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Theory of constraints contributions to outbound logistics

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Theory of Constraints contributions to outbound logistics

Fernando Bernardi de Souza

São Paulo State University (UNESP)

Av. Eng. Luiz Edmundo C. Coube 14-01

Bauru, SP, 17033-360 - Brazil

fbernardi@feb.unesp.br

Tel: 55 (14) 3103-6122

Sivio R. I. Pires

Methodist University of Piracicaba (UNIMEP)

Rodovia do Açúcar, Km 156

Piracicaba, SP, 13400-911 - Brazil

sripires@unimep.br

Tel.: 55 (14) 3124-1515

POMS 19th Annual Conference

La Jolla, California, U.S.A.

May 9 to May 12, 2008

Abstract

This article aims to present the main contributions of the Theory of Constraints (TOC) to

outbound (distribution) logistics. It presents the thesis defended by TOC, according to which

the current ways of managing outbound logistics, based mainly on sales forecasts, batching

policies, and indicators that do not privilege the supply chain performance as a whole, lead to

difficulties in handling the trade-off between logistics (stock and transportation) costs and

stock-outs levels. This conventional logistics management is labeled by TOC as pushed

distribution, in contrast with its defended approach called pulled distribution. Considering the

few scientific texts existing on the subject, this article claims to present, based on a theoretical

research, the technical aspects of the approach suggested by TOC and discuss the assumptions

it is based on, in an attempt to contribute to the expansion of outbound logistics knowledge

and widen the analysis of supply chain management.

Key words: Outbound logistics; distribution; pull system; Theory of Constraints.

1. Introduction

Since its inception about two decades ago, the Theory of Constraints (TOC) has developed

quickly and stood its ground as an important model for business management (WATSON et

al, 2007). In this sense many management issues have been studied, structured and presented

under TOC perspective. Some good examples are the approaches for performance indicators

based on the Throughput Accounting (CORBETT, 1998; NOREEN et al., 1994), the study of

Drum-Buffer-Rope and Buffer method Management for Production Planning and Control

(GOLDRATT, 2004; UMBLE & SRIKANTH 1990; STEIN, 1997), the proposal of the

Critical Chain as an alternative for project management practice (GOLDRATT, 1997;

CERVENY & GALUP, 2002), and even use of Thinking Process for problem identification,

solution and implementation development (GOLDRATT, 1994; DETTMER, 1997).

However, there are areas appertaining to operation management in which TOC precepts have

been little explored, as in outbound logistics, within the emerging area of Supply Chain

Management (SCM). Given the very small number of bibliographical sources on the theme,

this study intends mainly to be a conceptual revision, structuring a text gathering the main

concepts and information on the contributions brought by TOC on this subject.

Given its range, this article is interested especially in some logistic aspects of SCM. As the

concepts Logistics and SCM are sometimes mixed up, we should have in mind the work of

COOPER et al (1997), who had already reported that executives of leader corporations in

their segment, who were implementing the state of art in SCM, understood the wider range of

SCM, compared to Logistics processes and functions. The same authors point out that the

Council of Logistics Management of the USA had changed their definition of Logistics,

classifying it as a subset of SCM, seeing the two concepts not as synonyms, and defining

Logistics as “that part of supply chain processes that plans, implements and controls the

efficient flow and storage of goods, services and related information between the point of

origin and the point of consumption in order to meet customers' requirements.

Based on a theoretical research, this article proposes a conceptual rehearsal aiming to identify

the main contributions that TOC can bring for distribution management practice in SCM

perspective, for the redefinition of the ways of planning and distributing stocks as well as

conceiving, evaluating and controlling some of the transactions within the Supply Chain (SC).

For such, this text has the following organization: Next topic briefly presents some of TOC

basic principles, important for the better understanding of this article. The third topic

introduces some views defended by TOC, which challenge some typical presuppositions of

distribution management practice and of SCM. In its fourth topic, this article relates some

additional points of the TOC methodology. The principal actions proposed by TOC regarding

distribution logistics are discussed in the fifth topic of this article. In the sixth topic, final

considerations are delineated.

2. Theory of Constraints

The TOC approach initially recognizes that every organization must be understood as a

system, that is, a set of elements among which there is an interdependence relation. Next step

emphasizes the widespread accepted notion that every system has a purpose or goal, and so,

every action taken by any part of the system must be judged by its impact on the whole

system. For this reason, it is imperative to define measures that allow the evaluation of the

impact of any subsystem, and any local action in this subsystem (GOLDRATT, 1990a).

If companies are formed by interrelated elements with a common goal, the system constraint

must be defined as anything that significantly prevents a system from achieving a better

performance considering that goal. Moreover, every system has to present at least one

constraint. Otherwise, the system performance would have to be infinite. Likewise, there will

always be very few constraints, as there is always a single weaker link in a chain

(GOLDRATT, 199b).

The constraint may be physical, such as a machine with limited capacity, or lack of raw

material, but more often it is either a policy or behavior constraint. Policy constraints often

arise when the company environment changes, while the company policies remain unchanged.

Most significantly, policy constraints are usually within the control of the organization

management. Behavioral constraints occur when performance measures or policies, even after

been changed, lead into behaviors that are ingrained and constrain a system’s performance.

TOC contains a range of tools and techniques for addressing physical, policy and behavioral

constraints (MABIN & BALDERSTONE, 2003).

Based on this, TOC provides 5 focusing steps that must be followed in order to assure an

effective ongoing improvement (GOLDRATT, 1990a):

a) Identify the system constraint (s);

b) Decide how to explore the system constraint (s), that is, increase the system throughput,

completely eliminating any kind of waste in the system constraint;

c) Subordinate everything else to that decision, which implies in ensuring that all other

system elements work towards exploring the constraint, and should be evaluated in function

of how well they are accomplishing that objective;

d) Elevate the system constraint(s), that is, increase the system throughput by means of

increasing the investment volume towards the constraint, for example, investing in new

market segments to elevate a market constraint;

e) If in a previous step a constraint was broken, return to the first step, but prevent inertia

from becoming the system’s constraint.

Goldratt (2004) affirms that the target of a capitalist enterprise is to make money in the

present as well as in the future. It must be evaluated in terms of its Net Profits (NP) and

Return On Investment (ROI). In order to judge the impact a local action has on the NP and the

ROI, TOC uses three performance measures:

- Throughput (T): rate at which the system generates money by means of sales;

- Investment (I): all the money captured by the system;

- Operating Expense (OE): all the money that the system spends transforming investment into

throughput.

Goldratt (1990a) comments that Throughput can be understood as all the money entering the

company minus the amount paid to suppliers. That would be the money the company

generated. The amount paid to the suppliers is the money generated by other companies.

According to Corbett (1998), the throughput has two sides: the Revenue and the Total

Variable Costs (TVC). The use of the terms cost and variable may lead to a confusion

regarding cost accounting measurements. TVC is the cost that varies for every extra unit

produced (in most cases its only raw material). This will tell us how much money the

company generates with the sale of one unit of the product. All other costs are included in the

OE category. Schragenheim and Dettmer (2001a) reinforce that the definition of throughput

does not include the direct (or even indirect) labor costs, unless they are returned by piece.

According to Goldratt (1991), the relation between the three financial measurements (T, I and

OE), the NP and the ROI becomes immediate. NP is T minus OE, while ROI is T minus OE,

divided by I.

