Other Completed Projects (Under this grant)

Logistics Problems in Warehousing and Distribution of Perishable Goods at Tropicana’s Northeast Distribution Center


Grant #: 992500 NCTIP #: 992513 NJDOT #:
Sponsor #: Task Order : 0


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Principal Investigator:

Athanassios K. Bladikas, Ph.D.
Department of Industrial and Management Engineering,
New Jersey Institute of Technology, (973) 596 - 3649
bladikas@njit.edu

Arijit Sengupta, Ph.D.
Department of Engineering Technology
New Jersey Institute of Technology, (973) 642-7073
sengupta@njit.edu

Background:

Of the four principal Distribution Centers (DC) in the U.S., the Jersey City, N.J. DC is responsible for the supply of Tropicana juices in all states in the Northeast U.S., and all Canadian provinces. Jersey City houses a unit load capacity Automated Storage and Retrieval System (ASRS) that is fully integrated into an Automated Warehouse System (AWS). The center handles chilled premium orange juices, and blended juices from concentrate as well as shelf stable juice products from either Florida or local co-packers. Products vary according to package size, and juice type and style, giving rise to approximately 200 Stock Keeping Units (SKU), each facing random demand from customers. Juices arrive already palletized and variously prepackaged, and are unloaded according to demand, and moved into the ASRS area.

Tropicana has sales offices located throughout North America and around the world, which take orders from customers, forecast demand, and help plan production at their facilities in Florida. The customer service department at the Jersey City facility handles customer service, decides sales promotion and handling juices that are close to expiration date, and coordinates with the sales department and customers on other issues.

There are three major problem areas related to the current practices in Tropicana. The first is the ordering policy of the individual retailers; second is the central ordering of juices that are shipped to the distribution center from Florida; and third is how to combine marketing strategies with inventory levels and other factors.

In summary, the problem that the Tropicana distribution center faces is a complex multi-echelon inventory problem, in which the product has limited lifetime. The demand is random, as well as a function of the ages of juices. The ordering of individual customers and the distribution center have to work cooperatively, taking into consideration that the finite capacity on each train that is shared by all types of items. Another feature that is different from what is developed in the multi-echelon inventory literature is that, when an item’s lifetime approaches the expiration date, it does not vanish, but it becomes less valuable (because it is sold at a lower price). Finally, there are priorities among customers because customers with their own trucks have to be loaded first. All these features make the problem analytically not tractable. As a matter of fact, none of these features mentioned above has been addressed in the multi-echelon setting. Our objective in this project is to develop heuristic methods to approach these problems.

Objective:

The objective of this research effort is to investigate and seek solutions for the logistics issues associated with Tropicana’s Distribution Center at Jersey City, N.J., where unit-trains bring inventory to be distributed by trucks to customers. With today’s emphasis on meeting customer requirements and improving service, getting the right product to the right customer at the right time has become a standard. Time is of the utmost importance for high volume, delivery-sensitive environments. Speed and accuracy of transactions, information flow, and distribution processes are essential for industries that deal with short shelf -life and product life cycles, particularly consumer packaged goods, food and beverage and high-tech markets (Dilger 1999).

Abstract:

Tropicana’s Jersey City, N.J. principal distribution center (DC) houses a unit load capacity Automated Storage and Retrieval System (ASRS) that is fully integrated into an Automated Warehouse System (AWS). The center handles chilled premium orange juices, and blended juices from concentrate as well as shelf stable juice products from multiple areas. The products vary according to package size, and juice type and style, giving rise to approximately 200 Stock Keeping Units (SKY), each facing random demand from customers.

The Jersey City Distribution Center (DC) of Tropicana is responsible for the supply of Tropicana juices in all states in the Northeast U.S., and all Canadian provinces. Premium orange juice from Florida represents approximately 65% of the shipments, and has an approximate shelf life of 65 days. The Jersey City DC receives five Tropicana Unit trains from the production facility in Florida weekly. Each train has approximately 45 refrigerated cars. Juices arrive already palletized and prepackaged in paperboard containers and plastic and glass bottles. Two types of unloading procedures are currently in practice: cross-docking and warehousing. Cross docking normally is used for customers receiving a single product types or transfers to a smaller distribution center in Whitestone, NY. Each train usually contains 8 to 10 railcars that can accommodate cross-dock delivery.

