Chapter 5 Conceptual Framework in Supply Chain Design
5.3 Critical Issues in Conceptual Model Structure
The supply chain structure of NB industry consists of four echelons: (1) GC- and KC-suppliers, (2) assembly plants, (3) configuration hubs, and (4) customer zones.
Each SC echelon has a set of control parameters that affects the performance of other components. Some critical issues concerning developing conceptual model are discussed as follows:
A. Model Structure in Strategic-level Planning
Notebook-comuter industry is characterized by multi-product, multi-echelon, and component procurement/bare-bones assembly/full-set configuration/full-set distribution system. Supply chain design in strategic-level planning can be considered as an integrated and flexible facility network configuration. It optimizes bare-bone and full-set flows throughout the supply chain, gives the optimal number and locations for assembly plants, and regional configuration hubs, and provides the best assignment of configuration hubs to customer zones. A multi-objective function, conceptual structure is shown as Figure 5.3, can be formulated to minimize cost, while ensuring a sufficient amount of volume flexibility.
The first objective function (Z) minimizes the total fixed and variable costs. The second objective function (W) represents the volume flexibility, which can be calculated by the sum of the following flexibility performance measures:
(1) Assembly plant volume flexibility, which is measured as the differences between plant capacity and plant capacity utilization, and thus represents the available plant capacity.
(2) Configuration hub volume flexibility, which is calculated as the differences between the available throughput and demand requirements, and thus represents the available configuration capacity.
Figure 5.3 Conceptual Structure in Strategic-level Planning Model
B. Model Structure in Operational-level Planning
Given the output (decision variables) of the strategic-level sub-model, customer demand requirements, minimum required service and flexibility levels, cost and lead-time data, and bill of material data, variable costs can be estimated under uncertainty. Also, various operational variables can be determined by optimizing inventory variables such as lot sizes, reorder points, and safety stock. Four sub-models are considered in operational level: (1) GC-module control, (2) bare-bone assembly control, (3) bare-bone stockpile control, and (4) full-set configuration control. The GC-module control and full-set configuration control sub-models are solved using analytical techniques, while the bare-bone assembly control and bare-bone stockpile control sub-models are simultaneously optimized using non-linear programming (A multi-objective function should be developed to incorporate all cost, customer service level (fill rate), and flexibility (delivery)
Objective 1
Input Data
¾Transportation cost
¾Component-module supply cost
¾Fixed cost for logistics facilities
¾Production cost at assembly plant
¾Throughput cost at configuration hub
¾BOM (bill of material) data
¾Full-set demand at customer zone
¾Production capacity at assembly plant
¾Throughout capacity at configuration hub
¾Average demand at customer zone
Outputs
¾Supply chain total cost
¾Supply chain total volume flexibility
¾Quantity of component-module shipped from supplier to assembly plant
¾Quantity of bare-bone assembled at assembly plant
¾Quantity of bare-bone shipped from assembly plant to configuration hub
¾Binary variable : if the assembly plant open or not
¾Binary variable : if the configuration hub open or not
¾Binary variable : if configuration hub serves customer zone or not Objective 1
Input Data
¾Transportation cost
¾Component-module supply cost
¾Fixed cost for logistics facilities
¾Production cost at assembly plant
¾Throughput cost at configuration hub
¾BOM (bill of material) data
¾Full-set demand at customer zone
¾Production capacity at assembly plant
¾Throughout capacity at configuration hub
¾Average demand at customer zone
Outputs
¾Supply chain total cost
¾Supply chain total volume flexibility
¾Quantity of component-module shipped from supplier to assembly plant
¾Quantity of bare-bone assembled at assembly plant
¾Quantity of bare-bone shipped from assembly plant to configuration hub
¾Binary variable : if the assembly plant open or not
¾Binary variable : if the configuration hub open or not
¾Binary variable : if configuration hub serves customer zone or not
tradeoffs). The interactive relationships between each control sub-model are presented in Figure 5.4.
