Simulation

Supply Chain Redesign for Resilience using Simulation

Daniel Dumke — Mon, 13 Aug 2012 07:30:40 +0000

Today we have a look at current research regarding the improvement of resilience within a supply chain.
In their 2012 paper “Supply chain redesign for resilience using simulation” Carvalho et al. analyze supply chain resilience on the basis off a Portuguese automotive parts manufacturer.

Methodology

As indicated by the paper’s title the authors main method is a simulation study. The simulation model is based on the results of a case study. Semistructured interviews were conducted to gather the relevant data off a Portuguese automotive supply chain.

But first, the authors analyze the current literature on supply chain design and resilience (figure 1).

Figure 1: Literature Review Supply Chain Design (Carvalho et al., 2012)

Simulation model

The structure of the supply chain model is shown in figure 2. The Portuguese automaker has a capacity of over 180,000 vehicles per year and all vehicles are customized.

Figure 2: Supply Chain Structure (Carvalho et al., 2012)

Arena 9.0 in conjunction with Microsoft Excel has been used to implement the model of the supply chain.
The processes which have been identified rely on the SCOR process definitions. Figure 3 shows the simulation model flowchart.

Figure 3: Supply Chain Processes (Carvalho et al., 2012)

Supplier lead-times were estimated together with the case study participants using triangular distributions (figure 4).

Figure 4: Input Data Lead Times between Suppliers (Carvalho et al., 2012)

Performance was measured using two key performance indicators: lead-time and total cost.
Overall six scenarios were designed by the authors. One containing the base scenario without using any strategy to reduce risk, one using a redundancy-strategy, and another one implementing a flexibility-strategy. These scenarios were then duplicated to generate one group with a disruption in the material flow between supplier 2_1 and 1_1 and another group without any disruption.

Figure 5 shows the total cost performance in different scenarios. Scenarios to 4 and 6 are affected by the disruption.

Figure 5: Simulation Results in different Scenarios (Total Cost, Carvalho et al., 2012)

The authors conclude

Two strategies widely used to mitigate disturbance ad- verse effects on SCs were considered (flexibility and redundancy) and six scenarios were designed. To evaluate the different scenar- ios designed, two performance measures were defined and com- puted for each SC entity, Lead Time Ratio and Total Cost.
The results of the simulation allowed to compare SC behavior after the occurrence of the disturbance under the two SC resilience design strategies. Both strategies are effective in reducing the neg- ative effects of the disturbance on SC performance. When the flexibility strategy is applied the Total Cost of the SC is less, in comparison with the redundancy strategy and the Lead Time Ratio is better.

Conclusion

Since my own research revolved in parts around my own simulation model I have two comments on this specific implementation, but I would like to share with you:

I’ve seen this already in other papers: the description of the scenarios is really bad. For one there is no overview summarizing the key differences between each of these scenarios, furthermore the description of how these scenarios are implemented in the supply chain model lack in detail.
Another key aspect to simulation modeling is the validation off the model’s output. In this case the authors are using real input data from the case study, but there is no mention if they also compared the model’s output with the real supply chain.

The conclusion of the authors also highlights the difficulty of interpreting simulation results. What could one learn from this study? Redundancy and flexibility can be used to reduce risk?
For me this insight does not qualify as a groundbreaking revelation. At least not in 2012.

Reference:

Carvalho, H., Barroso, A.P., Machado, V.H., Azevedo, S., & Cruz-Machado, V. (2012). Supply chain redesign for resilience using simulation Computers & Industrial Engineering, 62, 329-341 DOI: 10.1016/j.cie.2011.10.003

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strategy

Modeling Defaults of Companies in Multi-Stage SC Networks

Daniel Dumke — Mon, 28 May 2012 14:07:06 +0000

Agent-based supply chain models are build using small entities (agents), which might represent a single company.
Each of the agents has its own goals and rules of operation programmed into a computer. The interaction between several agents of this kind leads to a more realistic and complex behavior of the system.

