SIMULATION [Supply Chain Management] Submitted by Miss Praealyn Thamachakkul ID

SIMULATION
Supply Chain Management

Submitted by
Miss Praealyn Thamachakkul
ID: 27035390
Present
Professor Terrence PereraCONTENTS
Page
INTRODUCTION 3
CASE STUDY
Simulation model of multi-compartment distribution in 4
the catering supply chain
Modelling complexity in the automotive industry 5
supply chain
– The Impact of Inventory Inaccuracy on Retail Supply Chain 6 Performance: A Simulation Study
Simulation modelling for food supply chain redesign 7
: integrated decision making on product quality, sustainability and logistics
SYSTEMATIC REVIEW 8
CONCLUSION 9
REFERENCE 10
INTRODUCTION
Simulation is a popular technique for the exploration, design and optimization of complicate systems. Simulation is the replication of a dynamic process in a model to arrive at conclusions that can be used for reality. Simulation using facilities that a computer program to model real system and changing the simulation parameters to describe and analyst their behavior.

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For the structure of a simulation system that was developed at the Logistic and Supply chain. It has been applied to many areas such as the design, production planning and control methods, design and logistic evaluation of production systems, education of company employees as well as the validation and development of models of the production process. The simulation model includes a reproduction of a production system and order control.
The heart of the model is a universal simulation that presents a specific production system through the description of its resources, the material flow control mechanism and the order data. The number of orders to be processed, the due dates of order and processing dates can be changed by manipulating the demand on the production system as well as by changing the integrated functions for production planning.

CASE STUDY 1 : Simulation model of multi-compartment distribution in the catering supply chain
The efficient distribution system of high performance is needed to anticipate market developments in the catering supply chain (CSC) in the Netherlands. This case using simulation to analyze a multi-compartment distribution system that reaches to customer demands for shorter lead times, increased delivery frequency, improved quality of product and process. The objective of this paper is to describe a stochastic simulation model of the multi-compartment distribution of fresh produce, chilled products, dry groceries and frozen products applied to case situation in the CSC.

Normally, in the multi-compartment distribution scenarios, a lorry has three different compartments as frozen products (18°C), fresh produce and other chilled products 2-4°C. and. These compartments have limited capacity in terms of the number of containers and trolleys.

For alternative logistic scenarios, all product groups will be transported by the wholesaler’s DCs for fresh produce and other chilled products. The delivery frequency varies with the logistic scenarios.

The delivery frequency can be defined as the number of times a product group is delivered in a week. All catering outlets in a special route will be supplied via the same scenario. This case assumed the number of delivery moments is equal to the number of order moments. The lead times are set for all scenarios. The timing is fixed for a six-time frequency delivery and for a three-time frequency delivery as shown in fig 1

Fig 1: Logistic scenario: delivery frequency per week to the catering outlet
In each scenario, the wholesaler delivers the whole packet of product groups to a special number of outlets every delivery day. In scenario (3,1,1) for example, frozen products and dry groceries will be delivered once a week, other chilled products and fresh produce and three times a week. On Monday, for example, the first three outlets in a route get dry groceries and frozen products, on Wednesday, the second three etc. The amount of consumption is equally spread over the days until the next delivery arrives. The current situation is simulated as a reference scenario. In scenario (6,6,6), the delivery frequency is maximal for all product groups. Scenario (6,1,1) is approximately with respect to the delivery frequency the same as the current scenario except for a difference in distribution structure. Scenario (3,1,1) seems to be a realistic scenario for small catering outlets. In scenario (3,3,3), the delivery frequency for dry groceries and frozen products is higher than in the current situation. These scenarios were judged to be the most relevant for performance analysis of the CSC among all possible scenarios.

The result of this case is in one scenario (3,1,1), total costs decrease with 14% compared to the current scenario just as the travel distance. In scenario (3,1,1), fresh produce and other chilled products are delivered three times a week and dry groceries and frozen products both once a week. In scenarios (6,1,1) and (3,3,3), total costs are almost the same as in the current scenario. An increase in delivery frequency for dry groceries and frozen products satisfy the demands of the CSC and can be achieved using multi-compartment distribution with decreasing integral cost. A sensitivity analysis for order amount has shown the simulation results to be primary stable.

