Advanced Planning Systems

8/10/2019 Advanced Planning Systems

1/23

Advanced Planning Systems as an Enabler ofLean Manufacturing

March 24, 1999

Jeffrey K. Liker

Principal and Senior Lean ConsultantOptiprise, Inc.

Karl Burr

Vice President

i2 Technologies


2/23

Problem

Manufacturing companies throughout the world in many industries are

adopting lean manufacturing methods, a fundamental shift from traditional

mass production. The original model for lean manufacturing is the Toyota

Production System. Toyota runs their system with remarkably little information

technology and relies heavily on simple, visual, manual signals to manage

scheduling and material flow such as kanban cards and strategic buffer

stocks i.e. market places. Yet, in American companies, while lean systemsare being implemented on the shopfloor, in parallel information technology

departments are implementing new information technologies for enterprise

integration and shopfloor control. Thus, the question arises: In what ways can

appropriately applied information technology significantly enhance the

performance of lean systems? In other words, how can we bring together

these parallel activities so they work in concert to drive value?

In this paper we focus in particular on Advanced Planning and Scheduling

(APS) for shopfloor production as an enabler of lean manufacturing.Advanced Planning and Scheduling systems begin with a representation or

mathematical model that mirrors the actual supply and manufacturing system.

The model can be used to optimize selected parameters of the system as a

whole and can be updated almost instantly as conditions change and new

data is generated. The forerunners to modern APS, like MRP and Finite

Forward Scheduling packages, were used to schedule push systems and

generate schedules down to the level of individual machines and staffing

requirements. This has changed. Modern Advanced Planning and Scheduling

Systems are more sophisticated, simulating pull systems by projectingconsumption by customers. By contrast, lean manufacturing emphasizes that

operations should be directly tied together through pull systems driven by

customer demand.

Our focus on shopfloor advanced planning systems tackles head on the role

of these computerized planning systems in lean manufacturing, as the

shopfloor is the place where lean manufacturing most strongly advocates pull

systems. An analysis of the total supply chain is beyond the scope of this

particular white paper.


3/23

We will argue that while APS has traditionally supported scheduling of

traditional manufacturing, it can also be used in a somewhat different way to

substantially enhance pull systems. Moreover, we will argue that in most

factories the conditions are such that a blend of lean and scheduled systems

makes the most sense for optimal performance. As an example, many plants

these days are moving toward build-to-order manufacturing. Dell is one of the

best known, but many other companies are following suit. Furniture is

another example, but even automotive companies are starting to envision

making cars to real customer orders. With a large number of end productsand products actually delivered based on real customer orders one would

need a warehouse the size of a small city to keep a few of each kind of end

product in safety stock. The mass customization solution is generally to use

combinations of standard parts to make a large variety of end products, and

do the assembly that differentiates products at the latest possible stage in the

process. Some components are very commonly used in a large proportion of

orders so these can be kept in a parts supermarket and replenished based on

kanban. Other components are more particular and their demand fluctuates

widely so a scheduled system is needed based on forecasts which areupdated with customer orders in real time as they come in. This seems to be

an ideal scenario for appropriate mixes of pull and scheduled systems.

We will begin with a general overview of lean manufacturing concepts

focusing on information and material flow. We will then discuss the role of

APS as an enabler of a pure lean manufacturing system. We will then

describe a number of different scenarios in which APS might be introduced,

pure scheduling, pure lean, and mixed models, and consider the potential

value of APS in these different models. Finally, we will consider what thismeans for implementing APS to support lean manufacturing.

What is Lean Manufacturing?

Lean manufacturing is a philosophy which seeks to shorten the lead time from

customer order to manufacture and delivery of product by eliminating waste.

Waste is anything that does not add value to the product from the customers

perspective. Taiichi Ohno is often given credit for creating the first lean

manufacturing system at Toyota, now called the Toyota Production System(TPS). Just as Henry Fords system was built on many innovations

developed by other people, Ohno learned from wherever he could find good

ideas. Ohno faced a crisis and had to develop a better way since Toyota

after World War II had little cash, little space, and was trying to create a car

company from the ground up to serve a small and diverse customer base in

Japan only Thus he needed to develop a highly efficient manufacturing


4/23

Taiichi Ohno realized that material sitting in inventory was wasteno value-

added activity was being performed on it. In fact, Henry Ford had written

about that very idea. And Ohno attacked waste with a vengeance

eliminating warehouses and creating flow of materials whenever possible. For

example, having a set of machines on one side of the plant that built to

inventory which was then moved to the other side of the plant and fed into

another set of machines led to extra handling and material waiting to be

processed. So Ohno would move the machines together into a product layout

(for example, a manufacturing cell) and material would flow. In the processhe discovered that quality improvedsince defects coming from the

preceding process were immediately detected by the succeeding process

before a lot of defects had time to be made. He also found that it was better

to stop and fix defects than to let them build up to be repaired laterthe basis

for the now famous Andon system. Inspection and repair were waste.

