Week 3 Learning Activities
Using Bath and Body Works as a model, describe how you would develop a master schedule. Make sure you describe the relationship between the production plan and the customer demand forecast.
The format for developing a master schedule has several specific fields. Use this weeks assigned resources to identifythe five most important rows you would find in a master schedule. Make sure you describe why its important to include this information in a master schedule and whythe ones chosen are most important.
Only need 5-6 paragraphs
OR-Notes
J E Beasley
OR-Notes are a series of introductory notes on topics that fall under the broad heading of the field of operations
research (OR). They were originally used by me in an introductory OR course I give at Imperial College. They are
now available for use by any students and teachers interested in OR subject to the following conditions.
A full list of the topics available in OR-Notes can be found here.
Master production schedule
Introduction
The master production schedule (also commonly referred to as the MPS) is effectively the plan that the company
has developed for production, staffing, inventory, etc.
It has as input a variety of data, e.g. forecast demand, production costs, inventory costs, etc and as output a
production plan detailing amounts to be produced, staffing levels, etc for each of a number of time periods.
This production plan:
operates at an aggregate level (that is it does not usually go into great detail about parts to be used, etc –
hence the name aggregate planning); and
is cost driven, that is it attempts to meet the specified requirements at minimum cost.
The idea of a master production schedule can best be illustrated by means of an example.
Example
In our example we have just a single product being produced.
Production takes place each period (week) either in the normal (regular) production shift or in overtime associated
with that shift. There is only one shift (i.e. not operating a two/three shift system – such as with “round-the-clock”
working).
Completed items can also be “bought-in” from a subcontractor (at a cost).
We are allowed to hire/fire workers (again at a cost). Backorders are also allowed (recall here that backorders are
customer orders that cannot be satisfied in the required period, but the customer allows the order to remain open to
be fulfilled in a later period). Lost sales are not allowed.
The diagram below illustrates the situation and the types of factor with which we are dealing graphically.
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http://people.brunel.ac.uk/~mastjjb/jeb/or/contents.html
The data for the example we consider is as below, where we have shown the initial data entry screen from the
package.
In the above screen we have chosen the “General LP Model”. This is the most general of the options allowed by
the package. LP stands for linear programming and is a generalised way of modelling decision problems. To ease
data entry we have not crossed the “Part Time Allowed” box – if we had then we would have had the option of
dealing with part time employees.
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http://people.brunel.ac.uk/~mastjjb/jeb/or/lp.html
We have also not crossed the “Lost Sales Allowed” box – if we had then we would have allowed lost sales. In
general a company may allow lost sales because the company finds that customers simply do not backorder – i.e. a
lost sale is automatic if the product is not immediately available; or the company is prepared to allow lost sales as it
may be better to allow orders to be lost than to allow such orders to become backorders (thereby incurring
backorder costs).
The remaining boxes have been crossed and so we can deal with:
overtime
hiring/firing
subcontracting
backorders
In our example above we have just 4 periods (weeks) – this is our time horizon (planning period). We are dealing
with employees working hours in each week. Two employee hours are required to produce one unit of each
product and the initial number of employees is 10. At the start of the planning period there is no initial inventory
(nor are there any backorders).
The data for our example entered into the package in the light of the choices made at the initial screen is as below:
The meaning of each of these lines of data is given below:
Forecast Demand – this is the forecast demand for the product in each of our 4 periods (weeks).
Initial Number of Employee – this is the initial number of employees in each week, here just the 10 employees we
have currently.
Regular Time Capacity in Hour per Employee – this is how many regular hours each employee works per week,
here 35 hours
Regular Time Cost per Hour – this is the cost per hour of regular time worked, here 15
Undertime Cost per Hour – this is the cost per hour of not using a worker to their full regular capacity, here zero
Overtime Capacity in Hour per Employee – this is the maximum number of hours each employee can work in
overtime per week, here 10 hours
Overtime Cost per Hour – this is the cost per hour of overtime, here 25
Hiring Cost per Employee – this is the cost of hiring one employee, here 500
Dismissal Cost per Employee – this is the cost of dismissing (firing) one employee, here 2000
Maximum/Minimum Number of Employee Allowed – here we can set limits on the maximum and minimum
number of employees, here M signifies there is no limit on the maximum number and the minimum number is 8. In
general there may be an upper limit on the number of employees due to physical capacity constraints.
