Technical Note: Wicked Messes & Strategy Dynamics€¦ · World Model – Customer Satisfaction,...

© 2017 Dialectic Simulation Consulting, LLC

Website: www.dialecticsims.com Inquire at: [email protected]

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Technical Note: Wicked Messes & Strategy Dynamics

Prepared by Dialectic Simulation Consulting, LLC

Objectives ....................................................................................................................................... 2

What is a Wicked Mess? ................................................................................................................. 3

How do I know if I have a wicked mess problem? ......................................................................... 4

Which problem solving approach is best to use with wicked messes? ........................................... 4

Which Simulation Science Approach to Use? ............................................................................ 6

How Dialectic Simulation Consulting solves wicked messes using strategy dynamics. ................ 8

What are the skill sets Dialectic brings to engagements? ........................................................... 9

Annex A - What does a Strategy Dynamics Engagement Look Like? ......................................... 10

Annex B – Commitment Requirements from Client .................................................................... 11

Annex C – 1/10th-1/20th Scale Diorama of Sharing Economy Strategy Dynamics Model ........... 12

Strategic Architecture – Provider Coverage (Leased & Portfolio) ....................................... 13

World Model – Customer Satisfaction, Availability & Usage Sector .................................. 17

Additional Analysis Options ................................................................................................. 21

Figure 1: Sharing Inc.'s Wicked Mess ........................................................................................... 2

Figure 2: Sharing Inc's, Wicked Mess ........................................... Error! Bookmark not defined.

Figure 3: Problem Classes .............................................................................................................. 4

Figure 4: Boundaries of Effectiveness of Process Improvement Efforts ........................................ 5

Figure 5: Locating Problem-Solving Approaches against Problem Classes .................................. 5

Figure 6: High Level Overview of Engagement Approach .......................................................... 10

Figure 7: FTE Commitment Estimates ......................................................................................... 11

Figure 8: Sector Overview of Simulation Model .......................................................................... 12

Figure 9: Actual Simulation Model .............................................................................................. 13

Figure 10: Sector in Focus – Provider Coverage (Leased & Portfolio) ........................................ 14

Figure 11: Detail of Provider Coverage Sector ............................................................................. 15

Figure 12: Elements of a Model.................................................................................................... 16

Figure 13: Portfolio Values by Strategy ....................................................................................... 17

Figure 14: Sector in Focus - Customer Satisfaction Sector .......................................................... 18

Figure 15: Detail of Customer Satisfaction Sector ....................................................................... 19

Figure 16: Lookup Function for Effect of Customer Satisfaction on Desired Rentals ................. 20

Figure 17: Strategy 2: Comparison Across two Variables - Availability vs. Actual Rentals ....... 21

Figure 18: Identifying Drivers of Change across Dissimilar Units & Scales ............................... 22

Figure 19: Causal Analysis - What Creates Share Revenue ......................................................... 23

Figure 20: Confidence Boundaries across 200 Randomized Scenarios....................................... 24

http://www.dialecticsims.com/



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Objectives

This technical note can be read as a companion to the executive white paper that explains the

dilemma of Sharing, Inc. or as a stand alone document by itself.

The intended audiences for this document are specialists in analytics, strategists, and/or program

managers to help them understand at a deeper level how simulation science can help solve a

wicked mess problem. However, the note is written at a conceptual level, one does not need to be

able to perform calculus to follow along.

As discussed in the executive whitepaper we developed a 1/10th – 1/20th diorama simulation of

“Sharing, Inc.” a make-believe company in the Sharing Economy that provides lodging,

transportation, delivery services, or other “by-the-sip” services to subscribing members. Sharing,

Inc. provides these services to retail subscribers through a network of “in-house” providers or by

leasing providers from other networks as-needed. Seeking a growth plan for the next eight years

senior management compared two different marketing plans against a projected baseline. The

results are reproduced below in Figure 1.

Figure 1: Sharing Inc.'s Wicked Mess

Members (People)

500,000

375,000

250,000

125,000

0

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84

Time (Month)

Peo

ple

"Members (People)" : Baseline

"Members (People)" : Strategy1

"Members (People)" : Strategy2

Reserves (USD)

2 B

1 B

0

-1 B

-2 B

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84

Time (Month)

US

D

"Reserves (USD)" : Baseline

"Reserves (USD)" : Strategy1

"Reserves (USD)" : Strategy2

Employees

20,000

15,000

10,000

5000

0

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84

Time (Month)

Peo

ple

Employees : Baseline

Employees : Strategy1

Employees : Strategy2

Change in Employees (People/Month)

2000

1000

0

-1000

-2000

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84

Time (Month)

Peo

ple

/Mo

nth

"Change in Employees (People/Month)" : Baseline

"Change in Employees (People/Month)" : Strategy1

"Change in Employees (People/Month)" : Strategy2




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The results are a wicked mess. In one strategy a seemingly ideal result – fast growth – led to to

unanticipated management behavior, hiring too many employees too fast. That reaction led to

financial hardships and the need to layoff employees to right-size the company. An all too

frequent scenario in fast-growing companies. But what are these wicked messes? Whether a new

company, an established Fortune 50 or a government agency why are they easier to describe in

hindsight but seemingly impossible to articulate, or solve, looking forward? This technical note

seeks to shed some light on these questions.

The objectives in this note are to first explain what wicked mess problems are and how they

evolved from ongoing research into classes of problems. Second, how does one know they are

facing a wicked mess? If one is facing a wicked mess what are the best problem-solving

approaches and why? Are there better applications to use or not use? Finally, how does Dialectic

approach solving wicked mess problems and what kind of team is needed on these sorts of

challenges? In Annex A and B are overviews of our deployment approach as well as estimated

time commitments on a standard engagement.

The scale diorama simulation model of Sharing Inc., is explained in overview in Annex C. This

section also gives just a very brief highlight of some of the capabilities of system dynamics and

simulation science.