There are several different schools for the quantitative analysis of supply chain risks. Simulation is one of them and agent-based models show several distinct advantages: They are both easier to understand and allow for a more complex system behavior, than other quantitative methods.

After this introduction I would like to have a look at a current agent-based supply chain model, which analyzes the effect of bankruptcies on supply chains. The full paper can be downloaded here.

Model

The authors focus on a comprehensive view on the supply chain: several stages (horizontally and vertically) are modeled. Figure 1 shows an exemplary supply chain.

Figure 1: Exemplary Supply Chain Structure (Mizgier et al. 2012)

Each circle represents one node or agent, the connections are drawn as lines.
The model features five additional characteristics worth mentioning:

Price dispersion (prices can vary between companies)
Evolution of supply chain topology (links between companies can be changed, by the agents themselves)
Network reconfiguration (the reconfiguration is based on the price)
Production dynamics (output is determined by the invested working capital)
Dynamics of costs of production (random changes to the environment every five periods, lead to changes in the cost function of the companies)

Last but not least, companies may go bankrupt if they are not able to perform their short therm debts. There are no loans.

Results

First, the network performs as expected. During the first period turbulences can be observed. Figure 2 shows the utilization of working capital during the simulation (1 equals 100%).

Figure 2: Capacity Utilization (Mizgier et al. 2012)

After 200 iterations a typical start scenario might look like figure 3.

Figure 3: Network Structure after Initialization Period (Mizgier et al. 2012)

Starting from this state the

The firms with the best profit/cost ratio are growing and adding new suppliers, whereas the working capital of the firms whose sales price is higher than the mean price of the given stage is slowly decaying and results in the defaults of firms.

A stable state might look like figure 4.

Figure 3: Network Structure after Stabilization (Mizgier et al. 2012)

The authors deduce three implications from those results:

The first implication is that during the process of assessment of the company’s risk exposure, managers should keep their focus on the global structure of the supply chain network instead of being restricted to the own portfolio of suppliers and customers.

Secondly, as a result of the dynamics of the topology of the supply chain network, strong competition in prices and fast changing technology, even the most reliable firms should be monitored and constantly re-evaluated in terms of their production capacity and risks associated with their structure of connections.

Third and most important, managers should find ways to cut costs and reinvest the free cash flows in new technology of production, which will allow further cost reductions and the development of new innovative and cheaper products.

Conclusion

Being stuck in the supply chain can lead to negative consequences, when ripple effects cause multiple suppliers and customers to default. Furthermore, these supply chain partners might also not be the most efficient ones, and these prices affect each participating company.
All in all, a great paper, but I would have liked to read more about the authors’ efforts to validate and verify the model’s integrity.

Reference:

Mizgier, K., Wagner, S., & Holyst, J. (2012). Modeling defaults of companies in multi-stage supply chain networks International Journal of Production Economics, 135 (1), 14-23 DOI: 10.1016/j.ijpe.2010.09.022

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analysis

Improving Performance in Food Supply Chains by Reducing Uncertainty

Daniel Dumke — Mon, 12 Sep 2011 15:51:00 +0000

Today’s article is from the late 90s, but sets a great example for research methodology in supply chain risk management. But don’t worry, I will focus on the results, since they’re very interesting as well. The objective of today’s article (Supply Chain Management in Food Chains: Improving Performance by Reducing Uncertainty) is to show strategies (here called principles) to reduce uncertainty, and at the same time show the beneficial effects of reduced uncertainty.

Methods

Three methods were deployed to ensure the validity of the results. Based on a case study (1) of a chilled salads supply chain sources of uncertainty were generated and improvement principles designed. The analysis of the case study’s processes was also used to feed into a simulation model (2) of the supply chain, which was validated using (another) pilot study (3).
The approach is shown in figure 1 and is meant to ensure that the results presented below are worth reading.

Figure 1: Research Approach (van der Vorst et al., 1998)

Results case studies

The sources of uncertainty, which were uncovered during the process analysis in the first case study, can be found in figure 2.