Initial Inventory (+) or Backorder (-) – the initial inventory available or backorders outstanding, here zero
Maximum/Minimum Ending Inventory – here we can set limits on the maximum and minimum number of
product units in stock at the end of each week, here M signifies there is no limit on the maximum number and the
minimum number is zero. In general there may be an upper limit because we have a limited space in which to store
stock. The minimum number corresponds to safety stock that may be kept in case of unforeseen demand.
Unit Inventory Holding Cost – this is the cost of holding one unit in stock at the end of each period, here 3
Maximum Subcontracting Allowed – this is the maximum number of product units we are allowed to buy in from
the external subcontractor, here there is no limit on the amount that may be bought in. In general there may be a
limit on the total amount the subcontractor can supply to us each period.
Unit Subcontracting Cost – this is the cost of each unit bought from the external subcontractor, here 60
Maximum Backorder Allowed – this is the maximum number of backorders allowed at the end of each period,
here there is no limit on the number of backorders that can be held at the end of each period.
Unit Backorder Cost – this is the cost of each backorder outstanding at the end of each period, here the M signifies
that each backorder is very expensive. The effect of M here will be to ensure that (if at all possible) backorders will
be avoided.
Other Unit Production Cost – this is the cost of producing one unit of the product that is not already accounted for
by employee costs – here zero
Capacity Requirement in Hour per Unit – this is the number of employee hours that are required to produce one
unit of the product, here 2 hours
In order to ease understanding of the problem most of the above data items take the same value in each and every
period (week). However it would be perfectly possible for them to have different values in each week.
Consider for a moment this example as we have defined it so far. We have a single product, are planning over 4
time periods, have regular time and overtime, can buy from an external subcontractor, and are allowed to hire and
fire employees. Some of the decisions we must make are shown below:
Period 1 2 3 4
Amount to produce using regular time ? ? ? ?
Amount to produce using overtime ? ? ? ?
Amount to purchase from subcontractor ? ? ? ?
Number of backorders ? ? ? ?
Number to hire ? ? ? ?
Number to fire ? ? ? ?
You can see from this matrix that there are already 24 decisions which we have to make. For such problems
decision models (such as the decision model used within the package) are much better at decision-making than
people. Moreover such models can guarantee to make decisions at minimum cost, something people cannot do.
Note here that even the (cheap) package used here is extremely flexible in terms of the situations it can consider.
Solution
The solution to the problem is shown below:
It can be seen that we immediately hire more employees, and that these are employed throughout the planning
period of 4 weeks. Note that the number hired is 10.57 – i.e. it includes a fraction of an employee. This often
happens in aggregate planning and can usually be ignored (simply round to the nearest appropriate whole number).
Reflect that we are producing a plan for production over a 4 week period. It is unlikely that our demand forecasts
will be completely accurate and hence this rounding need not concern us unduly.
With 10+10.57 = 20.57 employees working 35 hours a week we have a regular time capacity of 20.57×35 = 720
employee hours (approximately) and at 2 hours per unit produced this corresponds to a regular time production of
360 units – precisely as above, i.e. over the 4 week planning period we are planning to work all of our employees
to their full regular time capacity. As can be seen above we are planning no overtime or subcontracting.
Note the build into inventory that occurs in various periods. As inventory costs us money let us be clear about why
the above (the minimum cost solution) involves build into inventory. It is to meet future demand. Demand for the
product increases over the 4 week planning period (from 250 to 450 units) and the package has determined that the
most cost-effective way to ensure that this demand is met is to build into inventory in earlier periods. Note that an
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alternative strategy to meet this increased demand would be to buy from the subcontractor, were this cheaper the
package would have adopted that strategy.
The costs associated with the package solution can be seen below:
The production and employment strategy given above is the minimum cost strategy since the package uses linear
programming to calculate a schedule for production and staffing that meets the forecast demand and also satisfies
the other constraints that we place upon the problem at minimum cost. It would be impossible to find the minimum
cost solution manually – consider the solution shown with an increase in employment and with varying amounts
built into inventory – could that ever be produced by a person in the time (fraction of a second) it takes the package
to produce it?
Level and chase strategies
We may be interested in a solution that consists of a fixed number of workers (a level strategy). This can be seen
below where we have fixed the maximum and minimum number of employees to 10 (the current number) in each
and every period.
