Material Requirements Planning (MRP)

1
Material Requirements Planning (MRP)
Unlike many other approaches and techniques,
material requirements planning works which is
its best recommendation.
Joseph Orlicky, 1974
2
History

• Begun around 1960 as computerized approach to
  purchasing and production scheduling.
• Joseph Orlicky, Oliver Wight, and others.
• APICS launched MRP Crusade in 1972 to promote
  MRP.

3
Key Insight Assumptions

• Independent Demand finished products
• Dependent Demand components
• It makes no sense to independently forecast
  dependent demands.
• Assumptions

1. Known deterministic demands. 2. Fixed, known
production leadtimes. 3. Infinite capacity. Idea
is to back out demand for components by using
leadtimes and bills of material.
4
MRP Procedure

• 1. Netting net requirements against projected
  inventory
• 2. Lot Sizing planned order quantities
• 3. Time Phasing planned orders backed out by
  leadtime
• 4. BOM Explosion gross requirements for
  components

5
Inputs

• Master Production Schedule (MPS) due dates and
  quantities for all top level items
• Bills of Material (BOM) for all parent items
• Inventory Status (on hand plus scheduled
  receipts) for all items
• Planned Leadtimes for all items

6
Developing a Master Production Schedule

• Disaggregate the production plan into sku by sku
  forecasts corresponding to a manageable unit of
  time.
• Record customer orders accepted to date.
• Project on hand inventory as follows
• It It-1 MPSt - max(Ft or COt)
• where It projected inventory balance at the
  end of period t.
• MPSt production quantity due in period t
• Ft forecast of demand in period t
• COt customer orders booked for shipment in
  period t

7
MPS (cont.)

• Calculate production quantity (MPS) required to
  maintain inventory at or above safety stock
  levels
• Calculate the quantity of end-items marketing has
  Available to Promise (ATP) potential customers.
  
• ATP1 I0 MPS1 - CCO
• ATPt MPSt - CCO
• where ATPt available to promise in week t
• I0 current on-hand inventory
• MPSt MPS quantity in week t
• CCO cumulative customer orders until next
  MPS

8
MPS Numerical Example
Factory lot size for this sku 100 units Safety
stock required 0 units Time bucket 1 week
1
2
3
4
5
6
7
8
Forecast
30
30
40
40
50
50
60
20
Customer orders
45
20
12
10
0
0
0
0
Projected on-hand
50
5
75
35
95
45
95
35
15
MPS quantity
100
100
100
Available to promise
5
68
90
100
Average Inventory 50 Orders Placed 3
9
Bill of Material for the Chair
1347 Chair
Level 0
172 Leg Asm
148 Seat
047 Back Asm
Level 1
64 Leg (4)
58 Spindle(2)
71 Brace (2)
07 Back
91 Peg (2)
Level 2
1 Pine
1 Dowel
1/4 Dowel
1 Dowel
1 Pine
Level 3
Note 1Pine is treated at lowest level in which
it occurs for MRP calculations.
10
Exploding MRP Records

• Calculate Gross Requirements (GR) as the total
  demand derived from all higher order inventory
  items.
• Input open orders, called Scheduled Receipts
  (SR), for inventory items that have not been
  completed or not yet received from a vendor.
• Calculate Projected On-hand Inventory (POI)
• It It-1 SRt PRt - GRt
• Where It Projected On-hand Inventory in
  period t
• SRt Scheduled Receipts due in period t
• PRt Planned Receipts in period t
• GRt Gross Requirements in period t

11
Exploding MRP Records (cont.)

• A Planned Receipt (PR) is generated when
  projected on-hand inventory drops below safety
  stock and should be sufficient to raise
  projected on-hand inventory to at least equal
  the safety stock quantity.
• A Planned Order Release (POR) signifies when an
  inventory order should be placed and is
  normally the planned receipt offset by the
  items leadtime.

