Adaptive Logistics and Inventory Control – LH07-KTP

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Adaptive Logistics and Inventory Control – LH07-KTP

This project supports a dynamic, statistical, and data-driven approach in developing efficient systems to support economic growth of a process taking in account key


Collaborative: Orion Security LSP/KTP Enterprises

Research Team:

Aurelie Thiele, Charalambos A. Marangos, John R. Spletzer

Universities Involved:

Lehigh University

Start Date:


End Date:



KTP is adopting new technology in the manufactured housing industry. This research was applied implementing a dynamic data-driven approach to inventory management under cyclical demand to test the new technology by identifying key parameters as input to the algorithm development process.
KTP is a start-up company bringing innovative design to the manufactured housing industry. The long-term goal of the company is to develop a superior manufactured building technology that may be replicated in multiple regions around the world to solve the problems of affordable housing and speedy construction with structural characteristics required to withstand severe wind, earthquake, temperature and insect problems. Achievement of this goal required a close attention to the raw material supply chain and inventory management.

The project discovered the need to include extensive detailed variables with teams assigned to gather data in the following areas:

* Steel tube gauge and dimensional configuration strength impact
* Supplier availability for tube forming by dimension and minimum order quantity
* Make vs. buy decision by volume
* Shipping cost absorption methods
* EPS supply and dimensional tolerance options
* Misc. materials parameters

These parameter values are required input to the algorithm development process.
In conclusion, this project

* Realized that a macro design change could have greater supply chain efficiency impact than a sub-optimized micro approach.
* Discovered that use of less optimal, over-designed material with greater availability may reduce total cost and cycle time.
* Determined that there are critical mass volume step functions that must be met before the data-driven inventory control algorithms are meaningful.
* Provided new “pre-implementation” approaches to supply chain and inventory management design for start-up companies that may result in significant savings due to sub-optimization avoidance.