Detailed Production Planning
&
Shop-Floor Control
Dealing with the Problem Complexity
through Decomposition
Corporate Strategy
Aggregate Unit
Demand
Aggregate Planning
(Plan. Hor.: 1 year, Time Unit: 1 month)
Capacity and Aggregate Production Plans
End Item (SKU)
Demand
Master Production Scheduling
(Plan. Hor.: a few months, Time Unit: 1 week)
SKU-level Production Plans
Manufacturing
and Procurement
lead times
Materials Requirement Planning
(Plan. Hor.: a few months, Time Unit: 1 week)
Component Production lots and due dates
Part process
plans
Shop floor-level Production Control
(Plan. Hor.: a day or a shift, Time Unit: real-time)
Disaggregation and
Master Production Scheduling
(MPS)
The (Master) Production Scheduling Problem
Capacity Company Product Economic
Consts. Policies Charact. Considerations
Placed Orders
Forecasted Demand
Current and Planned
Availability, eg.,
•Initial Inventory,
•Initiated Production,
•Subcontracted quantities
Master Production
Schedule:
When & How Much
to produce for each
product
MPS
Planning
Horizon
Time
unit
Capacity
Planning
MPS Example: Company Operations
Grain cracking
(1 milling
machine)
Fermentation
(3 40-barrel
ferm. tanks)
Mashing
(1 mashing tun)
Boiling
(1 brew kettle)
Bottling
(1 bottling
station)
Filtering
(1 filter tank)
Fermentation Times:
Brew
Pale Ale
Stout
Ferm. Time
2 weeks
3 weeks
Winter Ale
2 weeks
Summer Brew
2 weeks
Octoberfest
8-10 weeks
Example: Implementing the Empirical
Approach in Excel
# Fermentors:
1
Microbrewery Performance
Week
# Fermentors Req'd
Feasible Loading?
Min # Fermentors Req'd
Fermentor Utilization
Total Spoilage
Pale Ale
Week
Demand
Scheduled Receipts
Fermentors Released
Inventory Spoilage
Inventory Position
Net Requirements
Batched Net Receipts
Scheduled Releases
Fermentors Seized
Total Fermentors Occupied
Stout
Week
Demand
Scheduled Receipts
Fermentors Released
Inventory Spoilage
Inventory Position
Net Requirements
Batched Net Receipts
Scheduled Releases
Fermentors Seized
Total Fermentors Occupied
0
Unit Cap:
200
1
0
2
0
3
0
2
0%
0
2
0%
0
2
0%
0
Fermentation Time:
0
1
45
200
1
100
255
Fermentation Time:
0
1
35
150
2
2
115
3
Shelf Life:
20
4
0
5
0
6
0
7
0
8
0
9
0
10
0
2
0%
0
2
0%
0
2
0%
0
2
0%
0
2
0%
0
2
0%
0
2
0%
0
4
5
6
7
8
9
10
50
40
40
40
40
40
40
40
40
205
165
125
85
45
5
-35
35
-40
40
-40
40
3
2
3
4
5
6
7
8
9
10
40
30
30
40
40
40
40
50
50
75
45
15
-25
25
-40
40
-40
40
-40
40
-50
50
-50
50
Computing Inventory Positions and
Net Requirements
Inventory Position:
IPi = max{IPi-1,0}+ SRi+BNRi -Di
(Material Balance Equation)
(IPi-1)+
SRi+BNRi
i
Di
IPi
Net Requirement:
NRi = abs(min{0, IPi})
Problem Decision Variables:
Scheduled Releases
# Fermentors:
1
Microbrewery Performance
Week
# Fermentors Req'd
Feasible Loading?
Min # Fermentors Req'd
Fermentor Utilization
Total Spoilage
Pale Ale
Week
Demand
Scheduled Receipts
Fermentors Released
Inventory Spoilage
Inventory Position
Net Requirements
Batched Net Receipts
Scheduled Releases
Fermentors Seized