Figure 5.4 Interactive Relationships between Each Control Sub-model
In component-module control sub-model, the optimal values of “optimal batch size” and “optimal fill rate” can be determined by the first derivative of the total cost function in this subsystem, and then relative parameters, such as “unit cost involved in module control”, “inventory holding level”, “reorder point”, “expected demand over a replenishment lead-time” and “average total replenishment lead-time” can be calculated via analytical process. “optimal fill rate” and “average total replenishment lead-time” are inputs in bare-bone assembly control sub-model.
In bare-bone assembly and stockpile control sub-models, the optimal values of
“optimal production batch size”, “optimal fill rate” and “expected replenishment lead-time” are determined by considering the cost, fill rate, and delivery flexibility tradeoffs. Then, the relative parameters “unit production cost” and “total production lead-time” can be calculated and “total production lead-time” is the input in bare-bone stockpile control sub-model. Similarly, parameters like “unit stockpile cost”, “reorder point”, “expected demand over a replenishment lead-time” and
“expected replenishment lead-time” can be calculated in bare-bone stockpile control
Component-module Control
Optimal batch size of component-module at assembly plant
Optimal fill rate for component-module at assembly plant
Analytical process
Determinants of component-module control at assembly plant
¾Inventory holding level
¾Standard deviation of replenishment lead-time demand
¾Expected demand over a replenishment lead-time
¾Average total replenishment lead-time Input Data
¾Order setup cost
¾Unit component-module holding cost
¾Unit backorder penalty cost for shortage
¾Expected lead-time of component-module from supplier to assembly plant
¾Expected delay time of component-module at supplier side
¾Component-module availability (fill rate) at supplier side
Bare-bone Assembly Control
⎟⎟
Optimal production batch size for bare-bone at assembly plant Determinants of bare-bone assembly
control at assembly plant
¾Unit production cost
¾Total production lead-time
¾Production processing time
¾Component-module delay time
Input Data
¾Production setup cost
¾Unit processing cost
¾Unit work-in-process holding cost
¾Production setup time
¾Waiting time at work station Optimal fill rate
for component-module at assembly plant
Average total replenishment lead-time
Bare-bone Stockpile Control
⎟⎟
Optimal production batch size for bare-bone at assembly plant
Optimal fill rate of bare-bone at assembly plant
Determinants of bare-bone stockpile control at assembly plant
¾Unit stockpile cost
¾Reorder point inventory level
¾Expected demand over production lead-time
¾Expected replenishment lead-time
¾Standard deviation of replenishment lead-time demand
Input Data
¾Unit holding cost
¾Unit transportation holding cost
¾Normal transportation lead-time
¾Expected transportation lead-time
¾Cost of initiating an expedited production order
¾Standard out-of-stock delivery time
Total production lead-time
Full-set Configuration Control
⎟⎟
Optimal final configuration batch size at hub
Optimal fill rate for full-set at configuration hub
Analytical process
Determinants of component-module control at assembly plant
¾Unit cost of throughput
¾Reorder point inventory level
¾Replenishment order-up-to level
¾Expected demand over a replenishment lead-time
¾ Standard deviation of replenishment lead-time demand
Input Data
¾Unit holding cost
¾Order setup cost
¾Unit backorder penalty cost for shortage
¾Unit final configuration cost
Expected
Optimal batch size of component-module at assembly plant
Optimal fill rate for component-module at assembly plant
Analytical process
Determinants of component-module control at assembly plant
¾Inventory holding level
¾Standard deviation of replenishment lead-time demand
¾Expected demand over a replenishment lead-time
¾Average total replenishment lead-time Input Data
¾Order setup cost
¾Unit component-module holding cost
¾Unit backorder penalty cost for shortage
¾Expected lead-time of component-module from supplier to assembly plant
¾Expected delay time of component-module at supplier side
¾Component-module availability (fill rate) at supplier side
Optimal batch size of component-module at assembly plant
Optimal fill rate for component-module at assembly plant
Analytical process
Determinants of component-module control at assembly plant
¾Inventory holding level
¾Standard deviation of replenishment lead-time demand
