This study aims at developing a series supply chain network models for a high-tech product manufacturer who operates multiple plants at different regions. According to the specific issues emphasized in different parts, the planning frame of this dissertation includes strategic level and timeframe between tactic and operational levels. The research scope is shown as Figure 1.1.
(1) Raw material vendor
. . . . . .
(2) Manufacturing plant (3) Customers
(distributor/retailer) (4) End user (consumer)
. . . .
. . . .
Figure 1.1 The research scope
The research approach with respect to different parts is described as follows.
Following past literature in the field of supply chain network design problems, the first and third parts of this dissertation apply mixed integer programming (MIP) formulations and attempt to minimize the average total cost per unit product subject to constraints such as satisfying customer demand in various geographic regions, relationships between supply flows and demand flows within the physical configuration and the production limitation of different size plants. The average total cost per unit-product in the study is given by the sum of inbound, production and outbound cost and constructed, respectively by analytical approaches.
The second part devises a reliability evaluation method for the manufacturer in assessing how well the results of a supply chain network design perform under potential abnormal demand fluctuations. In the study, the capacity utilization is assumed to be the basic criterion for evaluating the reliability of the manufacturing plants under demand fluctuations. To lessen the impacts of the unreliable situations on the overall performance, this study investigates the advantage and disadvantage brought by the adjustment decisions under different fluctuant demand, by analytical approaches.
Finally, this study develops the mathematical programming models and proposes adjustment procedures for the supply chain network for determining the optimal adjustment decisions, as they cope with different abnormal demand fluctuations. The judgment on performing an adjustment or do nothing is also investigated by comparing between the results if no adjustments are made and if adjustment are made during the duration of an abnormal state.
In the forth part of this dissertation, this study integrates the logistics aspect in the supply chain and customer demand analysis into one model, and aims at analyzing the impacts of service delivery strategy on customer choices and customer demands for the
manufacturer’s product with time and spatial dependent demand, and to incorporate demand-supply interaction into network design. The logistics cost, including transportation cost and inventory cost, are formulated by analytical approaches to tackle the impacts of time-dependent demand from various customers, service frequencies with respect to different customers and different assignments of manufacturing plants to customers on manufacturer total costs. This study further deals with dynamic and time-sensitive customer demand, and investigate how service cycle durations affects customer demand for manufacturer products. This study applies a binary logit model to determine customer choice probabilities for manufacturer products. The dependent variables include product prices and delay in receiving products from the manufacturer, where these factors are influenced by the manufacturer’s delivery service strategy.
Furthermore, a nonlinear mixed integer programming model is formulated for determining the optimal number and duration of service cycles for different customers and the plants assignment decisions, by maximizing profit for the manufacturer subject to demand-supply equality.
In the fifth part of this dissertation, this study applies a binary logit model to determine consumer choice probabilities for both Internet and conventional shopping.
The model further captures variations in consumer characteristics by employing consumer income distribution and individual logit model to estimate and aggregate the expected choice probability of choosing Internet shopping for all consumers. Then, the average logistics cost functions for discriminating service strategy is formulated by an analytical approach. Because of various numbers of orders accumulating during different service cycles during the entire study period, the average logistics cost during the study period is estimated using the weighting average method based on service cycle number and duration. Combing the choice probability function with the average
logistics cost function, this study further devises a mathematical programming model for determining the optimal number and duration of service cycles during the entire study period by considering the relationship between consumer demand and logistics costs and assuming that Internet store operators are seeking to maximize profit. Due to the complexity in solving a nonlinear programming problem, some approximate methods are required and the greedy algorithm is applied in this study due to its simple implementation and speed. The initial values, including the number and duration of service cycles, are randomly generated. Then the greedy algorithm is applied to obtain the best results for service duration for a specific number of service cycles. To verify this optimal solution, this study tests a variety of initial values for the duration of a specific number of service cycles. After several trials, the optimal duration for a specific number of service cycles can then be determined.
In sum, this dissertation applies network design modeling techniques, non-linear mixed integer programming formulation, disaggregate choice and demand forecast models to develop a series models on analyzing high-tech firms’ decision, such as the plant capacity and production allocation among the manufacturing plants, dispatching decisions, reliability evaluation and adjustment and delivery service strategies, as they cope with production and shipping economies, demand characteristics, demand-supply interaction in an uncertain environment. The objectives functions, costs and constraints considered in different parts of this dissertation are listed in Table 1.1.
