Prime Pantry Optimization : a cost analysis and deep-dive in process improvement

Amazon's Prime Pantry is a specialty business that focuses on selling household goods such as toilet paper or bottled water. The business is a part of the consumables portfolio that has consistently met or exceeded forecasts. However as the business grows and lower Average Sales Price (ASP) items are introduced, optimizing the current fulfillment solution is critical to ensure profitability through growth. In 2016, there was a $662B market opportunity to fulfill consumables between the $0-5 ASP range. As a relatively high velocity consumables business, the Pantry platform is well situated to help address this opportunity as well as address fulfillment of items below a $5 ASP. This thesis analyzes two primary initiatives: 1) The first is to determine the ideal characteristics (inbound profile, cubic velocity, demand) of Stock Keeping Units (SKUs) that will determine how new SKUs can be added to Amazon Pantry while maintaining a positive profit through an in depth analysis of inventory management strategies, 2) The second is to design a low cost fulfillment solution for this chosen product profile while maximizing throughput and capacity through process flow changes and automation where necessary. Part I analysis determined general tenets for inventory management correlating item characteristics with cost. Key recommendations included palletizing larger items and storing smaller items in smaller quantities to decrease obsolescence costs. The study showed it was necessary to make strategic decisions at a SKU level. Therefore, a dynamic model was created to change the inputs based on characteristics for new or existing SKUs to output the operational cost implications on the network. Part II analysis showed that splitting process paths significantly improved throughput and capacity for Pantry operations. The thesis shows that with a large scale operation, consolidation of process paths is not necessarily cost efficient or operationally beneficial. This is shown through an in depth analysis of a new picking process, pick to rebin. An additional design analysis of an automated sortation system investigates further operational improvements. Each of the initiatives outlined above will provide additional savings to the Pantry business. Though the Low ASP analysis generated savings of $2.27M, only a fraction of the SKUs were analyzed. If case replenishment is automated or more pallets are used in the field, these savings will increase since the Variable Cost Per Unit (VCPU) of handling these items will decrease. The pick to rebin initiative can save the company an additional $432,000 annually with a VCPU improvement of $0.018. Lastly, the OPEX machine would save $312,000 annually since the improvement over manual pick to rebin is a VCPU improvement of $0.013. In total, these savings amount to $3.02M annually. Though the cost improvements are fairly significant, these improvements could prevent the business from needing to open new sites at the current frequency required and will improve the business's current operations immensely. Additionally, it will enable the business to introduce even lower ASP items profitably while improving the customer experience.

Department dc:contributor.department

Leaders for Global Operations Program at MIT

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Dokras, Nupur Satchit

Advisor dc:contributor.advisor

• Donald Rosenfield and Julie Shah.

• Mechanical Engineering.
• Sloan School of Management.
• Leaders for Global Operations Program.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/111873

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/111873