OPT Report By: Adrian Chu, Department of Industrial & Systems Engineering, University of Washington, Seattle, Washington 98195. November 12, 2009. The Goal Every manufacturing company has one goal to make money. In order to do so, a company must consider three important methodologies of measuring progress toward the goal: throughput, inventory and operating expense. Throughput is the rate at which the system generates money through sales (Goldratt 60). An item must be sold, not just produced for an item to be considered throughput. In addition, production managers must work with sales and marketing people to ensure that the most optimal quantity is produced. Inventory is all the money that the system has invested in purchasing things which it intends to sell (Goldratt 60). Accountants typically consider inventory an asset on the balance sheet. Many companies fail because they neglected the importance of having sufficient cash flow. Work in progress or products that don t sell are illiquid and cannot readily be converted into cash or equivalents. Operating expense is all the money the system spends in order to turn inventory into throughput (Goldratt 61). Many people consider value added labor an asset as it increases the value of the product being made. However, it is simpler just to consider labor as overhead since both inventory and operating expense are negative while throughput is positive. OPT Game: Initial Strategy First, I decided to make a list of the variables and constraints. Setup time and production time for each unit is given. Another constraint is that only one red and one green station can be used at once. So my first strategy is to keep stations running and productive as long as raw material stocks are available. Since we have unlimited customer demand, overproduction would not be an issue. Next, my plan is to keep A and B running at the same time and then C and D running at the same time while making changes as needed. My initial batch consisted of 50 Raw Material As and 100 Raw Material Bs since I knew B was produced faster than A. I had made some calculations using information provided. Please 1
see attached appendices for more information. It is clear that we should make larger quantities in each batch due to lengthy setup times. The following is the result of my first round: Week One Results from my first round of the OPT Game. OPT Game: Revision of strategy 1 One major issue I noticed after playing the first round is that the yellow stations stayed idle for too long. Since yellow is independent of the other stations, we must have it start setting up from the very beginning to avoid any wasted time. Another fault is that I purchased too many raw materials, which caused raw material costs to exceed sales revenue. From calculation, we need to make at least $2,500 to break even on our operating expenses. The profit potential for each unit is $40 per unit, so we need to build 63 units in one week to avoid bankruptcy. However, if we assume our inventory will be $0, then we can survive week one having just $1,000 profit, which equals to only 25 units. So our immediate goal should be to make at least 25 63 units, dependant on the amount of inventory we have on hand. 2
Week One Results from my second round of the OPT Game. OPT Game: Revision of strategy 2A This time we survived week one. However, we still experienced negative cash flow, which means if we don t make up for that, we will not survive week two. With only $120 left, it means that we must make $2,380 in order to meet the break even point for week two (60 items). In comparison to the first trial, the idle time for red was reduced from 13% to 8%, green increased from 1% to 43% while yellow decreased from 68% to 58%. From red, we can tell that it is a bottleneck as throughput increased as a result of a lower red idle time. Green is definitely not the bottleneck as throughput increased while idle time drastically increased. This proves that idle time is not necessarily a bad thing. Who cares if we have to pay them wages for sitting? Yellow idle time decreased by 10 percent due to increased final assembly production, but it is normal for it to be higher since it depends on red and green. One thing I forgot here was that I could click Sell near the end of the work day on Friday to sell the individual products that hasn t been fully assembled and turn it into cash. 3
Week Two Results from my second round of the OPT Game. OPT Game: Revision of strategy 2B It s easier said than done! In this round, I failed to sustain the full optimization of the bottleneck (red stations) and completed fewer units that last week (30 vs. last week 32). The new strategy is to make sure that red stations are never idle. We must subordinate everything else to the bottleneck (Goldratt 301). 4
Week One Results from my third round of the OPT Game. OPT Game: Revision of strategy 3A We once again survived week one with having the same 8% idle rate for red stations and more productive green stations (38% vs. 43% last one). However this week we were more successful since we have 18 pre production parts at Station E versus 10 for our previous round. This will give us a slight head start for week two. At the end of this week, we have $110 cash, which means we will need to produce and sell 60 units. 5