An important aspect of TOC regards its orientation towards improvement efforts. For TOC, it

is more sensible to focus improvement efforts on increasing Throughput than reducing

Operating Expense. The reason being that there is no theoretical limitation for Throughput

increases - and even practical limits are in the vast majority of times very far from the actual

Throughput. Efforts towards Operating Expense reduction would have a theoretical zero limit,

while the practical limit would be well above that, limiting a true process of continuous

improvement (GOLDRATT, 1990a).

Throughput orientation is an organization-wide focus on increasing throughput, rather than

reducing operating expenses, as means of improving financial performance, and awareness

that financial performance is limited by constraints. Throughput orientation is supported by

the use of performance measurement systems and decision making systems that facilitate

throughput maximization through management of constraints (BOYD & GUPTA, 2004). As

will become clear here, TOC approach for outbound logistics has a definite throughput

orientation, challenging some important management presuppositions.

3. Some presuppositions challenged by TOC

TOC proposed methodology challenges some implicit or explicitly premises, frequently taken

for granted by of Supply Chain Management users and academicians and, more specifically,

by the physical distribution management of the product to the points of sale. Each one of

these presuppositions are introduced and discussed below, in the TOC perspective.

3.1 The SC cooperation dilemma in SC and pushing stock practice

When there is conflict between a local optimum (sector performance) and a global optimum

(company performance), the local optimum should be discarded. Such is not the case in a

supply chain made of several business units. When there is conflict between a local optimum

(business unit performance) and a global optimum (supply chain performance), neither the

local optimum nor the global optimum should be discarded (GOLDRATT & GOLDRATT,

2007).

Competition is moving more and more towards the supply chain sphere. The result is that the

supply chain performance is becoming more and more vital to the member companies. Thus,

the global optimum cannot be discarded. A clear reason being that satisfied final customers

bring more sales, which positively contribute to the SC profitability only if they realize that

the SC delivers them good value. This reasoning also assumes that sale maximization can be

created only if SC members play their roles in a synchronized and cooperative way

(SIMATUPANG et al., 2004).

On the other hand, each company’s objective avoids the sacrifice of its own performance in

favor of their supply chain performance. So, local optimum cannot be discarded either. From

another perspective, members of a SC will only realize the benefits of cooperation if their

profit levels are expanded as a result of this practice, as each company is accountable to their

shareholders (SIMATUPANG et al., 2004).

Conflict between local and global in a company is strong evidence that the performance

measures used to evaluate each area are wrong. Conflict between local and global in a supply

chain is strong evidence that the rules that govern the business relationships between the

companies are wrong. Therefore, new rules allowing companies to work together eliminating

such conflict are needed (GOLDRATT & GOLDRATT, 2007).

The first key presupposition is that the chain members usually think that cooperation practice

in SC implies a reduced bargaining power. They presume that minimizing costs of each SC

member improves the whole chain performance. Usually, each SC member focus decisions on

maximizing sales from immediate downstream partners (myopic revenue), and minimizing

(myopic) costs from the relationship with its immediate upstream partners (SIMATUPANG et

al., 2004).

The source of the so called myopic revenue is payment transfer from exchanging stocks

between the trading partners, as a stock transaction between SC members is considered a sale.

Besides, practices that reduce myopic costs indicate a compensation policy that encourages

self-optimization, such as seasonal promotions, sub-optimizing retailers’ performance, to the

revenue expense of the SC as a whole (SIMATUPANG et al., 2004).

The result is that each member tends to think according to organization boundaries, instead of

considering the SC as a whole. Each company improves their own revenues ignoring the

impact of their actions on other SC links. Therefore, on many occasions, individual

performance maximization happens to the expense of the whole SC performance, and usually

the actual SC performance maximization is not reached (SIMATUPANG et al., 2004).

Goldratt & Goldratt (2007) assert that the mentioned practices are in total conflict with what

any executive manager knows: while the final SC customer has not bought, none of the chain

companies has sold anything. Transactions within the chain should not be registered as sales.

A sale should only be acknowledged when the final customer finished the purchase. At that

moment the paid money should be distributed among the chain links participating in the

supply of the sold item. In order to align each SC member practice with this statement, the

authors reason that SC should be able to reduce replenishment times to the extent that they are

shorter than the current payment periods. Item 3 of this chapter introduces how TOC

methodology for outbound logistics concurs to replenishment time reduction.

An important undesirable effect of traditional management practice regards how they

stimulate pushing stocks along the SC. If a sale is considered finished when goods are

transferred to the upstream link, increasing revenues, there is a clear tendency of each link to

push stocks upstream in the SC (myopic sale).

When under pressure of local ideal solutions, manufacturers tend to push their stocks to the

distribution channels as soon as they are manufactured. Cost accounting usually rewards these

manufacturers in the short term, registering this stock movement in the accounting log as sale

and profit, although no final customer has performed any purchase. Accordingly, the

distributor is strongly motivated to push their stocks downstream the SC, to improve stock

turnover measure. This way, manufacturer, distributor and each SC further link apply

themselves to pushing stock until it arrives at the shop and/or the final consumer (KENDALL,

2005).

There is also a marketing reason for such behaviour. Many products have a customer

tolerance time shorter than their replenishment times, requiring their availability on the

shelves for immediate purchase. The general practice of the chain members is, therefore, to

keep them as close as possible to the final customers, usually at the retail level. This is a

typical downstream product push behavior, in order to increase consumption. However, the

push behavior requires a good forecasting model, in order to predict where and when the

stocks will be needed (GOLDRATT & GOLDRATT, 2007; SCHRAGENHEIM, 2007).

Kendall (2005) complements that in a stock overloaded SC, that is, with pushed stocks, the

majority of stock is in retail, while there is little or no stock in the manufacturer premises. On

the other hand, it is usual that customers do not find in shops what they are looking for.

Kendal (2005) lists two other reasons for retailers keeping so much stock, with the related

costs and risks, even though facing frequent stock outs. According to the author the reasons

are:

- Many manufacturers and distributors offer discounts for purchased quantity, for each order;

- Freight policies that punish small orders.

There are some other reasons mentioned by Goldratt & Goldratt (2007), which encourage

manufacturers and distributors to push stocks to the points of sale:

- For many products, the more of them displayed, the bigger the sales. This aspect is justified

by Visual Display reasoning;

- When a shop is full of stock, it has to sell it;

- When a shop buys large quantities from a supplier, its cash-flow is reduced and so is its

capacity of buying from competitors;

- The more stock is pushed, the greater the chances of obtaining more space in the retailer’s

merchandising.

There are many negative effects from this policy. Goldratt & Goldratt (2007) and Kendall

(2005) mention the following:

- Pushing great quantities to the point of sale puts the retailer in conflict with suppliers, the

relationship deteriorates and if the shop finds a better alternative, it reacts against the supplier.

- It also dramatically increases the supplier consumption variability, as a great sale is followed

by a long period with no sale at all;

- In many cases, as there is too much unsold stock with distributors and retailers, orders to

manufacturers and distributors take too long to reflect demand changes from the customers’

side, taking too long to react to new market trends.