Total Fermentors Occupied
Stout
Week
Demand
Scheduled Receipts
Fermentors Released
Inventory Spoilage
Inventory Position
Net Requirements
Batched Net Receipts
Scheduled Releases
Fermentors Seized
Total Fermentors Occupied
0
Unit Cap:
200
1
0
2
0
3
0
2
0%
0
2
0%
0
2
0%
0
Fermentation Time:
0
1
45
200
1
100
2
2
255
3
Shelf Life:
20
4
0
5
0
6
1
7
1
8
0
9
0
10
0
2
0%
0
2
0%
0
2
100%
0
2
100%
0
2
0%
0
2
0%
0
2
0%
0
6
7
8
4
5
9
10
50
40
40
40
40
40
40
40
40
205
165
125
85
45
5
165
125
85
200
200
1
1
Fermentation Time:
0
1
35
150
115
3
2
3
4
5
6
1
7
8
9
10
40
30
30
40
40
40
40
50
50
75
45
15
-25
25
-40
40
-40
40
-40
40
-50
50
-50
50
Testing the Schedule Feasibility
# Fermentors:
1
Microbrewery Performance
Week
# Fermentors Req'd
Feasible Loading?
Min # Fermentors Req'd
Fermentor Utilization
Total Spoilage
Pale Ale
Week
Demand
Scheduled Receipts
Fermentors Released
Inventory Spoilage
Inventory Position
Net Requirements
Batched Net Receipts
Scheduled Releases
Fermentors Seized
Total Fermentors Occupied
Stout
Week
Demand
Scheduled Receipts
Fermentors Released
Inventory Spoilage
Inventory Position
Net Requirements
Batched Net Receipts
Scheduled Releases
Fermentors Seized
Total Fermentors Occupied
0
Unit Cap:
200
1
0
2
1
3
1
2
0%
0
2
100%
0
2
2
Fermentation Time:
0
1
45
200
1
100
255
Shelf Life:
20
4
1
5
0
6
1
2
100%
0
2
100%
0
2
0%
0
3
4
5
8
1
9
1
10
0
2
100%
0
7
2
NO
2
200%
0
2
100%
0
2
100%
0
2
0%
0
6
7
8
9
10
50
40
40
40
40
40
40
40
40
205
165
125
85
45
5
165
125
85
200
200
1
1
Fermentation Time:
0
1
35
150
115
3
2
3
4
5
6
1
7
8
9
10
40
30
30
40
40
40
40
50
50
75
45
15
175
135
95
55
5
155
200
200
1
1
1
1
200
200
1
1
1
1
Fixing the Original Schedule
# Fermentors:
1
Microbrewery Performance
Week
# Fermentors Req'd
Feasible Loading?
Min # Fermentors Req'd
Fermentor Utilization
Total Spoilage
Pale Ale
Week
Demand
Scheduled Receipts
Fermentors Released
Inventory Spoilage
Inventory Position
Net Requirements
Batched Net Receipts
Scheduled Releases
Fermentors Seized
Total Fermentors Occupied
Stout
Week
Demand
Scheduled Receipts
Fermentors Released
Inventory Spoilage
Inventory Position
Net Requirements
Batched Net Receipts
Scheduled Releases
Fermentors Seized
Total Fermentors Occupied
0
Unit Cap:
200
1
0
2
1
3
1
2
0%
0
2
100%
0
2
2
Fermentation Time:
0
1
45
200
1
100
255
Shelf Life:
20
4
1
5
1
6
1
7
1
8
1
9
1
10
0
2
100%
0
2
100%
0
2
100%
0
2
100%
0
2
100%
0
2
100%
0
2
100%
0
2
0%
0
3
4
5
6
7
8
9
10
50
40
40
40
40
40
40
40
40
205
165
125
85
45
205
165
125
85
200
200
1
1
Fermentation Time:
0
1
35
150
115
3
2
3
4
5
1
6
7
8
9
10
40
30
30
40
40
40
40
50
50
75
45
15
175
135
95
55
5
155
200
200
1
1
1
1
200
200
1
1
1
1
Infeasible Production Requirements
# Fermentors:
1
Microbrewery Performance
Week
# Fermentors Req'd
Feasible Loading?
Min # Fermentors Req'd
Fermentor Utilization
Total Spoilage
Pale Ale
Week
Demand
Scheduled Receipts
Fermentors Released
Inventory Spoilage
Inventory Position
Net Requirements
Batched Net Receipts
