Other information and websites
One research focus is on consumer direct delivery. Consumer direct (CD) refers to when consumers order goods online, and the vendor must efficiently coordinate the delivery of the product from a plant, store, or warehouse to the consumer's front door. It crosses "the last mile" to the customer and provides many opportunities for research.
CD is an emerging business model with the most famous example being grocery delivery services such as Peapod. It is expected that in the near future we will see a proliferation of many more home delivery services and options. Forrester Research expects online purchases by U.S. consumers to grow to $184 billion by 2004, or seven percent of total retail spending that year. Many of the companies that have started to provide home delivery services have found, however, that direct delivery poses an enormous logistical challenge and are struggling to create direct delivery strategies that are profitable and can satisfy customer expectations. For example, the difficult challenge of matching grocery store prices while incurring high distribution costs led to the declaration of bankruptcy by Webvan.
The fulfillment process for most consumer direct businesses can be divided into three phases:
(1) order capture and promise
(2)order sourcing and assembly
(3) order delivery
Efficient order sourcing and assembly can be rather difficult with most grocery orders, for example, involving roughly 50 individual SKU's with multiple temperature requirements, but this research effort focuses primarily on the interactions between order promise (deciding on a delivery time) and order delivery (devising efficient delivery schedules). Better integration of order promise and order delivery decisions has the potential to substantially improve profitability, especially for those consumer direct businesses offering "attended" deliveries. Attended deliveries are those where the consumers must be present and may be necessary for security reasons (e.g. expensive computer equipment), because goods are perishable (e.g milk, flowers), or because goods are being picked up or exchanged with the consumer (e.g. dry cleaning, videos/DVDs), and are a vital feature of many consumer direct service models. To avoid delivery failures as much as possible, it is customary in attended home delivery services for the company and customer to mutually agree on a narrow delivery window, or time slot. Thus, our focus is on altering or filtering the orders captured in conjunction with creating better routing algorithms for such an application.
In 2002, we formally defined the home delivery problem that includes several of the key properties of consumer direct and forms a basis for new decision technologies in that area:
We construct a set of delivery routes for a specific day in the not too distant future. Requests from a known set of customers for a delivery on that particular day arrive in real-time and are considered up to a certain cut-off time, which precedes the actual execution of the planned delivery routes. We have to accept or reject each delivery request as it arrives. We assume that each request, if accepted, consumes d_i of the vehicle capacity and results in a revenue of r_i. There is a homogeneous set of m vehicles with capacity Q to serve the accepted orders. At each point in time t, we know for each customer i the probability p_i(t) that customer i will place a request for delivery between t and the cut-off time. The requests are equally likely to occur at any point in time, so we have no information on the order in which requests may be realized. The objective is to maximize the total profit resulting from executing the set of delivery routes, i.e., total revenues minus total costs, where we assume that total cost depends linearly on the total distance traveled. To increase the level of service, we guarantee that the actual delivery will take place during a 1-hour time slot on the day of delivery. The 1-hour delivery time slots are non-overlapping and cover the entire day, e.g., 8.00 - 9.00, 9.00- 10.00, ..., 19.00 - 20.00. We assume that for each delivery request we have a time slot profile that identifies which time slots will be acceptable to the associated customer. If we accept a request for delivery, we have to commit, at that time, to an acceptable time slot from the profile.
The reference for this defintion is:
Campbell, A. and M. Savelsbergh, 2005, "Decision Support for Consumer Direct Grocery Initiatives" Transportation Science, Volume 39, Number 3, Pages 313-327.
No datasets currently available.
I have written several articles involving consumer direct delivery. These include:
Campbell, A. and M. Savelsbergh, 2005, "Decision Support for Consumer Direct Grocery Initiatives", Transportation Science, Volume 39, Number 3, Pages 313-327.
Campbell, A. and M. Savelsbergh, 2005, ``Incentive Schemes for Attended Home Delivery Services", Transportation Science, Volume 40, Number 3, Pages 327-341.
Campbell, A. and Savelsbergh, M. (2003) "Decision Support for Consumer Direct Delivery", presented by Ann Campbell at Odysseus Workshop on Freight Distribution in Mondello, Italy, May, 2003 (pdf).
Campbell, A. and Savelsbergh, M. (2003) "Dynamic Routing for Home Delivery Problems", presented by Martin Savelsbergh at IMA Workshop (ppt).
The following are some of the papers on this or related problems:
Bent, R.W and Van Hentenryck, P. (2002) "Scenario-Based Planning for Partially Dynamic Vehicle Routing with Stochastic Customers", Department of Computer Science, Brown University, CS-02-08.
Bertsimas, D. (1992) "A Vehicle Routing Problem with Stochastic Demand", Operations Research,Volume 40.
Bramel, J., Simchi-Levi, D. (1996) "Probabilistic analyses and practical algorithms for the vehicle routing problem with time windows", Operations Research, May/Jun 1996, Volume 44 No. 3; pp. 501-510.
Gendreau, M., Laporte, G., and Seguin, R. (1995) "An Exact Algorithm for the Vehicle Routing Problem with Stochastic Demands and Customers", Transportation Science, Volume 29.
Guglielmo, C. (2000), "Can Webvan deliver the goods?", Inter@ctive Week, February 7, 2000.
Lin, I. and Mahmassani, H. (2002) "Can Online Grocers Deliver?: Some Logistics Considerations", Transportation Research Record, Volume 1817, pp. 17-24.
Punakivi, M., Holmström, J. (2000) "Extending e-commerce to the grocery business - The need for reengineering fleet management in distribution", Logistik Management, Vol. 2, No 1.
Punakivi, M., Saranen, J. (2001) " Identifying the success factors in e-grocery home delivery", International Journal of Retail and Distribution Management, Vol.29, No.4.
Punakivi, M. (2000) "The Value Added of Route Optimization in Home Delivery", working paper.
Punakivi, M., Yrjola, H., and Holmstrom, J. (2001)"Solving the Last Mile Issue: Reception box or Deliverybox?", International Journal of Physical Distribution and Logistics Management, Vol. 31, No. 6.
Saranen, J. and Smaros, J. (2001) "An Analytical Model for Home Delivery in the New Economy", working paper.
Småros, J., Holmström, J. and Kämäräinen, V. (2000) "New service opportunities in the e-grocery business", International Journal of Logistics Management", Vol. 11, No. 1
Smaros, J., Holmström,J. (2000), "Reaching the consumer through e-grocery VMI", International Journal of Retail & Distribution Management", Vol. 28, No 2.
Yrjola, H. (2001)"Physical Distribution Considerations for Electronic Grocery Shopping", International Journal of Physical Distribution and Logistics Management, Volume 31, No. 6.
Other Information and Related Websites
Consumer Direct Information
AMR Research - "Consumer Direct Initiatives Make a Comeback"(pdf)
Consumer Direct Forum FAQ (pdf)
www.descartes.com - a software vendor specializing in direct delivery
E-grocer sites
www.peapod.com - check out their delivery options
www.freshdirect.com - a growing e-grocer in New York
www.publixdirect.com - a home delivery branch of Publix grocery stores which are popular in the southeast
Contact me
Have you been working with consumer direct? If so, and you have papers, data or websites to post, please email me (ann-campbell@uiowa.edu).