Supply chain management simulation

Cincinnati Seasonings

Introduction

Cincinnati Seasonings is a supply chain scenario in the Supply Chain Management (SCM) Globe site. The objective of the exercise is to simulate some of the decisions that a real world supply chain manager might be faced with. In this simulation, we made decisions in each of the different weeks from 2 through to 9. Decisions were varied, in diverse realms related to supply chain management such as inventory management, transportation, warehouse locations and adjustments to supply and demand assumptions. The aim was to balance the need for low operating costs, having enough inventory but not too much (low inventory costs) and so on. The aim was to run the simulation for a period of 20-30 days in order to serve as an introduction to the field of supply chain management and get a deep dive into some of the complexities involved in achieving supply chain management targets.

Overview of Cincinnati Seasonings

Cincinnati Seasonings markets a product called the Spicy Cube in the Midwestern United States. In this simulation, the company only has this one product. There were a number of different facilities for the company in the simulation – retail stores, warehouses and factories. There were vehicle options, both trucks and airplanes, that could be used to get the Spicy Cube to market. The objective was to evaluate the baseline level of the company’s supply chain operations and then make recommendations for improvement. After that, the recommendations would be evaluated based on the outputs of the simulation, providing a realistic look at the types of feedback that a supply chain manager would have to deal with.

The Initial Supply Chain Scenario

In the initial scenario, Cincinnati Seasonings is based in Cincinnati, and that is where the factory is. The company has a warehouse in that city as well, and it uses that warehouse to distribution throughout the Midwest. Other facilities included a Fort Wayne warehouse, a Louisville warehouse, Seasonings DC warehouse, the Indianapolis retail store and the seasonings factory. The initial scenario was to run for 30 days, to see how well the decisions that the student made would play out.

Each “store”, or location had both a production capacity and a demand, so the first step was to think about which facilities had surplus production and which did not. There were costs associated with each location as well, and these needed to be taken into account, because cost management is one of the most important aspects of the simulation. There were also five different trucks of three different sizes.

These trucks were to be assigned to a different route, taking into account the costs associated with those routes, the capacity of the trucks, and the level of excess demand or production at each of the different facilities. There was also the question of the frequency of the deliveries.

An example of a problem that needed to be addressed is that the Cincinnati factory produced 10,000 per day and had zero demand. Conversely, the Louisville store had high demand of 7500/day but very low production. In fact, the starting point had more production/day than there was demand/day, a fairly obvious constraint on supply chain management, if it is assumed that the factory will run full-time. There were initial settings in terms of having a price, weight and size for each Spicy Cube.

Accomplishments:

The initial run of the simulation did not last long, but the objective was to make adjustments and see if the simulation could be made to run to the target length of time. Some initial calculations could provide a sense of direction but the process also included a lot of trial and error. Essentially, the trial and error and the simulation itself showcase one of the means by which supply chain management is done. Different simulations allow for the full range of variables to be tested, so that the variables that have the most impact on results are identified fairly early on the process.

All told, the simulation was run a number of times just to get a feel for the variables that could be adjusted. There were some clear and obvious choices, for example if we were able to shift production in a way that allowed for the Cincinnati factory to close and that production be done in Louisville, such a move would be expected to have significant cost savings.

There were also several opportunities along the way to learn, both from the instructor and from peers, about some of the basic principles of supply chain management that are embedded into the simulation. By applying some of this learning, ideally the supply chain for Cincinnati Seasonings could be made to be much more efficient than the baseline level.

What Went Well

One of the things that went well with the simulation was the approach of focusing on the big picture things first, and then drilling down to the details. Because the initial simulation was highly ineffective, that pointed me in the direction of first focusing on keeping the supply chain running for longer. That meant weeding out some of the bigger errors, and just making sure that enough Spicy Cubes were being produced, and shipped to the stores.

Not surprisingly, while this approach helped to re-set the supply chain to a reasonable baseline level of performance, there were still many areas where it was inefficient. For example, locations would max out their storage, or the production schedule would be inefficient for the purposes of transportation efficiency. Juggling efficiency throughout the entire system was certainly a challenge.

However, this approach of tackling the big problems first did allow for me to learn how to identify those big issues – like the misalignment between having a factory in one location that produces most of the product, while not actually selling any, and then another location that sells most of the product but doesn’t produce any. Things like that seemed like pretty quick fixes.

Over time, running the simulation several times, I was able to focus on individual objectives. It seemed that there were always problems to solve, new areas for improvement that could be possible. If my goal was to reduce system wide storage costs, my solution could be anywhere, This approach was excellent for learning because it allowed me to understand the process of solving specific problems by looking at the system in a holistic manner. Sometimes the temptation might be to solve a problem directly, without considering the impacts of your decision on other areas of the supply chain. But this is the beauty of supply chain management – everything is connected and understanding how each part affects the others is key to becoming a master of supply chain management.

I was able to get the simulation up to 43 days, and I did this by applying the technique of fixing the big things first, understanding how the different variables influence one another, and then applying specific targeted fixes once the system was more or less working properly. I felt that I was learning a lot, and learning the way somebody would if they were hired to do this job.

Areas for Improvement

I think one of the areas for improvement, for me anyway, was in getting those initial computations figured out. The simulation is good because there are quite a few variables and so there is a near-infinite amount of decisions and adjustments that can be made. So there was a lot of trial and error, some creative problem solving and those types of skills. However, the impression I got was that if I had done more work on the computational side in the beginning I might have been able to understand some of the variables and interactions better up front, and made better decisions early on.

