SCM Globe Paper: Simulation of Cincinnati Seasonings


Added on  2019-09-30

15 Pages3532 Words304 Views
SAMPLE SCM GLOBE PAPERIntroductionThe company in the simulation is called Cincinnati Seasonings, located in Cincinnati, OH. The main product of the company is a mix of seasonings for Cincinnati-style chili. The company operates a seasonings factory and distribution warehouse (center) in Cincinnati, and delivers the product to stores in the surrounding area. The original scenario set-up is with the factory, distribution center (DC), and three stores: Louisville, KY; Indianapolis, IN; and Ft. Wayne, IN. The unit of shipment for the spices is labeled as "Spicy Cube", which is also how the unit of sale is recorded as well.The following tables show the baseline information for the scenario. FacilitiesDemandProductionOn handMax StorageFt. Wayne200800800Indy7003001000Louisville10005001500DC00400015000Factory03507003000Table 1.1 – Baseline Facility inventory and storage – this shows the demand for "Spicy Cube" units, on hand inventory, and max storage capacity.TrucksNameTypeCarrying CapacityDelay Between Runs(hrs)Starting LocationTruck 1Small4020DCTruck 2Small4020DCTruck 3Large11020DCTruck 4Med6010DCFact T 1Med608FactoryTable 1.2 – Shipment Trucks – trucks with carrying capacity, delay between runs, and starting locations.

RoutesTruckTotal Time (hrs)Stop/Drop/Pickup 1Stop/Drop/Pickup 2Factory to DCFactT10.3DC/30/0DC to LouisvilleTruck 13.8LV/20/0DC to LouisvilleTruck 23.8LV/20/0Dc to IndyTruck 33.8IN/50/0DC/IN/FWTruck 47.55IN/20/0FW/30/0Table 1.3 – Shipment routes – initial routes of each truck, total time of the route, and stops on the route showing quantities delivered and picked up.AccomplishmentsAfter much trial and error, I was able to finally get the hang of the simulation. I learned that it was better to make small changes and allow the simulation to run, in order to see the differences that were made. I also learned to watch the charts on certain facilities, which enabled me to understand what the problems were and anticipate which facility would cause a fail in the simulation. For example, when running the initial scenario, it was readily apparent that the Ft. Wayne store would exceed storagecapacity because the quantity demanded (and consumed) was less than the quantity delivered daily. My initial adjustments were to delivery quantities through changing the truck capacities and delivery quantities in the routes. Eventually I started to combine some routes and use larger trucks, which enabled me in the final simulations to achieve a just-in-time (JIT) delivery schedule – I was delivering daily quantities that exactly met demand of the stores. (My final numbers are listed in the next section showing the week-to-week comparisons.)As for costs, I noticed that transportation costs seemed to become normalized once I was able to set up the JIT schedule. I determined that, since the schedule was running JIT, there was no more tweaking left to accomplish, unless I used alternate modes of transport. The major cost differences were in facility andinventory value costs, based on storage capacity of the facilities. This is where I was able to make the largest savings in costs. (The costs will be detailed in the Week 8 and Week 9 comparisons) An example

is with the Ft. Wayne store, where I reduced the storage capacity of the facility to 100 units. The facility operating costs reduced from $130,200 to $37,200.Finally, in regards to where I could have improved, I tend to approach scenarios at face value and work within the parameters. It usually takes some time for me to be able to step away from a problem and attack it from a different angle – in other words, think "outside the box." I believe my final numbers are pretty good, but I am sure that there is a unique solution that incorporates something outside of the scenario that would improve the system. I think that having uncertainty built into the simulations helps users to be more aware of the need for finding different solutions that don't fit perfectly into the problem as presented. ComparisonNote: all simulations were stopped at 30 days.Week 2:FacilitiesDemandProductionFinal On handMax StorageFt. Wayne200580800Indy7002401000Louisville10008301500DC00677015000Factory030012503000TrucksTypeCarryDelayStartTruck 1Med6020DCTruck 2Med6020DCTruck 3Small4022DCTruck 4Small4010DCFact T 1Large1108Factory

RoutesTruckTimeStop/Drop/Pickup 1Stop/Drop/Pickup 2Factory to DCFactT10.3DC/110/0DC to LouisvilleTruck 13.8LV/60/0DC to LouisvilleTruck 23.8LV/50/0Dc to IndyTruck 33.8IN/30/0DC/IN/FWTruck 47.55IN/30/0FW/10/0The highlighted blocks show the changes that were made. All of the delivery routes were changed, as well as all of the trucks, to allow sufficient quantities to arrive at the stores. The Ft. Wayne store was reduced because of the large on hand quantity. The concerning trends here are the increasing quantities with the Louisville store, Factory, and DC warehouse. Week 3:FacilitiesDemandProductionFinal On handMax StorageFacility CostFt. Wayne200600800$130,200.00Indy7002301000$170,500.00Louisville10008101500$232,500.00DC00242015000$1,240,000.00Chicago1000300500$105,500.00Columbus30060300$58,900.00

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