Nov
22
Challenges with using supply chain simulation and optimization tools
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The use of simulation and optimization techniques within the supply chain environment is now commonplace. There is a myriad of software tools (many of which are baked into traditional ERP/MRP solutions or offered as add-ons). The concepts that simulation tools provide can offer many benefits such as improved accuracy, speed and ease of use. These can deliver robust models that can better equip strategists and business decision makers.
However, simulation and optimization is not perfect and like any tool or process have some things which they are not good at.
1. Your initial data is accurate right?
The key to simulation tools is that they require data – if that data lacks accuracy then the results obtained from any modelling will be questioned. For example if your looking to run a simulation to better understand long term costs and your utilizing data that has inaccurate cost estimates then your doomed to failure. It may sound obvious but simulation is not a cure for poor ERP data.
2. How robust is your demand profile?
Optimization is usually centered on a estimated demand over a period of time. You can modify the estimated demand profile and run various scenarios comparing the effect on the recommended solution; however, optimization can struggle where demand or other inputs are highly variable.
Where inputs, are not constant, but more dynamic some tools may require more manual intervention and review. This can be more important where the estimated demand required is more granular i.e. based on a daily (or greater) interval.
3. How did you come up with that?
Optimization also has a tendency to be a closed or “black box” method. This means accepting inputs, figuring numbers and offering a solution but where the calculation itself remains a mystery – the system just does it.
Due to the complexity of such systems, it is not always easy for the operator to fully understand the relationships of the different factors, how all of the inputs, variables and data work as unit. This can often be further hampered by the resellers themselves who fail to fully explain how the system defines results.
The significance of this problem can vary depending on the accuracy of results coupled with the intended audience. Imagine giving your presentation to the CEO and he asks “how did you come up with that” and you can’t provide an accurate answer!
4. It’s a system not a crystal ball!
Whilst the benefits of simulation include the ability to better understand the impact of dynamic events, systems will never predict everything. There are likely to be various issues that every system will struggle to predict – for example consider how obsolescence can be reviewed in long term planning or how events such as the Icelandic volcano eruptions in 2009 which caused huge issues for international flights could’ve been predicted. Remember it’s a tool not a soothsayer!
Summary
The key to any system is to know its limitations – the many benefits of simulation overcome its few shortcomings but businesses must be aware that it is heavily reliant on the data and variables that it is fed with. Simulation and optimization tools will not fix any existing issues that the business has with these and it will not completely plug the gap in operational knowledge – results will still need to be reviewed, interpreted and iterated to get maximum benefit.
Nov
22
Using Supply chain simulation and optimisation techniques to better equip your decision making.
Filed Under Blog | Comments Off on Using Supply chain simulation and optimisation techniques to better equip your decision making.
Thousands of companies around the globe now use supply chain simulation and optimization methods. Interest has risen significantly over recent years no doubt as a result of increased awareness of best practice coupled with a greater footprint from (and increased numbers of) software resellers. Simulation whilst not necessarily new includes concepts that help optimize processes and delivery structures helping organizations better manage risk and provide a degree of robustness to long term planning.
The use of simulation techniques can be wide ranging. However they are often commonly applied to logistics or distribution processes. For example, distribution centers commonly have their location and inventory make up simulated to better understand optimal storage locations, storage volumes and logistics routes.
Complex simulation and optimization scenarios can be lengthy to process and the results often require further review or discussion (the results of simulation are not commonly carried immediately through to an execution situation.) However there are some exceptions, with some stock modeling solutions being very well equipped to take results directly into operation and planning or procurement processes.
Simulation can be used both as a development or problem-solving tool. Supply chains, network planners, transportation planners, inventory optimizers and factory scheduling can use simulation techniques that incorporate best practice statistical methods including constraint-based rules to be better prepared for decision making in order to generate the most appropriate business model.
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A key challenge in simulation is that there can be many variables to track or calculate. Many organizations use supply chain simulation techniques to better understand the financial impact of change – whilst other organizations will be interested in what customer service levels can be maintained when changes are made to their supply chain or distribution network (e.g. reduced inventory levels or fewer distribution centers). Supply chain simulation helps via a “if I tweak that input I get that output” approach and therefore can often be an iterative process.
The more complex the organization, consider those with large global supply chain, numerous distribution centers, large volumes of product/parts, the more challenging the simulation. However optimization tools often excel in these situations as capability for data processing can help organizations better understand the risks that their organization face in establishing their supply chain. In such large supply chains, simulation allows modeling to be completed more quickly and accurately achieving faster buy in than standard empirical testing.
Simulations are usually time based and when running a simulation supply chain analysts can observe the behavior, and performance of the supply chain system over a period of time or as the inputs or variables change. In particular they can play close attention to cost and service levels.
Given this these tools are often applied at the outset of projects that have particularly long life cycles where an understanding of long term cost/service can be a key decision factor in whether such projects go ahead in the first place.
Used in this way, simulation and optimization can be a great tool in identifying bottlenecks that may affect performance. However, whilst this can be extremely useful , analysts must still maintain sufficient understanding of their network and those factors that can influence the performance of the system in order that it can be re-configured to deliver the best approach.