Scheduled Releases
Fermentors Seized
Total Fermentors Occupied
Stout
Week
Demand
Scheduled Receipts
Fermentors Released
Inventory Spoilage
Inventory Position
Net Requirements
Batched Net Receipts
Scheduled Releases
Fermentors Seized
Total Fermentors Occupied
0
Unit Cap:
200
1
1
2
1
3
1
2
100%
0
2
100%
0
2
2
Fermentation Time:
0
1
45
100
55
Shelf Life:
20
4
1
5
0
6
0
7
0
8
0
9
0
10
0
2
100%
0
2
100%
0
2
0%
0
2
0%
0
2
0%
0
2
0%
0
2
0%
0
2
0%
0
3
4
5
6
7
8
9
10
50
200
1
40
40
40
40
40
40
40
40
205
165
125
85
45
5
-35
35
-40
40
-40
40
1
Fermentation Time:
0
1
35
150
115
3
2
3
4
5
6
7
8
9
10
40
40
40
40
40
40
40
50
50
75
35
-5
5
160
120
80
40
-10
10
-50
50
200
200
1
1
1
1
A feasible schedule with spoilage effects
# Fermentors:
1
Microbrewery Performance
Week
# Fermentors Req'd
Feasible Loading?
Min # Fermentors Req'd
Fermentor Utilization
Total Spoilage
Pale Ale
Week
Demand
Scheduled Receipts
Fermentors Released
Inventory Spoilage
Inventory Position
Net Requirements
Batched Net Receipts
Scheduled Releases
Fermentors Seized
Total Fermentors Occupied
Stout
Week
Demand
Scheduled Receipts
Fermentors Released
Inventory Spoilage
Inventory Position
Net Requirements
Batched Net Receipts
Scheduled Releases
Fermentors Seized
Total Fermentors Occupied
0
Unit Cap:
200
1
1
2
1
3
1
2
100%
0
2
100%
0
2
2
Fermentation Time:
0
1
45
200
1
100
255
Shelf Life:
6
4
1
5
1
6
0
7
1
8
1
9
1
10
0
2
100%
0
2
100%
0
2
100%
0
2
0%
0
2
100%
45
2
100%
0
2
100%
0
2
0%
5
3
4
5
6
7
8
9
10
50
40
40
40
40
40
40
40
40
205
165
125
85
245
45
160
120
80
40
200
200
1
1
Fermentation Time:
0
1
35
150
115
3
2
3
4
1
5
6
7
8
9
10
40
30
30
40
40
40
40
50
50
75
45
215
175
135
95
55
5
5
150
200
200
1
1
1
1
200
200
1
1
1
1
Computing Spoilage and
Modified Inventory Position
Spoilage:
SPi = max{0, IPi-1-(SRi-1+SRi-2+…+SRi-sl+1)
-(BNRi-1+BNRi-2+…+BNRi-sl+1)}
Inventory Position:
IPi = max{IPi-1,0}+ SRi+BNRi -Di-SPi
(Material Balance Equation)
(IPi-1)+
SRi+BNRi
i
Di
SPi
IPi
The Driving Logic behind the Empirical Approach
Demand
Availability:
•Initial Inventory Position
•Scheduled Receipts due to
initiated production or
subcontracting
Compute Future
Inventory Positions
Net
Requirements
Future inventories
Lot Sizing
Scheduled
Releases
Resource (Fermentor)
Occupancy
Feasibility
Testing
Product i
Schedule
Infeasibilities
Master Production Schedule
Revise
Prod. Reqs
MRP II:
Manufacturing Resource Planning
&
Scheduling
The “MRP Explosion” Calculus
BOM
Lead
Times
Planned
Order Releases
MPS
Current
Availabilities
Lot Sizing
Policies
MRP
Priority
Planning
Example: The (complete) MRP Explosion
Calculus
Item BOM:
Alpha
B(1)
D(2)
C(1)
C(2)
E(1)
E(1)
F(1)
F(1)
Item
Alpha
B
C
D
E
F
Gross Reqs for Alpha
Period
Gross Reqs.
Item Levels:
Level 0: Alpha Level 1: B Level 2: C, D Level 3: E, F
Lead Time
1
2
3
1
1
1
6
7
8
9
50
Current Inv. Pos.
10
20
0
100
10
50
10
11 12
50
13
100
The “MRP Explosion” Calculus
External Demand
Level 0
Initial
Inventories
Level 1
Capacity
Planning
Level 2
Scheduled
Receipts