Figure 2: Sources of Uncertainty and Improvement Strategies (click to enlarge; van der Vorst et al., 1998)

For the pilot study not all of the above mentioned improvement principles have been employed. The authors evaluated the following strategies (figure 3).

Figure 3: Improvement Strategies used at the Pilot Case Study (click to enlarge; van der Vorst et al., 1998)

By implementing them a very drastic decrease in inventory levels could be achieved (examples see figure 4). Overall results can be found in figure 5.

Figure 4: Inventory Level at the Distribution Center before and after implementing the Improvement Strategies (van der Vorst et al., 1998)

Figure 5: Results of the Pilot Study (click to enlarge; van der Vorst et al., 1998)

Results simulation

The results of the pilot study were then compared to the simulation model. Overall there seems to be a good fit between the model and the real data (figure 6).

Figure 6: Validation of the Simulation Model by comparing with Real World Data (click to enlarge; van der Vorst et al., 1998)

Further results were generated using the simulation model:

For fast moving goods optimization could be achieved by increasing the delivery frequency both to the retailer and to the DC by a certain amount. Even though the picking cost rose, inventory levels could be decreased significantly.
Implementation of a computer aided ordering system lead to an improvement of supply chain performance between 10 and 20%.

Conclusion

For the authors the simulation model is a logical extension of the prior case studies. Using it makes it possible also to test alternatives which in reality might be too expensive or even impossible to test.
Another key lesson: Reduction of uncertainties again is a major driver of supply chain performance and therefore should not be neglected.

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Simulation as a Tool for Supply Chain Optimization

Daniel Dumke — Wed, 27 Jul 2011 13:54:00 +0000

Sometimes I am really amazed by the research topics of others. Even though I already read much about simulation and its potential benefits, up to now I have never seen a analysis of supply chain simulation performance on a larger sample. So I would like to share those insights here.

Simulation method

In this paper the authors (Manzini et al.) used a visual interactive simulation (VSI) approach for five case studies. In this case VSI refers to a simulation model that can be directly manipulated by the user of the model during simulation. So for example the effects of a change in inventory levels could be viewed directly after the fact.

This approach therefore allows for quick testing of different scenarios and possible strategies for optimization of the supply chain.

Case example

The authors elaborate five different cases where they employed the above mentioned methodology. I will just highlight one case example here: A make-to-order producer of packaging systems for the pharmaceutical industry. The produced machines consist of a large number of different components, which are supplied by external suppliers. The authors build a model containing the 18 major suppliers, which supply overall 53 modules for the final product.

Figure 1: Case Study: Comparison of Module Lead Times (Manzini et al., 2005)

Based on the simulation model different measures have been developed to reduce lead times from the suppliers. Figure 1 shows the effects of those measures for each of the 53 components. The lead times could be reduced in nearly all areas.

Case analysis

As in any multi-case study after the description of the different examples a cross case analysis and synthesis is necessary to compare the results and draw further conclusions. In this case the author calculate five indicators for each case study which describes the effort for the simulation study and the resulting benefits:

General indicators of model complexity:

NE index is the number of entities used in the simulative model so as to describe the real situations that can occur; and

PT takes into account the programming time in terms of man-hours dedicated by the programmer to develop the model.

General indicators of model effectiveness:

SI is the saving index, defined as the ratio between savings forecast and costs of simulation approach; this is introduced to show the economic feasibility of the simulative model;

SCI, simulation costs index, is the ratio between simulation costs and plant modification costs required for the supply chain optimisation so it deals with the simulation’s economic weight in a complete project; and

RI, readiness index, is the ratio between the lead-time of the simulative approach and real project time extension; it measures the capability of the dynamic model to give rapid solutions to a problem.

Result

The above mentioned indices are then used to compare the different case studies concerning the effective and efficient use of the VSI simulation method within each of the settings.

Figure 2 shows the savings index of the companies which compares the savings the company could achieve using the selected measures versus the cost of the simulation (more is better).