There are three major problem areas related to the current practices in Tropicana. First is the ordering policy of the individual retailers. At the moment, Tropicana manages the inventory orders for about 10% - 20% of the retailers. This process is called CRP or continuous replenishment program. The Tropicana customer service department administers the ordering of those individual customers. From the supply chain perspective, this is mutually beneficial for both the customers and the warehouse. The advantage of the warehouse is that it is able to centralize the demand information of individual stores in its replenishment decisions of juices shipped from Florida to Jersey City. The retailers benefit from in time delivery and less stock out cost. Individual stores contribute the other 80% - 90% of the orders, which are not under Tropicana’s control. This is subject to random variation and hence uncertainties of demand on the warehouse. One approach would be to create an incentive for the customers to entrust their ordering function to Tropicana. This is the so-called supplier-retailer coordination problem. A carefully designed coordinated system will benefit each and every player in the supply chain network. This may require the design of contracts or cost sharing agreements with the customers.

The second problem is the central ordering of juices that are shipped to the distribution center from Florida. Currently there are five trains of juices scheduled to arrive weekly from Florida. The company never ships partially filled trains from Florida. The Jersey City distribution center sometimes builds up inventory of certain classes of juices that are close to their expiration date, and the company has to get rid of them either at a very low price with sales promotion or donate them to charity. A carefully designed and sophisticated coordination of ordering policies will reduce the chances for these problems and result in savings. At the same time it will increase the fill rate because the additional capacity gained from more reasonable ordering can be used for ordering more juices of the type that cause trucks to wait in the yard.

A third problem is how to combine marketing strategies with inventory levels and other factors. Marketing strategies such as sales incentives can influence demand. Foreseeing an inventory build up problem, the company can use marketing (and mainly pricing) as a tool to either increase demand (when certain items build up) or reduce demand (when insufficient inventory is available).

In summary, the problem that the Tropicana distribution center faces is a complex multi-echelon inventory problem, in which the product has limited lifetime. The demand is random, as well as a function of the ages of juices. The ordering of individual customers and the distribution center have to work cooperatively, taking into consideration that the finite capacity on each train that is shared by all types of items. Another feature that is different from what is developed in the multi-echelon inventory literature is that, when an item’s lifetime approaches the expiration date, it does not vanish, but it becomes less valuable (because it is sold at a lower price). Finally, there are priorities among customers because customers with their own trucks have to be loaded first. All these features make the problem analytically not tractable. As a matter of fact, none of these features mentioned above has been addressed in the multi-echelon setting. Our objective in this project is to develop heuristic methods to approach these problems.

Task Description:

1. Identify the factors that are responsible for long truck waiting times. This issue has top priority because of the company’s recent initiative to reduce truck waiting times for 'live' carrier loads to two hours or less.

2. Analyze current order-picking policies and order picking operational impact. The order pick-up policy affects the performance of the ASRS. During order picking, the ASRS becomes engaged and regular receiving and shipping is halted. Furthermore, cross-dock order picking directly affects the truck waiting times.

3. Analyze shipping requirements versus available inventory. This involves Tropicana’s train planning, shipping load coordination, sales forecasting and inventory analysis. An example of the problem might be a case where the inventory level is at 50%, but the DC is unable to complete the current shipping requirements until the daily train arrives.
Project Budget: $189332 NCTIP: $104113 NJDOT: $0
Sponsor: $44900 Other: $40319

Starting Date: 6/1/2000  Completion Date: 5/31/2001

Milestones:

Months1 - 3 : Review and analysis of literature related to automated warehouse management systems, data collection and analysis of the present system, study of order pick
Months 2 - 8 : Data Collection
Months 3 - 6 : Data Analysis - Determination of truck waiting causes
Months 3 - 8 : Data Analysis - Interaction of inventory and transportation problems
Months 4 - 9 : Data Analysis - Interaction of customer service/demand and transportation problems
Months 6 - 10 : System modelin.
Months 10 - 12 : Preparation of final report.

Student/Minority Involvement :

2 graduate students (Dmitrijevic; Elefsiniotis)
2 undergraduate students from Engineering Technology; 5 undergraduate students from Industrial and Manufacturing Engineering.

Relationship To Other Projects :

Unknown

Technology Transfer Activities to Date:

None to date

Potential Benefits of Project:

Time, speed and accuracy of transactions, information flow and distribution processes are essential for industries that deal with short shelf -life and product life cycles, particularly consumer packaged goods, food and beverage and high-tech markets. This project will develop logistics capabilities for dealing with the complexities involved.

TRB Key Words:

Logistics, warehousing, distribution, freight transportation; truck waiting times.