Fall, 2007
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Chapter 7
Push and Pull Production
Control Systems
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Basic Definitions
 MRP. (Materials Requirements Planning). MRP is the basic
process of translating a production schedule for an end
product (MPS or Master Production Schedule) to a set of
requirements for all of the subassemblies and parts needed
to make that item.
 JIT. Just-in-Time. Derived from the original Japanese Kanban
system developed at Toyota. JIT seeks to deliver the right
amount of product at the right time. The goal is to reduce
WIP (work-in-process) inventories to an absolute minimum.
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Why Push and Pull?
 MRP is the classic push system. The MRP system computes
production schedules for all levels based on forecasts of
sales of end items. Once produced, subassemblies are
pushed to next level whether needed or not.
 JIT is the classic pull system. The basic mechanism is that
production at one level only happens when initiated by a
request at the higher level. That is, units are pulled through
the system by request.
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
Comparison
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These methods offer two completely different approaches to basic
production planning in a manufacturing environment. Each has
advantages over the other, but neither seems to be sufficient on its
own. Both have advantages and disadvantages, suggesting that
both methods could be useful in the same organization.
 Main Advantage of MRP over JIT: MRP takes forecasts for end
product demand into account. In an environment in which
substantial variation of sales are anticipated (and can be forecasted
accurately), MRP has a substantial advantage.
 Main Advantage of JIT over MRP: JIT reduces inventories to a
minimum. In addition to saving direct inventory carrying costs, there
are substantial side benefits, such as improvement in quality and
plant efficiency.
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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MRP Basics
 The MRP system starts with the MPS or Master
Production Schedule. This is the forecast for the
sales of the end item over the planning horizon.
The data sources for determining the MPS
include:
 Firm customer orders
 Forecasts of future demand by item
 Safety stock requirements
 Seasonal variations
 Internal orders from other parts of the organization.
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Schematic of the
Productive System (Fig. 7.1)
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
The Three Major Control
Phases of the Productive System (Fig. 7.2)
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Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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The Explosion Calculus
 The explosion calculus is a set of rules for converting the
master production schedule to a requirements schedule for
all subassemblies, components, and raw materials necessary
to produce the end item.
 There are two basic operations comprising the explosion
calculus:
• Time phasing. Requirements for lower level items must
be shifted backwards by the lead time required to
produce the items
• Multiplication. A multiplicative factor must be applied
when more than one subassembly is required for each
higher level item.
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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The Product Structure Diagram
 The product structure diagram is a graphical
representation of the relationship between
the various levels of the productive system. It
incorporates all of the information necessary
to implement the explosion calculus. Figure
7-3 (next slide) depicts an end item with two
levels of subassemblies.
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Typical Product Structure
Diagram (fig. 7-3)
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Trumpet and Subassemblies (Fig. 7-4)
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Product Structure Diagram
for Harmon Trumpet
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Explosion Calculus
Rules for translating gross requirements at one level to production
schedule at that level and requirements at lower levels.
Example
Basic Equation:
Net Req. = Gross req. - Scheduled Receipts - projected on hand
inventory
Basic Algorithm
1. Compute time-phased requirements
2. Determine Planned Order Release (LS)
3. Compute ending inventory
4. Proceed to next level (if any)
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Explosion Calculus
Schedule for end item A:
Week
8 9 10 11 12 13 14 15 16 17
Gross Req
77 42 38 21 26 112 45 14 76 34
Sch Rpt
12
6 9
Inv
23
Net Req
42 42 32 12 26 112 45 14 76 34
Schedule for item B (1 unit/2 weeks)
Week
6 7 8 9 10 11 12 13 14 15
Gross
42 42 32 12 26 112 45 14 76 34
Schedule for item C (2 units/4 weeks)
Week
4 5 6 7 8 9 10 11 12 13
Gross 84 84 64 24 52 224 90 28 152 68
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
Lot Sizing For MRP Systems
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The simplest lot sizing scheme for MRP systems is
lot-for-lot (abbreviated L4L). This means that
requirements are met on a period by period basis as
they arise in the explosion calculus. However, more
cost effective lot sizing plans are possible. These
would require knowledge of the cost of setting up for
production and the cost of holding each item. This
brings to mind the EOQ formula from Chapter 4,
which can be used in this context. However, there
are better methods.
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
Statement of the Lot Sizing Problem
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Assume there is a known set of requirements (r1,
r2, . . . rn) over an n period planning horizon.