Figure 2: Comparison of the Cases: Saving Index (Manzini et al., 2005)

Figure 3 shows the simulation cost index which refers to the cost of simulation versus the overall project costs for implementing the measures within the respective supply chain (less is better)

Figure 3: Comparison of the Cases: Simulation Cost Index (Manzini et al., 2005)

Figure 4 displays the readiness index. This describes the amount of time it took to execute the simulation versus the total project time necessary to make the changes (less is better).

Figure 4: Comparison of the Cases: Readiness Index (Manzini et al., 2005)

The authors conclude based on these results that the VSI method is very suited for deployment in a business setting.

Conclusion

The authors make a strong case for “their” simulation approach: visual interactive simulation and claim that it is especially useful from a cost / benefit point of view. Also the results in the cases are delivered quickly.

I would love to see another comparison including other simulation approaches, since at least from a theoretical point of view, the VSI is a very simplistic simulation method. There is no optimization selecting the best strategy from several alternatives, but the simulation modeler has to select and evaluate the “best” strategy by himself.

Nonetheless a simple approach has advantages from the complexity perspective. As always: Case studies are not representative, but should give a hint for further research, so go ahead and do some research!

Reference:

Manzini, R., Ferrari, E., Gamberi, M., Persona, A., & Regattieri, A. (2005). Simulation performance in the optimisation of the supply chain Journal of Manufacturing Technology Management, 16 (2), 127-144 DOI: 10.1108/17410380510576796

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Simulation

Alternatives for Developing Supply Chain Simulation Capabilities

Daniel Dumke — Mon, 13 Jun 2011 14:54:00 +0000

Today I would like to talk about a non-essential, but helpful part of supply chain management: Simulation.

Simulation can be used in a supply chain setting on many different levels. On a strategic level there are models to analyze scenarios for the optimal locations of one’s factory, on a tactical level inventory management and distribution policies are treated and on the operations side route-optimization is a generally used. Of course there are also non-simulation models for these tasks, but this article is not about the pros and cons of that.

The goal of this article is to give you an overview what general differences exist between the approaches to build a non-specialized capability for supply chain simulation.

Approaches for building simulation capabilities

Simulation tools can be categorized into the following scheme.

Data driven simulators, can be defined as a simulation model which can be parameterized by providing data (e.g. by using spreadsheets), it’s purpose is to model a specified set of systems.

Pros: Usually easy to configure for the user

Cons: Less flexibility, since the simulator can only interpret parameters which its programming allow; standard graphical representation, which fits for the set of systems may not be optimal for one special case.

Interactive simulator, builds on modules for supply chain elements. Those have to be arranged and parameterized by the user.

Pros: due to the modularity the visual representation can come very close to the real supply chain

The sub-model approach, is comparable to the module approach, but instead of providing closed modules, models for a sub-system are used.

Pros: The user can better specify the level of detail he needs and also have differing levels of detail for different supply chain tiers.

Cons: Increased requirements for the abilities of the user

Furthermore there are:

Simulator extensions, are extensions written for a general purpose simulation software, which enhance the capabilities to model a specific set of systems.

Object class libraries, are an even lower level where objects are predefined, but not bundled with a simulator, but built on a general programming language.

Comparison and trade-offs

After defining the different approaches the author then compares the different approaches (see figure 1 and 2).

Figure 1: Flexibility / Effort Trade-Off to develop a generic Simulation Capability (Jain, 2008)

Figure 2: Flexibility / Effort Trade-Off to develop a single Model (Jain, 2008)

There is no golden rule of choosing the right approach for building simulation capabilities, but there are some important criteria and trade-offs to select the best approach.

Figure 1 focusses on the development of a generic simulation capability, which could be used to simulate a wider range of problems. If the objective is to develop only one model for a specific purpose (figure 2) the major difference is, that the effort for using sub-models is there higher than using the interactive simulator, since the learning curve for the former is steeper.