Level N
Gross Requirements
Planned
Order Releases
(borrowed from Heizer and Render)
Computing the item Scheduled Releases
Item C
Period
Gross Requirements
Scheduled Receipts
Inventory Position: 20
Net Requirements
Planned Sched. Receipts
Planned Sched. Releases
1
2
3
20
20
40
40
4
40
5
6
12
7
10
40
28
18
72
Safety Stock
Requirements
Parent
Sched. Rel.
Item External
Demand
Synthesizing
item demand
series
Gross
Reqs
Projecting Net
Inv. Positions Reqs
and
Net Reqs.
Scheduled
Receipts
Initial
Inventory
8
9
90
18
-72
72
72
10
11
75
0
-75
75
75
12
0
75
Lot Sizing
Policy
Lot Sizing
Lead Time
Planned
Order
Receipts
TimePhasing
Planned
Order
Releases
Lot Sizing
• If affordable, a lot-for-lot (L4L) policy will incur the lowest inventory
holding costs and it will maintain a smoother production flow.
• Possible reasons for departure from a L4L policy:
– High set up times and costs => need for serial process batching to control
the capacity losses
– Processes that require a large production volume in order to maintain a
high utilization (e.g., fermentors, furnaces, etc.) => need for parallel
process batching
• Selection of a pertinent process batch size
– It must be large enough to maintain feasibility of the production
requirements
– It must control the incurred
• inventory holding costs, and/or
• part delays (this is a measure of disruption to the production flow
caused by batching)
• Move or transfer batches: The quantities in which parts are moved between
the successive processing stations.
– They should be as small as possible to maintain a smooth process flow
Some Lot Sizing Methods employed
in the traditional MRP framework
• Main focus: Balance set-up and holding costs
• Wagner-Whitin Algorithm for dynamic Lot Sizing
• Economic Order Quantity (EOQ): Compute a lot size using the EOQ formula
with the demand rate D set equal to the average of the net requirements
observed over the considered planning horizon.
• Periodic Order Quantity (POQ): Compute T = round(EOQ/D), and every time
you schedule a new lot, size it to cover the net requirements for the
subsequent T periods.
• Silver-Meal (SM): Every time you start a new lot, keep adding the net
requirements of the subsequent periods, as long as the average (setup plus
holding) cost per period decreases.
• Least Unit Cost (LUC): Every time you start a new lot, keep adding the net
requirements of the subsequent periods, as long as the average (setup plus
holding) cost per unit decreases.
• Part Period Balancing (PPB): Every time you start a new lot, add a number
of subsequent periods such that the total holding cost matches the lot set up
cost as much as possible.
Optimal Parallel Batching:
A factory physics approach
Model Parameters:
k: (parallel) batch size
ra: arrival rate (parts/hr)
t: batch processing time (hrs)
B: maximum batch size
ca: CV of inter-arrival times
ce: CV for effective batch processing time
Then
CT = WTBT + CTq+t
1
1
k 1
1 (k  1)k k  1
WTBT  [0   ... 
]