12
MRP Numerical Example
Lot size 25 units Safety stock 10 units
Leadtime 1 week
Item 172 Description Leg assembly
1
2
3
4
5
6
7
8
Gross requirements
100
0
100
0
100
0
0
0
Scheduled receipts
150
25
0
0
0
0
0
0
Projected on-hand
21
71
96
21
21
21
21
21
21
Planned receipts
25
100
Planned order releases

25
100
13
MRP Numerical Example (cont.)
Lot size 5 units Safety stock 5 units
Leadtime 2 weeks
Item 148 Description Seat
1
2
3
4
5
6
7
8
Gross requirements
100
0
100
0
100
0
0
0
Scheduled receipts
40
25
0
0
0
0
0
0
Projected on-hand
39
9
34
9
9
9
9
9
9
Planned receipts
75
100
30
Planned order releases

105
100
Action Notice 30 units ordered 2 weeks late
14
MRP Numerical Example (cont.)
Lot size 1 unit Safety stock 50 units
Leadtime 1 week
Item 047 Description Back assembly
1
2
3
4
5
6
7
8
Gross requirements
100
0
100
0
100
0
0
0
Scheduled receipts
72
0
0
0
0
0
0
0
Projected on-hand
19
50
50
50
50
50
50
50
50
Planned receipts
100
100
59
Planned order releases

59
100
100
Action Notice 59 units ordered 1 week late
15
MRP Numerical Example (cont.)
Lot size 200 units Safety stock 100 units
Leadtime 1 week
Item 064 Description Leg
1
2
3
4
5
6
7
8
Gross requirements
100
0
0
0
400
0
0
0
Scheduled receipts
200
0
0
0
0
0
0
0
Projected on-hand
120
320
220
220
220
220
220
220
220
Planned receipts
400
Planned order releases

400
16
MRP Numerical Example (cont.)
Lot size 50 units Safety stock 20 units
Leadtime 1 week
Item 071 Description Brace
1
2
3
4
5
6
7
8
Gross requirements
200
118
0
0
200
0
0
0
Scheduled receipts
100
0
0
0
0
0
0
0
Projected on-hand
20
52
52
52
52
52
52
52
52
Planned receipts
200
50
200
Planned order releases

200
250
Action Notice 50 units ordered 1 week late
17
MRP Numerical Example (cont.)
Lot size L4L Safety stock 100 units
Leadtime 1 week
Item Raw material Description 1 Dowel
1
2
3
4
5
6
7
8
Gross requirements
0
250
600
0
0
0
0
0
Scheduled receipts
300
0
0
0
0
0
0
0
Projected on-hand
100
150
150
100
100
100
100
100
100
Planned receipts
550
Planned order releases

550
18
Lot Sizing in MRP

• Lot-for-lot chase demand
• Fixed order quantity method constant lot sizes
• EOQ using average demand
• Other convenient lot size
• Fixed order period method use constant lot
  intervals
• Part period balancing try to make
  setup/ordering cost equal to holding cost
• Wagner-Whitin optimal method

19
Lot Sizing Example using EOQ

• EOQ

Note EOQ is a special case of fixed order
quantity.
20
EOQ Assumptions

• 1. Instantaneous production.
• 2. Immediate delivery.
• 3. Deterministic demand.
• 4. Constant demand.
• 5. Known fixed setup costs.
• 6. Single product or separable products.

WW model relaxes this one
21
Dynamic Lot Sizing Notation

• t a period (e.g., day, week, month) we will
  consider t 1, ,T, where T represents the
  planning horizon.
• Dt demand in period t (in units)
• ct unit production cost (in dollars per unit),
  not counting setup or inventory costs in period t
• At fixed or setup cost (in dollars) to place an
  order in period t
• ht holding cost (in dollars) to carry a unit of
  inventory from period t to period t 1
• Qt the unknown size of the order or lot size in
  period t

decision variable
22
Wagner-Whitin Property I

• Under an optimal lot-sizing policy either the
  inventory carried to period t1 from a previous
  period will be zero or the production quantity in
  period t1 will be zero.

Basic Idea of Wagner-Whitin Algorithm
By WW Property I, either Qt0 or QtD1Dk for
some k. If jk last period of production
in a k period problem then we will produce
exactly DkDT in period jk. We can then
consider periods 1, , jk-1 as if they are an
independent jk-1 period problem.
23
Wagner-Whitin Example

• Step 1 Obviously, just satisfy D1 (note we are
  neglecting production cost, since it is fixed).
• Step 2 Two choices, either j2 1 or j2 2.

24
Wagner-Whitin Example (cont.)

• Step3 Three choices, j3 1, 2, 3.

