Benefits of optimizing the supply chain network and beyond

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I came across the following article , ‘10 Guidelines for Supply Chain Network Infrastructure Planning’, in IndustryWeek, which discusses a methodology to reduce supply chain costs through the optimization of the network infrastructure. The authors discuss 10 things to keep in mind when tackling infrastructure optimization, which they say account for 75%-80% of total supply chain costs. I would like to expand on a couple of these points, focusing on optimizing the supply chain through the use of collaboration, simulation, and scenario comparison utilizing modeling tools. The supply chain network is composed of many different components acting in concert to deliver the required goods and services at the right time. The better these interdependent relationships are understood, the better the supply chain can be optimized. This raises the question how best to optimize such a complex set of data points. In order to get an accurate simulation, you must be able to obtain accurate data about each node in the network, and then utilize software to model it. These nodes can include, but are not limited to:

  • manufacturing plants,
  • supplier plants,
  • sub contractor plants,
  • warehouses, and
  • transportation routes.

This requirement would suggest that any simulation model must be able to incorporate multiple disparate data sources in a relatively easy and timely manner. Important data to obtain would be:

  • lead times,
  • inventory levels,
  • in transit times,
  • associated costs,
  • quality levels, and
  • service levels.

There are many others that could also be included to give a more accurate picture of the network. Once this data has been obtained (which may require some effort, especially with off shore suppliers), the next question then becomes, what do we do with the data? This is where the requirement for multiple scenarios becomes critical. Because these networks can be extremely complex with many factors influencing outcomes, we must have the ability to compare many different scenarios in order to determine a path forward. One scenario may give us a lower overall cost, but poorer customer service. Another may result in cheaper raw material costs, but lower quality levels and increased transportation costs. All these outcomes must be weighted in order to determine the most optimal design, -this requires some way to be able to compare multiple scenarios in a clear and efficient manner. The next step would then be to examine each scenario, and determine if the best parts of each can be combined into one ‘super’ scenario. As an example, one scenario may have a supplier with lower cost, but higher transportation costs. What if sourcing is split between a low cost/high transportation supplier and a higher cost/low transportation supplier? By managing the sourcing between the 2, an optimal outcome may be achieved (happy medium). This can only be determined by developing a complex model with multi-sourcing. If you take this case and apply it across the network, the number and complexity of the inter-relationships can soon become mind boggling. This is why a multi scenario engine which can rapidly calculate the various outputs of the model from its inputs is so vital. In order to implement the optimal supply chain network, it must first be designed as a whole to deliver the optimal results. Once the supply chain network has been successfully modeled, a powerful tool is now available for what if analysis. The maximal use of this tool can be realized by incorporating continuous improvement into the business development cycle and optimizing the network on a regular basis. But in order to make this an effective tool for continuous improvement, a plan must be generated quickly and easily analyzed, as time and resource constraints limit the amount of time to turn around the results and push improvements to the network. This is why speed and performance matter when implementing complex modeling solutions. Because the only way to realize the benefits of an optimal network model is to implement it, collaboration among all the players in the network (inside and outside the enterprise), is vital. This leads us to another key criteria for a simulation system - the ability of many users to access and collaborate on the development of the network model. Once the simulation, model, and collaboration pieces are in place, it raises the question: Can this environment be used to model other complex systems as well as the supply chain? What about the ‘sales chain’, the complex relationship between you and your customers? What about the complex network of relationships that exist between an enterprise’s internal resources (employees) and the enterprise? Financial models? As can be seen, there are a myriad number of networks in the modern world that can be modeled for the purposes of increasing efficiency, and thereby reducing costs or improving service levels.

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