Jain names the following criteria, which should be used to decide on the correct approach:

Effort for developing simulation model instance vs. effort for developing a generic simulation capability

Flexibility of the acquired knowledge

Base software to be used

Role of the user in the simulation process

Animation capabilities of the selected product

Utilization of standards (by the simulation software), e.g. in data import / export, model format

Maintenance efforts needed for the capability

Execution time of the simulator

Conclusion

I think Jain could have made a better job to make the difference clear between the decision which software tool to acquire and which capabilities are needed at a company.

These decisions are of course interrelated, but since especially for a larger company it may be also an option to buy capabilities externally or by employing specialists.

For a complete framework the goal orientation was missing as well. For me the first step in such a process would be to define the company’s goals which then lead to the needed capabilities.

But, the reason why I presented this article is that I still think it contains some good classifications to select a fitting approach for creating simulation capabilities.

Reference:

Jain, S. (2008). Tradeoffs in building a Generic Supply Chain Simulation Capability Proceedings of the 2008 Winter Simulation Conference, 1873-1881

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Business & Research Tools

Supply Chain Simulation and the Bullwhip Effect

Daniel Dumke — Wed, 20 Apr 2011 08:59:00 +0000

I already reviewed some articles by Denis Towill primarily because he does some interesting research on simulation and supply chains, but also because I like his clear style in his articles.

In one of his early papers (1992) he teamed up with Naim and Wikner and described state of the art strategies to fight the bullwhip effect or as it is called in the paper by its older name: Industrial Dynamics.

Industrial Dynamics

I already described the causes of the bullwhip effect and how to measure it in an older post. The bullwhip effect describes amplifications in demand within the supply chain. Exemplary effects can be seen in figure 1.

Figure 1: Effect of the Bullwhip Effect (Towill et al. 1992)

Improving SC Dynamics

One way to improve the supply chain dynamics is to introduce a leaner supply chain, which in turn will lead to lower lead times and lower amplifications.

In a lean supply chain stocks are located at a low level of “added value”, to keep the inventory cost lower.

The next decision to make is to find the right trade-off between stock levels and production rates. If you increase the flexibility of production, you might be able to reduce the inventory overall. There are two alternatives:

Fixed process rate / varying inventory levels

Varying process rate / fixed inventory

There are four sectors where where further improvements can be acchieved:

Industrial engineering improvements, eg. changing the product design or changing the layout of the production

Production engineering improvements, eg. integration and sequencing of processes to reduce lead times

Information technology improvements, eg. quicker data capture and electronic data interfaces

Operations engineering improvements, eg. pull system instead of push, shared planning

Optimization

To examine their points, Towill et al. design a simple supply chain simulation model with four stages (retailer, distributor, warehouse and factory). The results (figure 2) show that above mentioned improvements can lead to a reduction in the amplification.

Figure 2: Strategies for Reducing Demand Amplification (Towill et al. 1992)

The authors conclude that reducing the overall lead times in the supply chain may be an expensive solution. As an alternative they suggest to remove some stages from the chain which also leads to significant reductions in the amplifications. Probably the cheapest way to reduce the bullwhip effect may be to increase collaboration in the chain, in this case especially concerning forecasts and planning.

Conclusion

Simulation model cannot only help in theoretical exercises. There are very important applications in real life applications as well when the task is to optimize generic strategy recommendations like those mentioned above.

Reference:

Towill, D., Naim, M., & Wikner, J. (1992). Industrial Dynamics Simulation Models in the Design of Supply Chains International Journal of Physical Distribution & Logistics Management, 22 (5), 3-13 DOI: 10.1108/09600039210016995

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Simulation

Strategies

Advanced Concepts in Simulation

Daniel Dumke — Wed, 06 Apr 2011 19:00:00 +0000

Computer simulation has not been used on a professional scale until after World War II, and also then mostly for military uses like war games or simulations of atomic bomb explosions nowadays.