Up to this point there is no contradiction with APS as it is applied in traditional

systems. APS seek to optimize total systems and it is up to the analyst and

client to specify the scheduling strategies and optimization goals. If theobjective is to get material to flow through the system very quickly, subject to

real world constraints, APS can create a plan which if followed will maximize

throughput and minimize inventory. Thus the goalsof lean manufacturing and

APS can be congruent.

However, lean is more than a set of goals. It is a system of tools, beliefs, and

methodologies. One of Taiichi Ohnos breakthroughs was the recognition that

scheduling individual machines led to piles of inventory. In principle if

operation A and operation B are sequential processes, and each makes justwhat is scheduled exactly when it is scheduled, and the schedule is a good

one, material can flow through the plant with little buildup of inventory. But

Ohno observed from experience that focusing on planning individual

operations with the goal of product coming together where it is needed and

when it is needed, leads to waste. In fact, individual operations are decoupled

from their immediate customers and build what they expectthe next operation

will need, which is generally different from what the next operation really

needs. This is because schedules always change and schedulesare never

perfectly followed in the very dynamic environments of manufacturing plants.Thus, inventory builds up between operations and we are back where we

startedpush production.


5/23

Ohnos breakthrough was the concept of a pull system. If operation A,

instead of making assumptions about what operation B needs hour by hour

during the day, actually looks at what operation B needs and builds that, we

should be able to minimize inventory buildup. Even if operation B deviates

from the planned productionas long as operation A follows the lead of B and

changes what it builds accordingly the system is synchronized. Back in 1948,

Ohno began setting up pull systems where downstream operations withdrew

product from upstream processes and upstream process replaced what was

taken away.. By 1949 he eliminated most intermediate warehouses and tied

plants together within Toyota. In 1953 one of his breakthroughs was

implementing a supermarket system to pull product from upstream

operations to downstream operations. The supermarket system was based in

part on observations of American supermarkets. On the store shelf small

amounts of product are neatly and visually arranged and customers take what

they want. In response to what the customer takes away, the stock clerk

replenishes what is missing from the shelf. Ohno developed a similar system

in a Toyota machine shop. He kept the complete lineup of parts coming from

the upstream operations in bins on racks and attached cards (kanban) toeach bin. When the consuming operation (B) took away a box of parts he put

the kanban in a mailbox which was sent back to the producing machine A

which signaled A to make more to replenish the supermarket. A was then

building, with some lag time, what B was consumingthey were tied together

using this very simple method. Some parts, for example chassis, are large

and it does not make sense to have cards for each rack of 5 such parts, so

these parts are brought to the line based on another signala call signal.

The call signal can be as simple as pushing a button which triggers a light

indicating a call to bring another rack of 5 chassis.

TPS is a very customer-focused system. The idea behind the pull system is

to make just what the customer wants, as close as possible to when they

want it. The customer withdraws from the supermarket and upstream

operations replenish what the customer takes away. What then should

capacity and staffing plans look like? They should be sized to build at the

speed at which the customer is withdrawing product. That pace is referred to

as the takt timethe German word for meter or pace. Operations working

faster than the takt time have to build to inventory which is waste. Operationsrunning slower will not be able to keep up so they will shut down the customer

or have to run extra hours and also build up inventory.


6/23

While building to customer demand is the ideal, Ohno learned that, when

operating with little inventory, if a downstream operation suddenly withdraws a

lot of one product it could overwhelm the capability of the upstream operation

to replenish what was taken away from the supermarket, unless there was a

very large amount of inventory in the supermarket. To minimize the amount

of inventory held in the supermarket the consuming operation needs to

withdraw at a stable, leveled pace. So TPS argues that a condition for using

pull and keeping inventory down is to build at a stable, levelized pace. This

means that instead of building exactly in the sequence the customer

consumes product, actual customer demand plus forecasted demand should

be spread over some time horizon and manufacturing should build to that

leveled demand. Thus, even in the Toyota Production System there is a

place for a schedule and for forecasted demand.

