

Enterprise will reduce overhead, streamline processes, substitute technology for

manpower, and create incentives for positive change.” (40)

One response to the challenge of Sea Enterprise might be simply to

capitalize on the lessons learned from previous experience and incrementally

improve current Navy business processes. However, as recent events suggest,

Sea Enterprise must also promote the development of solutions capable of

supporting emerging military tactics. President Bush, aboard the USS Abraham

Lincoln, noted that: “Operation Iraqi Freedom was carried out with a combination


of precision and speed … Marines and soldiers charged to Baghdad across 350

miles of hostile ground in one of the swiftest advances of heavy arms in history”

When discussing the capabilities required to support concepts such as

Sea Basing and other emerging strategies, Vice Admiral Moore, Deputy CNO for

Fleet Readiness and Logistics, and Lieutenant General Hanlon, Commanding

General, Marine Corps Combat Development Command, asserted that the naval

services’ future logistics enterprise must: “leverage information to achieve

efficiencies and provide support at the time and place of greatest impact.” (82)

They went on to say that naval service logistics must “shift toward anticipatory,

responsive logistics.” (82)

Focusing specifically on the supply chain, the challenge of Sea Enterprise

then becomes three fold: First, given the recent glimpse at the future, how does

the supply chain need to change in order to support a broader spectrum of

conflict? Second, what are the lessons from the business revolution? Finally, how

are these lessons applicable to the naval services’ supply chain in order to

reduce overhead and improve effectiveness? Accepting Admiral Moore’s and

General Hanlon’s description as a starting point for the future characteristics of

the naval services’ supply chain, the next question becomes are there relevant

lessons from the business revolution?


Key Lessons from the Business Revolution

Complexity Science

One of the applicable lessons from the business revolution is complexity

science. Complexity science is not a new field of study, but a new approach for

studying complex, adaptive systems. Adaptive systems consist of numerous,

varied, simultaneously interacting parts, called agents. The goal of complexity

science is to uncover the underlying principles and emergent behavior of

complex systems, often

invisible using

traditional approaches.

The difference

between traditional

methods of analysis

and complexity science

involves a shift in focus

and methodology.

Traditional methods

rely on cause-and-

effect analysis: by

knowing all the factors that affect a situation, one can predict the outcome of the

situation. Conversely, complexity science holds that behavior is often

unpredictable and analyzing the factors of a situation may not gain the requisite

Birds Flocking

The basic flocking model consists of three simple steering behaviors which describe how an individual boid maneuvers based on the positions and velocities its nearby flockmates:

Separation: steer to avoid crowding local flock mates

Alignment: steer towards the average heading of local flock mates

Cohesion: steer to move toward the average position of local flock mates

Reynolds - Boids

Separation

Alignment

Cohesion

http://www.red3d.com/cwr/steer/


insight. As an example, complexity scientists discuss the steering behaviors of

birds: each individual bird maintains separation, alignment, and cohesion with the

other birds in the flock. (Sidebar) Given these three factors particular to each bird

in the flock, it is unlikely one would predict that the group of birds flock, but that is

what they do as emergent behavior from their steering behavior interactions.

Agent-Based Modeling (ABM)

To capitalize on the insight offered by complexity science, scientists and

corporations have developed Agent-Based Modeling (ABM) which uses

collections of autonomous decision-making entities called agents. Each agent in

the simulation assesses the current situation and makes decisions based upon

its set of rules. The rules themselves are not the essential product of the

simulation; rather the benefit comes from the interactions between agents and

the emergent behavior these interactions produce.

But to glimpse at emergent behavior requires numerous iterations – many

times the number required for traditional simulations – and until fairly recently,

there was insufficient computing power to make these multiple simulation runs in

a cost effective manner. However, because of recent capabilities and product

improvements, analysts can run the simulations hundreds or thousands of times

to develop a distribution of emergent behavior while incurring only nominal costs.

By comparing this behavior to historical data, the analysts validate the accuracy

of the model. Once validated, the model provides something that most traditional

approaches cannot: the ability to model changes to the system, such as


obstacles or bottlenecks, and predict how the real system agents would adapt to

these changes. This ability changes ABM from a purely analytical tool to a

predictive tool. ABM offers the potential to accurately model not only the main

elements of the naval services’ supply chain, but all the interactions and

“workarounds” that become such an integral part of the dynamic system. This

ability to extract useful information from agent interactions led Procter and

Gamble (P&G) to use ABM tools in an effort to reduce supply chain inventory.