One of the first scientific papers on simulation has been published in the late 70s by Ören and Zeigler. They aggregate some fundamental knowledge about simulation and suggest an conceptual model for simulation, which I want to introduce today from the perspective of a supply chain

Components of Simulation

Simulation programs can be described completely using the following terms:

Model Structure, describes the static and dynamic aspects of the model, like components of the model, variables and rules for interaction (supply chain context: echelons and elements of the echelon)

Model Outputs, contain all variables and functions that can be observed during and after execution (eg. lead times, profit of the chain)

Input Scheduling, when is the model fed with what inputs (eg. how is demand generated)

Initialization of Simulation, what are the starting parameters of the model (eg. are queues already loaded?)

Termination of Simulation, what are the conditions for model termination, may be after specific time or specific model states

Data Collection, how and when is the data during simulation collected

Simulator, the simulator carries out the model’s instructions to generate the new model states (nowadays the Simulator is usually a software like Arena, Anylogic or similar)

All those elements can be found within modern simulation tools, since they do not only include the Simulator component, but also aids to model and control the supply chain.

Simulation Models can be further characterized by the “language” they are using. First, is the model using a continuous or discrete time scale. Second, orientation the model: block- or expression-oriented; and last, what is the world view expressed in the model by differential equations, events or process interactions.

Experiments

After modeling a supply chain you can do experiments using the model, but of course also in reality (eg. increasing buffers or changing inventory policies). This would allow the experimenter to validate and compare the models output with the real changes. Of course in a supply chain context experiments in reality are confined to a very small range of parameter variation without compromising the ongoing operations.

This discussion focusses on the object of the experiment. But there are other components of an experiment. The experimental frame is defined as

a limited set of circumstances under which the system is to be observed or subject to experimentation,

so the settings which are used for above mentioned components of a simulation.

Advanced Concepts and Conclusion

The authors suggest some concepts for advancing simulation which are standard in nowadays simulation software. For example they describe a closer linking of model and experiment or demand capabilities for easy design of models and experiments.

So after all a very insightful article in the historical development of simulation. This has not been discussed here, but of course there are pros and cons for simulation. There are strong opponents of using simulation in the supply chain context (eg. Sodhi and Tang, 2009), since at least when using optimization there will always be an optimality gap. Instead mathematical modeling should be used.

But the huge amount of literature using different kinds of simulation (eg. reviewed here) shows that supply chain management can benefit very much from simulation, since the method is probably more easily understood, than some mathematical models.

Reference:

Oren, T., & Zeigler, B. (1979). Concepts for advanced simulation methodologies SIMULATION, 32 (3), 69-82 DOI: 10.1177/003754977903200301

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Case Study: Stress Testing Supply Chains

Daniel Dumke — Mon, 07 Feb 2011 15:27:00 +0000

Stress tests are an acknowledged method to test systems under extreme conditions. The method is not only used in engineering (eg. Great picture of a Boing wing stress test (Guardian Eyewitness)), but also in business, most notably and in the banking industry. But can this method also be used to test supply chain designs?

Method / Case Study

There is limited literature on using simulation to evaluate supply chains under stress. The authors took the following approach:

Determining the scope by setting the level of detail and defining the basic elements of the supply chain
Gather data, to improve the reliability the authors worked together with a company which provided the input data for the model. These included the bill of materials, network configuration, etc.
Next the model was implemented. Validity was ensured, by integrating different opinions and thoughts on the model

Results

The company was a supplier for the ministry of defense the major objective therefore was to, make sure that demand can be met during three different scenarios:

Normal operation, with defined volume level
Surge operation, with twice the volume level of normal operation
Mobilization operation, with four times the volume level of normal operation

The simulation showed that all stress scenarios could be finished successfully (each with some weeks of ramp up, of course). Furthermore some configuration changes helped the company to improve performance even more.

Conclusion

In this case stress testing helped the company: to gain a better understanding of their supply chain and to improve supply chain operations. It also helped the customer to gain confidence in the capabilities of its supplier.
I find it an interesting thought, that proven reliability, in this case based on a transparent simulation model, can support a company to gain new customers. But also other companies, which have a strong emphasis on supply chain risk management, are using stress testing for their supply chains, one example would be Dow Chemical.