In sum, central tenets of lean manufacturing include:

Takt time and Continuous FlowAll operations should ideally build at thepace of customer demand. Continuous flow is the ideal, building one

piece at a time, which tends to minimize waste, with all operationsbuilding to takt time. With a pure one piece flow only the first operation inthe continuous flow needs to be scheduled and all other operations followsequentially. Takt time is the pacemaker for the continuous flow.

Pull systems should be used when continuous flow is not feasible. In thiscase a small buffer (supermarket) is set up between operations and thefeeder operation replenishes what is taken away by the downstreamoperation. Ideally, only the final operation (beginning of final continuousflow) is scheduled and then all upstream processes build to replenishwhat has been consumed by their immediate customer.

Production leveling--While ideally the lean system would build only whatthe customer needs exactly as they need it, in reality customer demand isnot level. In a multi-product environment, an uneven demand (e.g., asudden surge in demand for one of the products) makes it difficult toservice that demand unless there is a large inventory of all end products.This surge in demand is particularly disruptive for upstream suppliers (i.e.,the bull whip effect). Lean manufacturing deals with this through

heijunka, i.e., leveling demand by creating an inventory buffer andreplenishing that buffer using a leveled schedule.


7/23

APS as an enabler to lean: Taking it to the next level

Lean manufacturing has been operating very effectively at Toyota using

primarily manual systems. Taiichi Ohno found that simplicity in manufacturing

was a virtue. A part of that simplicity was to use visual systems wherever

possible. Thus, the signals used to trigger more production were cards or

kanban. They could be color coded, they traveled with the material so it was

apparent if a kanban was missing, and operators and material handlers had to

do something deliberate and manual to order parts. Kanban gave operatorscontrol over the scheduling process. By sending back a kanban they were

literally sending an order to schedule capacity for the parts represented on the

kanban. As vehicles became more complex ultimately thousands of parts all

have cards attached to them. It is now common to have hundreds of

components coming just to the final assembly operation. Yet, Toyota is able

to handle that complexity adding only an automatic card sorter to sort out

cards coming back from suppliers. The cards now have bar codes on them

so that the card reader automatically updates a database by indicating that a

transaction has occurred and accounts payable and receivable can beupdated by computer.

The reason the card system is manageable is that the card systems represent

a large number of pull loops that are reasonably autonomous. So if the

injection molding department making instrument panel housings the operators

need only worry about the cards associated with the instrument panels and

the raw materials to be used for the I.P.s.

Most information systems used in manufacturing, e.g., MRP systems, arebased on a push system. Todays manufacturing applications in the APS

class are more sophisticated than MRP systems. These applications

concurrently take into account capacity and material constraints, calculate

dynamic lead times and are based on an accurate model of the production

system. So how can APS add value to a company that is implementing lean

manufacturing? In the next section we discuss how APS can provide the

planning engine while simple visual mechanisms are used for execution.

Planning in Support of Stable, Lean Systems

While the MRP systems of yesteryear were intended to be execution systems,

modern APS are designed as decision aids. They can help make decisions at

a variety of levels of granularitymacro capacity planning for the entire plant,

schedules for departments, and schedules for individual machines. If the

diti h th t l k b t b ff ti d th


8/23

What needs to be planned? The entire value stream needs to be planned,

even in a lean manufacturing system. Someone decided how many parts

should be in a bin, how many bins of product to put in the marketplace, how

many kanban to issue, etc. In other words, these are all buffers which should

be dynamically sized based on a global, optimal plan. The takt time is based

on a customer demand which has not happened yetit is in the future. Thus,

someone developed a forecast. Someone figured out how to take that

forecasted takt time and create a level schedule for final assembly. While it is

possible to do much of this manually, the computer does them faster. And in

some case, such as developing an optimal plan, it is not feasible to do all the

iterations required manually.

Let us consider each of the main features of synchronous material flow in lean

and how they can be enabled by APS:

Takt Time and Balanced OperationsThe design of a lean operation starts

with the takt time. What is the customer demand rate? Then ideally all

operations are designed to be balanced to the takt time. For manual

operations work elements are assigned to individual operators to load them

up to the takt time. Machines may be dedicated to a product family and their

cycle times matched to the takt time.

