Production Planning and Control

1
Production Planning and Control
Chapter 9 Advancement in Production Planning
and Control
Professor JIANG Zhibin Department of Industrial
Engineering Management Shanghai Jiao Tong
University
2
Chapter 9 Advancement in PPC

• Contents
• Optimized Production Planning
• Theory of Constraint Based Production Planning
• Advanced Planning and Scheduling
• Mass Customization and its Production Planning

3
Optimized Production Planning-Introduction (1)

• The most prevalent approach in the production
  planning is based on the concept of material
  requirement planning (MRP).
• The release time is obtained by shifting the
  expected output time back along the time scale
  by a period of the estimated average lead time
• The release quantity is derived by dividing the
  expected output by the estimated average product
  yield.

• However, MRP-based methods have three major
  drawbacks
• The lead time not only needs to be pre-specified
  but also is assumed to be static over the entire
  planning horizon
• The capacity is assumed to be infinite, which
  means the derived production planning may not be
  realized
• The production system is made nervous. Little
  adjustment in MPS changes the due date, requiring
  the recalculation of MRP.

4
Optimized Production Planning -Introduction (2)

• New methods need to be developed for production
  planning based on mathematical programming
• Time Dimension
• Space Dimension
• Corporate level planning production planning
• Shop floor level planning production lot planning

• Mathematical programming based optimized
  Production Planning Commonly used in production
  planning
• Linear Programming (LP)- the most widely used
  methods
• Dynamic Programming (DP)- for multi-periods
  planning
• Stochastic Programming (SP)-coping with the
  uncertainty.

5
Optimized Production Planning -L P (3)
Common LP model
x Decision variables f(x) Objective function
gi(x) constraints.

• Commonly used terms
• Objective function, constraints, right-hand side,
  feasible region, feasible solution, optimal
  solution.
• Features of LP
• Linearity the objective and all constraints can
  be expressed as a linear function of the
  decisions variables
• Continuity the decision variables should be
  continuous

6
Optimized Production Planning -L P (4)
Example Make the production planning of milk
product One barrel of milk can be made into 3kg
A1 by 12 hours or 4kg A2 by 8 hours. The profit
of A1 and A2 are 24/kg and 16/kg, respectively.
The supply of raw material, milk, is 50 barrels
per day. Capacity is 480 hours per day and the
production limit of A1 is 100kg at most.
50 barrels of milk/ day, 480 hours available/day,
and 100kg A1 at most
7
Optimized Production Planning -L P (5)
Raw material
Work hours
Constraints
Requirement constraints
8
Optimized Production Planning -L P (6)
Xi is continuous The barrels of milk is real
number

• Model Analysis
• Features of LP Linearity and Continuity

Proportion The contributions of xi to objective
function and constraints are separately
proportional to xi. Addition The contributions
of xi to objective function and constraints are
separately independent of xj.
The profit/ kg of A1,A2 is constant, and the
production quantity and time of A1,A2 from one
barrel of milk are constant.
The profit/ kg of A1,A2 is constant, and the
production quantity and time of A1,A2 from one
barrel of milk are constant.
9
Optimized Production Planning An Example in
Semiconductor Manufacturing

• The following are the assumption underlying this
  model
• The activity of the model are the production
  activity on each of wafer fab routes. Activity
  are measured in terms of the quantity of wafer
  released the quantity of output is measured in
  terms of good die.
• Each wafer type are assumed to provide a single
  type of die, thus wafer types and die types are
  synonymous. There can be alternative wafer fab
  routes for producing the same wafer type.

10
Optimized Production Planning An Example in
Semiconductor Manufacturing

• The following are the assumption underlying this
  model (Cont.)
• The overall planning horizon is divided into
  planning periods in which demands, capacities and
  productions rates are assumed to be held
  constant. The length of each planning period for
  each wafer fab facility may vary and is measured
  in terms of working days. The length of each
  period is measured in calendar days for the
  purpose of discounting cash flows in the
  objective functions.
• A production variable is defined as a quantity of
  a particular wafer type to be released following
  a particular route during a planning. An
  inventory variable is defined as the inventory
  level of a particular die type at the end of a
  planning period. A backorder variable represents
  the quantity of die demand that can not be
  satisfied on time at the end of a planning
  period.