What is a Wicked Mess? The evolution of wicked mess problems begins in 1973 with German professors Rittel & Webber

who identified wicked problems as a class of problems involving innumerable causes and strong

feedback between interdependent parts, which frequently can’t be reduced to a right or wrong

answer. Separately in 1974, Russell Ackoff, a Professor at the Wharton School of Business,

coined the term social messes to describe problems where numerous stakeholders held different

perspectives, problem solving approaches and disagreed on what constituted good or bad

solutions. Then in 1990’s trends of multi-national companies, globalization and the internet

connectivity caused these two classes of problems to merge and be applied to business

enterprises. This new class of wicked messes recognized by MIT Professors Peter Senge (author

of the “Fifth Discipline”) and George Roth combined elements of both wicked problems and

social messes.

Wicked messes challenge traditional scientific methods of hypothesis, experiment, interpretation

and replication. Every intervention is a one-off experiment in an adaptive system. Results could

be interpreted as both good and bad and wouldn’t necessarily help predict the results of the next

experiment as characteristics of the system changed over time.

Today, wicked messes continue to emerge in new areas due to the emergence of social media,

analytics, Big Data, cloud computing, globally distributed workforces, rapid M&A, incompatible

or competing business architectures and fragmented employee cultures. These contribute to the

increased frequency and difficulties senior leaders will face navigating wicked mess problems in

determining strategy and efforts to improve enterprise performance.




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How do I know if I have a wicked mess problem? If a company problem shares two or more of the below characteristics it is likely a wicked mess:

1. Different stakeholders have different views of the problem, specialized problem solvers

who are out of contact with the problem offer contradictory solutions – there is no one

“correct” view.

2. Most problems are connected to other problems with considerable ambiguity of how

pieces interconnect which makes them difficult to conceive of without computer

assistance.

3. Missing or uncertain data, numerous possible intervention points and no obvious solution

leads to considerable uncertainty on the strategic way forward.

4. Unidentified constraints due to business culture, corporate politics, financial situation or

technology infrastructure limit plausible solutions and create conflicts between

stakeholders and a great resistance to change.

A second way to understand if you are facing a wicked mess is to locate efforts within their

classification of problem. Figure 1 graphically locates the four classes of problems with textual

descriptors.

Figure 2: Problem Classes

Which problem solving approach is best to use with wicked messes? Simply realizing that you are confronting a wicked mess is often hard enough. Another challenge

is in identifying the correct problem-solving approach to use to address it. In Figure 2 are

Wicked Problems

Cannot be solved by solving

components in isolation due to

feedback loops and interactions

between parts.

Wicked Messes

Systems of interlinked problems

interact with the

misunderstandings, divergent

assumptions, and polarized

beliefs of different groups.

Tame Problems

Can be broken down and solved

independently or in isolation by

conventional methods.

Social Messes

People see different

perspectives and plan different

strategies for problem solving

based on their own mental

models. These problems

requires significant diplomacy

and alignment of interests.

Wicked Problems

Cannot be solved by solving

components in isolation due to

feedback loops and interactions

between parts.

Wicked Messes

Systems of interlinked problems

interact with the

misunderstandings, divergent

assumptions, and polarized

beliefs of different groups.

Tame Problems

Can be broken down and solved

independently or in isolation by

conventional methods.

Social Messes

People see different

perspectives and plan different

strategies for problem solving

based on their own mental

models. These problems

requires significant diplomacy

and alignment of interests.

Dynamic Complexity

Behavioral Complexity

Low High

Hig

hLo

w

Relationship between

cause and results are not

clear; many handoffs

exist. Large, complex

organizations are

examples of high dynamic

complexity.

Characterized by deep conflict in assumptions, beliefs, and perspectives. Difficult

to get people to agree on what should be done because they have different

mental models.

(Roth & Senge 1996)




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notionally plotted performance improvement projects (Lean, Lean Six Sigma, BPM etc.) as to

where they normally fall within the problem classification paradigm presented in Figure 1.

Figure 3: Boundaries of Effectiveness of Process Improvement Efforts

In retrospect, these projects are clustered into a band within the black lines marking the

boundaries of effectiveness. These boundaries exist between the lower edge of utility based on

limited return-on-investment and the upper edge of the limits of the tools themselves.

Figure 3 replicates in broad strokes my own research over time as to what kinds of problem-

solving approaches are best suited to the different classifications of problems.

Wicked Problems Wicked Messes

Tame Problems Social Messes


Tame Problems Social Messes

Process Improvement (e.g. LSS, Agile)

Discrete Event Simulations

Business Architecture Cultural Change

Data Science &

Analytics Simulation Science:

Agent Based Modeling

& System Dynamics Enterprise Transformation

Domain Specific

Simple

Problem Solving

Figure 4: Locating Problem-Solving Approaches against Problem Classes


Tame Problems Messes


Tame Problems Messes

Low High

Hig

hL

ow

No ROI

Too Hard!

Dynamic Complexity

Behavioral Complexity

Relationship between

cause and results are not

clear; many handoffs

exist. Large, complex

organizations are

examples of high dynamic

complexity.

Characterized by deep conflict in assumptions, beliefs, and perspectives. Difficult

to get people to agree on what should be done because they have different

mental models.




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Although it took decades of practical experience and simulation – we eventually settled on

simulation science as the best problem-solving method in addressing these kinds of problems.

This is due to the way simulations are constructed, their ability to incorporate all the

characteristics of dynamic complexity and how they aid in stakeholder understanding to combat

behavioral complexity – all key aspects of wicked mess problems.

Simulation science uses numerical step-based simulation models that can recreate an abstraction

of a real world system in order to study system behavior and performance. Strategies to solve

wicked messes in the real world are costly and risky one-shot attempts; and because

circumstances change, an alternative strategy can never truly be tested under the same conditions

to see if it would’ve performed better. Managers must commit to one choice and hope it is the

correct one. In the abstracted world of a simulation model however strategy experiments can be

replicated hundreds, even thousands of times. The full range of dynamics and conditions

necessary for success can be developed generatively with as many iterative permutations as

necessary.

