Capacity Measurement

Today's case study from Collier (2015) chapter 10 provided an opportunity to experiment with two capacity measurement formulas:

• capacity required for an individual work order (Ci)
• total capacity required for the shop (sum Ci)

These formulas are detailed on Collier (2015), p. 208.

Applying these formulas to the David Christopher case is a bit tricky, as the "set-up time" used by our orthopedic surgeons actually does vary with volume, unlike the standard set-up time in a factory setting. Most of you caught this variation, and accommodated for it in your calculations. Congratulations! Here's a video detailing the calculations for this case...

Now the question is what to do about the delta between Dr. Christopher's available capacity and demand? Many of you suggested variations on hiring another surgeon (maybe part time), hiring other non-physician staff to free up the doctors, and reducing set-up/changeover time.

Kristen, Michael, and others had an important conversation about the costs associated with hiring another physician, and the hope that increased capacity would generate new revenue. What we don't know from the case are demand trends that might change this hope into a reality. It would be helpful to have that info.

Personally, I'm a fan of having a fully rested physician operating on me, so I'm a supporter of the set-up times between procedures (many of you want to reduce those times). Let me suggest another non-hiring alternative: what about using one Friday per month as a surgery day? Most other Fridays would still be available for conferences, etc. Then if demand warranted additional capacity, another surgeon could be hired after X period of time. Something to think about.

Now, how might you be able to apply these capacity measurement tools to your case study companies?

Capacity Management

Today's discussion board posts provided a number of insights about how different businesses manage their capacity: the ability "to accomplish their purpose over a period of time." (Collier & Evans, 2015, p. 205). We can measure capacity in two ways:

• maximum rate of output per unit of time (e.g., number of pizzas/hour)
• units of resource availability (e.g., number of seats in McInnis Auditorium)

As many of your pointed out through your posts, capacity is relevant to both goods and services producing businesses. It can be impacted by facility size/location, process design, workstation or equipment. Watch this video for a helpful introduction.

It is important to distinguish capacity management from inventory management. When we're talking about capacity management, were talking about maximum rates of output or resources availability based on constraints imposed by the facility, process or workstation design, or equipment. Inventory availability will certainly impact our ability to run our processes at full capacity, but it does not determine capacity. For example, a movie theatre may have 500 seats (its capacity), but the number of customers may not fill the theatre at any one showing. This lower number of customers does not result in lower capacity.

The intent of today's assignment was to give you a head start on case study part 3, item 4: planning and control tools. Today's capacity management topic fits under that umbrella. See these posts in today's discussion board for helpful examples as you continue to develop your own final case study installment:

• Long and/or short term capacity management strategies: Andrew B., Lauren S., Michael G., Katie K. 
• Short-term capacity management via labor skills mix: Jacob S., Mario M.
• Short-term capacity management via labor capacity & schedules: Edem A. & Xia C., then see Ryan's post for thoughts about the down side of this method over the long-term.

Finally, Kyle presents an example of a company that has created a distribution system with a large amount of capacity flexibility. What specific tools from Collier & Evans (2015), chapter 10, do you see at work in Kyle's example?