25
Wagner-Whitin Example (cont.)

• Step 4 Four choices, j4 1, 2, 3, 4.

26
Planning Horizon Property

• If jtt, then the last period in which
  production occurs in an optimal t1 period policy
  must be in the set t, t1,t1.
• In the Example
• We produce in period 4 for period 4 of a 4 period
  problem.
• We would never produce in period 3 for period 5
  in a 5 period problem.

27
Wagner-Whitin Example (cont.)

• Step 5 Only two choices, j5 4, 5.
• Step 6 Three choices, j6 4, 5, 6.
• And so on.

28
Wagner-Whitin Example Solution
29
Wagner-Whitin Example Solution (cont.)

• Optimal Policy
• Produce in period 8 for 8, 9, 10 (40 20 30
  90 units)
• Produce in period 4 for 4, 5, 6, 7 (50 50 10
  20 130 units)
• Produce in period 1 for 1, 2, 3 (20 50 10
  80 units)
• Note we produce in 7 for an 8 period problem,
  but this never comes into play in optimal
  solution.

30
Wagner-Whitin Example
31
Problems with Wagner-Whitin

• 1. Fixed setup costs.
• 2. Deterministic demand and production (no
  uncertainty)
• 3. Never produce when there is inventory (WW
  Property I).
• safety stock (don't let inventory fall to zero)
• random yields (can't produce for exact no.
  periods)

32
Lot Sizing Using Fixed Order Period (POQ)
Lot size L4L Safety stock 0 units Leadtime
1 week
Item 1023 Description sprocket
1
2
3
4
5
6
7
8
Gross requirements
100
100
200
300
100
0
200
100
Scheduled receipts
100
Projected on-hand
75
75
0
0
0
0
0
0
0
Planned receipts
25
200
300
100
200
100
Planned order releases
25
200
300
100
200
100

Average Inventory 75/89.375 per week Orders
Placed 6
33
Lot Sizing Using Fixed Order Period (POQ)
Lot size POQ-3 Safety stock 0 units
Leadtime 1 week
Item 1023 Description sprocket
1
2
3
4
5
6
7
8
Gross requirements
100
100
200
300
100
0
200
100
Scheduled receipts
100
Projected on-hand
75
75
300
100
0
200
200
0
0
Planned receipts
325
500
100
Planned order releases
325
500

100

Average Inventory 875/8109.375 per week Orders
Placed 3
34
Lot Sizing Using Part-Period Balancing
Assume ratio of setup to holding 200
PPB make the carrying cost as close to the
setup cost as possible
Safety stock 0 units Leadtime 1 week
Item 1023 Description sprocket
1
2
3
4
5
6
7
8
9
10
GR
44
41
84
84
42
86
7
18
49
30
SR
PoH
120
79
35
126
84
0
74
67
49
0
0
PR
175
160
30
POR
175
160
30
86 units gt 0 part periods 93 units gt 7 part
periods 111 units gt 43 part periods 160 units gt
190 part periods 190 units gt 310 part periods
49 units gt 0 part periods 91 units gt 42 part
periods 175 units gt 210 part periods
35
Nervousness

• Note we are using FOP lot-sizing rule.

36
Nervousness Example (cont.)

• Past Due
• Note Small reduction in requirements caused
  large change in orders and made schedule
  infeasible.

37
Reducing Nervousness

• Reduce Causes of Plan Changes
• Stabilize MPS (e.g., frozen zones and time
  fences)
• Reduce unplanned demands by incorporating spare
  parts forecasts into gross requirements
• Use discipline in following MRP plan for releases
• Control changes in safety stocks or leadtimes
• Alter Lot-Sizing Procedures
• Fixed order quantities at top level
• Lot for lot at intermediate levels
• Fixed order intervals at bottom level
• Use Firm Planned Orders
• Planned orders that do not automatically change
  when conditions change
• Managerial action required to change a FPO

38
Safety Stocks and Safety Leadtimes

• Safety Stocks
• generate net requirements to ensure min level of
  inventory at all times
• used as hedge against quantity uncertainties
  (e.g., yield loss)
• Safety Leadtimes
• inflate production leadtimes in part record
• used as hedge against time uncertainty (e.g.,
  delivery delays)