Simulation science is very different from macro-econometric formulas based on statistical

models that attempt to distill complex behavior to a single equation. Instead simulations usually

consist of several hundred to several thousand equations allowing it to replicate complex

feedback mechanisms, non-linear behavior, incorporate both hard (quantitative) and soft

(qualitative) data. Parameters can be initialized and then react and change in the course of the

simulation to end at different values. Because simulation models are graphically formulated

(visually displayed in diagrams or images), they are more accessible to stakeholders than

analytical formulations consisting of complicated Greek symbols. This aids in education and

consensus-building which is vital for stakeholders to gain a common understanding of a wicked

mess.

Which Simulation Science Approach to Use?

Settling on simulation science as the appropriate method to resolve wicked messes the question

that follows is which types of simulation science to use of the three types available, discrete

event simulations (DES), agent based models (ABM) or system dynamics.

DES are used extensively in process improvement to model processes in isolation, as event based

activities disconnected from any larger system without feedback and typically only with linear

behavior. Because of these limitations DES falls within the boundaries of effectiveness of other

process improvement efforts and is not suitable for wicked messes.

ABM and system dynamics however both focus on replicating behavior patterns of highly

complex systems with the characteristics necessary to simulate wicked mess problems. ABM

does this by populating a system of agents each programmatically coded with its own rules of

behavior. Simulations allow these agents to interact with one another and the environment

according to their rules to generate a behavior pattern of interest. System dynamics approaches

the problem in reverse by taking an observed behavior pattern of interest and then developing a

system structure capable of replicating the behavior to provide explanatory theories as to exactly

what caused the behavior.




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Although this may sound semantically similar the difference between the two is key. Because

ABM relies on computer code and randomized interactions it is not always clear how to connect

the dots from agent code to observed behavior. This is useful for scientific studies but less useful

for business enterprises. System dynamics relies on integral calculations conducted across a

designed system structure. This means the simulation can be frozen, like a video, put into pause

and advanced frame-by-frame in sequence. Changes in behavior can be isolated to the exact

parameter interactions of the simulation that caused the behavior to change.

This is the powerful difference between system dynamics and ABM or even inferential statistics.

Both of the latter correlate the cause to the effect within their models. In system dynamics, at

least within the boundaries of the model, the causes are precisely known. This causality makes

system dynamics particularly well suited for business strategy. Strategy decisions can be

examined for the causal impact they have, desired or undesired, over time on multiple different

performance measures and other aspects of the enterprise. This leads to a crisp actionable list of

recommendations on what to do, and not do, in order to achieve the desired result of the strategy.

The final question in selecting how to address a wicked mess is what type of system dynamics

application to use? Over the decades, many applications of system dynamics have been

developed for different purposes. The standard method, for example is used by academics for

scientific research but may not be appropriate for use in business contexts. However, strategy

dynamics, released by Kim Warren in 2008 is designed specifically for business purposes of

strategy and performance.

Strategy dynamics focuses on speed of implementation in model development, enhancing utility

of the end result in creating actionable recommendations, and participation by the business

stakeholders throughout the modeling process. This informs the modeling process with the

subject matter expertise, increases the confidence of subject matter experts by having them help

build the model all without having to learn system dynamics first.




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Timeline in Wicked Mess Problem Solving

1960 Forrester develops System Dynamics

simulation science.

1973 Rittel & Webber identify Wicked

Problems.

1974 Russel Ackoff identifies Social Messes.

1988 Standard Method application of system

dynamics for scientific research.

1990’s Wicked messes become a matter of

concern for large enterprises.

1996 Roth & Senge identify Wicked Messes

in enterprises.

1998 ABM simulation science developed.

2008 Strategy Dynamics application of

system dynamics for business.

Today Continuing complexification of

business increases frequency and

severity of wicked mess problems.

How Dialectic Simulation Consulting solves wicked messes using strategy dynamics. At Dialectic, we intend to narrowly focus on the problem space of wicked messes and the

application of strategy dynamics. Nothing above should be taken to diminish the value of other

problem-solving approaches when used appropriately in the correct problem space. Those

capabilities however are commonly available either through internal departments or externally

contracted firms. Our value-add to internal efforts and differentiation with other firms however is

where the utility of those methods utility begins to fade based on the limitations of the tools. We

target the very specific challenges of wicked mess problems to:

1. Educate stakeholders to build a shared understanding, common consensus, and

confidence in what their wicked mess problem is with a graphical “English labeled”

models.

2. Reduce ambiguity by mathematically formulating the interdependence of both “hard” and

“soft” aspects of the system. This enables simulations to identify unanticipated outcomes

the human mind cannot conceive of without computer assistance.




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3. Reduce uncertainty by identifying the key leverage points and recommendation portfolios,

including quantitative justification of a strategic way forward.

4. Increase adoption through change by identifying the latent constraints within the

enterprise, as well as external to it, ensuring policy recommendations are plausible,

tangible and actionable.

5. Produce a portfolio of strategic steps that can be taken to achieve significant change in

the targeted performance measure while minimizing the risk of unanticipated

consequences.

What are the skill sets Dialectic brings to engagements?

Developing simulations is a capability not found in process improvement or analytics but can

amplify and enhance the value of those efforts where they exist. Where these efforts identify

dozens or even hundreds of disparate data elements and findings – a simulation model can tie

them together in a coherent fashion and allow simulation of the whole. Because the designs are

graphically depicted in English the models aid in educating stakeholders to build consensus and

confidence around the strategy. Successfully doing this requires a team with a blend of hard and

soft skills.

A Dialectic team consists of simulation modelers and a services owner. Our simulation modelers

typically hold Masters in Science, PhD’s or equivalent experience in system dynamics from

schools such as MIT, WPI, SUNY-Albany and Texas A&M. Our LSS MBB’s usually have ~15

years of prior experience in managing engagements and specifically are skilled facilitators on

how to bridge audiences of differing technical backgrounds to obtain needed information for

success.

We partner our teams by inviting subject-matter-experts from the client to join in focused

workshops and then at periodic check-ins. Outside of the workshops, some initial data-gathering

and milestone socializations the majority of the work can be conducted remotely with check-ins

conducted online. This limits the interruption and disruption of the client environment.




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Annex A - What does a Strategy Dynamics Engagement Look Like? As mentioned above strategy dynamics is designed for pressing business problems where time is

of the essence. A typical modeling engagement can be completed in four months. We incorporate

‘Agile methodology’ in our strategy dynamics efforts, breaking a four month engagement into

eight two week sprints. This engagement approach is depicted below.