Goldratt & Goldratt (2007) emphasize that even though keeping large stocks might be a way

of avoiding shortages, large stocks may also risk sales, as:

- When working with batches in every place, and associated long replenishment times, there is

a relatively long period between production and consumption moments. This means that in

environments in which the products have a short shelf life, many units are offered to

customers close to the due by date. Two things can happen in this case. The customer buys a

deteriorated or under the expectation product, with a risk for future sales of this product for

this customer. It can also happen that the customer decides not to buy product with a short due

by date, negatively affecting current sales;

- In many environments, products have a short market life, as new versions or similar

products are placed in the market very frequently. Keeping large stock of such products can

cause three kinds of typical situations: High obsolescence due to launchings of more modern

products; high revenue losses due to price reduction of older products, which in their turn,

reduce the new products market share; market share loss due to postponement of new

versions;

- Keeping large stocks implies in keeping high investment. In environments where cash-flow

is relatively limited, keeping large stocks of a range of products depends on cutting down

other products. Product variety offered to the point of sales is thus limited, reducing sales.

Item 3 of this article will show that TOC methodology for outbound logistics suggests that

stock should move along the SC in a pulled way, considering the real product purchase by the

final customers. However, for such change to be implemented, it is necessary to replace the

conventional practices mentioned above with more adequate ones, improving the SC

performance as a whole. For such, it is necessary to revise some common performance

measures regarding the SC. This is the subject of next topic.

3.2 Performance measures regarding the SC

There seems to be no clear link between criteria for SC performance measures and individual

performance measures. If individual decisions have no link to supply chain-wide performance

measures, then the chain members do not have internal indicators that will be affected by

collaboration and therefore they do not know whether or not the collaboration is working

(SIMATUPANG et al., 2004).

Smith affirms that the traditional approach of facing the dilemma between collaboration and

minimizing costs in each SC link, leads to a zero-sum game situation in which each chain

member bargains to save costs as much as possible, many times at the expense of other

partners.

For efficient results from competitive practices and strategies of SC management, it is

necessary to base on an adequate measure process. In a SC performance evaluation process,

the use of performance indicators in individual business unities should be replaced by the

perception of efficiency of SC as a whole.

According to Holmberg (2000) and Beamon (1999), the performance measure for the SC is

many times an extension of the use of “traditional” company indicators. According to Neely

et al. (1995), a systemic approach and generic application of SC performance measure had not

yet been created for that purpose. Beamon (1999) and Holmberg (2000) agree that there is not

connection or compatibility between the performance measures for the SC and its strategies.

For Simatupang et al. (2004), the solution direction is in ensuring that local decisions are

made regarding throughput measures, investment and operating expense, proposed by TOC

(see Corbett, 1998, for a better understanding of these measures). However, according to these

authors, the use of these measures implies in two challenges.

First, the chain members need to formulate a new replenishment policy that encourages the

chain members to align their operating decisions to maximize the total revenue of the SC. The

chain members need to focus on maximizing revenue from selling goods to the final

customers. This can be done with the right allocation of decision making to the chain member

with most intimate knowledge regarding such decision. If the SC is made of a retailer and a

producer, the retailer should focus on excellent customer services and capture the market

needs. The producer should be in charge of fast responses to the market needs. Thus, the

producer manages the retailer’s stocks, ensuring their availability.

The second challenge consists of new performance measures by the chain members, aligned

with SC global performance measures. Local performance measures should guide local

decisions to maximize global throughput, maximize individual throughputs at the same time

(SIMATUPANG et al., 2004).

In short, as Goldratt & Goldratt (2007) point out, traditional SC performance measure

approaches fail when they assume that if each chain member is managed as an isolated entity,

and its performance maximized, the chain as a whole will also maximize its benefits.

The use of distorted measures can also lead to a practice that is highly criticized by TOC: the

use of batches along the SC. Next item discusses this subject.

3.3 The batch policy and its relation with the forecast practice

According to Schragenheim (2002), TOC and Lean Manufacturing methodologies are in total

agreement regarding the huge damage caused by batching. For the author, even though TOC

recognizes that in some cases batching cannot be avoided, it is necessary to clearly define

when batching is absolutely necessary and to find a minimum size batch that overcomes a

particular obstacle, as in the case of very small batches transforming a non-constraint resource

into an active capacity-constraint resource (CCR), due to the accumulation of setup time.

The author highlights that it is important to verbalize it that way in order to come up with the

clear idea of what is the minimum batch that will not turn the specific resource into an active

CCR. Therefore, it is not necessary to increase batches only to improve efficiency measures.

The only place where efficiency is important is at the CCR.

Goldratt & Goldratt (2007) defend that batch practice significantly inflates the replenishing

time, consequently inflating the needed stock level along the SC. The authors define

replenishing time as the sum of three types of lead times: order lead time, production lead

time and transportation lead time (these definitions will be better discussed in item 4). The

effects brought by batch practice to these lead times are:

- Order lead time: As mentioned by Kendall (2005), SC links usually get discounts when they

place large quantity orders. Besides, as deciding and placing orders require time and effort,

each chain member chooses to work with batches;

- Production lead time: to improve local efficiency measures (saving setups), production

operations use large production batches;

- Transportation lead time: to improve transport costs, this is done in large batches. Some

orders are accumulated until the transport can be justified.

There is a narrow co-relation between batching and forecasting. Any stock production or

distribution involves some kind of forecast. Srchragenheim (2002) stresses that the order

point level, or the minimum level in stock management systems based on min-max logics, is a

rough forecast regarding possible sales during the replenishment time.

High stock levels due to batching policy and far-future forecasting encourage production

planners to schedule large batches. The supposed knowledge of what will be needed (from the

forecast) in the future, space availability to keep large stock and pressure for efficiency and

low costs lead to batches larger than the strictly necessary. Large batches are kept in the

system as a necessary practice, a fact of life (SCHRAGENHEIM & BURKHARD, 2007).

However, there are highly undesirable effects resulting from large batches practice. While a

large batch is produced or distributed, a large portion of those products is not necessary at the

moment, or even in the near future. Large batches increase the product replenishing times in a

factory, in a distribution center or in a shop, increasing the amount of stock kept in these

places. At the same time, large batches lower due date performance and the capability to

flexibly meet customers’ urgent needs, missing better service opportunities that would lead to

higher prices and/or larger sale volumes (SCHRAGENHEIM & BURKHARD, 2007).

Nevertheless, even with all the sophistication of the forecasting systems and high stock levels,

shortages occur regularly. The forecasting mechanism, no matter how good it is, cannot really

predict what the demand would be like (SCHRAGENHEIM, 2007). Schragenheim &

Burkhard (2007) assert that managers recognize forecasting limitation, but use it for lack of a

better method to assist them.

Investing in formal or computerized forecasting is not a better option. It seems that when

forecasting, some companies try to look too far ahead, creating huge batches, and then chase

the market trends using frequent re-forecasting to correct the actions taken before the last

forecast. One of the characteristics of any forecast is that its quality deteriorates very fast with

the widening of the horizon. If a forecast detailed by article and location for next week is

reasonably good, one for next month is exposed to many more variables, while predictions for

next year have no point at all (SCHRAGEHEIM, 2002).

The author remembers that most of the time what really counts is not the average demand, but

how much it might be. Hence, the concept of safety stock comes to mind. In many cases the

“noise” in the actual sales is the same as, or even more than, the official forecast, which tried

to predict the average demand. In addition, the stocks for protecting sales need to consider not

only demand fluctuations, but also replenishment time fluctuations. Until a replenishment

order is issued, sales depend on stock availability. In this sense, the safety stock serves not

only to cover deviations from the forecast, but also deviations from the replenishment time.