The solution for this level strategy is shown below.
http://people.brunel.ac.uk/~mastjjb/jeb/or/lp.html
It can be seen that with this solution we use both overtime and subcontracting but do not build into inventory. The
total cost of 63,400 is much more than the previous (non-level) strategy which had a total cost of 49475.71
If we change the hire/fire costs to zero and reset the limits on the maximum/minimum number of employees then
we will produce a chase strategy (ramp workforce up/down as required). This solution can be seen below.
Backordering
In our original situation considered above, with costs for hiring and firing, we were prohibiting backorders by
making them very expensive. Suppose now that backorders cost us 1 per period (week). The effect of this on the
solution is shown below.
It can be seen that we produce nothing – the cheapest solution is simply to allow backorders to build up over the
planning period. This seems silly and for this reason it is usual to insist that there are no backorders outstanding at
the end of the planning period. This is an assumption that is, by convention, applied and is a reasonable assumption
when planning over a relatively long time period. Moreover unless this is assumed it can happen that the best thing
for the company to do over the planning period is simply to allow backorders to build up (as above).
To ensure that there are no backorders outstanding at the end of the planning period we enter a zero for “Maximum
Backorders Allowed” in the last period (week 4), as below.
The solution is
which is effectively the same as the initial solution we considered. However to illustrate that backorders can play a
role suppose that we:
have a level strategy with exactly 10 employees in each and every period (week); and
restrict the subcontractor capacity in each and every period
then the input is as below:
and the output is:
where backorders do occur since production capacity (both regular time and overtime) together with subcontractor
capacity is insufficient to met demand in some periods.
Rolling horizon
In practice we would probably deal with the situation described above on a “rolling horizon” basis in that we
would get an initial production plan based on current data and then, after one time period (say), we would update
our problem/data and resolve to get a revised production plan. In other words even though we plan for a specific
time horizon (here 4 weeks) we would only ever implement the plan for the first week, so that we are always
adjusting our 4 week plan to take account of future conditions as our view of the future changes. We illustrate this
below.
Period 1 2 3 4 5 6 7 P = plan
P P P P D = do (follow) the plan in a period
D P P P P
D P P P P
D P P P P Master Production Schedule
6-1
6
MASTER PRODUCTION SCHEDULE
MGT2405, University of Toronto, Denny Hong-Mo Yeh
So far, in discussing material requirement planning (MRP), we have assumed that master
production schedule (MPS) is ready to be fed into MRP. In fact, human users involve in
MPS procedure much more than in MRP. MPS drives all kinds of planning including MRP
of an enterprise. MPS is so important that users involve intensively, while MRP is normally
an automatic computer procedure.
MPS Objectives and Data Sources
In this section, we discuss the importance of MPS and its input data. MPS itself is a major
input to the MRP.
Importance of MPS
A production plan is an aggregate plan that schedules product families in relatively
long time intervals. Master production schedule is used for individual end products
and in shorter time intervals. MPS is important in the following aspects:
1. It is the link between what is expected (production planning) and what is actually to
be built, i.e., material requirement planning and final assembly schedule (FAS, to be
discussed).
2. It develops data to drive the detailed planning, MRP. MPS is a priority plan for
manufacturing. It keeps priorities valid.
3. It is the basis for calculating the resources available (capacity) and the resources
needed (load). It provides devices to reconcile the customers demand and the plants
capability.
Master Production Schedule
6-2
4. It makes possible reliable delivery promises. It provides salespeople information on
available-to-promise (ATP) indicating when end products are available. ATP will be
discussed later.
5. It is a tool that can be used to evaluate the effects of schedule changes. It is a device
for communication and a basis to make changes consistent with the demands of the
marketplace and manufacturing capacity.
6. It is a contract between marketing and manufacturing departments. It is an
agreed-upon plan. It coordinates plans and actions of all organizational functions and
is a basis to measure the functions performance.
7. It provides management with the means to authorize and control all resources needed
to support integrated plans.
8. In the short horizon, MPS serve as the basis for planning material requirement,
production of components, order priorities, and short-term capacity requirements.
9. In the long horizon, MPS serves as the basis for estimating long-term demands on
the company resources such as people, equipment, warehousing, and capital.
MPS as a primary Input to MRP
MRP input data include MPS, external demand for components, forecasts of
independent demand for components, BOM, and fundamental data in item master such
as lead times, safety stocks, scrap allowances and lot-sizing rules. Among the above
data, MPS is the primary input to MRP. It enables MRP to translate the end item
schedules into individual component requirements. Therefore, MRP depends on the
validity and realism of the MPS for its effectiveness.
