Chapter 10
Operational Supply Chain Planning
JEREMY F. SHAPIRO
報告人:莊美雅
Content
Introduction
Taxonomies of Operational Planning
Problems
Modeling Systems for Operational
Planning
Vehicle Routing System for an E-
Commerce Company
Production Planning System for a
Semiconductor Company
Simulation Models and Systems
Final Thoughts
Introduction
Operational planning refers to short-term decision problems facing supply chain managers who execute the company’s business.
An important subclass of operational planning problems is scheduling problems，which are problems heavily focused on the timing and sequencing of decision.
Models and modeling systems for operational planning require far more customization than tactical and strategic planning
A class of Advanced Planning Scheduling(APS) systems
Taxonomies of Operational Planning Problems
The characteristics of operational planning problems ()
Planning over a short-term horizon during which demand for finished products，work in process，receipt of raw materials and parts from vendors are reasonably well known.
Focus decision making among activities located within a single facility or a geographical region that is small relative to the area served by the company’s entire supply chain.
Coordination of a number of time-dependent decisions associated with managing these activities.
Taxonomies of Operational Planning Problems
Production planning and scheduling
1. Discrete parts manufacturing
2. Process manufacturing
3. Job-shop scheduling
Taxonomies of Operational Planning Problems
Production planning and scheduling
1. Discrete parts manufacturing
individual machines produce a number of similar items
the machines are intermittently set up to make lots of each item
Planning horizons vary from a few days to several weeks.
Demands are assumed to be known
MTS(計劃生產) or ATS(計劃組裝)
Taxonomies of Operational Planning Problems
Production planning and scheduling
1. Discrete parts manufacturing
By the following types of decisions
Minimization of avoidable short-term costs，especially machine setup costs，overtime，and inventory holding costs.
Capacity planning for the production of multiple products.
Inventory planning for work in process and finished products.
Integration of multiple stages of production with varying production lead times.
Taxonomies of Operational Planning Problems
Production planning and scheduling
1. Discrete parts manufacturing
A discrete parts production planning model
INV
MPS
BOM
MRP
Shop
Orders
Purchase
Orders
Modeling
System
Figure
Material requirements
planning and modeling
system to compute
master production
schedule
aggregate
disaggregate
Taxonomies of Operational Planning Problems
Production planning and scheduling
2. Process manufacturing
Capital intensive(資本密集) companies
Problems
Machines and plants must be operated continuously and near capacity to realize a profit on investment.
Products flow continuously through several stages of processing.
Taxonomies of Operational Planning Problems
Production planning and scheduling
2. Process manufacturing
Problems
For each processing stage，product transformation activities can be smoothly adjusted as long as the equipment associated with the stage remains in the same major configuration(構形).
Intermittent changeovers in the major configuration of equipment，which are both time consuming and costly，are required to manufacture different classes of products.
Figure ()
Taxonomies of Operational Planning Problems
Production planning and scheduling
2. Process manufacturing
For each period of the planning horizon, the scheduling model addresses the following decisions and constraints ()
…..
Describing unit configurations and changeovers using 0-1 variables
The main effect of a change in configuration is the lost processing time.
… requires additional 0-1 variables that control before and after combinations of the changeovers.
Taxonomies of Operational Planning Problems
Production planning and scheduling
3. Job-shop scheduling
A number of jobs, each having a variety of tasks(作業), which may processed on different machines in different sequences, are undertaken.
Some tasks can be undertaken only if other tasks have been completed.
MTO 接單後生產, 目標:用最短時間完成jobs
The constraints fall into two categories
Precedence relationship among tasks associated with each job
…sequencing tasks from different jobs on each machine
Taxonomies of Operational Planning Problems
Production planning and scheduling
Hybrid Manufacturing Environments 混合式
Figure ()
Digestors process
Paper Machines manufacturing
Trim Operations job-shop
Finishing Operations scheduling
Taxonomies of Operational Planning Problems
Vehicle routing and scheduling
Routing：physical paths and sequence of stops visited by the vehicles
Scheduling：the timing of vehicle loading at DC, plants, or ports and the timing of deliveries at customer locations
Time windows 時窗
Problems
Local Delivery
Long-Haul
Integrated Manufacturing and Distribution Scheduling Decisions
Taxonomies of Operational Planning Problems
Human resources scheduling
. Airline crew scheduling
Optimization models are responsible for yearly cost savings totaling millions of dollars per year for individual airlines.
. Human resources scheduling is relevant to supply chain management
Drivers’ schedules with the route schedules
Modeling Systems for Operational Planning
System integration
Steps to follow in using a system
Real-time operational planning
Other use of a modeling system
Training, learning, and system evolution
The demands of operational modeling systems are
repetitively employed in a time-critical manner to support
decisions that often will be immediately carried out.
ATP(available-to-promise)
Modeling Systems for Operational Planning
System integration
To be effective, an operational modeling system must be fully integrated with other analytical and transactional systems maintained by the company, venders and customers.
Modeling Systems for Operational Planning
System integration
()
Operating
Modeling
System
Graphical
System
Figure
Operating modeling
system integrated with
other systems
Order Entry
System
Fulfillment
System
scheduler
Distributed
Orders
Orders
Inventory
Management
System
Semipermanent
Data
Inventory
Input data
Cost and
Capacity Data
Released Plan
Physical
counts
Replenishment
Other
Order
Details
Modeling Systems for Operational Planning
Steps to follow in using a system
input data
and correct input data
and adjust optimization model
and control parameters
and optimize the model
and manually adjust model
results
plan (all or part)
Modeling Systems for Operational Planning
Real-time operational planning
The system might be exercised one or a few times a day.
reoptimize the plan each time a new order is being considered
This need for real-time response suggests that the system run the optimization model and methods on a continuous basis.