There are at least two conditions in which a takt time analysis is not

straightforward and can be aided by APS. First, if the takt time changes over

time new calculations are needed to rebalance the system. As long as the

takt time can be predicted accurately and smoothed over a time period, the

manual calculations can easily be performed. (This of course assumes thereis a good forecasting method in place for computing the takt time, which itself

can be non-trivial.) But when the customer demand rate changes, there are a

myriad of calculations necessary to identify the optimal rates for the entire

product flow. When there are many different product lines all with changing

takt times, a lot of manual effort is necessary. In fact, in our experience, once

the takt time calculation has been done to design the operation it is the

unusual company that computes changes in takt time and rebalances the

operation on a regular basis. Toyota, in fact, does this monthly but they are

an example of an unusual company. With an APS system the computermodel can automatically be re-optimized with different takt times and the

implications for rebalancing computed.


9/23

Second, takt time calculations are straightforward when there is a dedicated

product line with variation on that product which have the same routing and

work content and all machines are dedicated to that product and run in the

same amount of available time. Takt time is the time available to operate in a

period divided by the customer demand for that period. So, for example, we

might find that the takt time for a component is one piece coming off the line

every 60 seconds and design to that one number. But what happens when

we have multiple products produced on the same final assembly line and

these products have different customer demand rates and go through

different operations? And what happens when we can not or do not run all

operations the same amount of time per day? Some operations may run 3

shifts, others 2 shifts, some with breaks, and others automatically without

breaks. While in principle we would like a continuous flow with dedicated

machines running the same amount of time and a product going through the

same process, this is often not practically feasible. Then we may find we

have different takt times for different products and even for different stages of

the process.

For example, let us say a part that includes plastic components is provided to

the customer at a rate of 2400 per day. The final assembly operation runs

one shift for 7 hours. Thus the takt time would be 11.25 seconds of

available time per piece and the operation would be set up for that. But a

plastic injection molding machine is slower than final assembly and runs 24

hours a day for a total of 23 hours of operating time (with one hour of

preventative maintenance). For the molding machine the takt time would be

34.5 seconds. So a little complexity has been added with just two operations

that run for different amounts of available time. Things start to really get

interesting if those injection molding machines are also being used to support

other products and the available time is a variablethat is it depends on the

allocation of machine time across products.

The point is not that these complexities represent an insurmountable barrier

to planning for lean manufacturing. Obviously Toyota has found ways to

make this work under a variety of circumstances. But the implications of

balancing to takt time for a process of any complexity is time consuming and

not always straightforward. And when there are many different products andprocesses in the plant, it is quite possible that no one will take the time to

optimize and update these rates on a regular basis and identify their

implications for capacity planning, staffing, and the balancing of the operation.

This is in fact what APS systems are especially good at as they include a

model of the entire system and can optimally balance the entire system.


10/23

Pull SystemsFor a process of any complexity there are many decisions

that have to be made to design a pull system. The basic concept is simple

enough. When you cannot build exactly to customer orders one piece at a

time you build the smallest lot sizes feasible and deliver those based on

signals from your immediate downstream customer that they need more. For

automotive parts this typically means building to a supermarketa small

inventory store between you and your immediate customer. The store

contains all the items the immediate customer, the consuming operation,

might want. As they withdraw from the store they send kanban back to

replenish what has been taken away.

The key decisions to make in this case are the pack size, how many to put in

a container, the marketplace size, how many kanban to put in the system, and

the frequency of the replenishment cycle by material handling. You can make

blanket assumptions, such as I want to have two hour inventory lineside and

at least 2 hours minimum and 4 hours maximum in the store, but this may be

more than needed for some products and less than is needed for others. The

reason for this can be seen in the formula for the number of kanban in the

system:

Kanban Quantity (Max.)

K = D (P + C)/ Q + SS

Where:

D = Demand (Consumption Rate in units per time period, like Takt Time)

P = Production Time per batch (order to replenishment)

! LEAD

C = Conveyance Time per batch (order to replenishment)

TIME

Q = Quantity per Kanban

SS = Safety stock.

Typically there is one kanban per container and the container size is then the

size of the order quantity. Thus, adding a kanban means adding an additional


11/23

automatic, there are different takt times for manual and automatic mirrorsand there should be more kanban for the automatic mirrors.

The greater the lead time the greater the number of kanban. Note thatthe lead time, which is the time required to replenish the marketplacewhen a kanban has been sent back, includes production time andconveyance time. Both of these will vary across components and thusdifferent number of kanban are needed depending on lead time.

Capacities of containers also vary depending on the size of the part.