The complete article can be downloaded here free of charge.

Reference:

Jain, Sanjay, & Leong, Swee (2005). Stress Testing a Supply Chain Using Simulation Proceedings of the 2005 Winter Simulation Conference, 1650-1657

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disruptions

Level of Detail in a Simulation Model

Daniel Dumke — Mon, 29 Nov 2010 14:48:00 +0000

After the last more general entries on managers perception of risk and measuring SC performance I wanted to make a detour back to the basics.
Simulation is one of the tools, which can be used for analyzing supply chain dynamics, optimization and to support corporate decision making.
One major question when starting a supply chain model has always been what level of detail should you choose? Someone could start with a single worker in an agent based simulation model and continue with the machine he is operating, but when the goal is to gain insights about the interaction between your company and your closest competitors this might be too much information.
To get a better grasp on this topic I read “The impact of different levels of detail in manufacturing systems simulation models” by J.F. Persson (Translation).

Experiment / Results

To test the importance of the level of detail in a simulation model Persson designed an experiment: He modeled the same process chain with three different levels of detail:

high level of detail
some aggregation
only main processes

After simulating this process and comparing the output measured in capacity utilization, inventory levels and blocked time (for bottleneck detection), he concluded that significant differences between the models have to be acknowledged (see figure).

Capacity at different Stages within the Models (Persson 2002)

Conclusion

So what are the important lessons for someone planning to develop and use a supply chain model:

Focus
Know your exact goals and focus on achieving them. If you want to analyze competitive interaction between companies, it might be a good idea to have a single company as your base entity
Minimization

The level of detail should be kept at a minimum, keeping the model simple. The reason is that (i) a simple model can provide a result and (ii) an unimportant detail in the model is hard to remove despite the negative impacts on simulation execution time.
Validation
It is very important to have constant validation for the model during the development process and final approval tests to prevent adverse effects from a wrong level of detail as well as other mistakes.

Reference:

Persson, J. F. (2002). The impact of different levels of detail in manufacturing systems simulation models Robotics and Computer-Integrated Manufacturing, 18 (3-4), 319-325 DOI: 10.1016/S0736-5845(02)00024-8

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Simulation

Disruption-Management Strategies for Short Life-Cycle Products

Daniel Dumke — Sat, 10 Jul 2010 07:05:00 +0000

In his 2009 paper Brian Tomlin analyzes strategies to mitigate disruption risks in a three echelon supply chain.

Setting

Focus in his research is a single company, with its suppliers and customers. The objective is to maximize expected utility, while demand and supply are uncertain. There are two products available which can be used as substitutes. The time horizon for the decision maker is one season where the products can be sold.

Three different sourcing structures are considered.

Different sourcing structures (Source: Tomlin 2009)

Variables / Attributes of the SC

The following attributes in the supply chain are used as free variables and can therefore be modified:

Suppliers
- Supplier reliability
- Correlation between supplier failures
- Proportion of order cost charged in the event of a failure
- Relative cost of emergency supplier
Products
- Contribution margin
- Demand uncertainty
- Demand correlation
- Substitutability
Firm
- Risk aversion of the decision maker

Strategies

The effects of three major adaption strategies on the risk exposure / expected profit are tested:

Supplier diversification
adding another supplier to lower the overall risk for failure
Contingent sourcing
adding a supplier which is only used in case the main supplier fails to deliver
Demand switching
shifting customer demand to a different product

There is also the possibility not to engage in any activity and accept the risks.

Results

It is shown that contingent sourcing is the preferred strategy to supplier diversification for the firm, if the failure probability of the supplier increases. Furthermore, demand switching is an effective strategy for controlling demand risk and therefore is the preferred strategy with low supply risk.

Higher levels of risk aversion lead to a preference for contingent sourcing over the other strategies.

Conclusion

Tomlin shows viable and effective strategies to increase profit (utility) in supply chain settings with disruption risk. He therefore calculates the expected profits in different situations and uses the results to give suggestion for situational optimal strategies to mitigate these risks.