¾Expected demand over a replenishment lead-time
¾Average total replenishment lead-time Input Data
¾Order setup cost
¾Unit component-module holding cost
¾Unit backorder penalty cost for shortage
¾Expected lead-time of component-module from supplier to assembly plant
¾Expected delay time of component-module at supplier side
¾Component-module availability (fill rate) at supplier side
Bare-bone Assembly Control
⎟⎟
Optimal production batch size for bare-bone at assembly plant Determinants of bare-bone assembly
control at assembly plant
¾Unit production cost
¾Total production lead-time
¾Production processing time
¾Component-module delay time
Input Data
¾Production setup cost
¾Unit processing cost
¾Unit work-in-process holding cost
¾Production setup time
¾Waiting time at work station Bare-bone Assembly Control
⎟⎟
Optimal production batch size for bare-bone at assembly plant Determinants of bare-bone assembly
control at assembly plant
¾Unit production cost
¾Total production lead-time
¾Production processing time
¾Component-module delay time
Input Data
¾Production setup cost
¾Unit processing cost
¾Unit work-in-process holding cost
¾Production setup time
¾Waiting time at work station Optimal fill rate
for component-module at assembly plant
Average total replenishment lead-time
Bare-bone Stockpile Control
⎟⎟
Optimal production batch size for bare-bone at assembly plant
Optimal fill rate of bare-bone at assembly plant
Determinants of bare-bone stockpile control at assembly plant
¾Unit stockpile cost
¾Reorder point inventory level
¾Expected demand over production lead-time
¾Expected replenishment lead-time
¾Standard deviation of replenishment lead-time demand
Input Data
¾Unit holding cost
¾Unit transportation holding cost
¾Normal transportation lead-time
¾Expected transportation lead-time
¾Cost of initiating an expedited production order
¾Standard out-of-stock delivery time
Bare-bone Stockpile Control
⎟⎟
Optimal production batch size for bare-bone at assembly plant
Optimal fill rate of bare-bone at assembly plant
Determinants of bare-bone stockpile control at assembly plant
¾Unit stockpile cost
¾Reorder point inventory level
¾Expected demand over production lead-time
¾Expected replenishment lead-time
¾Standard deviation of replenishment lead-time demand
Input Data
¾Unit holding cost
¾Unit transportation holding cost
¾Normal transportation lead-time
¾Expected transportation lead-time
¾Cost of initiating an expedited production order
¾Standard out-of-stock delivery time
Total production lead-time
Full-set Configuration Control
⎟⎟
Optimal final configuration batch size at hub
Optimal fill rate for full-set at configuration hub
Analytical process
Determinants of component-module control at assembly plant
¾Unit cost of throughput
¾Reorder point inventory level
¾Replenishment order-up-to level
¾Expected demand over a replenishment lead-time
¾ Standard deviation of replenishment lead-time demand
Input Data
¾Unit holding cost
¾Order setup cost
¾Unit backorder penalty cost for shortage
¾Unit final configuration cost
Full-set Configuration Control
⎟⎟
Optimal final configuration batch size at hub
Optimal fill rate for full-set at configuration hub
Analytical process
Determinants of component-module control at assembly plant
¾Unit cost of throughput
¾Reorder point inventory level
¾Replenishment order-up-to level
¾Expected demand over a replenishment lead-time
¾ Standard deviation of replenishment lead-time demand
Input Data
¾Unit holding cost
¾Order setup cost
¾Unit backorder penalty cost for shortage
¾Unit final configuration cost
Expected replenishment
lead-time
sub-model. “Expected replenishment lead-time” is the input in full-set configuration control sub-model.
In full-set configuration control sub-model, similar to the analytical process in component-module control sub-model, the optimal values of “optimal final configuration batch size” and “optimal fill rate” can be determined and then relative parameters, such as “unit throughput cost”, “reorder point”, “replenishment order-up-to-level”, and “expected demand over a replenishment lead-time” are calculated. Then, we can summarize the actual unit variable production cost for bare-bone at assembly plant (the sum of “unit cost involved in module control”,
“unit production cost” and “unit throughput cost”), “unit cost of bare-bone stockpile at assembly plant” and “unit transportation cost for bare-bone shipped from assembly plant to configuration hub”, which will be used as inputs to the strategic-level planning model.