In addition to all the above, typically the minimum quantity in themarketplace takes into account how much safety stock is needed. Thesafety stock depends on how much variabilitythere is in customerdemand, manufacturing, and supplied component lead timethat is, howreliable the processes are. This also varies across different products andmanufacturing processes.

The point here is that including the number of containers of product needed

for two hours of production is a highly simplistic decision rule. And once

made it is not likely to be adjusted often, except for periodically when perhapssomeone says, we are doing well, so lets see if we can get down to 1.5

hours. A better approach is to use data to optimally derive the appropriate

reorder point and number of kanban and update these calculations on a

regular basis. This is something APS is very good at maintaining a plan

instead of recreating and re-assembling the data each time. And APS can

look more broadly than between a feeder operation and the consuming

operation to consider stability in supply through the value chain to identify

appropriate marketplace sizes and kanban quantities.

As an actual example, in an automotive axle plant there were many

processes needed to make a wide variety of rear wheel drive axles for many

different light trucks. Ultimately there are well over 100 combinations of axles

produced in this plant on multiple assembly lines. The plant machines ring

gears and pinion gears which must ultimately mate. The ring gears alone go

through ten major processes prior to assembly. Between each process there

is a large inventory buffer of up to 60 hours worth of inventory. Material has

traditionally been moved on large pallets by forklift between each operation.

Material can spend weeks in the system. The plant is in the early stages of

moving to lean manufacturing and is switching over from the pallets to carts

on wheels that hold smaller quantities of material. The vision is to use these

carts as a kanban system, paint a certain number of squares on the floor, and

only build enough to replenish marketplaces of carts. Each stage in the

production process makes multiple types of gears. Some are dedicated to


12/23

The lean manufacturing support group in the plant is not sure how to size the

marketplace. They are not aware of what calculations are necessary. While

they could get a formula like we presented and calculate the appropriate

quantities they would have to do this for each stage in the process and for

each product meaning dozens of such calculations. Once these calculations

were made they are not apt to recalculate these quantities as conditions

change, something APS can easily do. APS also can help them analyze and

determine where they could get the most leverage in improving the system

using gaming techniques or what-if scenario development , e.g., where to

focus efforts on creating continuous flow or reducing changeover times.

An illustration of the value of APS for planning purposes can be seen by

walking through Figures 1-3. Figure 1 shows an empty kanban board with

hooks for hanging kanban. Each column is set up for kanban for that part.

Shown in parentheses are the maximum number of kanban that have been

allocated and thus the maximum number of containers of parts in the system

at any point in time. A material handler will pick up cards from the consuming

operation which represent containers of parts that have are being used inproduction and bring them to the producing operation. The material handler

will fill the board by placing the Kanban on available hooks from the bottom

up. The Green zone indicates no rush to make these parts. Cards in the

yellow zone are higher priority and cards in the red zone mean the

downstream operation may be starved for parts unless you build these right

away. For example, in Figure 1 there are 2 hooks in the red zone for Part A

so someone decided two bins is the minimum reorder quantity.

F i g u re 1 :E x a m p le K a n b a n S e q u e n c e B o a rd

. . . .

. . . .

. . . .. . . . .. . .

.

...

.

. . . .

. . . . ... . .

P a r t A (1 0 ) P a r t B (7 ) P a r t C (2 1 )

R e dR e d

Y e l l o wY e l l o w

G r e e n

N O T E : T h i s em pty kanba n boa r d has hooks for hang ing kanban E ach co lum n is s e t up for kanban f or tha t


13/23

Figure 2 shows a board loaded with kanban that have been coming back from

the consuming operation (e.g., assembly). In this case we have color coded

the Part A segment of the board with green indicating the cards for Part A are

almost into the red zone and production should be set up to make those parts

next. By contrast, there are only two cards back for Part C out of a totalpossible 21 cards so there is plenty of Part C in the supermarket and that is

the lowest priority for production. It should be clear that this simple manual

system is literally the schedule.

Figure 3 illustrates that when conditions change, e.g., customer demand rates

(volume or mix or both) change or perhaps there are problems with parts or

machines which require more safety stock for some parts, cards should be

added or subtracted from circulation. In this case conditions changed and the

APS systems calculated cards should be reallocated. For example, four morekanban should be added for Part A to increase the maximum WIP of Part A.

These will be allocated by adding two hooks to the green zone and one each

to the yellow and red zones. APS recommended the change in kanban and

can also recommended how to allocate the kanban (e.g., what should be the

minimum trigger point). The board and cards can be a very powerful, visual

control system which is loaded manuallybut the optimized planning for this

.