Suppose that there are 30 end products made from 5,000 components, parts, and raw
materials. MPS helps people to concentrate on the planning of the 30 independent end
items, and leave the other 5,000 dependent items to be processed automatically by
MRP.
External demands for components include service-part orders, interplant orders, OEM
orders, components needed for sales promotion, R&D, destructive testing, etc.
Forecasts of independent demand for components include service parts no longer used
in regular production which are better planned by time phased order point (TPOP).
After reviewing forecasts, the planners input the quantities they decide are reasonable
for such items as added gross requirements. External demands and forecasts for
Master Production Schedule
6-3
independent components normally are not incorporated in the MPS but are instead fed
directly into MRP as separate inputs.
Data sources for MPS
The data needed to develop an MPS include:
1. Customer orders.
2. Dealer orders.
3. Inventory replenishment orders.
4. Forecast for individual end products.
5. Interplant requirements.
6. Distribution center requirements.
7. Inventory levels for end products.
8. Safety stock.
9. Released production orders for end products.
10. Capacity constraints.
Time-Phased Order Point
Time phased order point (TPOP) is a technique similar to MRP logic. It is used to conduct
planning for independent demand items, where gross requirements come from a forecast,
not via explosion of the planned order releases of the parent items. TPOP can be used in
planning service part requirements. This technique can also be used to plan distribution
center inventories as well as plans for service parts. TPOP is an approach that uses time
intervals thus allowing for time-phased lumpy demands instead of average demand as in
ROP.
TPOP is a preferred alternative to reorder point replenishment techniques (ROP) for the
following reasons:
1. TPOP allows planning for known lumps in future demand; ROP accepts average
demand only.
2. TPOP provides information on future planned orders, which is the data required in
planning the needed resources. ROP only provides information for overall resources
requirement.
3. TPOP permits re-planning for requirements; this keeps relative priorities valid for
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6-4
all shop orders. ROP does not consider future requirements.
4. TPOP links planning for independent and dependent demands for items with both
types. Service part demand planning is an example.
ROP is to be discussed in chapter seven. TPOP differs from MRP in that TPOP covers each
individual item while MRP covers all the items in a product structure. The gross
requirements in TPOP are drawn from independent sources while the gross requirements in
MRP come from the explosion of higher level data. The planned order releases in TPOP are
not further exploded, but the POR in MRP are exploded to next level items.
From Production Plan to Master Production Schedule
Production plans and master production schedules differ in their precision. Production
plans are macro plans, while MPS are micro plans. Production planning is for
preparing resources to accomplish business objectives. Resource requirement planning is
used to reconcile business objectives with the resources available. MPS is the schedule of
end item production. It is a decision of manufacturing actions subject to the constraints of
capacity.
It is a set of decisions that determines manufacturing actions subject to capacity
constraints.
Rough-cut capacity planning is used to obtain a realistic MPS and therefore a realistic MRP.
Suppose the following production plan is for a product family X of three end products A, B,
and C: (The initial on-hand inventory for X is 500.)
Table 1: Production Plan for Product Family X
Month 1 2 3 4
Forecast 620 800 660 760
Production Plan 720 720 720 720
PAB 500 600 520 580 540
The on-hand inventory for X consists of the inventories of its end items shown in Table 2:
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Table 2: On-hand Inventory
Item On-hand Inventory
Product A 250
Product B 150
Product C 100
Product Family X 500
The master scheduler must devise an MPS to fit the constraints of the PP. MPS is derived
from the customer orders and the forecasts, but must not exceed the production plan
quantities. Table 3 is a valid MPS for the first two months.
Table 3: MPS for End Items
Item Week 1 2 3 4 5 6 7 8
A
GR 78 85 86 90 96 100 120 100
MPS 90 90 90 90 90 90 90 90
PAB 250 262 267 271 271 265 255 225 215
B
GR 48 50 46 52 58 60 70 55
MPS 54 54 54 54 54 54 54 54
PAB 150 156 160 168 170 166 160 144 143
C
GR 28 30 32 32 38 44 40 36
MPS 36 36 36 36 36 36 36 36
PAB 100 108 114 118 122 120 112 108 108
MPS Techniques
Master production scheduling is a time-phased order point (TPOP) procedure. The planned
order releases (POR) in the TPOP are the master schedules fed into the MRP system. MPS
are done for the MPS items (end products). In assemble-to-order (ATO) cases, a module is
defined as an MPS item, and all its ancestors must also be MPS items. Two-level master
production schedules are used in assemble-to-order cases. Related topics are discussed as
follows.