11
Optimized Production Planning An Example in
Semiconductor Manufacturing

• The following are the assumption underlying this
  model (Cont.)
• The demands are expressed in terms of time-phased
  die output requirements and are assumed to be net
  of initial die inventory and net of equivalent
  die output of the initial work-in-process (WIP).
  These demands are divided into prioritized
  classes that are loaded onto front end facilities
  by incremental LP calculation. Demands in class 1
  are loaded first, then demands in classes 1 and 2
  loaded subject to not exceeding backorder levels
  associated with class 1, etc. The formulation for
  all classes is the same, except for the values of
  the demands and the lower bounds on back order
  variables.
• Production is rate-based, i.e., the release
  quantity in a particular period is to be
  distributed uniformly over the period.

12
Optimized Production Planning An Example in
Semiconductor Manufacturing

• The following are the assumption underlying this
  model (Cont.)
• As a horizon condition, the wafer fab are
  required to enter steady-state, whereby
  production releases on each route are required to
  follow some constant rate in all periods falling
  within one total flow time of the planning
  horizon. The planning periods that overlap the
  interval beginning one total flow time for a
  route before the planning horizon until the
  horizon are termed frozen periods with respect to
  that route. Demands from each class in the last
  planning period are assumed to continue at the
  same rate forever.

13
Optimized Production Planning An Example in
Semiconductor Manufacturing

• Parameters
• g die type.
• i wafer fab route.
• l processing step (i. e., operation) on a wafer
  fab route.
• li the last step on wafer fab route i.
• k resource type (i. e., machine type).
• p, q planning period, p 1,2,,P. P is the
  planning horizon. An extra period P1 is appended
  to the planning horizon whose length is equal to
  the flow time of the longest fab route.
• r demand class, r 1,2,,R.
• Gr set of all die types appearing in r-th demand
  class.
• I r set of all wafer routes producing die types
  appearing in Gr.
• Kr set of all resource types loaded by routes in
  I r.

14
Optimized Production Planning An Example in
Semiconductor Manufacturing
The following values are assumed to be known and
constant
15
Optimized Production Planning An Example in
Semiconductor Manufacturing
Continued
16
Optimized Production Planning An Example in
Semiconductor Manufacturing
Variables
Short notation
17
Optimized Production Planning An Example in
Semiconductor Manufacturing
Maximize the discounted sum of (die output
revenue)-(raw material cost)-(die inventory
holding cost)-(cost of backordered die demands)
Objective function (For r-th demand class)

• Constraints
• Conservation of Die Demand
• Constraints relating Resource Capacity
• Variables ranges.

18
Optimized Production Planning An Example in
Semiconductor Manufacturing
(die output during the period) (inventory at
the end of period) (backorders at the end of
period) (demands at the end of period)
(die output during the period) (inventory at
the start of period) (backorders at the end of
period) (inventory at the end of period)
(backorders at the end of period) (demands at
the end of period)
(die output during the period) (backorders at
the end of period) (backorders at the end of
period) (demands at the end of period)
Constraints Conservation of Die
Demand Constraints relating Resource
Capacity Variables ranges.
19
Optimized Production Planning An Example in
Semiconductor Manufacturing
(machine hours required to process new releases)
(available machine hours for processing activity)
(machine hours required to flush initial WIP)
Constraints Conservation of Die
Demand Constraints relating Resource
Capacity Variables ranges.
0 (backorder variables) ( upper bound on
backorder quantity), and all other variables0.
20
Optimized Production Planning -Dynamic
Programming (DP)

• DP is an approach developed to solve sequential,
  or multi-stage, decision problems by solving a
  series of single stage problems
• DP tends to break the original problem to
  sub-problems and chooses the best solution in the
  sub-problems, beginning from the smaller in size
• DP follows the principle of best, that is the
  best solution of the problem will come by the
  combination of the best solutions of
  sub-problems, if the possible solutions of a
  problem are a combination of possible solutions
  of sub-problems
• DP can solve the multi-periods production
  planning problems.

