Topic: From Local to Global Optimization and Mutual Benefit in Outsourcing

Outlines:

From local to global optimization and mutual benefit in outsourcing

P. C. Yang

Industrial Engineering & Management Department,

St. John’s University

Taipei 25135

Global is better than local optimizations

Mutual benefit leads to long term relationship

•Mutual benefit will sustain long    term business relationship.

2. Non-mutual benefit will be resisted by either buyer or vendor.

Abstract (1/2)

•Two basic essences to build a collaborative SC:

–increase the joint total profit,

–ensure that all players benefit.

•The purposes are two folds;

–show how total profit increase is accrued from the collaboration,

–show how it is being shared between vendor and buyer.

Abstract (2/2)

•A collaborative pricing and replenishment policy is derived using a constrained nonlinear programming.

•Total profit increases from 3.6% to 54.3% approximately,

•The pricing strategy benefits both players.

Keywords

•Collaboration,

•pricing policy,

•Replenishment policy,

•Price sensitive demand,

•Shortage.

Essentials of a collaborative SC

•Knowledge, information, resources, processes, risks and profits sharing,

•Instant data access to the whole system,

•Just in time replenishment,

•Supply and demand synchronizing,

•Continuous replenishment by incorporating joint forecasting,

•Global optimization instead of local optimization,

•Lasting and mutual benefit partnerships, and others.

Assumptions

•Vendor’s replenishment rate is finite

•Demand rate is linearly decreasing with the unit price.

•Players have complete knowledge of each other’s information.

•Shortage is allowed and fully backlogged.

•Single item is considered.

•Single vendor and a single buyer.

Three cases

•Sequential optimization, which does not consider the buyer-vendor collaboration and price reduction.

•Global optimization, which considers the buyer-vendor collaboration without price reduction.

•Global optimization, which considers the buyer-vendor collaboration and price reduction simultaneously.

Figure 1. Sequential optimization (case 1)

Decision variables

•Q_i    Buyer’s lot size for case i, i= 1, 2, 3

•P_mi  Unit price for case i

•P_b3  Buyer’s purchase unit cost for case 3

•n_i     Number of deliveries to buyer for case i

•B_i     Maximum shortage level for case i

Buyer’s parameters

•C_b  Ordering cost, $ per order

•P_bi  Buyer’s purchase unit cost for case 1 and 2

•F_b   Inventory carrying cost per year and per dollar

•S_b   Shortage cost per year per unit

•TC_bi