. . . .

. . . .

. . .

. . . .

. . . .

. . . . .

. . . . .. ..

.

.

..

.

Part currently runningPart currently running

Part ready for next set-up

Figure 2: Planning the kanban system

RedRed

YellowYellow

Green

Part A (10) Part B (7) Part C (21)

The KanbanKanban system for the execution process ...


14/23

Production LevelingIt is not accurate to say Toyota does not schedule its

system but uses pure pull. Nor does Toyota build exactly what the customer

wants when we want it. In its pure form this would mean building exactly to

customer demand without consolidating orders in any way. In practice this

would mean orders would be very uneven with respect to mix and volume.Orders are to a degree random and random means at times you will get a

sudden rash of red vehicle orders or a rash of vehicles with manual mirrors. If

the assembly plant built directly to these orders they might quickly use of their

red paint supplies and manual mirror supplies in the marketplace. They would

then send an urgent message to the supplier to ship more, which may well

outstrip the small amount of inventory in the marketplace of the supplier. That

supplier will soon run out of unique components for the manual mirror from

their suppliers.

In fact, the ripple effect through the supply chain gets amplified at each stage

backward in the processreferred to as the bull whip effect. With a small

flick of the wrist at the handle end, a bull whip creates a large movement with

great destructive force at the end of the whip. Many studies using

mathematical modeling or simulation have demonstrated how small deviations

Figure 3: APS Can Enhance The Kanban System

. . .....

... . . .

. . . . ... . . . .. . . .

. . . .. . . .

. . .....

... . . ... . . ... . . . .

. . . .. . ... . . .

...

....

..

APS for plann ing ...

and for re-planning

RedRed

Green

YellowYellow

RedRed

Green

YellowYellow



Key: Red dots=kanban hooks added to board

Gray dots=kanban hooks taken away


15/23

It has long been recognized by Toyota that a lean system with little inventorydepends on having a level schedule:

The smoothing of production is the most important condition for production by

Kanban and for minimizing idle time in regard to manpower, equipment, and

work-in-process. Production smoothing is the cornerstone of the Toyota

Production System. (Toyota Production System by Yasuhiro Monden, 1998)

Toyota represents TPS as a house and often draws it with productionsmoothing, referred to as heijunka,as the foundation. Why would heijunka be

so critical? Because the entire production process and supply chain have

been finely balanced to takt time which assumes a certain mix of product.

When all the marketplaces and kanban quantities are sized to this takt time, a

wild swing in demand for a particular product cannot be handled by the

kanban. Recall that with kanban each stage of the process, e.g., the mirror

plant, only needs to know what has been taken away from their marketplace

to schedule production. They look and replenish what is taken away. A

sudden surge in demand, like a rash of orders for manual mirrors, will quicklyempty out the marketplace and overwhelm the ability of the production

operation to replenish it. Besides there are not enough kanban cards to place

on all the containers of material needed to fill a very large order.

While there has been a lot of discussion in the bull whip effect literature on

using current and accurate information to mitigate its effects, Toyotas solution

is to level production at each stage of the process and develop stable

manufacturing processes that can build to the leveled schedule. The

assembly plant uses a leveling algorithm to take the demand (actual orders +

forecast) and create a levelized sequence to spread out all variations of

product across the day so the producers of components for that product see a

level stream of orders coming to them. As a general rule they assume the

suppliers should plan for fluctuations of +/- 10% deviations from the levelized

schedule in a given day. Toyota commits to keeping their schedule within

those parameters and goes to extremes to build the mix and volume of

product committed to by the end of the day.

Suppliers also have a responsibility to keep their schedules level so their

suppliers do not see dramatic fluctuations in demand. Most suppliers do not

build directly to a truck, but generally have a finished goods marketplace as

well as a safety stock of product as a buffer to changes in their customers

demand. They then can build at a levelized pace to replenish the marketplace

and the safety stock (if needed)


16/23

A tool often used by Toyotas component suppliers to level their production isa heijunka box, which is also called a load-leveling box. It is used to level the

production schedule for the final assembly or production operation in each

supplier plant. Figure 4 shows a load-leveling box. The figure shown only

goes out from 8:00am to 10:30am but generally would cover the entire day. In

this case we have slots every 15 minutes of the day. This time interval, 15

minutes, is called the pitch, and is the frequency with which a material handler

will come to the box, take whatever card or cards are in that time slot, and

then withdraw the product type and amount on the card and bring it to the

finished goods area. The box is loaded by material planning following rules tolevelize production of the four products shown over the day. The box can be

loaded with kanban or may be loaded with shipping labels that go right on the

boxes which are brought to shipping.