21
Optimized Production Planning -Dynamic
Programming (DP)
The decision makers take some actions at the
first stage A recourse decision can then be made
in the second stage that compensates for any bad
effects that might have been experienced as a
result of the first-stage decision.

• SP is a framework for modeling optimization
  problems that involve uncertainty
• The most widely applied and studied stochastic
  programming models are two-stage linear programs
• Multi-stage linear programs have been extended,
  
• SP can tackle the uncertainty of future demand.

Each stage consists of a decision followed by a
set of observations of the uncertain parameters
which are gradually revealed over time.
22
APS-Overview of Planning and Scheduling

• Generally speaking, planning and scheduling
  jointly determine how, when, and in what quantity
  products will be manufactured or purchased. In
  essence, planning establishes what should be done
  and scheduling determines how to do it
• There is no agreed definition of planning versus
  scheduling. Many believe that the right and only
  way to achieve accurate due dates is to perform
  very detailed scheduling. Others believe that it
  is much more important to put more effort in the
  planning process. But most agree that the
  distinction between planning and scheduling is
  the trade-off of time horizon versus the level of
  detail.

23
APS- Definition
Advanced Planning and Scheduling (APS) is a
software system that uses intelligent analytical
tools to perform finite scheduling and produce
realistic plans.
24
APS-Overview (1)

• Its most important advantage over traditional
  planning approaches is that material and capacity
  are simultaneously considered as elements that
  may constrain production. This stands in marked
  contrast to the conventional MRP approach of
  independently planning material and then
  subsequently checking this plan against capacity
  to identify violations
• APS systems are able to generate plans and
  schedules very quickly. An APS engine can be
  designed to either look over a long time horizon
  (a few months) with less details or more details
  over a shorter period (a few weeks).
• APS covers various capabilities such as finite
  capacity scheduling or constraint-based
  scheduling at shop floor level
• Quite intuitive to say that the APS systems
  resolve (or attempt to resolve) the shortfall of
  the ERP system as a planning tool

25
APS-Overview (2)

• APS does Advanced Planning
• Consider business objectives
• Consider the organization of machines and work
  cells
• APS does Advanced Scheduling
• Consider plant capacity
• Consider business limitations

26
APS-the Scope (1)

• The scope of APS is not limited to factory
  planning and scheduling, but has grown rapidly to
  include the full spectrum of enterprise and
  inter-enterprise planning and scheduling
  functions
• Strategic and long-term planning
• Demand planning and forecasting
• Sales and operations planning (SOP)
• Inventory planning
• Supply chain planning (SCP)
• Available-to-promise (ATP)

27
APS-the Scope (2)

• Manufacturing planning
• Distribution planning
• Transportation planning
• Production scheduling
• Shipment scheduling
• Inter-company collaboration
• Source Bermudez, John. Advance Planning and
  Scheduling Is It as Good as It Sounds? The
  Report on Supply Chain Management. Advanced
  Manufacturing Research, Inc., March, 1998.)

28
APS- the Four-Part Model
AMRs APS Model
Source Advanced Manufacturing Research, Inc.
29
APS- Mathematical Technologies

• Linear Programming
• Genetic Algorithms
• Heuristics
• Constraint Based Programming (CBP)
• Source Shires, Nigel, (2005). Optimization
  Techniques and Their Application to Production
  Scheduling , White Paper, Preactor
  International, published on website
  http//www.preactor.com/whitepapers.asp.

