Supply Chain Risk Management

185

10

Using Probabilistic Models

to Understand Risk

AMR research, now part of Gartner, has been speaking about the com-

plexion of the 21st century supply chain for some time, and during that

dialogue the topic of probabilistic planning continuously arises. is

planning process is supported by stochastic demand management and

dynamic inventory planning.

In this chapter, we will discuss models that have been around for some

time, such as stochastic/ probabilistic models, deterministic methods,

discrete- event simulation, and digital modeling. We’ll also explore how

these methods are being leveraged to map out complex supply chains and

how leaders are appending risk assessments to scenarios supported by

these techniques. Next, we’ll introduce risk response planning, the logical

outcome of stress testing complex supply chains and modeling “what- if”

scenarios in an eort to develop a plan to manage risk scenarios. We con-

clude with several examples that demonstrate how leading companies are

leveraging these powerful and dynamic techniques to identify, assess, mit-

igate, and manage supply chain risks.

DEFINING THE MODELS

Stochastic/ probabilistic models are models where uncertainty is explicitly

considered in the analysis. Furthermore, stochastic/ probabilistic models

are procedures that represent the uncertainty of demand by a set of pos-

sible outcomes (i.e., a probability distribution) and that suggest inven-

tory management strategies under probabilistic demands.

1

Stochastic

186 • Supply Chain Risk Management: An Emerging Discipline

optimization (SO) methods are optimization algorithms that incorporate

probabilistic (random) elements, either in the problem data (the objective

function, the constraints, etc.) or in the algorithm itself (through random

parameter values, random choices, etc.), or in both. is concept contrasts

dramatically with the deterministic optimization methods, such as time

series analysis, linear programming, integer programming, the simplex

method, and regression models where the values of the objective function

are assumed to be exact and the computation is completely determined by

the values developed in the equations. (Table10.1 provides basic deni-

tions of some of the key terms used in this chapter.)

e most critical dierence between probabilistic and deterministic

models is that there is not an ounce of uncertainty explained or handled

in deterministic tools. erefore, the responsibility of handling any uncer-

tainty, complexity, and risk has been the responsibility of supply chain

TABLE10.1

Dening Key Terms

Technique Description

Time series

analysis

Deterministic approaches that use historical data to forecast future

requirements.

Regression

analysis

Deterministic models that represent the relationship between a

dependent variable [y] and independent variables [x].

Discrete-event

simulation

DES models the operation of a system as a discrete sequence of events

in time. Each event occurs at a particular instant in time and marks a

change of state in the system. While simulations allow

experimentation without exposure to risk, they are only as good as

their underlying assumptions.

Forecast error Represents the dierence between an actual value and a forecasted

value. e objective is to minimize forecast error and maximize

forecast reliability.

Stochastic/

probabilistic

models

Models where uncertainty is explicitly considered in the analysis.

Involves statistical procedures that represent the uncertainty of

demand by a set of possible outcomes and that suggest inventory

management strategies under probabilistic demands.

Design of

experiments

e process of setting up a series of tests or experiments to determine

what outputs result from dierent combinations of inputs.

Sensitivity

analysis

Involves systematically changing quantitative inputs or assumptions to

assess their eect on a nal outcome.

Linear

programming

A mathematical technique used in computer modeling (simulation) to

nd the optimal solution that maximizes prot or minimizes cost

considering a set of limited resources, such as personnel, funds,

materials, etc.