Part 8:00 8:15 8:30 8:45 9:00 9:15 9:30 9:45 10:00 10:15 10:30

Fig. 4: Load Leveling (Heijunka) Box

The box is used for scheduling production of multiple end products coming off one line. Material

handlers pick up the cards (kanban or a shipping label) in the verticle slots at the times indicated

across the top. The cards are sorted into the slots to level (spread out) the building of product types

and set the pace of production. The box above ends at 10:30 but would normally cover the work day.

A card is picked up every 15 minutes and delivered to manufacturing.


17/23

Figure 5 shows a load-leveling box along with a store and assemblyprocessa typical arrangement in a supplier plant. The material handler

withdraws product from the store every 15 minutes to bring to shipping in this

case based on the cards taken from the box. The assembly process is

replenishing the store based on some kind of production signal. They might

look at the store to see when product has gotten down to the red line or

perhaps when the material handler picks up product he or she takes a color

coded golf ball and rolls it down a chute in sequence to assembly so they

know what to build next. The results is a leveled pace of assembly and

leveled withdrawals of different products. Kanban can then be used to pullmaterials to assembly from earlier stages of production as assembly

consumes the materials.

When the process is stable and the operators and material handlers have the

discipline to execute it as planned it is a thing of beauty in its simplicity. Thelevel-loading box becomes the pacemakerthe heart beat of the system. So

why would we want to add information technology to such a simple and visual

system? Again, if the system is stable enough with a small number of end

products and the heijunka box can work then it is a great execution system

and need not be replaced by IT. But there are a number of reasons why APS

b hi d th dd tl t h ij k

Part 8:00 8:15 8:30 8:45 9:00 9:15 9:30 9:4510:00 10:15 10:30

F igure 5: Level Schedule: Heijunka Withdrawal Box

Heijunka box used for levelized kanban withdrawal.

Assemble to replenish store.

Assembly

Process

Gravity fedStore

Withdrawal

Kanban

Stop ProducingMust Replenish

HeijunkaBox

To ShippingDock

Part #

KJ-467

Production

Signal

Pull Partsto

Assembly


18/23

" Planning and loading the boxLike kanban, there is a lot of planningthat goes into heijunka. The pitch and kanban quantities need to be

calculated and the actual way of loading the box must be worked out.

Typically a set of rules are generated to load the box. Production control

is generally responsible for loading the box based on these rules. When

there are a number of products and the mix of demand does not exactly

match a multiple of the number of time slots available, it is not a trivial

task to figure out how to level-load the box. Moreover, often a number of

constraints must be considered simultaneously to load the box, such as

leveling out the product mix as well as optimizing what goes on a truckload based on multiple customers. And if there is a sudden demand for

certain products how should they be loaded in? Moreover, when there

are multiple heijunka boxes to load it can be an onerous task to figure out

the loadings of each box first thing in the morning. APS can be of great

value by specifying how to optimally load the box while simultaneously

considering multiple constraints.

" Dealing with unlevel demandToyota emphasizes that a condition for

using pull systems with suppliers is building a level production system.This means you need to have a very good forecast or a bank of orders

and then level that over a period of time. Toyota has been very fortunate

in the stable growth of its business and also limits the options in

customers order, through option packages (particularly outside Japan) to

simplify scheduling. As long as there is a relatively stable demand and

the supplier is building in steady state, the heijunka system leads to a

smooth and stable production flow. But there are many things that can

lead to instability and unlevel demand swings. One example is a sudden

surge in demand for aftermarket parts. As another example, if there is a

supply problem with another supplier which prevents building certain

models those will get removed from the schedule and other models will be

pulled ahead. This can lead to a sudden surge in demand to a supplier.

If a supplier is really running lean with little inventory then it will not take

long to affect the supplier, and the suppliers supplier. In that case, it may

be necessary to change the way the heijunka box is loaded. APS can

suggest what to do next in these cases as well as trigger plans to

accelerate ordering of needed raw materials.

When is Pull not Enough?

Toyota has worked hard to create the conditions needed to support pull

systems with very little inventory between producing operations and

consuming operations We already discussed the importance of developing a


19/23

One of the pioneers in the United States in promoting JIT systems andexplaining how they operate is Robert Hall.