30
APS-APS/ERP Integration
The comprehensive nature of todays APS
algorithms drives the need for copious amounts of
data--data that typically reside in an ERP
system. This means that attaining the full
benefits of APS is largely predicted on how well
it is integrated with ERP. When done well, both
systems benefit. Source Musselman, K., and
Uzsoy, R. (2001), Advanced Planning and
Scheduling for Manufacturing, in Handbook of
Industrial Engineering, 3rd Ed., G. Salvendy,
Eds., John Wiley Sons, New York.
31
APS/ERP Integration
APS/ERP Integration
Source Musselman, K., and Uzsoy, R. (2001),
Advanced Planning and Scheduling for
Manufacturing, in Handbook of Industrial
Engineering, 3rd Ed., G. Salvendy, Eds., John
Wiley Sons, New York.
32
APS- Software Providers

• Preactor
• SAP - Advanced Planner and Optimizer (APO)
• Oracle - APS
• Manugistics
• i2

33
APS-Preactor APS Screenshot
34
APS-SAP-APO Screenshot
35
Theory of Constraints (TOC)-Introduction

• The theory was first described by Israels
  physicist Dr. Eliyahu M. Goldratt in his book The
  Goal as a way of managing the business to
  increase profits in 1980s .

TOC is a proven method that can be used by
existing personnel to increase throughput
(sales), reliability, and quality while
decreasing inventory, WIP, late deliveries, and
overtime. Successful organizations also adopt
TOC to help make tactical strategic decisions
for continuous improvement.
36
TOC-Introduction

• The Theory of Constraints is based on the
  premise that
• Every real system, such as a business, must have
  within it at least one constraint. If this were
  not the case then the system could produce
  unlimited amounts of whatever it was striving
  for, profit in the case of a business..
  
• 
  Dr Eli Goldratt

37
TOC-Drum Buffer Rope
buffer
rope
drum

• Drum Buffer Rope (DBR) is a planning and
  scheduling solution derived from the Theory of
  Constraints (TOC).
• The fundamental assumption of DBR is that within
  any plant there is one or a limited number of
  scarce resources which control the overall output
  of that plant. This is the drum, which sets
  the pace for all other resources.
• In order to maximize the output of the system,
  planning and execution behaviors are focused on
  exploiting the drum, protecting it against
  disruption through the use of time buffers, and
  synchronizing or subordinating all other
  resources and decisions to the activity of the
  drum through a mechanism that is akin to a
  rope.

38
TOC-the Steps for Implementation

• Step 1 Identify the system's constraint(s)
• Step 2 Decide how to exploit the systems
  constraint(s)
• Step 3 Subordinate everything else to the above
  decision
• Step 4 Elevate the systems constraint(s)
• Step 5 If in the previous step, a constraint has
  been broken go back to step 1, but
  do not allow inertia to become the systems
  constraint

39
TOC- Types of Constraint

• A constraint is anything in an organization that
  limits it from moving toward or achieving its
  goal.
• There are two basic types of constraints
  physical constraints and non-physical
  constraints.
• A physical constraint is something like the
  physical capacity of a machine.
• A non-physical constraint might be something
  like demand for a product, a corporate procedure,
  or an individual's paradigm for looking at the
  world.
• THE MARKET
• CAPACITY
• RESOURCES
• SUPPLIERS
• FINANCE
• KNOWLEDGE OR COMPETENCE
• POLICY

40
TOC- Applications

• 1. Production Planning and Scheduling
• 2. Distribution and Supply Chain
• 3. Financial Management
• 4. Marketing
• 5. Strategic Planning
• 6. Project Management

41
TOC-the Principles of Applying TOC in Production
Scheduling

• Factory production rate is production rate of
  bottleneck work center
• Most of the buffer WIP should be waiting at
  bottleneck
• Bottleneck implies certain amount of idle time at
  other work stations
• Important to regulate bottleneck workload other
  work centers should serve the bottleneck, not
  optimize themselves.

