Chapter 12 – Page 6 – The OM Blog by Heizer, Render, & Munson

OM in the News: RFID for Tracking Surgical Implants

We note the increasing role of RFID (Radio Frequency ID) tags in our Process Strategy and Inventory chapters (Ch. 7, 12). Now, RFID Journal (Feb. 5, 2013) describes an exciting medical advance that will soon track surgical implants by placing the tags on implants and tools. The system, by Texas startup Innovapaedics, also includes a cloud-based server to store data and provide reporting to customers regarding the location, use and status of each instrument used during surgery, as well as devices implanted into patients.

Innovapaedics’ 3-5-year goal is to offer a “Smart Implant” solution that would include RFID tags and sensors permanently attached to implants. After an item is implanted into a patient, its RFID sensors would detect pressure and temperature changes, among other events, in order to track a patient’s healing process, as well as the device’s condition, and transmit that information to a reader. In the short-term, meanwhile, the company has developed MedEx, an RFID solution for tracking implants prior to their use within a patient, to track which items were used on that individual. The resulting data is incorporated into medical and billing records.

MedEx also enables hospitals to track surgical tools. A tag can be permanently attached to each surgical tool, and the tag’s ID number is linked to specific data about that tool in the MedEx. As a new tray of tools is created for use during a surgery, each tag is interrogated as the tool is placed into the tray and linked to that tray’s RFID number. Post-surgery, the tools are cleaned and sterilized, and are then placed in a tray once more. MedEx stores a record of which tools belong in that tray, and displays an alert if the wrong tool is placed there, or if a tool is missing. The software cannot only track the tray in which a specific tool is stored, or to which patient a particular implant has been administered, but also enable the reordering of inventory based on which implants were used.

Discussion questions:

1. Why is this an important OM advancement?

2. Describe other medical applications of RFID tags already in use–(see Chapter 5).

Guest Post: The Distribution Game– A Perfect Class Engagement Activity

Our Guest Post today comes from Dr. Chuck Munson, who is Professor of Operations Management at Washington State University.

It’s not always easy to find an activity that combines true learning with fun and competition. For more than 15 years I have been successfully using “The Distribution Game” in undergraduate supply chain management and MBA operations classes. The game was originally designed by Peter Jackson and John Muckstadt at Cornell http://people.orie.cornell.edu/~jackson/distgame.html, and it can still be downloaded for free (although it may require a modern platform conversion).

Each day for 200 days, the player must choose how many units to order for three retail locations and the supplying warehouse (so this is a multi-level inventory problem). It takes 15 days for the product to reach the warehouse from the supplier and 5 days to reach the stores from the warehouse. Demand is random. The animation is a little bit crude by today’s standards, but it’s still quite effective to see little trucks carrying products across the screen each day.

I like the game because it can be played whether or not inventory formulas have been taught. Students can try to use common sense and some can perform quite well doing so; nevertheless, they seldom beat my performance that’s completely formula-driven. Imbedded in the game are issues of safety stock, balancing setup and holding costs, lead time effects, and lumpy demand at the warehouse. We can usually get through about four games during an hour in the computer lab or with laptops in class.

Game parameters can change: I vary the demand distribution, relative holding and setup costs, and lead times. For me, the most important learning outcomes are: (1) equalize total holding and setup costs, (2) the warehouse should order in integer multiples of the combined retailer order sizes, and (3) the pipeline should be empty when time runs out.

Have fun, and consider awarding prizes to the winners!

Video Tip: A Two-Bin Kanban Inventory System at St. Clair Hospital

This 7 1/2 minute video from Pittsburgh’s St. Clair Hospital is a great tool to use when you are teaching inventory management in Chapter 12. St. Clare started with a “par level” system that often led to stock outs for nursing supplies. With the help of an industrial engineer, the hospital converted 28 supply areas over a 10 month period to a much more efficient two-bin kanban system. With the system in place, St. Clare went to zero stock outs and zero manual requisitions sent to its materials management department.

The video discusses Toyota’s kanban pull model and illustrates a realistic, interesting way that a hospital controls its inventory. It also makes the point that analysis of data is a critical first step of the process of changing any floor plan or stock area. The video closes with interviews of nurses who are happy that “everything is under budget since the new system” is in place.

Just click below to view the video.

http://www.youtube.com/watch?v=yjSwwPF5BUU

Teaching Tip: Using Real Data for Inventory

For lots of varied reasons many of us spend substantial class time on inventory. The Heizer / Render text covers inventory in Chapter 12 (managing inventory and inventory models), Chapter 14 (lot sizing), and Chapter 11 (measuring assets committed to inventory and inventory turnover).

One way to engage students in discussions of inventory, and perhaps understand its significance better, is to have them use real turnover data for companies or industries they know. We show how to do this in Chapter 11, Examples 5 and 6, where we use Home Depot and Pepsi data. Assignments where students compute and compare these ratios can be facilitated by the use of annual reports as most annual reports provide enough data for the calculations. Alternatively, data on an aggregate basis are available and down loadable from the U.S. Census Bureau.

Additionally, Quarterman Lee has just published some Wholesale Inventory Turn Data that you and your students might find interesting for developing industry comparisons. His raw data are from the U.S. Census Bureau, but he has organized it into 18 broad categories and more than 260 subcategories. He has a modest fee of $6 or $8 to access the down loadable data.