As will be better discussed later, the direction of the TOC solution is to try not to look so far

ahead and keep to a short lead/replenishment time. The emphasis is on very low stocks. That

means that within a short time all the stock will have gone. In order to continue to satisfy the

customer, replenishment of those low stocks needs to be done very fast. There should be no

batching unless absolutely necessary. TOC focuses on the several stages of replenishment

time and how to reduce it to the minimum possible.

The amount of stock is the expression of the maximum expected sale until next

replenishment. When stock levels are correct, they reflect demand expectations. In fact, stock

level forecasts are not necessarily more accurate than any other forecast, but they include an

intuitive uncertainty factor, because the stock level protects the maximum expected sale. TOC

defends the idea that an initial forecast should be made to first define the stock levels,

managing them dynamically, ensuring that they always reflect the maximum expected sales

until next replenishment (SCHRAGENHEIM & BURKHARD, 2007).

3.4 The distribution generic conflict

Previous discussions showed the difficulty in aligning interests of SC members with the SC as

a whole, the damages caused to good SCM practices by distorted performance measures, and

how batch practices not only increase needed investments regarding stocks kept by the chain

but also, and mainly, cause large sale losses.

A question to be discussed at this point is what the real objective of an outbound logistics

system is. Several articles in the literature mention that a good logistics system should reach a

double objective: to reduce logistics related costs ensuring a good service level, usually in

terms of product quality and availability of the right product at the right place at the right

time.

This double objective indeed reflects the main conflict, or generic conflict, of the distribution

environment. Using a TOC Thinking Processes tool (called Conflict Resolution Diagram,

Conflict Cloud or simply Cloud) a conflict cloud can be used to describe dilemmas or

conflicts between two conditions necessary to a common objective. For further explanation of

this diagram, see Goldratt (1994) or Dettmer (1997).

Picture 1 shows a typical conflict cloud, mentioned by Goldratt & Goldratt (2007).

Picture 1: The generic dilemma of outbound logistics

Dilemma

D. Hold lessinventory

D. Hold lessinventory

A. Profitabledistribution system

B. Reduce costsB. Reduce costs

C. Protect salesC. Protect sales D’. Hold moreinventory.

D’. Hold moreinventory.

The upper part of the diagram can be read as follows: “For a profitable distribution system

(A), costs must be reduced” (B to A on the diagram). The reason for that is obvious. Reading

entities D to B relationship, we have: “In order to reduce costs, we must hold low stock”.

Here, a presupposition is that levels are important elements of costs in a distribution system.

More stock means more investment, more cash-flow limitation and more obsolescence.

The lower part of the diagram can be thus read: “For a profitable distribution system (A),

sales must be protected” (C to A on the diagram). Again, the premise here is evident. Finally,

in D’ to C relationship, we have: “In order to protect sales, we must hold more stock”. A

premise here is that shortage levels are inversely proportional to the stock level kept in the

system. Another premise here is that, in distribution systems, replenishing time is usually

long, sales cannot be precisely predicted for each place and final product and replenishing is

not totally reliable. Otherwise, it would be possible to protect sales without the need of large

stocks.

Goldratt &Goldratt (2007) affirm that many conventional management approaches use to deal

with conflicts finding an intermediary solution between the conflicting necessary conditions.

The authors, however, say that these are “lose-lose” solutions, for neither side of the diagram

are fully served.

For them, the only way for a “win-win” solution consists of challenging at least one of the

presuppositions sustaining the causality connections of the diagram. The presupposition

challenged by the authors and the basis for TOC outbound methodology is the one sustaining

connection D` to C. The problem presented by the authors, facing the discussions in 2.3, is

how to operate with a more accurate forecast, with a replenishing time significantly shorter

and improve the replenishment reliability without the need of a more accurate forecasting

system; without risking margins nor investing more in replenishing (not to risk the

requirement B on the diagram) and without necessarily having to replace or improve current

suppliers.

The way Goldratt & Goldratt (2007) describe the generic distribution conflict and point the

way to follow (challenging connection D’ to C) brings us to some interesting conclusions.

Firstly, TOC outbound methodology is not centered in finding a mechanism to reduce total

costs in the system, or even finding an optimum point of tradeoff cost versus service level. To

protect sales reducing replenishing time and increasing replenishment reliability is the final

objective, which should be obtained at the lowest possible cost. The main objective is having

very good availability of the items at all points of sale. Item 3 will show that cost reductions

are obtained simultaneously in implementations of TOC methodology, ensuring the necessary

condition B on the diagram. This is not, however, the main focus. Another premise here,

therefore, is that additional efforts to ensure product availability allow sale increase in short

and long terms, making up for occasional cost increase.

Another premise here is that conventional outbound logistics systems, when pushing stocks

along the SC, cause high shortage rates.

Next item presents how TOC methodology for outbound logistics deals with the mentioned

presuppositions, reaching, in its perspective, the main objective of distribution systems, that

is, high availability of all stocked items, without risking, but rather reducing, related logistics

costs.

4. The TOC pull distribution methodology

The TOC proposition for distribution is nothing more than an application of the five focusing

steps described above.

The first step is to identify the system constraint. The SC members must focus in the SC

constraint, because the SC performance as a whole depends on its constraint performance. As

said before, the constraints can appear in different ways, such as raw material supply, low

production, difficulties in distribution, market, etc. However, in many cases, the constraint is

actually a reflex of wrong rules and policies, based on questionable presuppositions, like

those presented in section 2 (SIMATUPANG et al., 2004).

A direct way to identify the constraint in a SC made of distribution channels is to remember

that the constraints govern the system's throughputs. We can thus ask the following question:

Which elements govern the distribution system’s throughputs? The answer seems immediate:

consumers wishing to buy. This means that the larger the number of consumers interested in

buying, the larger the system’s throughput. Therefore, the constraint in a distribution system

is the market, understood as each final customer wishing to purchase a product offered by this

distribution system (GOLDRATT & GOLDRATT, 2007).

After identifying the constraint, the second step is to decide how to explore the system’s

constraint. Not being able to explore the constraint in a distribution system implies not being

able to serve the customer wishing to buy. Therefore, exploring the system’s constraint means

having the right stock in the right place at the right time. This is the only way not to waste this

market constraint (GOLDRATT &GOLDRATT, 2007).

The third step is to subordinate all other activities to the constraint. This means to change

traditional rules and measures that hinder any activity from supporting the constraint

exploration (SIMATUPANG et al, 2004). This s the big challenge: subordinating the whole

distribution system to the constraint exploration to keep right stock in the right place at the

right time (GOLDRATT & GOLDRATT, 2007).

For such, five great actions are inherent and essential parts of TOC methodology

(GOLDRATT & GALDRATT, 2007):

1. Establish the factory (or central) warehouse;

2. Establish the target stock for each place and for each product according to the formula;

3. Work with “daily orders” and “frequent replenishment”;

4. Manage stock levels according to the three areas (buffer management).

5. Train subsystems to manage execution based on money-day measures.

Each of these great actions is presented in detail below.

4.1 Aggregating stock in the factory or in the warehouse

It is well known and documented in the literature (TAYLOR, 2004; CHOPRA & MEINDL,

2003) that demand variability in the point of supply is much lower than in the point of

consumption. Information of how many items of the same SKU (Stock Keeping Unit) will be

sold on a national basis is much more precise than the same information on a regional basis.

At the opposite extreme, in the point of sale level, where many SC actually sell something,

the accuracy of this information given by forecast models is very poor.