42
TOC-Capacity and Bottlenecks in Production

• Capacity is defined as the available time for
  production (excluding maintenance and other
  downtime)
• A bottleneck (constraint) is defined as any
  resource whose capacity is less than the demand
  placed upon it
• A non-bottleneck is a resource whose capacity is
  greater than the demand placed on it
• A capacity-constrained resource (CCR) is one
  whose utilization is close to capacity and could
  be bottleneck if it is not scheduled carefully

43
TOC- An Example in Semiconductor Manufacturing

• SLIM (Short Cycle Time and Low Inventory in
  Manufacturing) is a project carried by Prof.
  Leachman at University of California at Berkeley.
  
• SLIM is a set of methodologies and scheduling
  applications for managing cycle time in
  semiconductor and its main method is TOC.
• Between 1996 and 1999, Samsung Electronics Corp.,
  Ltd., implemented SLIM in all its semiconductor
  manufacturing facilities and achieve great
  success
• During the presentation at the 2001 Franz
  Edelman Award Competition, Yoon-Woo Lee,
  president of Samsungs semiconductor business,
  stated The financial impact of SLIM was
  significant. We increased revenue
• almost 1 billion dollars through five years
  without any additional capital investment, and
  our global DRAM market share increased from 18
  percent to 22 percent.

44
TOC- An Example in Semiconductor Manufacturing
WIP between photo steps at normal time
Fab Process
The pipe represents the production line, and its
width represents the maximum flow rate or
capacity at various process steps. SEC fabs were
designed so that the photo machines are the
bottlenecks, and other machines have surplus
capacity. Thus the pipe in the figure narrows at
each photo step. When all the machines are up
and the process is in control, the photo
machines are the bottlenecks, and the largest
concentrations of WIP is at those points.
45
TOC- An Example in Semiconductor Manufacturing
WIP between photo steps at disturbance
The case of process or equipment trouble at a
non-photo manufacturing area of the fab is
depicted by a constriction of the pipe at a
non-photo step. If this condition persists, the
WIP at a photo machine can become exhausted. A
buffer is needed, proportional to the risk of
trouble in that layer. For example, if Layer j of
the process experiences more trouble than Layer
j1 the bottleneck step immediately following
Layer j should be awarded a larger buffer than
the bottleneck step following Layer j1.
46
TOC- An Example in Semiconductor Manufacturing
The way to solve the problem

• The philosophy underlying SLIM is to distribute
  WIP to put the fab in the best position to cope
  with the next disruption. That is, while the
  equipment and process are working well, the fab
  should strive to move as much WIP as possible to
  the photo bottleneck, to be prepared for the next
  disruption.

47
CS-Introduction

• With the increasing competition in the global
  market, the manufacturing industry has been
  facing the challenge of increasing customer
  value
• More importantly, quality means ensuring customer
  satisfaction and enhancing customer value to the
  extent that customers are willing to pay for the
  goods and services
• A well-accepted practice in both academia and
  industry is the exploration of flexibility in
  modern manufacturing systems to provide quick
  response to customers with new products catering
  to a particular spectrum of customer needs
• The key to success in the highly competitive
  manufacturing enterprise often is the companys
  ability to design, produce, and market
  high-quality products within a short time frame
  and at a price that customers are willing to pay
• In order to meet these pragmatic and highly
  competitive needs of todays industries, it is
  imperative to promote high-value-added products
  and services
• Mass customization enhances profitability through
  a synergy of increasing customer-perceived values
  and reducing the costs of production and
  logistics.

48
CS-Introduction

• Mass customization is producing goods and
  services to meet individual customers needs with
  near mass production efficiency
• Mass customization is a new paradigm for
  industries to provide products and services that
  best serve customer needs while maintaining
  near-mass production efficiency. Contradicted two
  sides

• Mass production demonstrates an advantage in
  high-volume production
• Satisfying each individual customers needs can
  often be translated into higher value, however,
  economically not viable

49
CS-Introduction
Economic Implication of Mass Customization

• Mass customization is capable of reducing costs
  and lead time by accommodating companies to
  garner economy of scale by repetitions.
• With flexibility and programmability, companies
  with low to medium production volume can gain an
  edge over competitors by implementing MC
• Mass customization can potentially develop
  customer loyalty, propel company growth, and
  increase market share by widening the product
  range.