39
Safety Stock vs. Safety Leadtime
Average Inventory 20
Average Inventory 40
40
Safety Stock vs. Safety Leadtime (cont.)
Average Inventory 30
41
Master Production Scheduling (MPS)

• MPS drives MRP
• Should be accurate in near term (firm orders)
• May be inaccurate in long term (forecasts)
• Software supports
• forecasting
• order entry
• netting against inventory
• Frequently establishes a frozen zone in MPS

42
Rough Cut Capacity Planning (RCCP)

• Quick check on capacity of key resources
• Use Bill of Resource (BOR) for each item in MPS
• Generates usage of resources by exploding MPS
  against BOR (offset by leadtimes)
• Infeasibilities addressed by altering MPS or
  adding capacity (e.g., overtime)

43
Capacity Requirements Planning (CRP)

• Uses routing data (work centers and times) for
  all items
• Explodes orders against routing information
• Generates usage profile of all work centers
• Identifies overload conditions
• More detailed than RCCP
• No provision for fixing problems
• Leadtimes remain fixed despite queueing

44
Production Activity Control (PAC)

• Sometimes called shop floor control
• Provides routing/standard time information
• Sets planned start times
• Can be used for prioritizing/expediting
• Can perform input-output control (compare planned
  with actual throughput)
• Modern term is MES (Manufacturing Execution
  System), which represents functions between
  Planning and Control.

45
MRP Conclusions

• Insight distinction between independent and
  dependent demands
• Advantages
• General approach
• Supports planning hierarchy (MRP II)
• Problems
• Assumptions especially infinite capacity
• Cultural factors e.g., data accuracy, training,
  etc.
• Focus authority delegated to computer

46
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47
Toyota Production System

• Pillars
• 1. just-in-time, and
• 2. autonomation, or automation with a human touch
• Practices
• setup reduction (SMED)
• worker training
• vendor relations
• quality control
• foolproofing (baka-yoke)
• many others

48
The Seven Zeros

• Zero Defects To avoid delays due to defects.
  (Quality at the source)
• Zero (Excess) Lot Size To avoid waiting
  inventory delays. (Usually stated as a lot size
  of one.)
• Zero Setups To minimize setup delay and
  facilitate small lot sizes.
• Zero Breakdowns To avoid stopping tightly
  coupled line.
• Zero (Excess) Handling To promote flow of parts.
• Zero Lead Time To ensure rapid replenishment of
  parts (very close to the core of the zero
  inventories objective).
• Zero Surging Necessary in system without WIP
  buffers.

49
The Environment as a Control

• Constraints or Controls?
• machine setup times
• vendor deliveries
• quality levels (scrap, rework)
• production schedule (e.g. customer due dates)
• product designs
• Impact the manufacturing system can be made much
  easier to manage by improving the environment.

50
Inherent Inflexibility of JIT

• Sources of Inflexibility
• Stable volume
• Stable mix
• Precise sequence
• Rapid (instant?) replenishment
• Measures to Promote Flexibility
• Capacity buffers
• Setup reduction
• Cross training
• Plant layout

51
Capacity Buffers

• Problems
• JIT is intrinsically rigid (volume, mix,
  sequence)
• No explicit link between production and customers
• How to deal with quota shortfalls
• Buffer Capacity
• Protection against quota shortfalls
• Regular flow allows matching against customer
  demands
• Two shifting 4 8 4 8
• Contrast with WIP buffers found in MRP systems

52
Setup Reduction

• Motivation Small lot sequences not feasible with
  large setups.
• Internal vs. External Setups
• External performed while machine is still
  running
• Internal performed while machine is down
• Approach
• 1. Separate the internal setup from the external
  setup
• 2. Convert as much as possible of the internal
  setup to the external setup
• 3. Eliminate the adjustment process
• 4. Abolish the setup itself (e.g., uniform
  product design, combined production, parallel
  machines)

53
Cross Training and Plant Layout

• Cross Training
• Adds flexibility to inherently inflexible system
• Allows capacity to float to smooth flow
• Reduces boredom
• Fosters appreciation for overall picture
• Increase potential for idea generation

54
Cross Training and Plant Layout (cont.)

• Plant Layout
• Promote flow with little WIP
• Facilitate workers staffing multiple machines
• U-shaped cells
• Maximum visibility
• Minimum walking
• Flexible in number of workers
• Facilitates monitoring of work entering and
  leaving cell
• Workers can conveniently cooperate to smooth flow
  and address problems