Figure 5: High Level Overview of Engagement Approach

The four key phases of the workshop represent milestone elements of developing a simulation

model capable of explaining a wicked mess performance problem and offering a strategy.

The strategic architecture represents a simulation model of the enterprise itself, anchored on the

performance measure of interest, modeled to the extent necessary to capture the major

interdependent parts that may impact the measure. This is not a model of the entire company,

but bounded to focus on the problem at hand. The strategic architecture is next located within

the world model. The world model depicts all the external systems within which an enterprise

operates: customers, supplies, competitors, government regulators, economic factors, changing

demographics. Again the selection criteria of what is included in the world model is based on the

problem at hand. Once a simulation model has been completed, numerous scenarios are

conducted against this. These can include “what if” scenarios designed with input by

stakeholders as well as computer-generated algorithmic optimization analysis to examine the

model and identify points of interest a human perspective might not be able to identify. Finally

the strategy & policy proposals are the concrete actions: whether strategic changes, mergers,

backlog or process improvement efforts, cultural change requirements – necessary to achieve the

performance desired with a minimum of unanticipated consequences.

Focus:

SPRINT 1 SPRINT 2 SPRINT 3 SPRINT 4 SPRINT 5 SPRINT 6 SPRINT 7 SPRINT 8

Objectives:

Kickoff

Socialization

with Leadership

Team

Socialization

with Select

Stakeholders

Scenario

Testing

Socialization of

Iteration 3 &

Feedback

Team Modeling

Workshop

Team Modeling

Workshop

Create Backlog

of Improvement

Ideas

Priotization of

Strategic Ideas

SME Interviews SME Interviews SME Interviews

Final

Presentation

Deliverables:

Iteration 1:

Simulation

Model of the

Strategic

Architecture

Iteration 2:

Strategic

Architecture

within the

"World"

system-of-

systems

Iteration 3:

Scenario

Analysis

Iteration 4:

Simulation

Supported

Strategy &

Proposed

Policies

Validation, Confidence Building & Testing Throughout

Strategic Architecture World Model Scenarios

Strategy & Proposed

Policies




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Annex B – Commitment Requirements from Client Based on our past experience supporting clients we understand that the impact on personnel time

is key to know up front. We also are emphatic that modeling not be a “black box” effort but

instead believe that cooperative development, phase by phase, increases confidence and

understanding of the simulation model with stakeholders. Although every engagement is

different a high level overview of the key roles from the client, as well as estimated participation

time, is included below.

Figure 6: FTE Commitment Estimates

Outside of the workshops, socializations and as-needed interview and data gathering session,

much of the simulation modeling work can be conducted remotely/

1The executive champion sets strategy and vision for the overall

project. Ensures access to resources and prioritization of

effort. Second line of escalation.

As NeededExecutive Champion

Role FTE

Commit

Capability Est # of

FTE’s

Executive Steering

Committee

1-2hours

monthly

Senior leadership and executive sponsors for the enterprise-wide

program. Sets strategy and acts third line of escalation.

Includes the Executive Champion, Outcome Owner, Dialectic

Services Owner, Dialectic Executive Partner and Dialectic

Data Scientist.

~Varies

Outcome

Owner/Project

Manager

25%-50% The client Outcome Owner liaisons with the Dialectic Services

Owner and participates in all stand-ups (physical or virtual).

They are the primary point of contact with the Dialectic

Services Owner and is the first line of authority for client in

relation to this work. They are responsible for ensuring access

and availability of SMEs when needed. They may also be an

owner of the final product and thus provide critical customer

input into the modeling to ensure the product meets the needs

of the client.

1

Subject Matter Expert

(Workshop Weeks)

100% during

identified

workshop

weeks

Dedicated subject-matter experts in the targeted non-state actor

threat organization. Will work hand-in-hand with our modeling

team to develop the strategy dynamics strategic architecture

and world model components during two workshop sessions

(more if required.) Ideally should be a veteran with deep cross-

functional expertise in multiple areas, “knows where the bodies

are hidden” in terms of data sources, has a wide network of

relationships to call upon if a question arises and is open/eager

to participate in new or innovative approaches.

~Varies (3-8)


(Non- Workshop

Weeks)

25-50% Outside of the workshop weeks the SME’s join stand-ups

(physical or virtual) and work with Dialectic modelers to obtain

and research data sets, whether from internal sources or

publically available sources to assist in completing the model.

They will also be involved in first-line validation and

socialization activities.

~Varies (3-8)

1The executive champion sets strategy and vision for the overall

project. Ensures access to resources and prioritization of

effort. Second line of escalation.

As NeededExecutive Champion

Role FTE

Commit

Capability Est # of

FTE’s

Executive Steering

Committee

1-2hours

monthly

Senior leadership and executive sponsors for the enterprise-wide

program. Sets strategy and acts third line of escalation.

Includes the Executive Champion, Outcome Owner, Dialectic

Services Owner, Dialectic Executive Partner and Dialectic

Data Scientist.

~Varies

Outcome

Owner/Project

Manager

25%-50% The client Outcome Owner liaisons with the Dialectic Services

Owner and participates in all stand-ups (physical or virtual).

They are the primary point of contact with the Dialectic

Services Owner and is the first line of authority for client in

relation to this work. They are responsible for ensuring access

and availability of SMEs when needed. They may also be an

owner of the final product and thus provide critical customer

input into the modeling to ensure the product meets the needs

of the client.

1


(Workshop Weeks)

100% during

identified

workshop

weeks

Dedicated subject-matter experts in the targeted non-state actor

threat organization. Will work hand-in-hand with our modeling

team to develop the strategy dynamics strategic architecture

and world model components during two workshop sessions

(more if required.) Ideally should be a veteran with deep cross-

functional expertise in multiple areas, “knows where the bodies

are hidden” in terms of data sources, has a wide network of

relationships to call upon if a question arises and is open/eager

to participate in new or innovative approaches.

~Varies (3-8)


(Non- Workshop

Weeks)

25-50% Outside of the workshop weeks the SME’s join stand-ups

(physical or virtual) and work with Dialectic modelers to obtain

and research data sets, whether from internal sources or

publically available sources to assist in completing the model.