50
CS-Introduction
Technical challenges

• The essence of mass customization lies in the
  product and service providers ability to
  perceive and capture latent market niches and
  subsequently develop technical capabilities to
  meet the diverse needs of target customers
• To encapsulate the needs of target customer
  groups means to emulate existing or potential
  competitors in quality, cost, quick response
• Therefore, the requirements of mass customization
  depend on three aspects time-to-market (quick
  responsiveness), variety (customization), and
  economy of scale (volume production efficiency)
• Successful mass customization depends on a
  balance of three elements features, cost, and
  schedule.

51
CS-Introduction
Maximizing reusability

• Maximal amounts of repetition are essential to
  achieve the efficiency of mass production, as
  well as efficiencies in sales, marketing, and
  logistics, which attained through maximizing
  commonality in design, which leads to reusable
  tools, equipment, and expertise in subsequent
  manufacturing
• Customization emphasizes the differentiation
  among products.
• An important step toward to this goal is the
  development and proliferation of design
  repositories that are capable of creating various
  customized products
• Dynamic stability a firm can serve the widest
  range of customers and changing product demands.
• To achieve mass customization, the synergy of
  commonality and modularity needs to be tackled
  and needs to encompass both the physical and
  process domains of design.

52
CS- Introduction
Product platform

• The effectiveness of a firms new product
  generation lies in
• Its ability to create a continuous stream of
  successful new products over an extended period
  of time
• The attractiveness of these products to the
  target market niches
• The essence of mass customization is to maximize
  such a match of internal capabilities with
  external market needs
• A product platform is impelled to provide the
  necessary taxonomy for positioning different
  products and the underpinning structure
  describing the interrelationships between various
  products with respect to customer requirements,
  competition information, and fulfillment
  processes
• This implicates two aspects
• to represent the entire product portfolio,
  including both existing products and proactively
  anticipated ones, by characterizing various
  perceived customer needs, and
• to incorporated proven designs, materials, and
  process technologies

53
CS- Introduction
Integrated product life cycle

• Mass customization starts from understanding
  customers individual requirements and ends with
  a fulfillment process targeting each particular
  customer,
• The time-to-market can be achieved by telescoping
  lead time
• Product realization should simultaneously satisfy
  various product life cycle concerns, including
  functionality, cost, schedule, reliability,
  manufacturability, marketability, and
  serviceability, to name but a few
• The realization of mass customization requires
  not only integration across the product
  development horizon, but also the provision of a
  context-coherent integration of various
  viewpoints of product life cycle.

54
CS-Design for mass customization

• Design for mass customization (DFMC) aims at
  considering economies of scope and scale at the
  early design stage of the product-realization
  process
• The main emphasis of DFMC is on elevating the
  current practice of designing individual products
  to designing product families
• There two basic concepts underpinning DFMC
• Product family architecture
• Product family design.

55
CS-Understanding DFMC
56
CS-Product family

• A product family is a set of products that are
  derived from a common platform
• A product family targets a certain market
  segment, whereas each product variant is
  developed to address a specific set of customer
  needs of the market segment
• The interpretation of product families depends on
  different perspectives.

57
CS-Modularity and commonality

• There two basic issues associated with product
  families modularity and commonality
• Modularity tries to separate a system into
  independent parts or modules that can be treated
  as logical units
• Decomposition is a major concern in modularity
  analysis
• Modularity is achieved from multiple viewpoints,
  including functionality, solution technologies,
  and physical structures
• There are three types of modularity involved in
  product realization functional modularity,
  technical modularity, and physical modularity
• The interaction between modules is important in
  characterizing modularity.
• As for functional modularity, the interaction is
  exhibited by the relevance of functional features
  (FFs) across different customer groups

58
CS-Modularity and commonality (Continued)

• The commonality reveals the difference of the
  architecture of product families from the
  architecture of a single product
• Corresponding to the three types of modularity,
  there are three types of commonality in
  accordance with functional, design, and process
  views
• Functional commonality manifests itself through
  functional classification, that is, grouping
  similar customer requirements into one class,
  where similarity is measured by the Euclidean
  distance among FF instances
• A class of products is described by modularity
  and product variants differentiate according to
  the commonality among module instances.