Week Two Results from my third round of the OPT Game. OPT Game: Revision of strategy 3B This time we produced fewer main products, but focused onto selling spares. However, I was not aware of the fact that the number of spares we can sell cannot exceed the number of main products. Yet, this made me discover one thing: spares are essential for the success of the company as they produce more profit than the main product itself! Overall net cash flow was $1,840 for this trial while it was $2,790 for the previous trial. So this is an improvement. In addition, I noticed that having on time deliveries is crucial for the most efficient manufacturing system. Next plan: make sure we sell spares and non spares accordingly. 6
Week One Results from my fourth round of the OPT Game. OPT Game: Revision of strategy 4A Net cash flow increased to $1,300 for this round. Idle time for red reduced to 5%. Idle time for green also reduced to 8%. Net cash is $250, so we only need $2,250 worth of goods to make it past week two. We need to sell the same amount of each part to maximize our profitability. For this round, I used the following algorithm for a week: 1. Start A with 30 and start B at 60. Start setting up E. 2. If B is finished, then start C and D. 3. Once C is done, start A. 4. Once D is done, start B (make sure not to overspend on raw materials, so that we have 2500 for the end of the week). 5. Sell main products (limited by the number of goods finished by Station A). The remaining B C D products, sell as spares. The algorithm provided us the most successful week one results out of all the previous trials. 7
Week Two Results from my fourth round of the OPT Game. OPT Game: Conclusion Although net cash flow is still in the negative, our plant survived week two with $630 in free cash! We are on our way to profitability. In the future, I will make sure to set the game speed down at the critical points to avoid wasted idle time. An example of a critical point was when Station C was ready to feed to the Station D which already finished setup, but there was waste of a few minutes (in game simulation time) since I was not able to click the mouse fast enough. Another situation was that at the very end I tried to maximize my sales for Product 1 spares, but I didn t want to restock as much raw materials, so I did so in increments of 5. But while at speed 10 on the training mode, the raw material stock stood at zero for a few minutes (in game simulation time). This resulted in a 4% idle time for the bottleneck Red resource. According to Goldratt, bottlenecks should never be idle, as they control the speed of the entire system. Hence, it was suggested that even an older machine should be used to make sure the bottleneck process (Goldratt 190). However, it is perfectly legitimate to have non bottlenecks stay idle since the closer you come to a balanced plant, the close you are to bankruptcy (Goldratt 86). 8
The algorithm from week one was revised slightly to emphasize selling of Product 1 spares, as none of those were sold in the first week. Near the end of the second week, the B C D production line was stopped, since it was impossible to start running any new raw materials from scratch for that line while Station A was overly busy with customer demand. Increasing throughput is a major objective for any industrial enterprise as doing so will enable the factory to make money. In the most recent trial of the OPT game, I did not maximize customer demand due to lack of time and different production constraints. I sold 58 units of the main product, which means there will be customer demand for 58 Product 1s and 58 Product 2s. Due to the time limitations, I was only able to sell 33 Product 1s and 42 Product 2s. Additionally, I feel that more optimization should be done such that the result will have equal amounts of sales for each product, as once again spare products are more profitable. The system is made such that you cannot start producing Product 2s from scratch unless you stop producing Product 1s, since Stations A and C are both red resources. Inventory is another key indicator of a plant s success. Overall, I ended up with 2 inventory parts. Yet both of which could have been sold if I had been given more time as they qualify as Product 1 spares. One issue with inventory control was that when I moved an item that could be sold as Product 1 to Station E, it could not be sold as a spare product again (or I didn t know how to). Yet, these are not really significant issues as in a real manufacturing plant, the two inventory items could actually have been sold since there was customer demand for it. Thus, basically, we have no inventory whatsoever which is a key reason for the plant s success. As Goldratt says, inventory is also an expense (Goldratt 87). Operating expense is an element in the simulation that is beyond our control. It is $2500 per week no matter what. So this simplified the simulation so that we only had to be concerned about two of the indicators: throughput (sales) and inventory carrying costs. Overall, there was more customer demand than production, so we can try to better optimize our bottlenecks (approach 0% idle time) to meet customer demand. To maximize profit, we could sell one main product and one spare at one time and try to keep sales levels more balanced. Inventory carrying costs were very low over the course of the two week simulation (3 percent of all assets, the other 97 percent is cash). Although the company did not yet recoup its initial investment, this simulation was a rewarding experience. Source: Goldratt, Eliyahu M. The Goal: A Process of Ongoing Improvement. Great Barrington, MA: The North River Press, 2004. 9