Both the set up cost, K, and the holding cost, h,
are given. The objective is to determine
production quantities (y1, y2, . . ., yn) to meet the
requirements at minimum cost. The feasibility
condition to assure there are no stockouts in any
period is:
j
j
 y  r
i 1
i
i 1
i
for 1  j  n.
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Methods
One could apply the EOQ formula by defining
but there are better methods.
1 n
   ri
n
i 1
Property of the optimal solution: every optimal solution orders
exact requirements: that is,
y  r or y  r  r ,. . ., or y  r  r  ...  r
1 method
1
1
1
2 this property
1
n
One
that
utilizes
is1 the2Silver Meal
Heuristic. The method requires computing the average cost
for an order horizon of j periods for j = 1, 2, 3, etc. and
stopping at the first instance when the average cost function
increases. The average cost for a production quantity
spanning j periods, C(j), is given by:
C ( j )  ( K  hr2  2hr3  ...  ( j  1)hrj ) / j
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Methods (continued)
Another method that is popular in practice is part period
balancing. Here one chooses the order horizon to most closely
balance the total holding cost with the set-up cost.
Finally, a third heuristic is known as the least unit cost heuristic.
Here one minimizes the average cost per unit of demand (as
opposed to the average cost per period as is done in the Silver
Meal heuristic.) The average cost per unit of demand over j
periods is given by:
C ( j )  ( K  hr2  2hr3  ...  ( j  1)hrj ) /(r1  r2  ...  rj ).
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Methods (concluded)
 Experimental evidence seems to favor the
Silver Meal Heuristic among the four
discussed as the most cost efficient.
 Optimal lot sizes can be found by using
backwards dynamic programming.
 A heuristic method for lot sizing subject to
capacity constraints is discussed in this
section.
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
Shortcomings of MRP
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 Uncertainty. MRP ignores demand uncertainty, supply uncertainty,
and internal uncertainties that arise in the manufacturing process.
 Capacity Planning. Basic MRP does not take capacity constraints
into account.
 Rolling Horizons. MRP is treated as a static system with a fixed
horizon of n periods. The choice of n is arbitrary and can affect the
results.
 Lead Times Dependent on Lot Sizes. In MRP lead times are
assumed fixed, but they clearly depend on the size of the lot
required.
 Quality Problems. Defective items can destroy the linking of the
levels in an MRP system.
 Data Integrity. Real MRP systems are big (perhaps more than 20
levels deep) and the integrity of the data can be a serious problem.
 Order Pegging. A single component may be used in multiple end
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
items, and each lot must then be pegged to the appropriate item.
www.izmirekonomi.edu.tr
Fall, 2007
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Introduction to JIT
 JIT (Just In Time) is an outgrowth of the Kanban system
developed by Toyota.
 Kanban refers to the posting board where the evolution of the
manufacturing process would be recorded.
 The Kanban system is a manual information system that
relies on various types of cards.
 It’s development is closely tied to the development of SMED:
Single Minute Exchange of Dies, that allowed model
changeovers to take place in minutes rather than hours.
(The mechanics of a typical Kanban system are pictured in
Figure 7-8.)
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Kanban System for Two Production
Centers (Fig. 7-8)
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Features of JIT Systems
Small Work-in-Process Inventories.
Advantages:
1. Decreases Inventory Costs
2. Improves Production Efficiency
3. Reveals quality problems (see Figure 7-10)
Disadvantages:
1. May result in increased worker idle time
2. May result in decreased throughput rate
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
River/Inventory Analogy
Illustrating the Advantages of Just-in-Time
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Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Features of JIT Systems (continued)
Kanban Information Flow System
Advantages:
1. Efficient tracking of lots
2. Inexpensive implementation of JIT
3. Achieves desired level of WIP
Disadvantages:
1. Slow to react to changes in demand
2. Ignores predicted demand patterns
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Kanban Information System vs
Centralized Information System (MRP)
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Features of JIT Systems (continued)
JIT Purchasing System
Advantages:
1. Inventory reduction
2. Improved coordination
3. Better relationships with vendors
Disadvantages:
1. Decreased opportunity for multiple sourcing
2. Suppliers must react quickly
3. Potential for congestion
4. Suppliers must be reliable.
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr
Fall, 2007
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Comparison of MRP and JIT
Major study comparing MRP and JIT in
practice reveals:
 JIT works best in “favorable” manufacturing
environments: little demand variability, reliable
vendors, and small set up times
 MRP (and ROP based on Chapter 5 methods)
worked well in favorable environments (comparable
to JIT) and better in unfavorable environments.
Asst. Prof. Dr. Mahmut Ali GÖKÇE, Izmir University of Economics
www.izmirekonomi.edu.tr