“This phenomenon stems from the fact that fluctuations average out on the aggregated events

(assuming they are independent events). If we predict sales at 100 different locations, we

might get an answer that sales in an average location will range from 10 to 25 units a day. If

we ask the same question on the overall quantity that we need to manufacture, we will get a

much more accurate answer – probably something ranging from 1650 to 1850”

(SCHRAGENHEIM, 2007).

Exploring this phenomenon, an important part of the model proposed by TOC for managing a

supply chain is to keep the stock at the divergent point, where the stock can be used to serve

many different destinations, and using a pull mechanism from the destination to replenish.

This method ensures keeping the minimum stock necessary to support demand in the several

points of consumption (SCHRAGENHEIM, 2007).

In order to have the product available at different locations, it is recommended to aggregate

the stocks at the source and build a plant or central warehouse (PWH/CWH). If the

organization is a manufacturer, the entity is called a Plant warehouse (PWH), i.e., the

warehouse for the finished products in the factory. If the organization is a distributor, the

entity is called a central warehouse (CWH). The largest part of stock is kept in this

warehouse. According to statistics principles, this aggregation guarantees a more stable

system than keeping stock at different points of consumption. At the point of consumption,

the stock level is very limited (SCHRAGENHEIM, 2007).

Once a certain point of consumption sells a unit, the sold unit will be replenished as soon as

possible from the PWH/CWH. When transportation time from the PWH/CWH to the point of

consumption is very long, a regional warehouse (RWH) might be needed between the

PWH/CWH and the points of consumption. A regional warehouse will behave as a point of

consumption to the PWH/CWH and as a central warehouse to the served point of

consumption. The idea is to pull from the PWH/CWH only based on consumption from the

RWH (SCHRAGENHEIM, 2007).

4.2 Establishing the target stock for each place and for each product

Next step in the TOC methodology is to determine the initial stock level for each SKU in each

stock point in the SC. The objective is to set the right stock levels to ensure that each potential

costumer will find the desired products available for purchase. Goldratt & Goldratt (2007)

suggest that these initial levels should be set according to the following factors: average

replenishment time; average demand within replenishment time; fluctuations of demand

within replenishment time; fluctuations in replenishment time; loss caused by shortage;

customer tolerance time; cost of holding stock.

Conventionally, stock levels are established based on sale forecast for a certain period, plus a

necessary security level due to inherent forecast mistakes and unpredictable replenishing

times. However, TOC considers that forecasting covers only a small part of relevant

information, even when prediction mistakes are considered.

Replenishment time, as well as its fluctuations, is much more under management control than

market demand. Replenishment time is at least as relevant for decision as forecasting.

Furthermore, even when all factors above are considered, how to know if one of those

parameters has suffered an alteration or what its magnitude? For example, the emergence of a

new competitor. The solution direction proposed by TOC is (GOLDRATT & GOLDRATT,

2007; SCHRAGENHEIM, 2007):

- keeping a target replenishment stock that effectively buffers sales. The target stock should

include both on-hand stock and orders in the pipe. The actual protection is, of course, the on-

hand stock.

- Drastically reduce the replenishment time;

- Establish the right priority system for the orders in the way, which depend on on-hand stock

level that protects the on going sales;

- Constantly monitoring target stock based on the actual protection / the on-hand stock

The target replenishment stock, for each place and SKU, is determined as the “maximum”

sales predicted within the average time of replenishing times a factor that considers the non-

reliability level of replenishing time (GOLDRATT & GOLDRATT, 2007).

TOC methodology marks that target stock, or buffer stock, must also encompass

replenishment orders. As it is not possible to determine which will be the on-hand stock level

at any point in the future because of continuous fluctuation of both demand and replenishment

time, TOC suggests keeping the total stock in the system, the on-hand stock and the stock in

the pipe, constant at the pre-determined target level.

An extremely important complement of TOC methodology is the dynamic buffer

management, with which it is possible to know if the target replenishment should be changed

and when. This subject will be detailed in item 4.4.

4.3 Obtaining daily orders and replenishing frequently

Replenishment lead time (RLT) is defined as: The period between the moment an item is sold

and the moment it is replenished from the previous link in the supply chain. As mentioned

before, RLT is divided into three separate parts (SIMATUPANG et al., 2004; GOLDRATT &

GOLDRATT, 2007; SCHRAGENHEIM, 2007).

- Order Lead Time (OLT): Period between the moment an item is sold and the moment a

replenishing order is issued. In other words, order frequency of the same SKU.

- Production Lead Time (PLT): Period between the moment the manufacturer/supplier decides

to issue the order and the moment that the order is produced;

- Transportation Lead Time (TLT): the time it actually takes to ship the finished product from

the supplying point to the stock location.

TOC suggests challenging all of these 3 elements in order to cut RLT to a bare minimum. By

cutting RLT, the supply side factor becomes less dominant, and the following is achieved

(GOLDRATT & GOLDRATT, 2007; SCHRAGENHEIM, 2007):

- The needed stock level at the points of consumption (and at the WHs) is lower, since it

needs to cover fewer demand days;

- Fluctuations in supply time decrease as supply time decreases;

- The needed sales forecast for new products is much more accurate, since the forecasting

error becomes larger as we need to forecast more into the future (the trajectory becomes

wider).

- The ability to respond much faster to actual demand is evident.

TOC principles try to encourage initiatives that trim different elements of RLT. These are the

general guidelines (SCHRAGENHEIM, 2007):

- OLT: if possible, cut it down to zero, usually meaning trying to replenish daily at each point

of consumption what was consumed that day. This means a total overcoming of the

presuppositions supporting batch policies (reorder point, min-max), as pointed out before;

- PLT: Simplified Drum-Buffer-Rope (SDBR), TOC methodology for shop floor production

management (see SCHRAGENHEIM & DETTMER, 2001; SCHRAGENHEIM, 2002)

should be implemented to significantly reduce PLT. The priority of manufactured parts

should be tied to their stock level at the plant WH. This will be detailed further in this article.

As seen before, the establishment of a PWH, by itself, allows PLT reduction when it cuts

down urgent orders and their malefic effects on the waiting-in-line time;

- TLT: try to find transportation alternatives. Finding closer raw material or part suppliers is

also a possibility in many cases. Usually this is the part of RLT where the least can be

changed, so every possibility should be considered.

In order to significantly reduce RLT, especially regarding OLT, TOC methodology is based

on a policy according to which the supplier should manage the clients’ stocks. The clients,

therefore, should not place replenishment orders any more, but only inform the supplier, on a

daily basis, the amount sold of each SKU. With this information, the supplier can replace

what has been sold as fast as possible, always to ensure total product availability to the

clients. This mechanism, however, does not suggest the need to consign stock. What is really

important is that the point of sale should inform daily what has been sold and the supplier

should replenish it as frequent as possible (SIMATUPANG et al, 2004; GOLDRATT et al,

2000)

Other aspect that must be marked is the importance of a PWH in terms of feasibility of the

daily order practice by the client (retailer, wholesaler or distributor). Without the PWH, the

factory itself would not encourage clients to order daily everything that was sold that day,

since this would probably force the factory to plan many small batches, spending too much

time with setup. With the introduction of the PWH, the factory gets replenishing orders

directly from its stock at the PWH, not having to produce one type of item to serve one client

even when that item is available in other client’s stock. This softens production in the factory,

since each batch is naturally dimensioned in function of the whole market needs, and not only

of one client (GOLDRATT et al, 2000).

