The Impact of Variability on Process Performance

RadPad Scenario 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: Processing Time Data (in minutes)

RadPad Scenario How many boards can you make in 20 hours? Station #1 CT: 16.97 CT: 16.93 CT: 15.77 CT: 17.13 CT: 17.00 How many boards can you make in 20 hours?

Process Simulation Station #1 Station #2 Station #3 Station #4 Station #5 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.

Production Lines and Buffers Station 1 Station 2 Completed 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? What if Station 2 is capable of producing 6 units per hour? Assume that each hour, the production of station 1 is delivered to the buffer in front of station 2. Answer: 2; 4 Actual Production = Min[Potential production, available WIP] Actual Production =

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? Team Total Production The average throughput should be about 52 units – much less than the 62 needed.

Game 1 Result: Why: Solutions: Results: Variability is processing times resulted in lower than expected throughput. Why: Statistical fluctuations and dependent events. Station 5’s output is dependent on Station 4’s output. Example: Station 4 (in period t) Station 5 (in period t+1) Total out (t+2) Low Low Low Low High Low High Low Low High High High Thus, instead of getting high output 50% of the time, we end up only getting it 25% of the time due to the dependency. Solutions: Reduce variability: training, maintenance, inspection of materials 2. Increase WIP

Game 2 Initial Buffer size between each station is 8. What is the total production over 20 hours? Team Total Production The average throughput should be about 52 units – much less than the 62 needed.

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? Team Total Production The average throughput should be about 52 units – much less than the 62 needed.

Production Simulation Summary Variability hurts! 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,… Note that this has significant implication for JIT production systems (systems with very small buffers)– what are they? Need to reduce variability Still need some buffers. Idea can be applied to supply chains as well – Stations are like companies. What’s the solution? Increase inventory; Disadvantage: costly Reduce variability; e.g. Toyota Production System