CASE STUDY 2 : Modelling complexity in the automotive industry supply chain
Regarding Kevin Turner using simulation to investigates to changes the design of the automotive industry supply chain to improve performance and solve the problem of high stocks cause of complexity include product variety, demand seasonality and consumer behavior. High stocks is a reason that performance of the supply chain has failed to meet customer expectations in terms of delivering the exact specification desired within time
Due to dealers create problems such as quality, reverse logistics and inventory but distribution centres and postponement can reduce these problems. In the 1990s, companies such as BMW, VW, Rover began to introduce distribution centres for reducing stock held at dealers. Postponement? clearly offers particular advantages to manufacturers with long distribution times, e.g. vehicles supplied to the UK from assembly plants in Japan.
This research using computer simulation is an experimental process by used data that defines a “base scenario” similar to the supply chain to clarify the model application, with the simulation run several times to determine the performance with varying levels of stock. Then repeat the process with two alternative scenarios are distribution center and postponement
Base scenario is dealer stock uses a build-to-forecast. Cars are allocated to dealers for production, and all stock is held by the dealers. Although their first priority is selling their own stock and orders in the production schedule. The transfer system efficiency is reduced to reflect the fact that some potential transfers will not be feasible due to the distance between dealers.
Scenario 1 is distribution centre. Stock at dealers is restricted and most stock is held at a single distribution centre Cars are no longer allocated to dealers, but remain available to all dealers until they leave the distribution centre to removes negotiating problem between dealers.The sales priority is amended to give greater priority to customer orders and sales from the distribution centre, with cars held in the dealer showroom given lower priority.

Scenario 2 is a postponement. By reduced number of option packages to eight .This could be achieved by fitting four options at the distribution centre to meet the customer requirements as alloy wheels, alarm, satellite navigation and fog lights. The option packages fitted at the assembly plant would be the eight possible combinations of three options as sunroof, air conditioning and in-car entertainment.
The result of this case study is variation in service level as stock is reduced from 53 days to 10 days and even lower for the alternative scenarios. At 20 days of stock giving a better service level of alternative scenarios than 53 days of base scenario. Postponement gives only a small improvement at high stock levels. The distribution centre can make all stock available to all dealers without the costly dealer transfer process. While postponement reduces lost sales it has little effect on the source of sales. There is a small increase in the proportion of customers satisfied from the production schedule cause of the lower variation of specifications produced. At low stock levels the alternative scenarios perform better on this measure than the result of base scenario at high stock level. However, postponement gives only a marginal improvement compared to the introduction of the distribution centre alone. An alternative scenario in which the assembly plant would show postponement having a greater impact. The model produces a range of additional performance measures such as the age profile of stock (older vehicles are likely to be sold at a discount, with significant impact on dealer margins) and on-time delivery (which may have an impact on customer satisfaction).

CASE STUDY 3 : The Impact of Inventory Inaccuracy on Retail Supply Chain Performance: A Simulation Study
Regarding Elgar using simulation to study the relationship of inventory inaccuracy and performance in a retail supply chain by simulating a three echelons supply chain with one product in which end-customer demand is exchanged between the echelons. In the base model, without alignment of physical inventory and information system inventory, inventory information becomes inaccurate due to low process quality, theft, and items becoming unsaleable. In a modified model, these factors that cause inventory inaccuracy are still present, but physical inventory and information system inventory is aligned at the end of each period and compare the two models.

Our simulation model uses discrete and constant time intervals. Demand, orders and other variables related to the physical flow of products are continuous variables.
For the result of the base case, The theft level is decreased from 1.5% to 1.3 -1.1% will be a positive impact on the performance measure cause of takes the lost item value into account. For unsaleable, the cost measure that includes the lost item value improves significantly, As for theft is because of the direct impact of a decrease in unsaleable on the lost item value cost component. The other performance measures do not show significant improvements. This can be attributed to the low default value for unsaleable compared to theft. For an increase of process quality, both inventory accuracy and the two cost measures improve significantly, but not for an out-of-stock level. The cost measures show significant improvements before the change in inventory inaccuracy becomes significant.
For a modified model of supply chain performance that eliminated inventory inaccuracy. This is done by assuming that physical inventory and information system are aligned at the end of each period. An elimination of inventory inaccuracy notably improves all performance measures. This case varied each factor starting with the best case (i.e. perfect process quality, no unsaleable and theft). For more than level 0.5% of theft becomes significant in improvement. For level 0.2-0.5% of unsaleable is no significant change in the performance measures. This is in contrast to the results above for theft and can be explained by the fact that in our model most items are shipped by the distributor or sold by the retailer in the same time period in which they are received. The effect is that, in contrast to theft, most unsaleable items are detected within the time period in which they become unsaleable, and information system inventory is adjusted.