There is a tradeoff between the additional cost one might invest in raising the frequency of

shipments and the cost of having lower availability. By increasing delivery frequency,

availability is improved, whereas the cost of shipment increases. By cutting down frequency,

the chain will have to pay with either lower availability or with higher stock levels at the

points of consumption. In most cases, the extra cost will be covered by the increased revenue

(SCHRAGENHEIM, 2007).

4.4 Dynamically managing buffers

As said before, it is essential in TOC pull distribution methodology that the supplier should

control clients’ stock. Basically, for each SKU and for each place controlled by the supplier,

the supplier should replenish the client’s stock as soon as the product is sold (equivalent to a

min-max policy of stock control, but here the min level equals the max level), or replenish

what has been sold along the day. However, as the objective is to ensure product availability,

adjustments in stock levels will be needed whenever one of the parameters considered in the

target dimensioning is altered.

Thus, the supplier uses buffer management to control buffer size adjustments. Corrective

action is taken to decrease or increase the buffer when required. As the buffer size reflects the

consumption pattern, the supplier should watch the consumption of the buffer as the basis to

determine appropriate actions (SIMATUPANG et al., 2004).

Such adjustments are made using the Dynamic Buffer Management (DBM) technique.

According to DBM, stock buffers should be divided into three areas, equal at first, called

green, yellow and red areas. Depending on the dynamic behavior of on-hand stock, DBM

establishes some criteria to adjust the replenishment level (SIMATUPANG et al., 2004;

GOLDRATT & GOLDRATT, 2007). Yuan (2003) gives some interesting contributions to

DBM technique.

Most of the time, on-hand stock would be significantly less than the replenishment level. At

the same time, it is expected that it would be above a certain level, the limit below which

would be considered as “almost losing sales”. Thus, the three zones of replenishment level

can be defined as (GOLDRATT & GOLDRATT, 2007):

- Green: when on-hand stock is close to the theoretical maximum;

- Yellow: the middle level, where the normal on-hand stock should be;

- Red: when there is risk of impossibility to deliver all the demand.

If during a period equal to replenishing time, the on-hand stock level invaded too far into the

red area, the target should be increased. If during a period equal to the replenishing time, the

on-hand stock level is always in the green area, the target level should be reduced. Such

reduction or increase, unless in case of special reason, should be done subtracting or adding to

replenishment level an amount equivalent to a whole area, that is, a third (33%) of the target

level. (GOLDRATT & GOLDRATT, 2007).

Since TOC mechanism is based on reacting to the actual market demand, and adjusting the

buffers accordingly, if the market demand picks up, the buffers will be increased, creating a

mechanism that allows stock-outs only for very limited time periods. Therefore, lost sales due

to stock outs of high-sale items are minimal with TOC methodology. Because lower stock of

all items are kept and the quantities are further decreased when consumption is low, low-sale

items are much less of a problem as their quantities are minimal. Therefore, using pull

distribution is very effective in eliminating lost sales (SCHRAGENHEIM, 2007).

Due to the throughput orientation of TOC methodology, adjustments in replenishment levels

have as main objective to keep a certain amount of stock allowing a small probability of

shortages. According to DBM, shortages are not only avoided adjusting buffers levels, but

also taking acceleration actions whenever an “almost losing sales” situation is identified.

When red level is hit, actions should be taken not to threaten product availability. The idea

behind DBM can be summarized as identifying situations where the planned protection is

almost exhausted. Once such a local case is identified, a warning is issued, resulting in high

priority being given to the problematic orders and then utilizing the rest of the protection to

remedy the disruption. Almost exhausting the protection means very low on-hand stock, low

to the extent that it could be exhausted before any reinforcement could arrive. This emergency

level, sometimes called the red-line level, represents an exception that should trigger action.

Once the red zone has been penetrated, the supplier should take action to speed up the

replenishment until the buffer has reached the top of the green zone. The default for the

emergency level is one-third (33%) of the replenishment level (SCHRAGENHEIM, 2002).

If replenishment level is too low, then there must be too many cases where the on-hand stock

was especially low. So, cases of “almost lost sales” should be considered as an indication of a

too low protection level. When on-hand stock gets too low, there should still be time to take

actions to bring in the replenishment fast. Therefore, in most cases no actual damage would

occur, but a warning is issued. If on-hand stock is too low for the average replenishment time,

then replenishment level should definitely be increased (GOLDRATT & GOLDRATT, 2007).

The buffer status of a product is the ratio of the quantity missing in the replenishment level

divided by the replenishment level. A buffer status of 70 percent means that the on-hand stock

is 30 percent of the replenishment level. Considering the default value of the emergency level

and the buffer status of 70 percent, the emergency level has been penetrated. The buffer status

for each product and for each location dictates the priorities on the distribution system

(SCHRAGENHEIM, 2002; SCHRAGENHEIM, 2007).

Managing the three zones is a simple but effective method to forecast the appropriate stock

level to protect deliveries. It judges the robustness of the current protection. Like any forecast,

it assumes no sudden change in reality. When coupled with very flexible and fast logistical

response, it can identify trends pretty fast and then we do not need regular forecast for short

term decisions (SCHRAGENHEI, 2007).

Two last decisions must be pointed out regarding DBM method. Besides dynamically

adjusting stock, keeping them in adequate levels and informing situations requiring delivery

speed-up actions, DBM has a third objective, regarding continuous improvement process.

Whenever the red level is invaded, the system user should identify the cause of delay,

triggering resolution actions. Continuous improvement techniques based on lean

manufacturing philosophy are highly recommended here (GOLDRATT, 1990a).

The other point is the need of a specialized information system (IS). Due to the large volume

of information dealt by DBM, it is impossible to do all the necessary calculations without a

computerized IS. For example, if a medium size supplier works with 50 different SKU and

keeps them stored in 1000 different places (RWH, distributors, wholesalers and retailers), the

system will manage 50000 (50 x 1000) buffers simultaneously. Finally, it should be registered

that DBM technique is not included in the great majority of the available ERP systems, being

then necessary to get external software, which should be fully focused on TOC processes and

decision making (SCHRAGENHEIM, 2007).

4.5 Measuring performance under a SCM perspective

For Simatupang et al (2000), companies have competed more frequently in SC. To become

competitive, they should look outside their own limits, that is, at the SC as a whole. As a

fundamental principle, so that the whole (SC) can be improved, it is not enough that each link

(the companies inside the chain) improves, but the cooperation between the links has to

improve.

A decisive point to make SC work as a business unity, ensuring that a sale is only a real sale

when the final customer buys a product in the SC, is the existence of a performance measure

saying when it is safe to rely on a certain client or supplier.

Supply chain collaboration assumes that there is a good amount of reliability between chain

members. For example, the retailer relies on the supplier to manage the stock and ensure its

availability and the supplier relies on the retailer to send timely and accurate information

about sales and to deliver good customer service. The SC cooperation success, therefore,

depends a lot on how each member will keep to the cooperation scheme. Cooperation

performance measures consist of a group of measures specifying how the cooperation process

is evaluated by individual members and by the SC as a whole (SIMATUPANG et al, 2004)

Simatupang et al (2004) also assert that performance measures should be able to induce the

chain members to follow a global plan of the SC, creating throughput by delivering right

products at the right time in the right place, using not more than a certain stock level and a

certain operation expense level. Since global operation measures are measured by throughput,

stock and operation expenses, and each member should control the global plan execution in

their particular area, so the chain members should measure their performance in terms of the

impact on the throughput, stock and operation expenses measures of the chain as a whole

(SIMATUPANG et al, 2004).