59
CS-A comparison of modularity and commonality
Issues Modularity Commonality
Focused objects Type (class) Instances (members)
Characteristic of measure Interaction Similarity
Analysis method Decomposition Clustering
Product differentiation Product structure Product variants
Integration/relation Class-member relationship Class-member relationship
60
CS-Product variety

• Product variety is defined as the diversity of
  products that a manufacturing enterprise provides
  to the marketplace
• Two types of variety can be observed functional
  variety and technical variety
• Functional variety is used broadly to mean any
  differentiation in the attributes related to a
  products functionality from which the customer
  could derive certain benefits
• Technical variety refers to diverse technologies,
  design methods, manufacturing processes,
  components and/or assemblies, and so on that are
  necessary to achieve specific functionality of a
  product required by the customer. It may be
  invisible to customers
• While functional variety is mostly related to
  customer satisfaction from the marketing/sales
  perspective, technical variety usually involves
  manufacturability and costs from the engineering
  perspective.

61
CS-Product variety (Continued)

• These two types of variety result in two
  different variety design strategies functional
  variety strategy and design for functional
  variety strategy
• Since functional variety directly affects
  customer satisfaction, this type of variety
  should be encouraged in product development
• A design for functional variety strategy aims
  at increasing functional variety and manifests
  itself through vast research in the business
  community, such as product line structuring,
  equilibrium pricing, and product positioning
• A design for technical variety tries to reduce
  technical variety so as to gain cost advantages.

62
CS-Variety Leverage Handling Variety for Mass
Customization
63
CS-Product family architecture

• A well-planned product family architecture (PFA)
  provides a generic umbrella for capturing and
  utilizing commonality, within each new product
  instantiated and extends so as to anchor future
  designs to a common product line structure.

64
CS-PFA and Its Relationships with Market Segments
65
CS-Composition of PFA

• The PFA consists of three elements the common
  base, the differentiation enabler, and the
  configuration mechanism
• Common bases (CBs) are the shared elements among
  different products in a product family
• Differentiation enablers (DEs) are basic elements
  making products different from one another. They
  are the source of variety within a product
  family
• Configuration mechanisms (CMs) define the rules
  and means of deriving product variants. Three
  types of configuration mechanisms can be
  identified selection constraints, include
  conditions, and variety generation.

66
CS-Composition of PFA (Continued)

• Selection constraints specify restrictions on
  optional features because certain combinations of
  options are not allowed or feasible or, on the
  contrary, are mandatory
• Include conditions are concerned with the
  determination of alternative variants for each
  differentiation enabler. The include condition of
  a variant defines the condition under which the
  variant should be used or not used with respect
  to achieving the required product
  characteristics
• Variety generation refers to the way in which the
  distinctiveness of product features can be
  created. It focuses on the engineering
  realization of custom products in the form of
  product structures.

67
CS-Basic Methods of Variety Generation
68
CS-Synchronization of multiple views

• The strategy is to employ a generic, unified
  representation and to use its fragments for
  different purposes, rather than to maintain
  consistency among multiple representations
  through transformation of different product data
  models to standard ones.

69
CS-Representing Multiple Views of Product Family
within a Single Context
70
CS-Product family design

• Under the umbrella of PFA, product family design
  manifests itself through the derivation processes
  of product variants based on PFA constructs.

71
PFA-Based Product Family Design Variant
Derivation through GPS Instantiation
72
CS- PFA-Based Product Family Design Variant
Derivation through GPS Instantiation
73
CS- Manufacturing and production planning

• Competition for mass customization manufacturing
  is focused on the flexibility and responsiveness
  in order to satisfy dynamic changes of global
  markets. The future major trends are
• A major part of manufacturing will gradually
  shift from mass production to the manufacturing
  of semi-customized or customized products to meet
  increasingly diverse demands
• The made-in-house mindset will gradually shift
  to distributed locations, and various entities
  will team up with others to utilize special
  capabilities at different locations to speed up
  product development, reduce risk, and penetrate
  local markets
• Centralized control of various entities with
  different objectives, locations, and cultures is
  almost out of the question now. control systems
  to enable effective coordination among
  distributed entities have become critical to
  modern manufacturing systems.