They will also be involved in first-line validation and

socialization activities.

~Varies (3-8)




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Annex C – 1/10th-1/20th Scale Diorama of Sharing Economy Strategy

Dynamics Model

This section provides an overview of a conceptual model, built at scale, we created based on an

arbitrarily selected wicked mess problem of improving the performance measure of memberships.

Obviously a diorama is just a model and the structure depicted below is based entirely off our

assumptions, limited understanding, and synthetic data that we have created. The results

therefore are unlikely to match any actual Sharing Economy company performance. However the

diorama demonstrates key elements of a strategy dynamics approach in solving wicked mess

problems.

The model depicts a startup operation with a 500 providers of services secured by lease, 100

initial employees and $50M in starting capital. The simulation runs for 8 years and is designed to

test different strategies for acquiring memberships and the impact those strategies might have on

various business operations.

There are nine sectors to the model depicted in aggregate below. The sectors are split into a

strategic architecture and a world model. The strategic architecture represent the hypothetical

Sharing Economy company capabilities, assets and resources.

Figure 7: Sector Overview of Simulation Model

Members

Advertising

Capability

Competitor

Adoption

Provider Coverage

(Leased & Portfolio)

Member

AdoptionMarket

Employees &

Shift Coverage

Cash Flow

& Reserves

Customer Sat,

Availability &

Usage

Revenue

Expenses

WORLD MODEL

STRATEGIC

ARCHITECTURE

PERFORMANCE

MEASURE




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Amongst these nine sectors are nearly 117 equations. Typically, during model development each

sector would be developed with client subject matter experts and socialized with stakeholders to

build confidence. Then the model is assembled in aggregate to be tested as a whole. This is

important because even as a scale diorama, the entire model is a bit bewildering to look at as a

whole:

Members

(People)

PotentialMembers(People)

Rate Members Abandon

Market (People/Month)Subscriptions

(People/Month)

Conversions

(People/Month)

Total Market

Awareness Rate

(People/Month)

Market Share

(Pct)

Normal Contact Rate

(People/People/Month)

Normal Word of MothAdoption

(People/People/Month)Conversion through Word

of Mouth (People/Month)

Table f/ Effect of Market

Share (Function)

Effect of Marketshare on

WoM Adoption (Dmnl)

People Contacted

<Members

(People)>

Total Aware

Population

<Potential Members

(People)><Members

(People)>

Adoption fromAdvertising

(People/Month)

Advertising

Conversions (Pct)

Advertising Effectiveness perCohort

(People/People/Cohort)

Cost of Advertising to aCohort

(USD/Cohort/Month)

Advertising Budget

(USD/Month)

Advertising Cohorts

(Cohorts)

Churn Rate

(People/Month)

Subscription Length

(Months)

SharingCompany

LeasedProviders

(Providers)

Portfolio Acquisition

(Providers/Month)

Cust Sat

(Pct)

Provider Transfers

(Providers/Month)

Average ProviderTransfers

(Providers/Month)Change in Average ofPortfolio Transfers

(Providers/Month/Month)Averaging Time

(Months)

Reported Sharing CompanyPortfolio Providers to Acquire

(Providers)

Time to Acquire

Providers (Months)

SharingCompanyPortfolio

(Providers)

Total Portfolio

(Providers)

Cost to Maintain

(USD/Provider/Month)

Portfolio Acquisition Price


<Total Portfolio

(Providers)>

<Portfolio Acquisition

(Providers/Month)>

Total Expenses

ProcurementExpenses

(USD/Month)

MaintenanceExpenses

(USD/Month)

<Advertising Budget

(USD/Month)>

Subscription Revenue

(USD/Month)

SubscriptionPrice

(USD/Person)

Total Revenue

Reserves

(USD)Cashflow

(USD/Month)

Monthly Accounting

(Month)

<Members

(People)>

PROVIDER

COVERAGE (LEASED

& PORTFOLIO)

MEMBER

ADOPTION

ADVERTISING

CAPABILITY

CASHFLOW &

RESERVES

Competitor

Members

CompetitorConversions

(People/Month)

Competitor Churn

(People/Month)

CompMarket

Share (Pct)

CompNormal ContactRate


CompNormal Word of MothAdoption


CompConversion throughWord of Mouth(People/Month)

CompTable f/ Effect of

Market Share (Function)CompEffect ofMarketshare on WoM

Adoption (Dmnl)

CompPeople

Contacted

CompTotal Aware

Population

<Potential Members

(People)> <Competitor

Members>

CompAdoption fromAdvertising

(People/Month)

CompAdvertising

Conversions (Pct)

<Subscription Length

(Months)>

<Potential Members

(People)>

COMPETITOR

DYNAMICS

STRATEGIC

ARCHITECTURE

WORLD

MODEL

<Cust Sat (Pct)> Effect of Cust Sat on

Abandoment

Abandon Rate

Time to Abandon

Normal Rentals perMonth

(Shares/Person/Month)

Avg Per Share

Revenue (USD/Share)

Desired Monthly

Rentals (Shares/Month) <Members

(People)>

Share Revenue

(USD/Month)

Employees

Coverage

(Shifts)Increase inCoverage

(Shifts/Month)

Desired Goverage

(Shifts)

Coverage Gap

(Shifts)

Time to Hire/Fire

Employees

Change in Employees

(People/Month)

Coverage perEmployee

(Shifts/Person)

<Total Portfolio

(Providers)>Normal Shifts per

Provider(Shifts/Providers)

<Coverage perEmployee

(Shifts/Person)>

Employee Change to

Close Gap (People)

Quarterly Reporting

(Months)

Desired Change in

Shifts (Shifts)

Coverage Reporting

Time (Months)

Minimum Staff

Requirement for

Minimum Staff

Table f/ Requirement

of Minimum Staff

<Employees>

Employee Morale

Morale Impact on

Cust Sat

<Morale Impact on

Cust Sat>

<Employees>

Cost per Employee

(USD/Month)

Wages

(USD/Month)

Days to Prep

Provider (Months)