It is necessary, then, to use measures indicating if the clients or suppliers are operating

according to the plan suggested by te chain. According to Fogarty and Blackstone Jr. (1991),

Goldratt et al Simatupang et al (2004) and Goldratt & Goldratt (2007), there are two ways of

following a production plan: Producing in advance (forming stock in excess) or producing

with delay (risking or reducing throughput).

About this, Goldratt & Goldratt (2007) suggest two indicators to identify and monitor these

two types of plan dysfunction: throughput-dollar-days (TDD) and inventory-dollar-days

(IDD). A third measure is local-operating-expense (LOE).

TDD is a reliability measure that evaluates the faults in terms of commitment with clients.

TDD is the final result of “things that should have been done but were not”. Therefore, TDD

measure should consider the importance of the commitment that was not complied with in the

customer’s view (GOLDRATT & GOLDRATT, 2007).

TDD is the indicator proposed by TOC to control the program execution regarding delivery

deadlines. From the stock management in control points, it is possible to identify which

production orders are late, that is, should have arrived, but have not. The objective is to reach

a TDD equal to zero (GOLDRATT, 2000).

Goldratt & Goldratt give an example as how TDD measure affects people`s behavior and

local decision making. Take for instance a supplier late to deliver two parts. The client’s

purchase staff is inquiring about these parts. Each part costs U$ 100. How would the supplier

know which of these parts should be prioritized? What additional information does the

purchase staff have to guide them in their efforts to speed up delivery?

Supposing now this supplier knows that this part is waiting in the assembly line of a system

worth U$ 100,000, while the other is sold as a spare part. Then, supposing the client judges

the supplier’s performance in terms of TDD and reports daily to the suppliers with those

measures. When the suppliers know that they are being evaluated under the TDD measure,

they will behave accordingly. They will speed up the right orders without waiting for the

clients (which usually happens too late) to inquire about them.

Other behavior allowed by TDD measure occurs when it is compared with indicators such as

the due-date-performance (DDP). For instance, assume two orders should be delivered today

but the company in charge informs that only one can be delivered. By DDP criteria, the

company will probably focus on the easier order, minimizing the risks of a negative effect in

the DDP. The monetary value of each order is not considered by DDP. However, if TDD

measure is used, the monetary value of the order is explicitly considered, and the focus turns

to the order with a higher value, usually corresponding to the one requiring more effort to be

concluded.

IDD, on the other hand, is a system effectiveness measure. It measures the stock value and the

time it stays in a certain place. The final result of things that should not have been done but

were done nevertheless is stock excess. When measuring stock excess, two things are

important: the period until when the stock is necessary and the value of the excess. The

objective is having the least IDD necessary to ensure reliability (GOLDRATT &

GOLDRATT, 2007).

IDD is calculated multiplying the excess stock value by the number of extra days it is kept

for. It is important to mark that IDD allows the item-by-item control, always according to the

item plan, differently, therefore, from conventional stock control indicators based on Stock

Turnover or on average coverage time.

IDD is especially efficient in eliminating staff’s tendency of processing material in advance to

avoid inactivity. With this measure, the employee realizes that advancing the plan has no

point, and is negatively measured by the action. The same reasoning is valid when

“punishing” a company that generates stock only to improve local efficiency measures. The

company should follow the SC production plan instead of local immediate needs

(GOLDRATT & GOLDARATT, 2007).

Finally, LOE is an efficiency measure. It includes only expenses under control of the

measured links, such as wages and refusals, and it does not include any type of allocation

(GOLDRATT & GOLDRATT, 2007). LOE measures variations between real and planned

expenses (SIMATUPANG et al, 2007).

It should be clear that TDD should be the primary or main SC measure, while its target value,

it is good to remember, is zero. IDD and LOE are secondary measures. The target is to reduce

them without risking the primary measure. This implies that companies or chains using the

measures suggested by TOC should take actions that in priority would maximize the SC

throughput, even if it eventually implies in punctual increases of IDD or LOE (GOLDRATT

& GOLDRATT, 2007).

Besides using the three local measures to control SC operations, chain members can also use

them to judge how the other partners contribute to the global measures. The supplier can use

IDD to evaluate performance regarding the retailer’s stock. The supplier can also provide the

same measures to their suppliers, so that they can evaluate the supplier’s stock performance

(SIMATUPANG et al, 2004).

On the other hand, the retailer can evaluate the supplier’s delivery performance using TDD.

Accordingly, the supplier can use TDD to evaluate their own supplier’s delivery performance.

The supplier will be responsible, therefore, for results measured by TDD as well as by IDD.

This way, TDD and IDD are performance measures that help SC members to have

accountability uniformity. This means that, if the retailer wants a prompt answer for certain

products, the supplier will fulfill the request. On the other hand, the supplier will be able to

judge how fast the retailer will sell the same products. The same relationship should occur

between supplier and sale staff. This accountability chain stimulates SC members to improve

their mutual answers in order to satisfy the final customer (SIMATUPANG et al., 2004).

Simatupang et al. (2004) suggest performance measures derived from and aligned with TDD

and IDD concepts to support decisions by both manufacturers and retailers. According to the

authors, while the former should be supported by performance measures focused on the

constraint and on protective stock (buffers) monitoring, the latter should use measures

regarding their clients’ needs identification and satisfaction.

5. Some additional aspects of TOC methodology

The important actions culminating in TOC pull distribution methodology bring even further

benefits to the SC. Some of these benefits are commented below.

5.1 TOC methodology impact on total SC stock levels

Contrary to what could be expected at first, the introduction of a PWH or a CWH does not

increase the total stock levels in a system, but it actually reduces them. This reduction occurs

due to several interdependent factors (GOLDRATT & GOLDRATT, 2007):

- As the target stock depends on the “maximum” consumption within replenishing time, the

smaller the variability, the smaller this ”maximum" value. In the plant warehouse, the

consumption variability is significantly lower than in the regional warehouses. Therefore, the

amount to be kept in the factory warehouse is much lower than what was reduced in the

regional warehouses;

- As forecast accuracy deteriorates with time length, when drastically reducing replenishing

time the regional warehouses also reduce their “maximum” consumptions, that is, the stock

protection level that the RWH should keep is lower. This way, there would be a smaller

chance of product shortage in a RWH and excess of the same product in other RWH.

- In many factories, a significant production lead time portion refers to the queue time. On the

other hand, an important component of the queue time are urgent orders, that take a priority

position in the planning, delaying other orders. As a significant share of urgent orders occur

due to shortage of an item in a regional warehouse, even when this item is found in the system

(in other regional warehouse), keeping stock in the factory warehouse will drastically reduce

this type of problem. Urgent orders will only occasionally occur in case of shortages in the

factory warehouse, due to sharper PWH level forecast accuracy.

- As one of the main reasons for production lead time variation are the urgent orders that “skip

the queue”, then the reduction of urgent orders numbers should also provide considerable

reduction of non-reliability on the factory replenishing time.

- As mentioned before, the introduction of the factory warehouse allows daily orders from the

RWH, reducing their lead time. This practice additionally reduces the stock levels kept in the

regional warehouses.