If inventory inaccuracy is due to low process quality, eliminating inventory inaccuracy only has a significant impact on the cost measures, but not on the out-of-stock level.

In our final analysis, we vary the modified model and assume that, at the same time as inventory inaccuracy is eliminated, the relevant factor that causes inventory inaccuracy improves as well. Specifically, we assume that each factor improves by approximately 80% compared to its default value. This means that process quality improves to 0.8%, theft decreases to 0.2% and unsaleable to 0.1%. The results for stolen and unsold items will focus on the impact of a change in theft level and unsaleable on the cost measure, including the value of lost goods. For unsaleable. The cost measure which excludes item cost is the only one that does not improve significantly. Process quality improvements in cost measures and only later on the scale available outside the stock. This corresponds to the above results.

CASE STUDY 4: Simulation modelling for food supply chain redesign : integrated decision making on product quality, sustainability and logistics
According to the case study pineapple supply chain using simulation to compare logistic cost, product quality decay, energy use and CO2 emissions of two import supply chain.The import of fresh pineapple from Ghana and South Africa about 35 tons on a yearly to Netherland. Pineapples require particular temperature, moisture, ventilation conditions. Intact pineapples can be kept for several weeks, but cut pineapple has a much more restricted shelf life. Keep ability of cut pineapple is around six to nine days at a fixed temperature 4 C. Biological variation within the same batch causes differences in initial quality of cut pineapple such as different packages of cut pineapple may have a different pattern of quality decay at the same temperature. Each package of cut pineapple is provided with a guaranteed best-before-date at maximum temperature 4 C of the storage, which is equal to ‘the current date plus 6 days’
A large amount of pineapple is delivered to Netherland by costly air transport so importer needs to reduce the cost of transportation by finding alternative ways of transportation as by sea and also concern with quality decay and product shrinkage cause long transportation times. Therefore, Importer using simulation to redesign food supply chain. They identified several alternative food supply chain (FSC) designs two alternative scenarios as below
(1) Producing and cutting pineapples in Ghana then transport by air to the Netherlands and distributing the cut pineapples to retail outlets (the air chain).

(2) Producing pineapples in Ghana, sea transport of intact pineapples, cutting in the Netherlands and distributing the cut pineapples to retail outlets (the sea chain).

KPI of this FSC is the distribution cost along supply chain (focus on transport, warehouse and leave out the cutting process cost), the energy and emissions during distribution (CO2 emissions are considered at 73 g CO2 is per MJ direct energy use), and the product quality when arriving at the retail store that is measured by the remaining selling time at the retail outlet, The remaining keep ability of the product at a storage temperature of 4 C , The percentage of products for which the BBD is not reached yet, but has a yeast concentration which is not acceptable anymore.

This case using ALADINTM simulation to design scenarios by setting the model elements, for example, applying ship transport VS air transport. Alternative designs of the product supply chains were simulated, visualized, and analyzed. By changing the environmental conditions such as using new packaging materials. Applying new logistical concepts changes the control and product flows which impact costs and changes the keeping quality of the pineapples and the environmental load.

The result of this case as below
For responsible of all logistic cost of sea transport is 60%, air transport is over 70%
For energy during transport of sea transport is 50%, air transport is 85%
For keep ability of sea transport is four days, with a variation from less than 3.5 days to more than 4.5 days and air transport is average below five days, with a variation from less than 3.5 days to more than 5.5 days.

