System Dynamics, A Normative Approach to Forecasting

By :-Sudhanshu SharmaShikhar KhandelwalAnkit BorasiManish Boudh

• Forecasting

• History

• System Dynamics

• Case Study

Index

• Forecasting is the process of making statements about events whose actual outcomes have not yet been observed.

• Forecasting is a tool used for predicting future demand based on past demand information

What is Forecasting?

Demand for products and services is usually uncertain.

Forecasting can be used for…• Strategic planning (long range planning)

• Finance and accounting (budgets and cost controls)

• Marketing (future sales, new products)

• Production and operations

Why is Forecasting Important?

From the March 10, 2006 WSJ:

Ahead of the Oscars, an economics professor, at the request of Weekend Journal, processed data about this year's films nominated for best picture through his statistical model and predicted with 97.4% certainty that "Brokeback Mountain" would win. Oops. Last year, the professor tuned his model until it correctly predicted 18 of the previous 20 best-picture awards; then it predicted that "The Aviator" would win; "Million Dollar Baby" won instead.

Sometimes models tuned to prior results don't have great predictive powers.

What’s Forecasting All About?

• Forecasts are more accurate for groups or families of items.

• Forecasts are more accurate for shorter time periods.

• Every forecast should include an error estimate.

• Forecasts are no substitute for calculated demand.

• Key issues in forecasting• A forecast is only as good as the information included in the forecast (past data) .

• History is not a perfect predictor of the future (i.e.: there is no such thing as a perfect forecast).

• REMEMBER: Forecasting is based on the assumption that the past predicts the future! When

forecasting, think carefully whether or not the past is strongly related to what you expect to see in

the future.

Some general characteristics of forecasts

Forecasting Models

Forecasting Techniques

Subjective Assessment

Methods

Exploratory Methods

Normative Approach

Individual SubjectiveProbability assessment

Jury of Executive Opinion

Sales Force Composite

Formal survey’s & Market research

based assessment

Scenario Development Method

Delphi Approach

Cross Impact Matrices

Curve Fitting Methods

Analogy Methods

Morphological Research

Relevance Tree

System’s Dynamics

Catastrophe Theory

• Exploratory approaches generally deal with questions of what may, might, or could possibly happen on the basis of the forces at play.

• Normative forecasting almost always reflects the needs of an organization and, therefore, is goal-oriented.

• Goal-oriented forecasting tends to take into account an organization's purpose, its mission, and, most importantly, its expected achievements in the future.

• Normative forecasting addresses the question of "what ought to we do."

• Normative forecasting is usually associated with large organizations, both public and private, as an important component of decision making and a factor in resource allocation.

• Institutions, organizations, and governments have always had interest in the longer-term future.

• Normative forecasting is a much narrower aspect of that unstructured look to the future.

• The key elements that separate normative forecasting from any other kind of speculation or enunciation of goals are its systematic, comprehensive, and public aspects––public, in this case, meaning open to examination and review by people other than the planners and forecasters themselves.

• The normative forecast consists of two essential parts. First is the statement of a goal or set of goals for a specific time.

Second is the analysis in detail of how to reach the goal or goals.

History of Normative Forecasting

• The statement of the goal itself must be realistic and take into account a general awareness of present and future circumstances, resources, social, scientific, and technological contexts, etc

• Crucial to the process is the detailed analysis, which reveals the specific steps or stages that must be met and how they will be met at specific times in moving toward the goal.

• the function of a normative forecast is to allow an organization to orchestrate its resources in a highly targeted way in order to achieve a goal

• Normative forecasting, jumps ahead and states some goal or objective that may be substantially or only apparently discontinuous with the trends currently at play.

• Having defined that future goal, the forecaster backs away from that future to the present to identify the necessary steps for reaching the goal.

• The planners had to consider capabilities required to meet the goal. What kind of physical facilities are required?

Methods and Techniques

• Normative forecasting has had its most refined development in the service of physical technological systems.

• It recently enjoyed a vogue in the business world using a different nomenclature.

• Dynamics :- Dynamics is the study of forces that causes motion in a body.

• System dynamics is a methodology and mathematical modeling technique for framing, understanding, and discussing complex issues and problems.