1 He lists a number of conditions

that make pure pull systems very difficult to implement effectively:

1. Despite stable demand, the assembly of the final product cannot be

executed in a level enough fashion to provide steady demand for

upstream operations. This can happen when the assembly plant is not

stable or when the product inherently takes different amounts of time with

every unit (e.g., there is testing involved that takes different amounts of

time per unit).

2. Some operations must be started in advance of pull signals. This

happens when operations require special, lengthy, or difficult setups

which cannot be simplified or significantly shortened and must be

scheduled in advance.

3. The product is made in so many options, and the demand for each option

is so small or unstable that it is impractical to carry buffer stocks for all

parts everywhere in the process. This is the case of masscustomization and is found in industries like furniture where each order is

customized and assembly builds only complete orders regardless of the

mix within it.

4. The high defect level causes too many interruptions to permit continuous

flow, and the state of the technology is such that the defect level cannot

be reduced significantly.

5. Products must be produced as integrated batches throughout the process

for reasons of quality control or certification. An example of this is

pharmaceuticals.

When any of these conditions or a combination of them apply, it will probably

be necessary to schedule in addition to pull. There is no reason why it is

necessary to choose one or anothermixed models are quite possible. For

example, a schedule can be developed for purposes of having material and

people available but then some type of visual signal can actually be sent from

downstream operations to start production.


20/23


21/23

Implementing APS to Support Lean

Figure 6 summarizes the use of APS as a planning system to enable a lean

execution system. Not only does APS enable planning which then sets up

what can be a simple, visual execution system for running the shop, but the

plan can be updated as the day goes on and conditions change, and the plan

can be visible to everyone throughout the supply chain. So for example, if the

level-loading formula changes at the first-tier supplier, upstream suppliers can

see the new demands that will be placed on them and long-lead time items

that require scheduling can be scheduled appropriately. The two loops areinterconnected to illustrate that what happens on the shopfloor during

execution should feed data into APS so plans can be updated.

To effectively implement APS to support lean there needs to be a fit between

the planning and execution system. This means that the APS implementer

needs to understand the material control execution system so the APS is

Advanced Planning

System for Lean

Lean Execution

Systems

Demand Planning

Levelized Sequence

Kanban Quantities

Supermarket Sizes

Visibility through

supply chain

Load-leveling systems

Mixed-model production

Continuous flow processing

Pull systems

lean supply chain

management

Figure 6: Advanced Planning to Support Lean Execution

Part 8:00 8:15 8:30 8:45 9:00 9:15 9:30 9:45 10:00 10:15 10:30


22/23

Most manufacturing plants, even in progressive companies, are in a transitionto lean manufacturing. For example, they may have plans for continuous flow

assembly cells, but they are not fully implemented. They may have plans for

pulling material from feeder manufacturing processes to the assembly cells

but they are still at the stage of scheduling production. There may be efforts

to improve preventative maintenance, but downtime is still a problem. In

these cases APS cannot be set up solely to support the future lean system

this would waste the capability of the system. Instead, it should be used to

improve the scheduling needed during the transition to lean. Through

carefully adjusting the optimization models it is possible to use APS to helpguide the plant through the lean transition. For example, the model can

reveal where the bottlenecks are to achieving flow and therefore help

prioritize the lean initiatives. It may show clearly that reducing setup time at a

certain machine is a prerequisite for sending level pull signals back to raw

material suppliers, as well as quantify the benefits of doing this. This

information can be a powerful tool both for prioritizing lean initiatives, as well

as for providing ammunition to persuade skeptics who are challenging the

lean systems.

As we described, the most promising systems are those which use mixed

models of pull and lean systems. This requires a whole new way of thinking.

Traditional scheduling experts simply assumed the entire plant would be

scheduled using the computer system. Lean thinkers often just assume the

entire plant will use pull systems and scheduling is not needed, except to set

up a leveled schedule for final assembly. Mixed models require an

understanding of both sets of methods, and thinking across paradigms, to

identify what the conditions are and the best solutions in different parts of the

operation.

What this means is that those with APS expertise have to work as a team with

the lean manufacturing experts to design, implement, and improve upon the

planning and execution systems in concert. The days of the APS expert

collecting a little data, running the model in a back room, setting it up to

schedule the system based on present day assumptions, and leaving are

overif they were ever effective. In lean terms the APS expert must go to the

GEMBAto where the product is madestudy the process and the lean

plans, and work with the lean experts design and implement a compatiblesystem.


23/23

Advanced Planning Systems

Documents

Transcript of Advanced Planning Systems