Initial Lease Seed of

Providers (Providers)

Daily RentalAvailability(Shares)

Daily Services Available

to Rent (Shares/Month)

Rentals

(Shares/Month)

Monthly Availability per

Service (Shares/Provider)

<Normal Rentals perMonth

(Shares/Person/Month)>

Desired Rental

Availability (Shares)

<Members

(People)>

Gap in Rental


Change in Rental


Monthly Reporting

(Months)

Transfers to CloseAvailability Gap

(Providers)

Shift Coverage

<Desired Goverage

(Shifts)>

<Monthly Availability per

Service (Shares/Provider)>

Average Rental


Lookup for

Availability on Rentals

Effect of Availability

on Shares

<Quarterly Reporting

(Months)>


(Months)>

Weekly Reporting

(Months)

<Monthly Reporting

(Months)>

Weekly Availability

Average (Months)

Initial Reserves

(USD)\

Lease Cost

(USD/Provider) Transfer Expenses

(USD/Month)

<Provider Transfers

(Providers/Month)>

Lookup for Effect ofCustomer Sat on Desired

RentalsEffect of Customer Sat

on Desired Rentals

Source ofLeased

Providers(Providers)

AverageCustomerSat (PCT)Change in Avg Cust

Sat (PCT/Month)

Time to Form

Perceptions (Months)

Initial Customer

Sat

<Average Customer

Sat (PCT)>

Desired Daily Rentals

(Shares/Month)

<Desired Daily Rentals

(Shares/Month)>

EMPLOYEES &

SHIFT COVERAGE

<Average Rental

Availability (Shares)>

<Total Portfolio

(Providers)>

<Subscription Revenue

(USD/Month)>

<Share Revenue

(USD/Month)>

CUSTOMER

SATISFACTION,

AVAILABILITY & USAGE

PERFORMANCE

MEASURE:

MEMBERSHIPS

Figure 8: Actual Simulation Model

This is why examination is often based on sectors. We’ve selected two sectors in the following

pages to depict, close up, model structure, described what is being observed and then provide a

few illustrations of how the simulation can be used to gain insights. All graphs are pulled from

the same scenario initially presented – two alternate marketing strategies aimed to increased

Membership vs. a Baseline. The marketing campaign depicted by Strategy 1 increases the

effectiveness of advertising as well as word of mouth sales. The marketing campaign depicted by

Strategy 2 focuses on helping current subscribes share their experiences on social, increasing the

rate at which they contact non-subscribing potential members.

Strategic Architecture – Provider Coverage (Leased & Portfolio)

As shown in Figure 8 this sector is located in the Strategic Architecture of the Sharing Economy

company as it represents resources, assets or capabilities the enterprise can bring to bear on the

wicked mess at hand. The Provider Coverage Sector represents – based on assumptions – how




14

the Sharing Economy company might try to balance its provider portfolio during periods of

growth via leased providers versus securing it’s own providers directly affiliated with its own

company to build a permanent portfolio.

Figure 9: Sector in Focus – Provider Coverage (Leased & Portfolio)

The actual model structure in that sector is detailed in Figure 9.

Members

Advertising

Capability

Competitor

Adoption

Provider Coverage


Member

AdoptionMarket

Employees &

Shift Coverage

Cash Flow

& Reserves

Customer Sat,

Availability &

Usage

Revenue

Expenses

WORLD MODEL

STRATEGIC

ARCHITECTURE

PERFORMANCE

MEASURE




15

SharingCompanyLeased


Portfolio Acquisition

(Providers/Month)

Provider Transfers

(Providers/Month)

Average ProviderTransfers

(Providers/Month)Change in Average ofPortfolio Transfers

(Providers/Month/Month)Averaging Time

(Months)

Reported Sharing CompanyPortfolio Providers to Acquire

(Providers)

Time to Acquire

Providers (Months)

SharingCompanyPortfolio

(Providers)

Total Portfolio

(Providers)

Cost to Maintain


Portfolio Acquisition Price


<Portfolio Acquisition

(Providers/Month)>

PROVIDER

COVERAGE (LEASED

& PORTFOLIO)

STRATEGIC

ARCHITECTURE

Coverage Reporting

Time (Months)



<Normal Rentals perMonth

(Shares/Person/Month)>

Desired Rental


<Members

(People)>

Gap in Rental


Change in Rental


Monthly Reporting

(Months)

Transfers to CloseAvailability Gap

(Providers)




(Months)>


(Months)>

Weekly Reporting

(Months)

<Monthly Reporting

(Months)> <Provider Transfers

(Providers/Month)>

Source ofLeased


<Average Rental

Availability (Shares)>

Figure 10: Detail of Provider Coverage Sector

Beginning in the upper left information known to managers – the number of current members

and the normal rentals per month, are used to establish a desired rental availability. This is then

compared to the number of available rental slots the current portfolio provides to determine if

there is a gap, delayed in part by the time it takes to report on this information. Transfers are

made between the stock (box) of Source of Leased Providers to the Sharing Company Leased

Providers to meet surge demands. Over time, the level of transfers is monitored to determine

how many Portfolio Acquisitions of permanent providers need to be made through procurement.

Together these two sources of providers combine to determine the Total Portfolio of providers.

When the portfolio is larger than demand, efforts are made to return Leased Providers to the

Source of Leased Providers portfolio and the inventory of Portfolio Acquisitions can also be

reduced.

Each label is in English terms, and would be customized to fit specific any actual client

nomenclature and vocabulary. The “unit of measure” is identified in parentheses so readers

understand what that particular parameter is calculating. Although the information received

initial on demand is expressed in (Shares), this is converted to (Providers) by the time decisions




16

on portfolio size are made. Consistency of units of measure throughout the system is extremely

important to replicate real world conditions. Information should reflect what is available to

decision making managers when it is available, including if it has errors in it.

Focusing in on one specific aspect the common elements of a system dynamics model can be

identified. Behind each English-termed parameter is the mathematical calculation being

conducted.

