Page 92 - The Handbook for Quality Management a Complete Guide to Operational Excellence

78 I n t e g r a t e d P l a n n i n g S t r a t e g i c P l a n n i n g 79

• Market. Not enough demand for a product or service.
• Resource. Not enough people, equipment, or facilities to satisfy the
demand for products or services.
• Material. Inability to obtain required materials in the quantity or
quality needed to satisfy the demand for products or services.
• Supplier/vendor. Unreliability (inconsistency) of a supplier or
vendor, or exces sive lead time in responding to orders.
• Financial. Insufficient cash flow to sustain an operation. For
example, a company that can’t produce more until payment has
been received for work previously completed, because they might
need that revenue to purchase materials for a firm order that’s
waiting.
• Knowledge/competence. Knowledge: Information or knowledge to
improve busi ness performance is not resident within the system or
organization. Compe tence: People don’t have the skills (or skill
levels) necessary to perform at higher levels required to remain
competitive.
• Policy. Any law, regulation, rule, or business practice that inhibits
progress toward the system’s goal.
Note: In most cases, a policy is most likely behind a constraint from
any of the first six categories. For this reason, the Theory of Constraints
assigns a very high importance to policy analysis, which will be discussed
in more detail under “The Logical Thinking Process,” below.
Not all of these types apply to all systems. Material and supplier/vendor
constraints might not apply to service organizations. Market constraints
are generally not relevant in not-for-profit systems, such as government
agencies. But resource, financial, knowledge/competence, and policy
constraints can potentially affect all types of organizations.

Four Underlying Assumptions
Constraint management is based on four assumptions about how systems
function (Schragenheim and Dettmer, 2000, Chap. 2). These assumptions are:

1. Every system has a goal and a finite set of necessary conditions that
must be satisfied to achieve that goal. Effective effort to improve
system performance is not possible without a clear understanding
and consensus about what the goal and necessary conditions are.
2. The sum of a system’s local optima does not equal the global
system optimum. In other words, the most effective system does
not come from maximizing the efficiency of each system
component individually, without regard to its interac tion with
other components.

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