At 43 days, the performance was good, but that is not perfect. I think that one of the lessons in this simulation is that there is always room for improvement, and one should strive for continuous improvement as a means of approaching supply chain management, One would probably not “set it and forget it” until the simulation broke. Ideally, the process would be one of evaluating every day to see how the company is doing, to the point where I would have been able to identify well before day 43 that there were some issues that were going to arise. So strong predictive forecasting, computational work and that sort of thing would allow me to have continuous feedback and therefore make more adjustments on the fly, the sort of constant adjustments that would occur if one was doing the job each day.

Initially, when I was trying to build a functional supply chain, it was fairly easy to forget about certain key issues. I had realized that getting product to market is more important, so focused on that, and there were times when this focus on a functioning supply chain ended up resulting in an expensive system. I knew that making it work was a priority that costs could be optimized later. This was mostly correct but of course one of the things that would be ideal is to think of these things simultaneously, rather than one at a time. But this is what learning looks like.

When I shifted my focus to those specific individual issues, that also created some interesting outcomes. To focus on cost efficiency, when the system wasn’t really built with that in mind, delivered some quick wins at first, because the system wasn’t very efficient at all. But where the real challenge came in was getting to more refined, granular efficiency gains. In my case, I had to basically reverse an earlier decision, where I’d made a choice that seemed to deliver improvements (it did) but that move ended up hurting me later. Those are the kinds of mistakes that you don’t even realize are possible until you make them.

I would say that I identified many areas for improvement in my own performance. I enjoyed that there were so many teaching moments for me, opportunities to realize how I could have done better. It was a bit humbling, but I think it was important to be humbled a bit and realize how much work goes into designing even a fairly simple supply chain system, and running it is a full-time job. I came away from this simulation with a real appreciation for how difficult this can be, especially when you’re trying to deliver lots of different types of results – getting goods produced and to market, cutting costs and saving time are all goals that can be synergistic but sometimes they also seem to run into conflict with one another, forcing you to make a choice between one or the other. You just have to intuit, depending on the company’s situation, which trade-offs are worth making and which ones are not.

One change I would definitely make any time in the future when such a situation arises – simulation or otherwise – is to have a vision from the beginning. By the last week, I’d incorporated a lot of knowledge and made many changes, so that the supply chain for Cincinnati Seasonings looked very different from the starting weeks. But none of that was done with any sense of vision. I didn’t have financial targets or operating efficiency targets. So I was really doing piecemeal changes and maybe only at the end started to have a sense of what my vision would be. If I had this vision right from the beginning, I think I would have made better decisions their entire way through, and decisions that would have a high degree of consistency to them as well.

Comparison Between Week 2 and Week 9

There were fairly big differences between Week 2 and Week 9. I know that for Week 2 all I really wanted to do was get the simulation up and running for more than two days. I did not expect to have an elegant solution. I opted to pursue an approach that would get the system up and running, and worry about adjustments and improvements later. So the performance in Week 2 was only 12 days. That was actually disappointing, and I thought that I would be able to do better. But I didn’t worry, and instead I realized that you don’t win the championship in the preseason. So I went to work learning about how to make better decisions, and trying to learn more about how the simulation works. The result was really more of a slow and steady approach to getting to the 43 days by the end.

I think that more or less I was able to tackle problems in order of their importance, so that the big gains were made first. But I genuinely appreciated the role that creative problem solving played in this simulation – when you realize you can build a new facility, or change the size of the product, you start to realize just how much control you might have with your recommendations, as long as you can prove that they will have the right financial benefit.

By Week 9 I was at 43 days. I don’t want to say that I was unhappy with the results, but I knew that there were more improvements to be made. I was just getting the hang of changing multiple variables, so I know that given more simulations I would have been able to continue to make improvements.

It was interesting to see how much changed, however. I ended up making a lot of different decisions and radically transformed the supply chain by the time I was done. I thought this was quite interesting, just to see how much transformation can be done in trying to optimize. I imagine there are so many different ways to get to a really good number but that they all involve a complete overhaul.

What If Scenario

Intuitively, I thought that using air would not make much sense for Cincinnati Seasonings. Air travel is very expensive compared with trucks, and the distances for shipping in this scenario are not that great. They are natural for truck freight. Sure enough when looking at the numbers, it just makes no sense to use air freight when trucks are a viable option.

The most likely scenario for using air would be if there was a need for something very quickly. For the most part, that can still be accomplished with trucks given the geographic span of Cincinnati Seasonings’ market, however. In such an emergency scenario, it would probably be better just to have a stockout, given the cost of air freight and the likelihood that the stockout might only last a few hours. If the company ever found itself in that situation where air freight was needed that would probably represent some sort of failure along the supply chain, either in the data that was being gathered and communicated, or in the decisions that were made by the supply chain manager.

Conclusion

To do the Cincinnati Seasonings simulation exercise over again, there are a couple of things that I would do differently. First, I would try to have more of a vision for what I wanted to accomplish, instead of tackling each problem piecemeal I think that approach allowed me to learn a lot through trial and error, but having a vision would deliver a much more coherent strategy all round. It would have been difficult for me to have a vision at the beginning of this class, but now I think I could start to have a good vision to guide me.