55
U-Shaped Manufacturing Cell
56
JIT IMPLEMENTATION
57
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58
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59
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60
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61
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62
Kanban

• Definition A kanban is a sign-board or card in
  Japanese and is the name of the flow control
  system developed by Toyota.
• Role
• Kanban is a tool for realizing just-in-time.
  For this tool to work fairly well, the production
  process must be managed to flow as much as
  possible. This is really the basic condition.
  Other important conditions are leveling
  production as much as possible and always working
  in accordance with standard work methods.
• Ohno 1988
• Push vs. Pull Kanban is a pull system
• Push systems schedule releases
• Pull systems authorize releases

63
One-Card Kanban
Outbound stockpoint
Outbound stockpoint
Completed parts with cards enter outbound
stockpoint.
Production cards
When stock is removed, place production card in
hold box.
Production card authorizes start of work.
64
MRP versus Kanban
MRP
Lover Level Inven-tory

Assem-bly
Kanban
Lover Level Inven-tory

Assem-bly

Kanban Signals
Full Containers
65
The Lessons of JIT

• The production environment itself is a control
• Operational details matter strategically
• Controlling WIP is important
• Speed and flexibility are important assets
• Quality can come first
• Continual improvement is a condition for survival

66
Push and Pull Definitions

• Push Systems schedule work releases.
• inherently due-date driven
• control release rate, observe WIP level

• Pull Systems authorize work releases.
• inherently rate driven
• control WIP level, observe throughput

67
Push vs. Pull Mechanics
PUSH
PULL
(Exogenous) Schedule
(Endogenous) Status
Production Process
Production Process
Job
Job
Push systems are inherently make-to-order.
Pull systems are inherently make-to-stock.
68
Push and Pull Line Schematics
Pure Push (MRP)
Stock Point
Stock Point
. . .
Pure Pull (Kanban)
Stock Point
Stock Point
. . .

CONWIP
Stock Point
Stock Point
. . .
Full Containers
Authorization Signals
69
CONWIP

• Assumptions
• 1. Single routing
• 2. WIP measured in units
• Mechanics allow next job to enter line each time
  a job leaves (i.e., maintain a WIP level of m
  jobs in the line at all times).
• Modeling
• MRP looks like an open queueing network
• CONWIP looks like a closed queueing network
• Kanban looks like a closed queueing network with
  blocking

70
CONWIP Controller
Work Backlog
PN Quant



LAN
Indicator Lights
R
G
PC
PC
. . .
Workstations
71
Push/Pull Interface

• Eliminate entire portion of cycle time by
  building to stock.
• Requirements
• Level demand.
• Relatively few distinct parts.
• Relatively constant product mix.
• Implementation
• kanban
• late customization (postponement)

72
Example - Custom Taco Production Line
Push/Pull Interface
Pull
Push
Cooking
Assembly
Packaging
Sales
Refrigerator
Customer
73
Example - Quick Taco Production Line
Push/Pull Interface
Pull
Push
Cooking
Assembly
Packaging
Sales
Refrigerator
Warming Table
Customer
74
Summary of the Push Philosophy

• Goal maximize production by minimizing
  disruptions
• Management Assumption we can sell what we make
• Manufacturing Objectives
• Spread resources evenly line balancing
• Decouple operations line pacing and MRP
• Dependable and efficient operations automation
  and forecasting
• Where Push Works Well
• High speed, paced assembly lines with
  standardized finished goods, single direction of
  flow and high demand
• Problems
• Limited applicability
• Huge investment
• Disruption devastate material flowswamp floor
  with inventory

75
Summary of the Pull Philosophy

• Goal Produce salable goods as quickly and
  efficiently as possible
• Management Assumption we make what we can sell
• Manufacturing Objectives
• Clean up the workplace (5Ss)
• Flow manufacturing GT/CM and cross training
• Level production with visual control JIT,
  kanban, SPC
• Manpower reduction
• Where Push Works Well
• Repetitive manufacturing of a small population
  of parts
• Problems
• Limited applicability implementation may take
  years
• Disruption devastate material flow line stops
• Unfocused process improvement