SharingCompanyLeased


Provider Transfers

(Providers/Month)

Total Portfolio

(Providers)



Change in Rental

Availability (Shares)Transfers to CloseAvailability Gap

(Providers)



<Monthly Reporting

(Months)>

Source ofLeased


Figure 11: Elements of a Model

For example behind the parameter of Source of Leased Providers is this formula:

"Source of Leased Providers (Providers) "= INTEG ("Provider Transfers (Providers/Month)",

"Initial Seed Vehicles (Vehicles)")

Units: Providers

This conveys to the software that the current value of providers is the integration of the rate of

change value of Provider Transfers with the last value of stock, and the stock is initialized at the

number of providers found in the Initial Seed. If depicted in traditional formulation (Greek letters

& symbols) the formula depicted by this sector would be over half a page long, and provide

almost no understanding to the untrained observer of what was occurring within all the symbols.

The four graphs below indicate the performance over time of the values of Source of Leased

Providers, Sharing Company Leased Providers, Sharing Company Portfolio of Providers and

Total Portfolio.




17

Figure 12: Portfolio Values by Strategy

The graphs demonstrate the impact of membership growth on the demand for providers. Demand

is initially met by pulling from the Source of Leased Providers and then evaluating long term

trends to build a Portfolio of Providers. Because the system is reacting to management’s

understanding of need (based on membership and estimated shares) and not actual need – more

providers are transferred than needed in some situations; resulting in back and forth churn.

This is synthetic data – but the behavior demonstrated can still provoke useful strategic

discussion. Is it realistic the that the pool represented by the Source of Leased Providers would

be drawn down so heavily? Does this reflect a need to change the Transfer or Portfolio

Acquisition purchase policies? Is there such a thing as “too-fast” growth? How does customer

demand translate into provider management? That’s the question we model in the next sector

which is composed almost entirely of “soft” variables, Customer Satisfaction and how that drives

Usage and Availability.

World Model – Customer Satisfaction, Availability & Usage Sector

As shown in Figure 12, this sector is located in the World Model – as it represents factors outside

of the control of the Sharing Economy company directly.

This sector depicts the interaction of paying subscribers to the Sharing Economy company and

their desire to use the service. As they schedule Rentals, this draws down on the availability of

providers. If there are insufficient providers available, they cannot take advantage of a Shared

service. This both limits revenue but also reduces satisfaction and may limit both the future

Sharing Company Leased Providers (Providers)

800,000

600,000

400,000

200,000

0

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84

Time (Month)

Pro

vid

ers

"Sharing Company Leased Providers (Providers)" : Strategy 2

"Sharing Company Leased Providers (Providers)" : Strategy 1

"Sharing Company Leased Providers (Providers)" : Baseline

Source of Leased Providers (Providers)

1 M

750,000

500,000

250,000

0

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84

Time (Month)

Pro

vid

ers

"Source of Leased Providers (Providers)" : Strategy 2

"Source of Leased Providers (Providers)" : Strategy 1

"Source of Leased Providers (Providers)" : Baseline

Sharing Company Portfolio (Providers)

60,000

45,000

30,000

15,000

0

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84

Time (Month)

Pro

vid

ers

"Sharing Company Portfolio (Providers)" : Strategy 2

"Sharing Company Portfolio (Providers)" : Strategy 1

"Sharing Company Portfolio (Providers)" : Baseline

Provider Transfers (Providers/Month)

80,000

40,000

0

-40,000

-80,000

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84

Time (Month)

Pro

vid

ers

/Mo

nth

"Provider Transfers (Providers/Month)" : Strategy 2

"Provider Transfers (Providers/Month)" : Strategy 1

"Provider Transfers (Providers/Month)" : Baseline




18

desire to partake of shares – and the likeliness a customer will recommend to a non-customer via

word-of-mouth to adopt the service.

Figure 13: Sector in Focus - Customer Satisfaction Sector

As in the previous sector, the detailed model structure of the Customer Satisfaction structure is

“blown up” and depicted in Figure 13.

Members

Advertising

Capability

Competitor

Adoption

Provider Coverage


Member

AdoptionMarket

Employees &

Shift Coverage

Cash Flow

& Reserves

Customer Sat,

Availability &

Usage

Revenue

Expenses

WORLD MODEL

STRATEGIC

ARCHITECTURE

PERFORMANCE

MEASURE




19

Normal Word of MothAdoption


Churn Rate

(People/Month)

Cust Sat

(Pct)

<Members

(People)>

Normal Rentals perMonth

(Shares/Person/Month)

Avg Per Share

Revenue (USD/Share)

Desired Monthly

Rentals (Shares/Month) <Members

(People)>

Share Revenue

(USD/Month)

<Morale Impact on

Cust Sat>

Days to Prep

Provider (Months)

Daily RentalAvailability(Shares)

Daily Services Available

to Rent (Shares/Month)

Rentals

(Shares/Month)

Monthly Availability per

Service (Shares/Provider)

Average Rental


Lookup for

Availability on Rentals

Effect of Availability

on Shares

Weekly Availability

Average (Months)

Lookup for Effect ofCustomer Sat on Desired

RentalsEffect of Customer Sat

on Desired Rentals

AverageCustomerSat (PCT)Change in Avg Cust

Sat (PCT/Month)

Time to Form

Perceptions (Months)

Initial Customer

Sat

<Average Customer

Sat (PCT)>

Desired Daily Rentals

(Shares/Month)

<Desired Daily Rentals

(Shares/Month)>

<Total Portfolio

(Providers)>

Figure 14: Detail of Customer Satisfaction Sector

For every Member there is a number of Normal Rentals per Month, which is the same used to

estimate in the previous sector the size of the portfolio. However this number is then modified by

the Average Customer Sat to result in an actual Desired Monthly Rentals. Daily Rental

Availability is provided by the Total Providers multiplied by the Average Rental Availability per

month. As providers are made available, the value goes up, as providers are rented, the values

go down.

This is an example of combining a soft qualitative variable such as a customer’s perception of

availability with a tangible variable such as portfolio size in a way that helps understand a

business result – Rentals contributing to Share Revenue. Each individual part of a strategy

dynamics model should be simple to understand in concept, but it is the ability to connect

hundreds of them together and simulate it that provides the power.