• Originally developed in the 1950s to help corporate managers improve their understanding of industrial processes, system dynamics is currently being used throughout the public and private sector for policy analysis and design.

Terms & definitions :- System Dynamics

• System dynamics is an approach to understanding the behavior of complex systems over time. It deals with internal feedback loops and time delays that affect the behavior of the entire system.

• The forecasts that come from calibrated system dynamics models are likely to be better and more informative than those from other approaches.

• System dynamics models can provide more reliable forecasts of short- to mid-term trends than statistical models, and therefore lead to better decisions.

Features of System Dynamics

• System dynamics models provide a means of detecting changes in industry structure, as part of an early-warning-system or on-going learning system.

• System dynamics models provide a means of determining key sensitivities, and therefore of developing more carefully thought out and robust sensitivities and scenarios.

• System dynamics models allow the determination of appropriate buffers and contingencies that balance risks against costs.

• In 1974, U.S. electric utilities made plans to double generating capacity by the mid-1980s based

on forecasts of a 7% annual growth in demand. Such forecasts are crucial since companies must

begin building new generating plants five to ten years before they are to come on line. But during

the 1975- 1985 period, load actually grew at only a 2% rate. Despite the postponement or

cancellation of many projects, the excess generating capacity has hurt the industry financial

situation and led to higher customer rates.

• The petroleum industry invested $500 billion worldwide in 1980 and 1981 because it expected oil

prices to rise 50% by 1985. The estimate was based on forecasts that the market would grow

from 52 million barrels of oil a day in 1979 to 60 million barrels in 1985. Instead, demand had

fallen to 46 million barrels by 1985. Prices collapsed, creating huge losses in drilling,

production, refining, and shipping investments.

A few examples

• “Just three weeks after announcing its new Aptiva home computer line, IBM is sold out through the year end and can’t fill all of its holiday orders. The

• shortage, which IBM attributes to conservative forecasting, means the company could forego tens of millions of dollars in revenue …”

System Dynamics Modeling

• Identify a problem.

• Develop a dynamic hypothesis explaining the cause of the problem.

• Create a basic structure of a causal graph.

• Augment the causal graph with more information.

• Convert the augmented causal graph to a System Dynamics flow graph.

• Translate a System Dynamics flow graph into DYNAMO programs or equations.

Critical Aspects

• Thinking in terms of cause-and-effect relationships.

• Focusing on the feedback linkages among components of a system.

• Determining the appropriate boundaries for defining what is to be included within a system.

Understand Cause & Effect• Causal thinking is the key to organizing ideas in a system

dynamics study.

• Instead of ‘cause’, ‘affect’ or ‘influence’ can be used to describe the related components in the system.

• Some are logical (e.g. physics)• Food intake weight• Money happiness• Fire smoke

• Some are not (e.g. sociology, economics)• Use of seatbelts reduced highway fatalities• Shortened daylight hours increased suicide rates

Feedback

• Thinking in terms of “cause and effect” is not enoughocean evaporation cloud rain ocean …

• Feedback: an initial cause ripples through a chain of causation ultimately to re-affect itself.

• Search to identify closed, causal feedback loops is one key element of System Dynamics.

• The most important causal influences will be exactly those that are enclosed within feedback loop.

Causal Loop Diagram (CLD)

• Represent the feedback structure of system.• Captures :-

• The hypotheses about the causes of dynamics.• The important feedbacks.

CLD Examples

• Salary v/s Performance• Salary Performance• Performance Salary

Salary Performance

• Tired v/s Sleep• Tired sleep• Sleep tired

Tired Sleep

Augmenting CLD 1 (Labeling Link Polarity)

• Signing: Add a ‘+’ or a ‘–’ sign at each arrowhead to convey more information

• A ‘+’ is used if the cause increase, the effect increases and if the cause decrease, the effect decreases

• A ‘-’ is used if the cause increases, the effect decreases and if the cause decreases, the effect increases.