As time passes, some subplans for scheduling some orders would be fixed and effectively taken out of the model.
Modeling Systems for Operational Planning
Other uses of a modeling systems
An operational modeling system may be effectively applied to study potential or proposed changes in the company’s operating environment.
Although such applications might appear sensible and worthwhile they can actually run counter to the culture of the organization.
Modeling Systems for Operational Planning
Training, learning, and system evolution
the scheduler must receive considerable training
Long-term learning aspect for both the scheduler and the company
The company should attempt to create processes for evolving the functionality of an operational modeling system.
Vehicle Routing System for an E-Commerce Company
Figure ()
Company background ()
Orders which are from its Web site, phone, fax are placed for next-day delivery between 9 . and 10 within delivery time windows of 2 hours.
An order is delivered free of charge as long as the total cost of items purchased exceeds a minimum.
Each city has a single depot. Each depot stocks around 8,000 SKUs.
Delivery 1000 orders in a typical day
The depots can capable of servicing 2000 orders per day.
Marketing and sales challenges
Vehicle Routing System for an E-Commerce Company
Routing system description and use
Every night ISRS is used to route orders for the following day.
ISRS has three major components
Interface and database management programs
GIS tool kit
Routing engine
Vehicle Routing System for an E-Commerce Company
Routing system description and use
The sequential steps in the routing process
retrieval
geocoding
time computations
solution optimization
editing of routing solution
solution release
assignment
Production Planning System for a Semiconductor Company
IMPRess
Manufacturing and marketing background
Semiconductors are manufactured in two major stages
Front-end stage
Back-end stage
Manufacturing process flow
Semiconductor manufacturing is capital intensive with plants with 24/7
binning, bin split ()
Figure
Production Planning System for a Semiconductor Company
Manufacturing and marketing background
Marketing and sales priorities
Three types of priority classes
Order-board classes
Inventory replenishment classes
Forecast classes
Build-to-level code 預先生產
Production Planning System for a Semiconductor Company
Planning and modeling approaches
The Planning cycle and the modeling analysis consists of three phases-Figure ()
planning
loading planning
for quotation planning
Heuristic decomposition scheme
Figure ()
Module 1~5
Production Planning System for a Semiconductor Company
Implementation
Figure ()
Installed a commercial demand forecasting software package
Designed and implemented a bill-of-materials(BOM) database
Upgraded the order entry system
Data management programs
Production Planning System for a Semiconductor Company
Results
The system went on-line in May 1992.
It was used each weekend to generate a production plan the revised availabilities in the quotations system.
Much higher portion of a given plan to be based on customer orders rather than on forecast.
The stability and the predictability of the plans were enhanced, and the number of planning personnel could be reduced.
Delinquent order line items fell from 5000 to less than 100.
Firm’s reputation grew and profit raised
Simulation Models and Systems
Simulation models are defined as descriptive models that permit managers or analysts to study the dynamic behavior of supply chain systems.
Deterministic simulation models
Stochastic simulation models(Monte Carlo simulation)
Simulation Models and Systems
Deterministic simulation
Describe a system’s dynamic behavior, assuming there are no random effects.
Involves state variable, such as ending inventory or a machine’s rate of production, describing a system at given points in time, and equations or other relationships describing how the state variables change over time as a function of decision and external events.
Simulation Models and Systems
Monte Carlo simulation
Describe a system’s dynamic behavior, assuming there are random effects.
It refers to the implementation and application of computer programs that mimic the behavior of supply chain and other business systems in response to random variables in key parameters affecting them.
Simulation methods are intuitive to understand, more intuitive than the optimization models and methodologies.
Simulation Models and Systems
Monte Carlo simulation
Inventory Example
Table Product demand distribution ()
Table Inventory simulation ()
Major requirements of Monte Carlo simulation ()
analysis of properties of the system
event simulation
number generation
analysis of results
Simulation Models and Systems
Monte Carlo simulation
Advantages
It provides insights into the system’s performance that cannot be obtained by deterministic optimization models.
It is particular useful in evaluating interdependencies among random effects that may cause a serious degradation in performance.
. Queuing system
Simulation Models and Systems
Monte Carlo simulation
Disadvantages
Considerable time and effort is required to construct and validate a Monte Carlo simulation of a complex system.
It provides no insights into how a system can be optimized, from the perspective of either operating the system and designing it.
It cannot be used to support operational decision making.
Simulation Models and Systems
Simulation software
Deterministic simulation
System dynamics is a well-elaborated methodology for deterministic simulation.
Software packages use causal feedback loops, flow diagrams, and other types of diagrams to describe the connections between factors affecting the performance of a dynamic system.
Monte Carlo simulation
Packages offer graphical model construction, templates, animation, curve fitting, and run-time debugging.
Simulation Models and Systems
Simulation vs. Optimization
Simulation 用來描述現況
Optimization 可以找出最佳解
Although the distinction between simulation and optimization may be clear from a formal, methodological viewpoint, it is less clear from a managerial or application viewpoint.
Final Thoughts
Many managers and system developers are confused about the capabilities appropriate for APS systems.
Supply Chain managers must distinguish between tactical planning and operations scheduling of supply chain decisions.
A second confusion is the extent to which operational decisions suggested by an analytical system should be based on historical rules or the results of optimization models.
Rule-based systems
Rigorous optimization models