76
Synchronous Manufacturing (Drum-Buffer-Rope)

• The goal is to make money not improve efficiency
• Evaluation requires a system-wide perspective.
• Throughput money generated from sales
• Inventory money invested in materials to sell
• Operating expense money spent to convert
  inventory into throughput
• Inappropriate levels of inventory damage a firms
  ability to compete
• Major obstacles to running an effective operation
  include
• Dependent events
• Statistical fluctuations

77
Synchronized Manufacturing focuses on operating
the plant as one synchronized system

• Four principles of managing bottleneck resources
  (CCRs)
• The marginal value of time at a bottleneck
  resource is equal to the throughput rate of
  products processed at the bottleneck
• The marginal value of time at a non-bottleneck is
  negligible
• Utilization of non-bottleneck is controlled by
  other constraints
• Flow through plant is controlled by most CCR
• DBR approach to improving system performance
• Identify system constraints
• Determine how to exploit constraints to improve
  performance
• Subordinate all parts of system to support step 2
• Carry out steps to improve system
• If bottleneck CCR is broken or moves, start again
  at step 1

78
Process Relationships and Product Flow in DBR

• A gateway is the point of entry for raw material,
  purchased parts, and information
• The manufacturing process points are
  manufacturing steps through which material and
  information flow.
• Flow through
• Divergent
• Assembly
• The final process represents the final step
  before shipment to customer

1
Gateway
Divergent point
2
3
5
Flow through points
4
6
Assembly point
7
8
Final process
79
VAT Analysis V Plants
Equipment and capital intensive process. Many end
items produced using very similar processing
techniques and raw materials Divergence points
provide several opportunities to misallocate
material
Final Process
Divergent points
Gateway
80
V Plant Misallocation of Material

• V plants tend to have one bottleneck
• misallocation of material and over production
  prior to bottleneck creates large inventory in
  front of bottleneck
• misallocation beyond bottleneck results in wrong
  finished goods inventory and increased load on
  the bottleneck
• Major concerns facing managers
• Large finished goods inventory
• Poor customer service
• Demand appears to change constantly
• Others complain about lack of manufacturing
  responsiveness
• Interdepartmental conflicts in manufacturing
• Traditional responses
• Finished goods not improved customer service -
  better forecasting
• Inefficiencies seen in plant - reduce direct
  labor
• Improve quality

81
DBR Applied
Final Process
Divergent points
Gateway
82
VAT Analysis A Plants
Equipment tends to be general purpose. Few end
items produced using very similar processing
techniques but unique components and raw
materials Assembly points provide opportunities
to misallocate resource capacity
Final Process
Assembly points
Gateway
83
A Plant Misallocation of Resources

• A plants tend to have many bottlenecks
• misallocation of resources caused by the use of
  large batches
• wave-like material flow lowers resource
  utilization
• when material arrives, overtime required
• material released early to maintain efficiency
• Major concerns facing managers
• shortages in assembly
• poor resource utilization and excessive unplanned
  overtime
• wandering bottlenecks and operation seems
  out-of-control
• Traditional responses
• Improve efficiency
• Control overtime
• Focus engineering on reducing unit cost

84
DBR Applied
Final Process
Assembly points
Gateway
85
VAT Analysis T Plants
Common components assembled into many end items
Unique component routings and raw
materials Assembly point provide opportunity to
misallocate material and capacity in the form of
component items
Final Process
Assembly point
Flow through points
Gateway
86
T Plant Misallocation of Material

• T plants tend not to have many bottlenecks
• misallocation of material at assembly point due
  to stealing
• Major concerns facing managers
• large finished good and component part
  inventories
• poor due date performance
• excessive fabrication lead times
• poor resource utilization in fabrication
• Traditional responses
• Improve off the shelf deliveries through better
  forecasting
• Improve off the shelf deliveries through improved
  inventory planning and control
• Reducing unit cost by improving efficiencies

87
DBR Applied
Final Process
Assembly point
Flow through points
Gateway
88
V-base and a T-top customer of the V-base is
another part of the plant
89
A-base with a T-top products that complete
processing through the A base are not shipped
90
V-base and an A-top items produced by V-base
become inputs into A-top
91
Complex combinations are also possible This is
an A and V topped with a T