Moreover, the decision variables with respect to the mathematical programming models developed in different parts are shown in Table 1.2.
Table 1.1 The objectives functions, costs and constraints considered in different parts of this dissertation
Objective function
Cost functions Constraints
Part 1
An integrated plant capacity and production model with economies of scales
Minimize the total average cost per unit product for high-tech product manufacturers
(1) Fixed cost (2) Inbound cost: raw material purchase cost and transportation cost
(3) Production cost: capital cost and variable production cost
(4) Outbound cost:
transportation cost
(1) Customer demand (2) Relationships
between supply and demand flows (3) Demand flows
within the physical configuration (4) Production limitation
of different size plants
Part 2.1
Supply chain network adjustment model in response to demand expansion
Part 2.2
Supply chain network adjustment model in response to demand shrinkage
Minimize total adjustment cost over months with expansive demand
Minimize total adjustment cost over months with shrunk demand
(1) Allocation cost (2) Extra material purchase
cost
(3) Difference in production cost
(4) Penalty cost (5) Transportation cost
(1) Allocation cost
(2) Difference in production cost
(3) Transportation cost
(1) Relationships between adjusted and unadjusted
production amount (2) Outsourcing amount
limitations
(1) Relationships between adjusted and unadjusted production amount
Part 3
Incorporating dispatching decisions into supply chain network design with production and shipping economies
Minimize the total average cost per unit product for high-tech product manufacturers
(1) Fixed cost
(2) Inbound cost: purchase cost, shipping cost and inventory cost
(3) Production cost (4) Outbound cost: shipping
cost and inventory cost
(1) Customer demand (2) Relationships
between supply and demand flows (3) Demand flows
within the physical configuration (4) Production limitation
Table 1.1 (continued)
Objective function
Cost functions Constraints
Part 4
Optimal delivery service strategy for high-tech product manufacturers with time-dependent demand
Maximize profit of the manufacturer throughout the entire study period
(1) Production cost (2) Logistics cost:
transportation cost and inventory cost
(1) Production limitation (2) Relationships
between the study period and service cycles
Part 5
Optimal delivery service strategy for Internet shopping with
time-dependent demand
Maximize profit of the Internet store operator
throughout the entire study period
(1) Transportation cost (2) Inventory cost
Relationships between the study period and service cycles
Table 1.2 The decision variables with respect to the mathematical programming models developed in different parts
Decision variables
Part 1
An integrated plant capacity and production model with economies of scales
(1) The capacity and production amount of the manufacturing plants
(2) The raw material amount from the vendors to the manufacturing plants
(3) Which manufacturing plants should produce how much production to serve customers in different regions
(4) The optimal capacity utilization of the manufacturing plants and the optimal number of active vendors
Part 2.1
Supply chain network adjustment model in response to demand expansion
Part 2.2
Supply chain network adjustment model in response to demand shrinkage
(1) Production reallocation among the manufacturing plants
(2) The optimal outsourcing firms as well as the outsourcing amount
(3) Whether or not performing an adjustment
(1) Production reallocation among the manufacturing plants
(2) Whether or not performing an adjustment Part 3
Incorporating dispatching decisions into supply chain network design with production and shipping economies
(1) The capacity and monthly production amount of the manufacturing plants
(2) The monthly procurement amount of key-component from suppliers to plants (3) Which manufacturing plants should produce
how much production to serve customers in different regions
(4) The average shipping frequency and shipment size between different combinations of
suppliers and plants and between those of plants to customers
Table 1.2 (continued)
Decision variables
Part 4
Optimal delivery service strategy for high-tech product manufacturers with time-dependent demand
(1) The optimal capacity and production amount of the manufacturing plants
(2) The optimal delivery service cycles and durations for the customers in different regions (3) The assignments of plants to customers during
each service cycle Part 5
Optimal delivery service strategy for Internet shopping with time-dependent demand
(1) The optimal delivery service cycles during the study period
(2) The time when the operator orders batch of each service cycle
(3) The number of items ordered in each batch ordering