In the volume pricing methodology, a seller initially establishes a price structure for a product which provides for lower prices as larger quantities of the product are purchased. The price structure is electronically made available to potential buyers of the product. For example, the price structure may be displayed on an Internet site. The sellers further establish an “open session” period during which orders for the product are accepted. During the open session period, multiple buyers are able to place orders for the product up to a maximum available quantity. At the end of the open session, the total quantity of products collectively ordered by all of the buyers is calculated. Based on the total quantity ordered, the final price to all buyers is the lowest price provided from the price structure regardless of whether the lowest price had been reached at the time a particular buyer placed their order during the open session.

E-commerce volume pricing

The demand aggregation system includes buyer sponsored DealRooms, seller sponsored DealRooms, and multiple buyer and multiple seller sponsored DealRooms. Moreover, the demand aggregation system includes a customer relations management (CRM) package. In the CRM package, information on buyer and prospective buyers are loaded into a database that can include information such as: individual name; company name and address; email address; phone number; cell number; products purchased; volumes; time of purchase. Further the CRM package can be integrated with the demand aggregation system as well as a customer resource planning system (e.g., MRP, ERP).

Demand aggregation system

The integration in production planning, scheduling, maintenance and quality has gained much attention from researchers in recent years. These areas are usually treated independently, which yielded independent models for each function. It is widely believed that these separate models will provide suboptimal solutions, due to the fact that they are interrelated. Proper understanding of this dependency will open a new avenue for more integrated models, and result in significant savings in operational cost and improved efficiency for any production system. This paper reviews the literature on models addressing different aspects of integration in these areas. This integration is treated in two different ways in the literature. The first way is to present models where a model is considered for one function taking into account the other. These models are referred to as interrelated models. The second way is to simultaneously model two or more elements of the production system. These models are referred to as integrated models. The objective of this paper is to review research in both interrelated and integrated modelling of production planning, scheduling, maintenance and quality to identify possible future directions.

Integrated models in production planning and scheduling, maintenance and quality: a review

Mechanisms are provided to facilitate buying and selling products utilizing social pricing. Sellers initiate sale of a product with volume discounts for a limited time. In other words, the more people that buy a product, the lower the price for the group. Buyers have a real incentive to become prosumers that not only purchase a product but also promote purchase by others. A social pricing system brokers interaction between sellers and buyer groups, and provisions mechanisms to aid promotion of products by consumers utilizing social networks and other online activities.

Promoting offers through social network influencers

Systems and/or methods are disclosed for interacting with a multiple criteria buying and/or selling system to conduct business electronically A dynamic discount card is disclosed according to an aspect of the present invention The dynamic discount card comprises an information access component for receiving information and a presentation component for displaying information to a user via the dynamic discount card

Dynamic discount card tied to price curves and group discounts

This paper considers the problem of selecting the optimal production run for a process with random linear drifts. A cost function per unit of finished product is derived. A search algorithm as well as a graphical method are suggested to find the optimal production run. Numerical examples are provided to illustrate how to determine the optimal production run. Finally, a sensitivity analysis of the model is performed.

M.A. Rahim

Papers

Optimal production run for a process with random linear drift