BA 302 - Systems & Processes

BA 302 - Systems & Processes

"All is Flux" Heraclitus (ca. 535 - 475 BC)

Learning outcomes:

Model simple business situations as systems and system states.

Recognize the basic system-theoretic parts of a business system; e.g., feedbacks, control points, elements, dynamics, black boxes, subsystems, etc.

Understand and illustrate a process as system dynamics.

Know the difference between and be able to provide examples of deterministic and stochastic processes.

Be able to provide examples of how different stochastic phenomena are governed by different probability distributions.

Understand the advantages of business process simulation.

p. 17: "The factory is operated as a closed system... Services operate as open systems"

p. 27: "Open-System view of services" (p.27):

O'Connell, Pyke, Whitehead (2006) Mastering Your Organization's Processes:

"The first vows sworn by two creatures of flesh and blood were made at the foot of a rock that was crumbling to dust; they called as witness to their constancy a heaven which never stays the same for one moment; everything within them and around them was changing." (Oeuvres Romanesques, Denis Diderot (1713-1784)).

"We want to encourage you to see more of the processes that surround you and to be able to place them in a wider perspective."

Some processes associated with London's Marble Arch:

Marble Arch, London 1837

Carrara marble: 200 MYA --> far future

Marble mining: 200 BC --> near future

City of London: 43 AD --> ?

Buckingham Palace: 1703 - 1837 --> ?

Marble Arch: 1828 --> ?

Moved to the entrance of Hide Park: 1851

Architect John Nash: 1752 - 1835

Note that these processes and histories are nested,stacked and interactive.

Problem: Can you think of other stacked and nested processes?

If indeed, "all is flux," is there a way for us to make sense of all this "change?" Can we create some stability or constancy so that we can start to lear and understand?

(One) answer: Systems Theory:

Heylighen et al.: "...however complex or diverse the world that we experience, its organization can be described by concepts and principles which are independent from the specific domain at which we are looking."
A system is a conceptualization of reality. !!!System is a cognitive (theoretical) construct.!!! and a !!! model !!!

Elements & their attributes.

Relationships.

Structure.

Systems are dynamic; i.e., they have behavior.

An element can be a system by itself (subsystem).

An element modeled as if it has no structure (elements & relationships) is a black box.

Open vs. closed systems.

Control elements: elements whose input is under control of a decision maker.

Feedback: an input to an element as a response to a previous output by the same element.

Example: heating system (people, thermostat, furnace, ducts, etc.).
Problem: Make a diagram of a real-world system that has at least one subsystem, one black box and one feedback. Is the system open or closed?

Process:

Websters: "A series of actions, motions, or occurrences; progressive act or transaction; continuous operation; normal or actual course or procedure; regular proceeding; as, the process of vegetation or decomposition; a chemical process; processes of nature."

System theory: Sequence of changes in the state of a system's elements (state transitions) (see also, Wikipedia).

Problem: What process(es) take place in the system you modeled a few minutes ago?

Deterministic vs. stochastic processes:

Deterministic processes:

Experience no random influences or fluctuations.

Outcomes are certain; i.e., fully determined by the initial state of the system and its dynamics.

Example I:

Initial state: You're in Corvallis, OR at the corner of Monroe and 6th street. You walk north onto 6th street.

Dynamic: turn right at each intersection.

Question: after processing 101 intersections, where will you be?

Example II:

Initial state: You have $1000 (present value - PV) in an interest-bearing account.

Dynamic: The value of the account is increased by 5% (interest) at the end of each year.

Question: What is the future value of the account in 25 years? ($3,386.35)

Example III: (Kehlman: Measuring the World)

Initial state: 0.

Dynamic: Add the next whole number to the current state.

Question: What's the sum after 1000 additions? (500,500)

Note that:

Regardless of how many times you compute the solution, it's always the same (certain) and fully determined(!!) by the initial state and the system's dynamics.

All three examples have analytic (closed-form) solutions; no need to compute all (or even any) of the intermediate states.

However: !!Not all deterministic processes have analytic solutions!!

Stochastic processes:

Experience randomness.

Outcomes are not certain as randomness changes the dynamics.

Example I:

Initial state: 0

Dynamic: Add the result of throwing a die to the current state.

Question: What's the sum after 100 throws?

Question: What's the most likely sum after 100 throws? (350) (and how likely is it that it is exactly 350?)

Example II (random walk):

Initial state: you're in the middle of a large, featureless plane.

Dynamic: move a fixed distance in a random direction.

Question: Where will you be after 10000 moves?

Example III:

You have a barge-unloading company. There are no barges at your dock waiting to be unloaded.

Dynamics:

Barges arrive irregularly. Most barges arrive relatively quickly after each other, but sometimes it takes a while before the next one arrives.

The unloading time for barges varies; some take longer than others.

You have three unloading cranes but one of the three is not very reliable; it breaks down a lot.

You have six employees but they sometimes get sick or take un unexpected day off.

Your employees are unionized and the talks between the dock worker union and the unloading companies are not going well.

Etc.

Question: For barges coming to your unloading facility in the next month, what will be their average wait time?

Note that:

Stochastic processes have many possible outcomes.

Outcomes often follow a specific probability distribution; e.g.,

Normal distribution

Uniform distribution

Poisson distribution (image)

Exponential distribution

We can use the characteristics of these distributions to estimate the likelihood of certain outcomes.

If we know, from experience, that the inter arrival times of barges follow a specific exponential distribution, we can estimate the arrival times of the next n barges.

If we know, from experience, that unloading times are normally distributed with a certain average and standard deviation, we can estimate the unloading times of these next n barges.

With these estimates, we can estimate the processing and hence, waiting times of the next n barges.

Once we know the structure and dynamics of a business process, we can model it; e.g., we can simulate it on a computer:

Explore the behavior of the system without risking the real-life process.

Do what-if analysis (aka scenario analysis).

Explore far into the future in just a few seconds or minutes of computer time.

Why is understanding processes so important?
- Processes are that what happens; they are the dynamics of the universe; including our own.
- Processes provide meaning to our world.
- Once you understand the process, you can simulate it, explore its behavior and make informed(!!) decisions on how to change it.
- We too "...want to encourage you to see more of the processes that surround you and to be able to place them in a wider perspective."
Homework

Test Questions:

Model an oil-change business as a system. What are some of its elements and the elements' attributes? What sort of state changes do they undergo? Does the system have any feedbacks? Any black boxes?

Provide two examples each of a deterministic and a stochastic system. Motivate your answer.

Match the Distributions on the left with the Plots on the right.

Distribution

Plot

Poisson

A.

Uniform

B.

Normal

C.

Exponential

D.

Would you expect most business processes to be deterministic or stochastic? Explain your answer.

What are some of the advantages of business process simulation?

Suppose that you have good reasons to believe that a specific business process has stochastic components but you do not know the distribution(s) that govern(s) these components. What course(s) of action would you have?

Is there a relationship between whether a system is open or closed and whether its processes are deterministic or stochastic?