P&G Case Study: Evolutionary business rules

In 1998, P&G had already achieved a 50% reduction in their inventory,

and was looking for an additional 25% reduction in an effort to control costs.

P&G’s desire to cut inventory seemed to run counter to their need to keep

products such as Tide and Comet on the store shelf. Using ABM, P&G found that

a “seemingly logical policy sending out only full trucks actually created

disruptions along the supply chain … [resulting in] supermarket shelves that were

empty of its key products.” (Bylinsky, 5) Supply chain agents within P&G’s ABM

recognized this self-induced obstruction and correctly modeled a new,

evolutionary approach: “letting some trucks travel with partial loads and making

delivery times more flexible.” (Bylinsky, 5) Not only did the proposed solution

meet predicted results, it exceeded them. After implementing the ABM

modifications, “Procter & Gamble Co. saves $300 million annually on an

investment of less than 1% of that amount” (Anthes, 1)


While the return on investment of the P&G example is impressive, similar

results might have been attainable by traditional methods and are evolutionary in

nature. On the other hand, what Air Liquide did with AMB was truly revolutionary.

Air Liquide Case Study: Revolutionary business rules

Air Liquide is a Houston-based industrial gas firm, which supplies “liquid

oxygen, nitrogen, and other gases to

10,000 customers from more than

300 sources through 30 depots,

using 200 trucks and 200 trailers.”

(Mucha) The scope and complexity

of Air Liquide’s supply chain was

daunting with “3 trillion daily

combinations among all its

constituent parts; it took 22 full-time

logistics analysts nearly half a day to

generate a delivery schedule that

would get every product to its

destination on time.” (Mucha) Using

ABM, the truck “agents” were not

only programmed to find the shortest

routes, but to remember those routes

and compare them with other routes

Radio Frequency Identification (RFID)

This tag, approximately the size of a shirt button, is:

a “smart object” implementation for item/object tagging that enables end-to-end asset awareness. At its core, RFID uses tags, or transponders that have the ability to store information that can be transmitted wirelessly in an automated fashion to specialized RFID readers, or interrogators. This stored information may be written and rewritten to an embedded chip in the RFID tag. When affixed to various objects, tags can be read when they detect a radio frequency signal from a reader over a range of distances and do not require line-of-sight orientation. The reader then sends the tag information over the enterprise network to back-end systems for processing. (Levine, 3)

Conceptually, the logistics supply chain could tag everything from pallets, boxes, even down to individual items if their size or importance demanded. This would provide the dynamic tracking visibility that so many other programs seek, but with a much higher degree of granularity in that each tag is able to know the contents of its attached container. Also, the cargo would now ‘know’ its destination, required delivery date, and associated cargo, which in turn would allow en route synchronization and adaptive rerouting when tied with the proper ABM system.


found, optimizing short-cuts and compiling new routes from sections of previously

optimized routes. Most importantly, because of the power of ABM, “just one Air

Liquide analyst is needed to create daily shipping and production schedules

across its numbingly complex supply chain in about two hours.” (Mucha) With the

proven cost savings and overhead reduction of P&G’s efforts and the manpower

reduction and adaptive supply chain of Air Liquide, ABM offers some potentially

revolutionary supply chain management lessons.

Real Time Modeling

The business examples demonstrated ABM’s ability to be both

evolutionary and revolutionary with its approaches to greater supply chain

effectiveness. But even in the Air Liquide example, the information optimized had

some time delay inherent to it – the analyst based the schedule on the known

conditions at a certain point the day prior. While the analyst was able to very

rapidly respond to a bottleneck or an obstacle such as an interstate shutdown,

the information he worked with was not the most current due to this time delay.

What if it were possible to remove that time delay? While a powerful tool in its

own right, one can greatly enhance ABM’s power by supplying the model with

real time data from the actual supply chain. Several technologies, including RFID

(sidebar) offer the potential for dynamic tracking. With the advent of low-cost

computing capacity, Agent-Based Modeling, and dynamic tracking technology,

the naval services have the potential to develop a real time adaptive supply

system.