74
CS-Managing variety in production planning

• Major challenge of mass customization production
  planning results from the increase of variety
• Facing such a variety dilemma, many companies try
  to satisfy demands from their customers through
  engineer-to-order, make-to-order, or
  assembly-to-order production systems
• The traditional approach to variant handling is
  to treat every variant as a separate product by
  specifying a unique BOM for each variant. This
  works with a low number of variants, but not when
  customers are granted a high degree of freedom in
  specifying products. The problem is that a large
  number of BOM structures will occur in mass
  customization production
• To overcome these limitations, a generic BOM
  (GBOM) concept has been developed.
• The GBOM provides a means of describing, with a
  limited amount of data, a large number of
  variants within a product family,while leaving
  the product structure unimpaired. The structure
  has three aspects

75
CS-A Generic Structure for Characterizing Variety
76
CS-The generic variety structure for souvenir
clocks
Structure items Ii Variety parameter Pj Variety instance Vj
3/hands Setting type Two-hand setting, three-hand setting
3/hands Color White, Grey, etc.
3/hands Size Large, medium, small
3/dial pattern Logo, mosic, scenery, customized photo, etc.
3/dial Size Large, medium, small
4/transmission Alarm Yes, no
4/core Alarm Yes, no
3/base Shape Round, rectangular, hexagonal
3/base Material Acrylic, aluminum, etc.
3/base Color Transparent, red, etc.
3/front plate Shape Rectangular,round, rhombus
3/front plate Material Acrylic, aluminum, etc.
3/front plate Color Transparent, red, etc.
1/label sticker Pattern HKUST, signature, etc.
1/paper box Type Ordinary, deluxe, etc.
77
CS-The generic variety structure for souvenir
clocks (continued)

• To understand the generic concept underlying
  such a variety representation, two fundamental
  issues need to be highlighted
• Generic item
• Indirect identification.

78
CS-Coordination in manufacturing resource
allocation

• Challenges of manufacturing resource allocation
  for mass customization include
• The number of product variety flowing through the
  manufacturing system is approaching an
  astronomical scale
• Production forecasting for each line item and its
  patterns is not often available
• Systems must be capable of rapid response to
  market fluctuation
• The system should be easy for reconfiguration
  ---- ideally, one set of codes employed across
  different agents
• The addition and removal of resources or jobs can
  be done with little change of scheduling systems.

79
CS-Major considerations of scheduling for
resource allocation

• Decompose large, complex scheduling problem into
  smaller, disjointed allocation problems
• Decentralize resource access, allocation, and
  control mechanisms
• Design a reliable, fault-tolerant, and robust
  allocation mechanism
• Design scalable architectures for resource access
  in a complex system and provide a plug-and-play
  resource environment such that resource providers
  and consumers can enter or depart from the market
  freely
• Provide guarantees to customers and applications
  on performance criteria.

80
CS-A Market Structure for Collaborative Scheduling
81
CS-A Market Structure for Collaborative
Scheduling (Continued)

• The satisfaction of multiple criteria, such as
  costs and responsiveness, cannot be achieved
  using solely a set of dispatching rules
• A price mechanism should be constructed to serve
  as an invisible hand to guide the coordination in
  balancing diverse requirements and maximizing
  performance in a dynamic environment.

82
CS-The Price Mechanism of a Market Model
83
CS-Message-Based Bidding and Dynamic Control
84
CS-High-variety shop-floor control

• The requirements of the new control systems
  include re-configurability, decomposability, and
  scalability to achieve make-to-order with very
  short response time

85
Chapter 9 Advancement in PPC

• End !