k
ra
ra
kra
2
2ra
CTq 
ca2b  ce2
2
k a2
ca2
ra
u
2
t ; cab 

;
u

t  1  k  ra t
1 u
(kta ) 2
k
k
From the above,
Remark: Notice that CT as
k  1 ca2 / k  ce2 u
k  1 ca2 / k  ce2 u
CT 

t t [

 1]t u1 but also as u0 !
2ra
2
1 u
2ku
2
1 u
Determining an “optimal” batch size
Let um  rat . Then u = um / k  k = um / u . Substituting this expression for k in the
expression for CT, we get:
um / u  1 ca2u / um  ce2 u
k  1 ca2 / k  ce2 u
CT  [

 1]t  [

 1]t
2ku
2
1 u
2um
2
1 u
Recognizing that
ca2u
um

ca2
k 
k

 0
, we set
ca2u
um
   0 and we get
1
u
2
  ce2 u
1
1
y (u )
1
y
(
u
)


(


c
)
CT  [ 

 1]t  [

 1]t where
e
u
1 u
2u 2um
2 1 u
2
2um
To minimize CT, it suffices to minimize y(u). This can be achieved as follows:
 1    ce2
dy (u )  (1  u ) 2  (   ce2 )u 2
1
2
2
*


0

(


c

1
)
u

2
u

1

0

u


e
du
u 2 (1  u ) 2
  ce2  1
1    ce2
1
and   0  u * 
which further implies that k *  ra t (1  ce )  ra t
ce  1
Remark: If ce2  0, the term  in the original expression for u* will significant. In that case,
ca2 1
*
we can set  
and obtain u* and k* as before.
um 1  ce
Finite-Capacity Planning & Scheduling
in the MRP II / ERP context:
Load Reports (Example)
Available
resource
time
150
100
50
1
2
3
4
5
6
7
8
Periods
Finite-Capacity Planning & Scheduling
in the MRP II / ERP context:
More Systematic Approaches
• Bottleneck-based scheduling in a cellular manufacturing context (Goldratt’s
Theory of Constraints approach):
– Each part (family) has its own production cell with a well-defined bottleneck
resource.
– Production is scheduled on the bottleneck resource and the schedule for the other
resources are organized around this schedule by taking consideration the expected
cycle times.
– Typically, a “cushion” of extra workload is maintained at the bottleneck in order to
prevent its starvation, in case of any disruptions in the upstream processes.
– If the bottleneck supports the production of more than one part types, a “singlemachine” scheduling problem arises naturally. This is addressed by selecting an
appropriate dispatching rule.
• Earliest Due Date (EDD) => minimizes maximum lateness (tardiness)
• Least slack (LS), where slack = difference between job due date and expected
completion time => tend to reduce average tardiness
• Shortest Processing Time (SPT) => minimizes average flowtime at the
bottleneck, and (by Little’s law) average WIP
• Other heuristics addressing different problem variations including weighted
performance measures, non-zero release times, etc.
Finite-Capacity Planning & Scheduling
in the MRP II / ERP context:
More Systematic Approaches (cont.)
•
•
•
•
Cases where the previous approach is not effective:
– There are more than one capacity-constrained resource
– Bottlenecks are shifting depending on the product mix
– There are operations involving parallel process batching
– Process routes are non-linear (e.g., due to routing flexibility, re-entrance, extensive
need for rework)
Remark: The semiconductor manufacturing operational context is a typical example of
all of the above.
A more global view of the system operations is necessary in order to support effective
and efficient scheduling.
Possible approaches
– Employ a set of pertinently selected dispatching rules at the different (critical)
resources, and assess its efficacy through simulation (possibly maintain a bank of
such rules for different operational conditions – meta-heuristics)
– Generate efficient (not necessarily optimal) global schedules by employing an
approach that searches for such a schedule in the space of feasible schedules
Typical approaches employed in the solution
of the job shop scheduling problem
•
•
•
Branch & Bound (B&B): Constructs all possible schedules incrementally, fathoming
options that are clearly suboptimal to some other options. Can generate optimal
schedules but it is very time consuming.
Beam search: Similar to B&B, but it employs additional heuristics to increase
fathoming.
Local search techniques: Given an initially constructed schedule, try to identify an
improved schedule that is obtained from the original one through a localized change
(e.g., through the change of the order of two jobs on a single machine); repeat. Also,
