Simplified Systematic Network Planning – step 3

STEP 3: ANALYZE SENSITIVITIES
In Step 3, the planner runs the model using optimization software; identifies any infeasibilities, and then troubleshoots.

Once free from infeasibilities, the planner runs and fine tunes the model, establishing a baseline that replicates current network performance.

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Model results for demand, costs and constraints are summarized on the Baseline Validation Worksheet. They are compared to the to the actual performance of the current network for the same time period. Notes and explanations address any changes made to the model and reasons for the variances between model results and actual performance. This exercise builds credibility. The smaller the variance, the more accurate the model and the greater the acceptance of model results

While appropriate optimization software must be used, the Simplified SNP procedure is not dependent on any specific algorithm or software, and there are many products that could be used. Most software conforms to the general structure of a user interface to view the data and results; a mid-layer wherein mathematical equations are generated; and an optimization engine which solves the problem.

Examples of simple modeling problems include:

1. Change in inventory policy decision.
2. Adding capacity to existing production for a specific product line.
3. Adding or closing branch in a region.
4. Transportation options: Less-Than-Truckload vs. Full truckload.
5. Impact of changing lot sizes for a product line.
6. New product introductions.
7. Make vs. Buy for one or few product lines.
8. Dealing with seasonality of demands (Overtime vs. Pre-building)

Examples of complex modeling problems include:

1. Greenfield analysis/new location analysis.
2. Strategic planning of capacity for all regions and all products.
3. Make vs. Buy for a business unit.
4. Annual budgeting based on sourcing.
5. Supply planning for all products across all regions.
6. Long range operations strategy

While modeling software generates lots of statistics, the key to successful projects is to present them at an appropriate level of detail, and to only present the most crucial results needed for the decisions at hand.

For MTT, as shown on the Baseline Validation Worksheet, the model results for demand and cost are almost identical to the actual results of the current network. And relevant constraints were respected. Demand and cost variances are less than one percent. Even so, explanations should be sought and presented. Any additional constraints that may have been discovered during fine tuning are noted as lessons learned for future modeling.

 

 

 

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