For six days after cutting BBD of sea transport is not realistic in this case. Five days after cutting 10.6% of all products have a keep ability which is less than the BBD code indicates (based on six days) but six days after cutting BBD of air transport seems to be realistic for this chain. A rather small percentage of all products 5.9% on average
SYSTEMATIC REVIEW
Simulation is an indispensable problem-solving methodology for the solution of many real-world problems. It is used to describe and analyze the behavior of the system, ask what-if questions about the real system, and help to design real system.
According four case study that using simulation tool that present many advantage of simulation for example investigate imperfect thing and find appropriate way to solve the problem in many industry, increase efficiency of supply chain such as integrated approach towards logistics, sustainability and product quality analysis of industry, re-design supply chain, analyze factor of inventory that impact on performance of retailer etc.
There are many advantages of the simulation tool. Firstly, Simulation tool helps to reduce cost because experimentation with the real system is likely to be costly. It is expensive to interrupt day-to-day operations to try out new ideas. Moreover, if the performance of new idea is worse, this may be costly in terms of loss customer dissatisfaction. Simulation can be made at the cost of the time it takes to alter the model and without any interruption to the operation of the real-world system. Secondly, Simulation tool helps to reduce time because experimentation with a real system may take many weeks or months (possibly more) before a true reflection of the performance of the system can be obtained. Depending on the size of the model and speed of the computer, a simulation can run many times faster than real time. Lastly, the Simulation tool is easy to control the conditions under the experiments because experiment of simulation tool can be repeated many times. When comparing alternatives it is useful to control the conditions under which the experiments are performed so direct comparisons can be made.

In contrast, Simulation tool also a disadvantage when they are used in an inappropriate situation. Firstly, Simulation software is not a cheap and high cost of model development if consultants have to be employed. Secondly, the Simulation tool is a time-consuming approach. This only adds to the cost of its use and that is mean the benefits are not immediate. Thirdly, Simulation tool requires an amount of data that is not always immediately available and much analysis may be required to put data in a suitable form simulation. Lastly, the Simulation tool is requires expertise. It requires skills in other things as conceptual modeling, validation and statistics, also skills to work with people and project management Which expertise is not always readily available.

In my point of view simulation tool is useful and important for every industry. The simulation tool is a recognized solution for real-world solutions in every industry and is an effective tool in planning and improving processes within the manufacturing industry. Simulation tools are useful for every industry. But in the same way that users have to consider using the right situation.

CONCLUSION
In spite of increasing competitiveness in all industries is driving the enterprises to strive to increase their supply chain performance. Among the techniques supporting supply chain. Simulation is an important role for all of the key features of quantitative analysis and evaluation. The results of this assignment demonstrate that simulation tools are more appropriate and useful for practitioners and researchers in the application of decision-making within the context of the supply chain.

The simulation tool helps to evaluate the performance prior to use of the system because it enables companies to conduct in-depth analysis effectively to make informed planning decisions. It helps to compare operating options without affecting the actual system and compress time to decide at a glance. These features are the result of a four case study in this work that demonstrates a different way to improve and increase the performance of logistics and supply chain.
REFERENCE
Banks, J. (Ed.). (1998). Handbook of simulation: principles, methodology, advances, applications, and practice. John Wiley ; Sons.

Fleisch, E., ; Tellkamp, C. (2003). The Impact of Inventory Inaccuracy on Retail Supply Chain Performance: A Simulation Study. White paper, Auto-ID Center.

Harrison, J. R., Lin, Z., Carroll, G. R., ; Carley, K. M. (2007). Simulation modeling in organizational and management research. Academy of management review, 32(4), 1229-1245.

Henderson, S. G., Biller, B., Hsieh, M. H., Shortle, J., Tew, J. D., ; Barton, R. R. Application of design of experiments on the simulation of a process in an automotive industry.

Jansen, D. R., Van Weert, A., Beulens, A. J., ; Huirne, R. B. (2001). Simulation model of multi-compartment distribution in the catering supply chain. European Journal of Operational Research, 133(1), 210-224.

Nyhuis, P., von Cieminski, G., Fischer, A., ; Feldmann, K. (2005). Applying simulation and analytical models for logistic performance prediction. CIRP Annals-Manufacturing Technology, 54(1), 417-422.

Stewart, R. (2004). Simulation: the practice of model development and use. Journal of Simulation.

Terzi, S., ; Cavalieri, S. (2004). Simulation in the supply chain context: a survey. Computers in industry, 53(1), 3-16.

Turner, K., ; Williams, G. (2005). Modelling complexity in the automotive industry supply chain. Journal of Manufacturing Technology Management, 16(4), 447-458.

Van Der Vorst, J. G., Tromp, S. O., ; Zee, D. J. V. D. (2009). Simulation modelling for food supply chain redesign; integrated decision making on product quality, sustainability and logistics. International Journal of Production Research, 47(23), 6611-6631.