The Impact of Variability on Process Performance

SkiBums.com is a manufacturer of ski equipment and apparel. SkiBums.com has just introduced a new type of snowboard called the RadPad. And since its prime target market is baggy-pants-wearing boarders who use the word “gnarly” far too much, it is anxious to release the product by the time of the winter X-Games. The product is fairly simple to make, requiring 5 steps performed in serial. The process is a “lean process” utilizing only a small amount of work-in-process inventory. The basic process will be nearly identical to the production line for the other snowboard RadX. Sample processing times for each step in the RadX process are provided in the table on the next page (a station consists of machines and people responsible for one step in the production). Your 17 year-old CEO explains to you that the process should be capable of producing 68 units per 20 hour period in order to meet the potential demand. He explains that underproduction could result in lost revenue and overproduction will result in higher operating costs. Would a replica of the current process for RadX be appropriate for the new RadPad? Is it too little or too much capacity? RadPad Scenario

RadPad Scenario: Processing Time Data (in minutes)

RadPad Scenario How many boards can you make in 20 hours? Station #1Station #5Station #4Station #3Station #2 CT: CT: 16.93CT: 15.77CT: 17.13CT: Station 4 has the longest cycle time, so it’s the bottleneck. Its throughput is 60/17.13 = 3.5 units/hour. This is the throughput of the line since station 4 is the bottleneck. We can make 20*3.5 = 70 boards in 20 hours if the cycle time is constant. What happens if the cycle time has variability? Let’s simulate and see.

Process Simulation Inventory buffers between stations; start out with 4 units in each. Each hour, each station will be able to produce between 1 and 6 units according to their die roll. Throughput rate (capacity) of each station? The amount each station produces and puts in inventory is limited by the amount of work-in-process inventory available. We will simulate production for 20 hours. Station #5 will count the actual production at the end of the simulation to calculate the throughput rate (capacity) of the system. Station #1Station #5Station #4Station #3Station #2

Production Lines and Buffers Station 1Station 2 Buffer If the buffer has 4 units of inventory and Station 2 is capable of producing 2 units per hour, how many units will be completed by Station 2 in one hour? 2 What if Station 2 is capable of producing 6 units per hour? 4 Completed Actual Production = min(capacity,inventory)

Game sequence Step 1: Production Roll the die. Take min(inventory, die roll) from your inventory and put it in front of you. Step 2: Replenishment Push over these units to the next stage’s inventory.

Game 1 Initial Buffer size between each station is 4. What is the total production over 20 hours? TeamTotal Production Average production = 55.8!

Game 1 Result: Throughput much less than 70!! Why: WIP limit and variability Solutions: Increase WIP limit, decrease variability

Game 2 Initial Buffer size between each station is 8. What is the total production over 20 hours? TeamTotal Production Average production = That’s better!

Game 2: Impact of Buffer Size

Game 3 Initial Buffer size between each station is 4. Use coin instead of die (heads = 3, tails = 4) What is the total production over 20 hours? TeamTotal Production Average production: 67.9 Much better! Notice that the variability in total production is lower as well.

Production Simulation Summary With limited WIP, production variability reduces the effective throughput rate (capacity) of the system. Why does variability occur? Machine and human variations, errors, raw material quality problems,… Variability hurts! What’s the solution? Increase inventory; Disadvantage: costly Reduce variability; e.g. Toyota Production System