Teaching Tip: Inventory Simulation Game

Inventory Simulation Game

An excellent exercise for a hands-on understanding of the impact and cost of inventory policies was developed by Keith Willoughby (Bucknell University) and Ken Klassen (Brock University). The objective, not unexpectedly, is to maximize revenue against one of three normally distributed demand functions while driving ordering, shortage, and holding cost to a minimum.

The game, He Shoots, He Scores, is described in Chapter 12 of the Instructor’s Resource Manual. The Student Instructions handout is also there. These are also available in myomlab (see below). EOQ material is presented in the text in Chapter 12 and 14, while lot-for-lot and PPB are presented in Chapter 14. All are available in POM for Windows software as are Wagner-Whitin, Period Order Quantity, and a User Defined option.

The IRM and the Instructor Resources Section of myomlab suggest 30 to 40 minutes, but with any summary / analysis that is tight. I have run the game in a 50-minute period, but even then, I needed to keep an undergraduate class moving. Any introduction to inventory policies or software mechanics suggests a longer class.

To enliven the class, I give points for the team with the highest profit (say 10 points for the highest profit) and reduce the scores down to 1 point. With ten 3-person teams, a 30-student class is easily accommodated. I let the students be as creative as they want in selecting and implementing an inventory policy (EOQ, PPB, Wagner-Whitten, or something of their own creation, etc.) and use any software they want.

Another option is to assign various inventory policies to teams and then compare the results. This has the advantage of moving the class to a more structured discussion of inventory management…but it is less fun.

The instructions, student handout, and excel spreadsheet are available in myomlab under the ‘Instructor Resources’ button on the left hand navigation. Once on the ‘Instructors Resources’ page, look under the heading ‘Instructor Supplements’ for the bullet point, ‘See the Simulation Games.’ This shows three simulation exercises; click on the first one, the ‘Inventory Simulation Game;’ then click on ‘spreadsheet’ on the left top of the first page to get to the Excel spreadsheet.

My experience with the exercise is all positive … I recommend it.

Video Tip: Inventory at Frito-Lay

If you cover the subject of Inventory Control (Ch.12), you may want to show the (8 min.) video, “Managing Inventory at Frito-Lay”. There aren’t too many more interesting products that students can relate to than potato chips…and this video goes from the farm to the truck to the plant to the truck to the store…in showing the production process. What makes it really exciting is that the whole journey often takes less than one day! We follow the inventory from the time the potatoes (12 semi-trailers full each day, at 50,000 pounds of potatoes per trailer) are loaded at a farm near each plant, unloaded, washed , sliced, seasoned, baked, bagged, boxed, loaded for delivery, shipped to supermarkets, and put on a shelf. It’s a real eye-opener to see how fresh the product at the store can be.

It’s also important for students to see that there are 4 types of inventory at Frito-Lay (and, of course, at other firms): raw materials (the potatoes, seasonings, packaging material, etc.), work-in-process, finished chips in a bag, and MRO. This may be an unusual product, in that a major raw material decays quickly, but the importance of inventory turns, W-I-P levels, and smooth production flow are all illustrated in this company’s excellent inventory management.

Jay and I really enjoyed filming this Frito-Lay series (our most recent featured company) and you will see why when you view the closing scene, filmed in my driveway. The company lent us a truck to use for the day and loaded it with hundreds of bags of chips of all brands. At the end of the day of filming our closing comments, we assumed we had to not only return the truck, but the massive quantity of chips as well. But we got to keep them! Since Jay couldn’t carry many back to Texas, we Renders ate chips for months….a big exception to my wife’s organic/healthy food house rules.

Teaching Tip: Teaching Inventory Modeling in the Real World

Our research shows that the most frequently covered topic in OM courses is Inventory Management (Ch.12). In that chapter, we do discuss the importance of record accuracy, cycle counting, and shrinkage. But what we do not discuss is the use of the numerous inventory models if inventory is only “partially observed”.

In today’s issue of Decision Line (Oct., 2010), an excellent article by Prof. Suresh Sethi, at U.Texas-Dallas, goes into the reasons for partial observation of inventory levels and then discusses how these impact modeling efforts. Here are 5 causes Suresh details:

1. Sales recorded wrong (eg, a clerk scans an item twice, when there were actually 2 different flavors of soup).

2. Misplaced inventory (eg, when items are stored dynamically, not in a fixed location). Suresh tells of a top retailer who discovered 16% of its items were misplaced.

3. Spoilage (eg, when customers tear open a package to look at the item inside, spill drinks on clothes, or scratch a car they test drive).

4. Product quality and yield (eg, when some items coming into the warehouse are unknowingly damaged).

5. Theft (eg, break-ins, employee pilferage, and customer shoplifting). The Limited, eg., recorded an inventory discrepancy of $142 million a few years ago—the equivalence of 21,000 ocean containers!

Suresh concludes, “By now it should be clear that the incomplete inventory information (i3) problem is quite common in practice, that policies in current use are neither optimal nor applicable”. He finishes the article by discussing 4 ways to classify i3 problems.

The real point worth making in class is that the models we discuss in Ch.12 depend on accurate record keeping, which may be impossible in a variety of real world companies.