Manufacturing Flexibility

Smart businesses are on the hunt to continually evaluate and transform how they use their assets. All too often, transformation becomes necessary to maximize resources in response to changing circumstances. In the case of a client of Quadrillion Partners, that change came in the form of a plant closure that forced production volumes to different locations— and threatened increased delivery times to critical customers.

Objectives

To determine options for pooling product family volumes across different plants globally—balanced against constraints like costs and customer delivery cycle times

  • What products should be produced, and where?

  • What are the costs and benefits of sourcing products from different regions?

  • What bottlenecks or capacity issues can arise?

Processes

Quadrillion’s client has built a complex and productive global manufacturing ecosystem.This is all before taking into account those all-important customer delivery commitments. For the end client, the FlexSim model needed to imitate every facet of the system to be useful—so that realistic scenarios on volume pooling could be run.
This meant planning a single simulation model that would link orders, customer locations, plants, costs, and current capabilities. Everything from machine speeds to staffing needs to customer logistic routes to the nuances of imports and exports would be considered. Quadrillion gathered and prepared a staggering amount of operational data on the system.

 

Since customer delivery cycle time was a key metric in this project, the model was fed geo-location data developed by Quadrillion that linked every customer ship-to site with every plant globally by postal code. The geolocation data was then used to calculate average delivery times in days for each product being shipped to each customer globally. This data was a critical excel input for the FlexSim model so that as volumes were pooled, the Quadrillion team could examine the impact on customer delivery times at both the customer order level and in a histogram by product family. The model also showed the trade-off in how quickly products can be delivered to an account versus where that order is made.

Results

The simulation model examined a variety of scenarios including volume pooling within regions, options to consolidate plants, and the changes resulting from new labor laws with variable staffing over the course of days. All the critical metrics were present in the model output: order demand by customer and product, capacity in and out of the plant, change over times, cycle times, customer delivery times, etc.


But the model also showed when equipment maxed out in a simulation run (known in manufacturing as a ‘bottleneck’ or congestion or blockage)—something that can only be discovered in a simulation model that considers the variability and interdependencies of real life operations. Once identified in the model, bottlenecks could be resolved with improvements in equipment speed, rebalancing demand across different sites, or replacing pieces of equipment.
 
Several immediate outcomes of the model were a decision to increase the pooling of volumes across the Asia region, a decision to consolidate from six factories to five, and an initiative to expand lower cost imports into higher cost regions for certain product families.

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