need a mechanism to avoid local optima.
– Simulated annealing: Seeks to avoid local optima by maintaining a non-zero
probability for transitioning to an inferior schedule. However, this probability is
reduced with the passage of time.
– Tabu search: Seeks to avoid local optima by pronouncing certain schedule changes
as taboo (these changes are apparent improvements that might attract the schedule
back to a local optimum)
– Genetic algorithms: Maintains an entire set of schedules at each iteration, and it
updates this set by replacing schedules of inferior performance with new schedules
resulting from the “combination” of the most efficient schedules currently
available; the synthesis of such new schedules is known as “crossover”. Also,
“mutation” provides additional schedules resulting from the local modification of
some single schedules.
Pegging and Bottom-up Replanning
(borrowed from Heizer and Render)
Some Limitations of MRP-based Planning
• The employment of fixed nominal lead times
– This problem is mitigated in case of a stable operational environment where
past experience and / or approximate formal models can provide insight for
setting lead times
– Lead time assessment is also facilitated by a well-structured, cellular shopfloor
• Possible system nervousness due to re-planning and the applied lot
sizing policies
– Potential remedies
• Firm orders
• Time fences
• L4L planning whenever possible
• Lack of an inherent mechanism for detecting and managing shop-floor
congestion – a purely “Push” approach
– However, it is possible to combine the planning visibility offered by the
MRP explosion calculus with more sophisticated production control
mechanisms that take advantage of the existing technology of
Manufacturing Execution Systems (MES).
The Revolution of
Just-In-Time (JIT) and
Lean Manufacturing
The essence of the JIT revolution and
Lean Manufacturing
• Try to reduce the system operational inefficiencies and the
resulting waste by identifying the sources of these inefficiencies
and working proactively to eliminate them as much as possible.
• In the emerging philosophy, inventories should be carefully
controlled and they should not function as the mechanism for
accommodating the system inefficiencies => Just-In-Time (JIT)
• The aforementioned effort should be an ongoing process
towards continuous improvement rather than one-time/shot
effort.
Targeting the sources of inefficiency
– input
• unreliable quality of raw material
• unreliable delivery times
– operation
• unreliable processes in terms of
– required processing times
– process outcome
• complex interacting process flows
• long set-up times
• unreliable (irresponsive and irresponsible) personnel
– output
• Highly variable production requirements in terms of
– production volume, and
– production scope
JIT enabling factors and practices
• Emphasis on quality at both the process and the supply side by promoting
– Statistical Process Control (SPC) theory and practice
– Quality certification programs
– Deployment of stable automated processes and foolproof practices (like checklists
and machines gauges) to guarantee the desired performance
– Employee empowerment and knowledge management (quality circles)
• “Tightening” of the supply chain by promoting
– Long-lasting and trustful relationships between the different parties in the supply
chain
– Timely and reliable information flow across these parties that takes advantage of
modern IT technologies, like
• Electronic Data Interchange (EDI), and e-commerce practices
• Real-time communications and global positioning systems
– Promotion of vendor owned and managed inventory practices that
• Establish economies of scale and protection to variability through pooling
• Enhance the demand visibility across the entire supply chain.
JIT enabling factors and practices (cont.)
•
Simplification of the process flows by promoting cellular manufacturing practices
– Dedication of separate production cells to product families with similar processing
requirements
– U-shaped layouts for facilitating employee sharing
– Employee cross-training for more flexible and higher utilization