Demand Time Fence (DTF)
DTF is a point of time in MPS. The DTF is set between the current date and the
planning time fence (PTF). The region between the current date and the demand time
fence contains actual orders that are frozen. Change of orders within DTF may cause
unstable production problems. No unanalyzed and unapproved changes are allowed
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for the MPS in this region. DTF is the earliest due date for taking a customer order.
Promising a customer order with a due date prior to DTF may cause late delivery. But
it does not mean that it is impossible to take an order with a due date earlier than DTF.
As long as there is enough available-to-promise (ATP) within the DTF, we can still
promise a customer order delivering before DTF.
Planning Time Fence (PTF)
PTF is set between DTF and the end of planning horizon. The region between DTF and
PTF contains actual orders and forecast orders. The region beyond PTF contains only
forecast customer orders. Between DTF and PTF, actual customer orders replace the
forecast quantities.
Now DTF PTF End of Planning Horizon
Region 1: Regin 2: Regin 3:
Frozen customer Customer orders replace Forecasts
Orders the forecasts
Figure 1: DTF and PTF
PTF is the accumulated lead-time for the end products. Related purchase orders or
manufacturing orders may have been released. Change of customer orders within PTF
may bring the necessity of rescheduling purchase orders or manufacturing orders. A
customer order with due date later than PTF can easily be changed for related
activities have not started yet.
MPS considers only the customer orders within DTF for it is not likely that any new
orders will fall in this region. MPS considers the larger of the customer orders and the
forecasts from DTF to PTF for new orders keep replacing the forecasts in this region.
If customer orders exceed forecasts, it means that the demand is underestimated, and
MPS considers customer orders. MPS considers only the forecasts for it is not likely
that many customer orders are received that early.
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Projected Available Balance (PAB)
Projected available balance is the projected inventory of the end items if the MPS
quantities are completed. MPS quantity is the quantity of end items that we planned to
manufacture. It includes the scheduled receipts and the firm planned orders (FPO).
Firm planned order is a common approach to describe MPS. Master schedulers are
required to firm all the planned order receipts (PORC) before PTF. That is, master
schedulers have to make a decision of what to produce from now to PTF. As shown in
Figure 1, MPS system considers as independent demand only the customer orders in
region 1, the larger of forecast and customer orders in region 2, and the forecast orders
only in region 3.
Available-To-Promise (ATP)
Available-to-Promise is the uncommitted portion of a companys inventory and
planned production, maintained in the master schedule to support customer order
promising. The ATP quantity is the uncommitted inventory balance in the first period
and is normally calculated for each period in which an MPS receipt is scheduled. In the
first period, ATP equals on-hand inventory plus MPS (if it is positive) less customer
orders that are due and overdue. In any period containing MPS schedule receipts, ATP
equals the MPS less customer orders in this period and all subsequent periods before
the next MPS schedule receipt. A negative ATP takes over prior periods ATP until it
turns from negative to zero or the prior periods ATP becomes zero.
Two-Level Master Production Schedule
It is a master scheduling approach where a planning bill of material is used to master
schedule end items or product families. Key features such as options and accessories
are frequently used in the two-level MPS procedure. For forecast demand, product
families are master scheduled and the usage ratio in the quantity-per of planning
BOM is used to calculate the gross requirement of the modules. For customer orders,
options and accessories are defined before the master production scheduling. In this
case, end items instead of families are master scheduled.
Multilevel Master Production Schedule
Master Production Schedule
6-8
A master scheduling approach that allows any level in an end items BOM to be master
scheduled. To accomplish this, MPS items must receive requirements from
independent and dependent demand sources. Higher level MPS items are scheduled
before lower level MPS items.
Case Study: MPS and ATP
Suppose there are two MPS items X and Y with BOM shown in Table 4. Please notice that
the parent part number in Table 4 should be BOM code; we assume all items have default
values for the BOM codes. The sources of independent demand are customer orders and
forecast. The demand time fence (DTF) is period 4, and planning time fence (PTF) is
period 10. The gross requirement from period 1 to period 4 includes actual customer orders.
From period 5 to period 10, the gross requirement in each period is the maximal of
customer order and forecast. The gross requirements include only forecasts beyond period
11. The projected on-hand (POH) and projected available balance (PAB) are identical to
those defined in MRP reports. We assume all the planned order receipts (PORC) are firm
planned orders thus the MPS is equal to the PORC. The calculation of POH, PAB, MPS,
and ATP are done in a TPOP procedure, as shown in Table 5 and Table 6.