An Illustration of Military Applications

As an example of ABM’s potential when supplied with dynamic tracking

information from the naval services’ supply chain, let us look at a critical node in

an existing supply chain: Sigonella, Italy. Currently, when ships deploy to the

Mediterranean, each group typically leaves an expeditor at Sigonella to rescue

frustrated cargo and ensure that all the cargo destined for the target group

actually makes it to that group. Expeditors rely on ship-to-shore communications

for priorities and a shore-based information system to know what cargo is

inbound or is lost en route for what ever reason. Additionally, the expeditor

maintains a list of priority cargo that takes precedence over other, lower priority

cargo. While the expeditor can be highly effective, he represents a manpower

intensive workaround to a supply chain problem. Additionally, the work of one

expeditor may well prove counter to the work of another, adding greater

inefficiency to the system.

Contrast the expeditor system with an ABM supply chain leveraging

dynamic tracking. In this system, each piece of cargo becomes its own expeditor.

Using RFID as an example, each tag retains knowledge of its host’s contents, its

destination, its required delivery date, and even associated cargo necessary for

this cargo to be useful for the end user. Since this data is stored on the RFID tag,

and not part of a remote system located at Sigonella, the loss of the facility or a

system at the facility does not destroy the required destination of the cargo.

Additionally, by capturing all dynamic tracking data via remote interrogation and

feeding it real time to the ABM, the system constantly learns and optimizes itself,


even allowing cargo synchronization with partner cargo en route, relieving

manpower requirements on the end user. This capability alone might make ABM

worth the cost of investment, but this new system really shows its strength when

something goes wrong.

Imagine that some terrorist faction detonates a bomb at Sigonella,

effectively shutting down the node and putting all the expeditors out of action. For

a traditional supply chain to react to this situation, news of the bombing must first

make its way back up the supply chain to the managers, potentially taking on the

order of minutes or as long as days. With the knowledge of the lost node, the

supply chain managers must determine alternate routes and enact those routes.

Then, still in a reactive mode, they must assess the impact that changing to

alternate routes has had on other nodes and adjust accordingly, potentially

routing too much cargo through ports with insufficient capacity. This further

congests the supply chain and potentially leads to individual supply chain

managers developing solutions that create even more congestion.

Now, take that same scenario, but this time using ABM to manage the

supply chain. Because of the dispersed nature of the ABM and the visibility

provided by dynamic tracking, the system could potentially recognize that there is

a problem with the Sigonella node before anyone even finds out that a bomb

went off. Recognizing the impact to cargo in the system, ABM considers the time

sensitive nature of shipments and automatically reroutes critical shipments.

Simultaneously, ABM down-grades the priority of items in the supply chain that


depend on other items unavoidably delayed. Finally, ABM, leveraging its

predictive nature and emergent behavior analysis capabilities, anticipates the

impact of routing changes on the entire system, preemptively eliminating the

potential bottlenecks. If cargo is somehow isolated from the master ABM

network, it still retains all of its destination information. Similar to mission specific

orders and commander’s intent, the cargo assesses the situation at the next

node and continues toward its intended objective.

Turning Supply Chains into Supply Networks

Lieutenant General Van Riper, USMC retired, spoke at a conference titled

Preserving National Security in a Complex World in September of 1999. During

his comments – A General Perspective on Complexity – General Van Riper

reminded his listeners, “if you do not cast your net widely and look at places that

traditionally Marines wouldn’t look, you are not going to find the right answers …”

(Van Riper, 179) Using complexity science and Agent-Based Modeling to

manage the naval services’ supply chain would definitely be a wide cast of the

net. However, the question remains: while the potential of cheaper, more

efficient, simpler, and less time consuming alternatives appear successful in the

business world, is it too great a hope to believe that they could produce the same

results for the naval services?

P&G was so impressed with the transformation of their supply chain, they

renamed it a supply network. According to Larry Kellam, P&G’s director of supply

network, “Chain connotes something that is sequential, that requires handing off


information in sequence … we believe it has to operate like a network …” where

all the parts are dynamically interacting. The payoff from successfully applying

this new way of thinking about logistics – the challenge of Sea Enterprise – holds

tremendous potential in both cost and effectiveness. By recognizing this potential

to transform how the military thinks about supply, the naval services have the

opportunity to lead the transition to the supply networks needed to properly

support tomorrow’s warfighting requirements. And this technique would adapt

well to cut across the bounds of the traditional service specific supply lines to

form the nucleus of a joint supply network.


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