Reference:

Tomlin, B. (2009). Disruption-management strategies for short life-cycle products Naval Research Logistics, 56 (4), 318-347 DOI: 10.1002/nav.20344

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Supply Chain Performance and its Topological Features

Daniel Dumke — Thu, 24 Jun 2010 08:04:00 +0000

Perhaps this research by Pero et al. can support small and medium sized companies with the design and redesign of its supply chain network.
The goal of the study was to analyze the connection between topological features of the supply chain and the resulting supply chain performance.

Method and Model

The authors used simulation techniques and statistical analysis to simulate a pull based supply network. The network consists of a retailer-, distributor- and manufacturer-level.
Demand and lead times were random, the supply chain performance was measured with stock outs that occured.

Results

The results show that:

An increasing number of nodes at each level increases the probability of stock outs.
The performance of the supply chain was not affected by the number of levels in the supply chain and the distance between the nodes.
Furthermore the number of sources for each node seems to increase the risk for stock outs.

Conclusion

Due to their simple structure the results are very compelling. To get the perfect supply chain performance all you have to do is following the above mentioned “rules” (results).

But you have to keep in mind the premises on which the model builds:
For example, supply chain performance is only measured in terms of “stock outs”. Stock out should never be the single performance measure you rely on, since there may not be a direct correlation with more important company goals like profit, …

Besides, it would be advised to model your own individual supply chain to accommodate for your specific configuration and needs. Especially strategic decisions like the supply chain configuration should be important enough to not take the shortcut.

Reference:

Pero, M., Rossi, T., Noé, C., & Sianesi, A. (2010). An exploratory study of the relation between supply chain topological features and supply chain performance International Journal of Production Economics, 123 (2), 266-278 DOI: 10.1016/j.ijpe.2009.08.030

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Improving Simulation in Supply Chain Research

Daniel Dumke — Tue, 09 Feb 2010 18:06:00 +0000

“Improving the rigor of discrete-event simulation in logistics and supply chain research” is a 2009 paper by Ila Manuj, JT. Mentzer and MR. Bowers.

The paper consists of three distinct sections:

Introduction of the SMDP (Simulation Model Development Process)

Evaluation of the current literature in comparison to the SMDP

Explanation of the SMDP using an example

Simulation Model Development Process

The SMDP is discribed as a normative model to create a rigor and trustworthy simulation model. The promise seems simple, eight steps ought to be followed to create a model which can be described sufficiently.

Problem formulation
Choice of dependent and independent variables
Validation of conceptual model
Data collection
Verification of computer model
Model validation
Performing simulations
Analysis

Following those steps can lead to a systematical description of the simulation and the underlying model. The result is a more complete model by using validating techniques like expert input and comparison with real world data.

Literature

The most important part for writing papers and theses is to not only generate this information but actually write it down.

The second part of the paper deals with this question. Are current simulation studies implicitly or explicitly following the above mentioned steps?

The answer is no. Especially steps 3 and 5 through 7 are not documented very well. Of course this does not mean that for example the simulation models have not been validated in some form, but very often there seems to be only very little documentation of the methods used to enable other scientists to reexamine the models.

Prove of concept

In the third part of the paper Manuj et al. show that it is actually possible to follow the steps by documenting a simple simulation model very exhaustively.

Summary

Overall I very much like the papers approach.

It provides a checklist for improving the model generation and simulation process.

The prove of concept already includes some risks though it is especially interesting read if you want to use this as a (very) basic model for SCRM.

The paper tackles a problem which is intrinsic to many articles in many papers, they are written to provide you with a glimse of the results. Very often the methodical foundations and side results are just ignored and not documented. Progress in science very much depends on the possibility to build on those models.

Reference:

Manuj, I., Mentzer, J., & Bowers, M. (2009). Improving the rigor of discrete-event simulation in logistics and supply chain research International Journal of Physical Distribution & Logistics Management, 39 (3), 172-201 DOI: 10.1108/09600030910951692

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