Table 4: BOM
Parent Part No. Component Part No. Qty-Per
X C 0.25
X D 1
Y D 1
Y E 1
E F 2
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6-9
Table 5: MPS and ATP for X
Part No.=X OH= 55 LT= 1 SS= 0 LS= 40 DTF= 4 PTF= 10
Period 1 2 3 4 5 6 7 8 9 10 11 12
Forecast 18 21 17 17 15 15 29 28 25 25 20 20
Cust. Order 19 20 15 25 12 18 14 16 20 20 15 15
POH 36 16 1 -24 1 -17 -6 6 -19 -4 16 -4
PAB 36 16 1 16 1 23 34 6 21 36 16 36
MPS(PORC) 0 0 0 40 0 40 40 0 40 40 0 40
ATP 1 3 22 10 20 5 25
Table 6: MPS and ATP for Y
Part No.=Y OH= 10 LT= 1 SS= 5 LS= 20 DTF= 4 PTF= 10
Period 1 2 3 4 5 6 7 8 9 10 11 12
Forecast 20 20 20 20 15 15 15 15 20 25 15 30
Cust. Order 30 20 20 15 11 8 0 20 5 5 20 0
POH -20 -15 -15 -10 -5 0 5 -15 -15 -20 -10 -20
PAB 5 5 5 10 15 20 5 5 5 5 10 5
MPS(PORC) 25 20 20 20 20 20 0 20 20 25 20 25
ATP 5 0 0 5 9 12 0 15 20 0 25
Available-to-promise (ATP) appears in the first period and those periods with positive MPS
quantities. The amount of ATP means the quantity that sales can promise the customers
during the period from current period to the period before the next positive MPS period.
The first period ATP (5) of Y in Table 6 is the on-hand inventory (10) plus MPS (25) minus
the accumulated customers in period 1 (30). The accumulated customer orders should
include the second period if the MPS in period 2 is zero. In calculating MPS (or PORC),
safety stock has to be considered to make PAB always above the safety stock. In
calculating ATP, we do not consider the safety stock; any stock can be promised to
customers.
Suppose we receive a customer order for 30 Xs to be delivered in period 7. The total
customer order in period 7 becomes 44. The result of MPS is shown in Table 7.
In Table 6, since there is no MPS in period 8, the MPS in period 7 must cover the demands
in period 7 and 8, and the ATP is 10 (40-14-16). In Table 7, the customer order quantity in
period 7 increases to 44, which makes the master scheduler to schedule a new MPS (40) in
period 8, and the MPS (40) in period 7 needs only cover the demand in period 7. Even so,
the customer order still exceeds the MPS by 4. To prevent ATP in period 7 from going
negative (-4), 4 units of ATP are moved from period 6 to period 7. The ATP in period 6
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6-10
should be 22 (40-18) if the next MPS is enough for the demand of customer orders. Since
the customer order exceeds MPS by 4 units in period 7, the ATP in period 6 decreases by 4
and only 18 remains.
Table 7: MPS Calculation with Insufficient ATP
Part No.=X OH= 55 LT= 1 SS= 0 LS= 40 DTF= 4 PTF= 10
Period 1 2 3 4 5 6 7 8 9 10 11 12
Forecast 18 21 17 17 15 15 29 28 25 25 20 20
Cust. Order 19 20 15 25 12 18 44 16 20 20 15 15
POH 36 16 1 -24 1 -17 -21 -9 6 -19 1 -19
PAB 36 16 1 16 1 23 19 31 6 21 1 21
MPS(PORC) 0 0 0 40 0 40 40 40 0 40 0 40
ATP 1 3 18 0 4 5 25
Distribution Requirement Planning
Distribution Inventory
Distribution inventory includes all finished goods held anywhere in the distribution
system. It consists of finished goods in warehouses and also in transit. There are
various types of distribution systems. Generally, a distribution has a central supply
warehouse that is supported by a factory, a number of distribution centers, and
customers. An example of distribution system is shown in Figure 2.
DC A DC B DC C
Central Supply
Factory
Customers
Master Production Schedule
6-11
Figure 2: A Distribution System
The purpose of holding inventory in distribution centers is to improve customer
service by locating stock near the customers and to reduce the transportation cost by
allowing the manufacturer to ship in full loads rather than in partial loads over long
distances.