The Effect of Customer Sat on Desired Rentals demonstrates how non-linear behavior, frequent

in wicked mess problems is represented:




20

Figure 15: Lookup Function for Effect of Customer Satisfaction on Desired Rentals

At every calculation the model makes, it inputs the current Average Customer Sat and then

provides an Output which is a percentage. In this way the actual value of Effect of Customer Sat

on Desired Rentals is dynamically changing and can reflect “human adaptation”, thus making

‘soft’ variables into ‘hard’ math.

What can we learn from this sector? We know that Strategy 2 resulted in more Members and

instead of comparing across all three strategies we can hone in on Strategy 2 to see this impact

across two variables. Below Strategy 2 is shown across the Availability (providers ready to rent

to customers) and Rentals (providers being rented). This shows periods of overage with idle

providers and insufficient availability relative to demand.




21

Availability vs. Rentals

400,000

400,000

200,000

200,000

0

0

0 6 12 18 24 30 36 42 48 54 60 66 72 78 84

Time (Month)"Daily Services Available to Rent (Shares/Month)" : Strategy 2

"Rentals (Shares/Month)" : Strategy 2

Figure 16: Strategy 2: Comparison Across two Variables - Availability vs. Actual Rentals

Additional Analysis Options

Behaviors can also be compared to parameters sharing dissimilar units and scales across different

sectors, to understand drivers of behavior. We already know that over hiring in response to rapid

Members growth led to poor financial behavior in Strategy 2. Now we can look at how

Availability demands of Members led to this over hiring. We compare Availability to Coverage

of shifts to Employees and focus the chart in on months 36-72. We compare the Availability

driven by




22

Selected Variables

6000 Shifts

400,000

20,000 People

3000 Shifts

200,000

10,000 People

0 Shifts

0

0 People

24 30 36 42 48 54 60 66 72 78 84

Time (Month)"Coverage (Shifts)" : Strategy 2 Shifts

"Daily Services Available to Rent (Shares/Month)" : Strategy 2

Employees : Strategy 2 People

Figure 17: Identifying Drivers of Change across Dissimilar Units & Scales

By year two growth in Members is strong driving increased Total Portfolio size which increases

Availability. However, as market saturation occurs it’s harder to get each new member and

growth begins to slow. This results in the chart above where Availability peaks at month ~46 and

begins declining. This is an overshoot – as Availability will continue to drop into a trough

reached in month ~68 before recovering. However, based on our shift coverage needs hiring has

continued right along side the growth resulting in more Employees than required. Employees

begin to be let go in month ~50…however the portfolio size still requires more shifts to cover

than the enterprise has employees and will result in other impacts until the coverage

requirements drop as the portfolio is downsized. This kind of cross-company interaction

between disparate elements is very difficult to perform quantitatively without the assistance of a

simulation.

A third way of looking at information is by tracing the causality of behavior. Because the system

is built deterministically and calculated with integrals, rather than statistics, causal analysis can

show precisely what-causes-what. Going back to compare all the two strategies vs. the baseline a

causal trace is performed on Share Revenue showing how that arrives from Rentals multiplied by

the Average Share Price.




23

Baseline

Strategy 1

Strategy 2

"Share Revenue (USD/Month)"

200 M

150 M

100 M

50 M

0"Rentals (Shares/Month)"

300,000

225,000

150,000

75,000

00 42 84

Time (Month)

"Avg Per Share Revenue (USD/Share)"

Baseline: 20 Strategy 1: 20

Strategy 2: 20

Figure 18: Causal Analysis - What Creates Share Revenue

The Share Revenue feeds into the Cashflow & Reserves sector which determines the overall

enterprise performance. This isn’t surprising, it stands to reason. However this is the benefit of a

simulation model dozens to hundreds of equations in size. Each component doesn’t have to be

very complex. They should be simple to understand and easily explained causally. However, by

following this trail of component-by-component causal analysis simulation scientists can identify

and isolate the very exact location and time when where complex behavior occurred. At these

locations exist the threshold points, tipping points, timing windows for intervention and other

areas of leverage.

Any one, or all, of the parameters in a model can be individually or simultaneously as a group

randomized in optimization runs. This can be done to identify optimal parameter configurations,

identify leverage points where intervention with a process improvement project will have greater

impact than others – or also to create confidence boundaries on potential outcomes.

For example, say we were interested in understanding the impact on marketing Strategy #2 on

variations in customer desire to use the service (# of shares) and what kind of shares they would

use it for (price per trip. We can simulate this by running 200 different scenarios varying the

values across a uniform distribution of Normal Shares per Month between 5 and 25

Shares/Month and Average Share Revenue between $7 – $40 USD. The results are then plotted




24

across confidence boundaries representing the range of which all results fall within and are

depicted below.

Figure 19: Confidence Boundaries across 200 Randomized Scenarios

The confidence boundaries show us the range in which the number of scenarios of Strategy 2

result based on changing customer behaviors – and can either build confidence, identify gaps or

suggest additions to strengthening the strategy.

These are just a handful of the numerous analysis options available in strategy dynamics that can

create additional value and insights in forming a strategy. In addition to crafting the overall set

of strategy recommendations using these analysis tools strategy dynamics can:

Recommend a backlog of project opportunities to focus process improvement efforts on

to alter individual elements of the system with a goal of effecting the whole.

Identify key performance measures to track as well as the boundaries within which they

need to perform.

Provide visual “what-if” depictions of what will happen to performance to drive home to

managers the impact of failing performance.

Identify areas to focus data science, consumer research and other efforts will gain the

most benefit while also noting which areas can remain “fuzzy” for lack of being a

leverage point in the system.

Test shock scenarios to ensure robustness of the strategy under unlikely events.

Strategy 2 Optimization

50.0% 75.0% 95.0% 100.0%

"Reserves (USD)"

5 B

2.5 B

0

-2.5 B

-5 B0 21 42 63 84

Time (Month)


50.0% 75.0% 95.0% 100.0%

"Total Portfolio (Providers)"

600,000

450,000

300,000

150,000

00 21 42 63 84

Time (Month)


50.0% 75.0% 95.0% 100.0%

Employees

10,000

7500

5000

2500

00 21 42 63 84

Time (Month)


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