•
Set-up time reduction through
–
–
–
–
•
The adoption of cellular manufacturing
Externalization of set-up times
Employment of flexible processes and pertinent auxiliary equipment like pertinent fixtures
Part standardization
Focus on repetitive manufacturing and promote the establishment of stable production
rates through
– Smoothing of the aggregate production requirements by appropriate quota setting
– Pertinent sequencing of the final assembly to support a desired product mix
– Use of buffer capacity (planned overtime) to protect against slippages from the target
production rates
– Component standardization
Institutionalizing the JIT practice
through the KANBAN-based
Production Authorization Mechanism
Station 1
Station 2
Station 3
Remarks:
• The KANBANS at each station cap the WIP at that station and they offer a natural
mechanism for reacting to various disruptions taking place in the system operation.
• In particular, production at each station is “pulled” as a result of the downstream
activity rather than “pushed” by an MPS-generated schedule.
• The KANBANS at each station should be set at a level that enables production
at the target rate
• A safe approach to set the KANBAN level at each station is by setting it initially to
the “historical” WIP level, and subsequently decrease it incrementally while
observing its impact on the production rate
• Frequent KANBAN changes are ineffective, since the production rate of the line is
rather insensitive to these changes, and they should be avoided
From KANBAN to CONWIP
Station 1
Station 2
Station 3
FGI
Why?
• It maintains the WIP cap but at the same time it offers more operational flexibility than
KANBAN.
• The unrestricted flow of WIP within the line enables better utilization of the (shifting)
bottleneck, and therefore, higher throughput.
• Less stress for the line operators since it enables them to work at the “natural pace” of the line.
• It enables more flexible scheduling of the line, since in the CONWIP operational context,
WIP is interpreted more generally as some aggregate amount of workload loaded into
the line (even measured in time-units, rather than number of parts) – new parts are pulled from
an available “work backlog” according to a pertinent set of dispatching rules.
• Easier to analyze and parameterize through the theory of closed-queueing networks.
• Remark: While the above features of CONWIP mitigate the rigidity of the KANBAN-based
shop-floor control, its “pull” nature still implies that it requires stable target production rates
in order to function well, and therefore, it is appropriate for repetitive manufacturing contexts.
A CONWIP-based “pull” framework
(borrowed from Hopp & Spearman)
Course Outline
•
1. Inventory Control Theory
–
–
–
–
The basic EOQ model and some of its variants
Replenishment coordinating approaches
Dynamic Lot Sizing
Statistical Inventory Control Models
• The News Vendor Model
• The Base Stock Model
• The (Q,r) Model
– An introduction to multi-echelon models
2. Factory Physics: A queueing-theoretic analysis of serial production systems
– Characterizing a flow line as a queueing system
– Some fundamental relationships between the line attributes and its performance indices
– The nature, role and impact of the operational variability
– An introduction to logical control of production systems
Course Outline
•
3. Integrating the Factory Physics insights to the OM practice
– Process Design, Capacity Planning and Line Balancing
– Hierarchical Production Planning
•
•
•
•
The classical hierarchical planning framework
Forecasting
Aggregate Planning
Master Production Scheduling (MPS) and Material Requirement Planning (MRP), and their
limitations
• Shop floor scheduling
– Just-in-Time (JIT) and Lean Manufacturing
•
•
•
•
•
The JIT philosophy
JIT practices and the KANBAN production authorization system
Shop-floor control based on the CONWIP production authorization model
Production Planning and Scheduling for CONWIP-controlled production systems
The JIT limitations
Thanks for being in the class!
Have a Nice Holiday!
But in the meantime,
please, give me your feedback in an evaluation! :)