Salary PerformanceTired Sleep

-

+

+

+

CLD with Positive Feedback Loop

• Salary Performance, Performance Salary

Salary Performance The better I perform

The more salary I get

The more salary I get

The better I perform

+

+

+

The more salary I get

The better I perform

Loop Dominance

• There are systems which have more than one feedback loop within them

• A particular loop in a system of more than one loop is most responsible for the overall behavior of that system

• The dominating loop might shift over time• When a feedback loop is within another, one loop must

dominate• Stable conditions will exist when negative loops dominate

positive loops

CLD with Combined Feedback Loops(Population Growth)

Birth rate Polulation Death rate-+

+ +

- -

CLD with Nested Feedback Loops

Sunshine

EvaporationA mount of

water on earth

RainClouds

Earth’s temperature-

+

-

+

+

+

+ +

+

+

+-

-

Self-Regulating Biosphere• Evaporation clouds rain amount of water

evaporation …

Exogenous Items

• Items that affect other items in the system but are not themselves affected by anything in the system

• Arrows are drawn from these items but there are no arrows drawn to these items

Sunlight reaching each plant

Density of plants

Sunlight +

+

-

-

Delays

• Systems often respond sluggishly• From the example below, once the trees are planted, the

harvest rate can be ‘0’ until the trees grow enough to harvest

# of growing trees Harvest rate

Planting rate+

+

-

-

delay

• If you would understand anything, observe its beginning and development.

~ Aristotle

Case study

• A System Dynamics Model of Submarine Operations and Maintenance Schedules

Introduction to case

• The extent to which a proposed military force will achieve operational objectives is a prime concern of defence planners. This case discusses the problem in the context of the exercise of sea power in distant waters and shows that a model of the whole problem would require a feedback analysis, for which an appropriate approach would be system dynamics.

• 1. Figure 1 gives a schematic influence diagram of the whole problem. Political circumstances dictate an operational need to have forces on station in a distant locality, perhaps to exert sover-eign control over an area of sea.

• 2. The operational need is translated by naval planners into a need for submarines on station, complemented by other forces, such as surface ships or long-range aircraft. If, for some reason, the required number of submarines are not on station then a discrep-ancy arises between requirement and fulfilment.

CYCLES IN SUBMARINE OPERATIONS

• Procurement cycle • Long term model• Intermediate term model

Procurement cycle• Service period : 16 months (fleet service period)• A cycle completes as :• 1. 1st fleet service period• 2. intermediate docking of 2 months• 3. 2nd fleet service period• 4. mid cycle service docking • (4 months maintenance period)• 5. 3rd fleet service period• 6. intermediate docking 2 months• 7. major refit.- return to dock for 2 years.

• After 3 cycles ship is decommissioned and scrapped• The operational life of unit is some 22 years.

• Cycles in submarine operations (a)submarine refit schedule, • (b) submarine docking cycle.

Long term model (Modeling of construction and refits)

• At the beginning of the simulation, construction commences on the first submarine in the batch.

• It is assumed that the construction yard is tooled, manned and stocked ready to commence con-struction and that the Navy will have men trained and ready to take it to sea when it is built.

• The construction yard starts building new submarines at 18-month intervals, with a 4-year period between starting construction and the submarine being available to enter its first fleet service period.

• Figure 3 shows a typical pattern of output from the long-term model, in this case for a fleet of six vessels. The solid line shows the progressive increase in boats available for sea service.

INTERMEDIATE-TERM MODEL

• Within the intermediate term model, units may be in any one of the following states:

• (a) preparing to sail on patrol.• (b) on the outward transit to the patrol area. • (c) in the patrol area. • (d) on the return transit having completed a patrol. • (e) in maintenance having returned to port; or, • (f) in port, available for sea duty having completed maintenance.

• During fleet service periods a unit may be tasked with a variety of training roles and take part in both national and allied exercise commitments.

Conclusion

• The models described in this paper have successfully represented the behavior of integer sub-marines over the long-term construction and refit and over the medium-term operational cycles. The results of the long-term model indicate unit availability within the major dockyard maintenance schedule for a batch of submarines ordered. The intermediate model examines patrol coverage in a given area of interest and highlights the occasions when units were required, but none were available to satisfy the demand to sail. In this way, these simple models could provide a framework for a broader policy model to embrace the wider issues indicated schematically in Figure 1

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