It is important to point out, however, that, even though very appreciated, TOC methodology

main objective is not to reduce stock level and associated costs, but actually to provide a fast

and flexible system that maximizes availability of each SKU in each storage place. This is a

clear throughput orientation.

5.2 Managing priorities in PWH

Usually, the manufacturers make to order (MTO). That means that each work order on the

shop floor is for a specific customer for a given due date. TOC for that environment

prioritizes the production orders based on their due dates (see Schragenheim & Dettmer,

2001).

When the SC embraces TOC pull distribution methodology, all the production and

distribution is guided by available stock levels along the SC, in a make-to-stock (MTS)

environment. Now, decisions based on due dates lose their point. “In this case, production

orders are not for a specific customer, and are just covering for consumption from the PWH.

Therefore, the right priority should be set not according to time, but rather in the same way

the priority in the stock locations for the SKUs was defined – the best priority mechanism is

to take the buffer penetration at the site as the priority for the work order that needs to

replenish it” (SCHRAGENHEIM, 2007).

“Both MTO and MTS orders use ‘Buffer Status’ to provide prioritization. The MTO Buffer

Status is based on penetration into the time buffer to produce the product while the MTS

Buffer Status is based on the penetration into the stock buffer. In an MTO environment, the

Buffer Status gives us information about the likelihood of meeting the due date. In an MTS

environment, Buffer Status tells us something about the likelihood of product availability in

the near future” (SCHRAGENHEIM &BURKHARD, 2007).

This ensures that the production is in line with the actual usage of the stock – if the stock is

depleted fast, the work order will be expedited throughout the production. Otherwise, it will

float on the excess capacity of the production system. Every entity in the supply chain is fully

aligned and synchronized with the goal of the system – to be responsive to the actual

consumption of stocks from next link in order to create availability otherwise unattainable

(SCHRAGENHEIM, 2007).

6. Final comments

Aiming to contribute to the knowledge of outbound logistics, this study presented TOC pull

distribution methodology, discussing the presuppositions on which it is based, as well as its

main characteristics. I should have been made clear that many of these characteristics are

specific to TOC methodology, such as the focus in improving SC throughput as a whole,

DBM technique and the use of measures based on dollar-day.

Nevertheless, maybe one of the main contributions brought by TOC is its throughput

orientation. TOC focuses mainly on the throughput increase, even though investment levels

along SC – and associated costs – and cross shipment expenses may be significantly reduced

with the proposal.

Many techniques associated to this methodology try to reach higher throughput levels, as the

text pointed out. A reason for this is that, due to difficulties in measuring actual shortage

levels (the clients do not always complain about the lack of a product, and when they do, this

information is not always properly dealt with by the SC information systems), the real volume

of missed sales is much larger than imagined.

Due to conventional ways of stock management along SC, Goldratt & Goldratt (2007)

consider that points of sale generally operate with shortage levels of at least 20%. Kendall

(2005), Goldratt & Goldratt (2007) and SCHRAGENHEIM (2007) mention some ways of

increasing throughput, among them the reduction of shortage percentage.

- Having enough stock for each product in the right place at the right time to serve the demand

improves customer services reducing shortage probabilities;

- The items which most suffer shortages are the most sold, possibly representing a significant

slice of sales in the point of sale. This means that reducing shortage and replenishing time of

these products, sales increase will be much larger that the proportional reduction of shortage

frequency;

- With lower stock per item, the point of sale does not have to keep large amount of low sale

products. This eliminates a behavior that is very harmful for sales, that is, the retailer will

invest a lot of effort to sell these slow mover items and block his display space at the expense

of the other items in the shop. Huge efforts that will be invested by the shopkeeper to sell

these slow mover items could have yielded much higher revenues from high runner items;

- With information about throughput and average replenishing time of each SKU and each

place, the supplier is able to identify the items with low return of investment, which can be

replaced by high turnover products, maximizing the financial return per display space;

- With a lower stock, therefore with higher available cash-flow and smaller space on the shelf

taken by each article, the distributor and the point of sale are able to offer a larger variety of

items, thus increasing throughput with the same physical space;

- Price reductions to get rid of too large stocks will be less frequent. This means that not only

profit margins will increase, but also price reductions of obsolete products will not spoil the

market for new products;

- With faster and more frequent replenishment, the time between production and availability

for the final client will be shorter. Products on the shelf will then be newer and consequently

more attractive. This can bring an important effect on the throughput from articles with due-

by dates;

- When the stock ends (in a frequency that should have been sharply reduced), replenishment

is faster and the probability of the customer change for the competitor’s product is smaller.

- TOC distribution methodology sharply reduces replenishing time. For many of the less

popular products, reduced replenishing time is shorter than consumer’s tolerance time. These

products can now be offered even without keeping them in stock. Liberating the distribution

system from the need to keep stocks of less popular products will cause a considerable

increase in the offered product portfolio.

TOC distribution methodology principles and techniques have been used for over a decade in

several companies from varied sectors. Kendall (2005) and Schragenheim (2007) present

some results based on their own experiences and information from other sources. Goldratt &

Goldratt (2007) affirm that implementations of TOC distribution methodology have presented

the following results:

- Decrease in system stock (typically 50%);

- Increase in sales (typically at least 20%);

- Increase in stock turnover (typically over 100%);

- Decrease in internal transferences between regional warehouses (typically to almost zero);

- Decrease of obsolescence typically to less than 50%);

- Operational Expense kept approximately the same;

- Significant improvement in relationship between clients and suppliers.

Therefore, it is clear that TOC brings a complementary proposal in many aspects and very

distinguishable in others in the way of managing some key processes and elements of SCM,

especially the outbound logistics. Therefore, besides the need of further research regarding

improvement of these techniques, it is necessary to take precautions before adopting such

methodology. Intensive practical and theoretical training on the methodology should be

considered obligatory in the first stages of the implementation process.

References

BEAMON, B. M. Measuring supply chain performance. International Journal of

Operations & Production Management, v. 9, n. 3, p. 275-292, 1999.

CERVENY, J.F.; GALUP, S.D. Critical Chain project management holistic solution aligning

quantitative and qualitative project management methods. Production and Inventory

Management Journal. Third/fourth quarter, p.55-64, 2002.

CHOPRA, S.; MEINDL, P. Supply Chain Management. Prentice Hall, 2003.

CORBETT, T. Throughput Accounting. Great Barrington: North River Press, 1998.

DETTMER, W. Goldratt's Theory of Constraints: A System's Approach to Continuous

Improvement. Ed. ASQC, Quality Press, 1997.

FOGARTY, D. W.; BLACKSTONE JR, J. H. Production & Inventory Management.

Cincinati: South-Western Publishing Co., 1991.

GOLDRATT, E.M., 1990. The Haystack Syndrome: Sifting Information Out of the Data

Ocean. Great Barrington: North River Press, 1990a.

GOLDRATT, E. M. What is thing called the Theory of Constraints, and how should it be

implemented. Croton-on-Hudson: North River Press, 1990b.

GOLDRATT, E. M. It’s not luck. Great Barrington: North River Press, 1994.

GOLDRATT, E.M. Critical Chain. Great Barrington: North River Press, 1997.

GOLDRATT, E.M. The Goal. Great Barrington: North River Press, 2004.

GOLDRATT, E. M. TOC insights into distribution. Available at http://www.toc-

goldratt.com. Accessed on March 20, 2007.

GOLDRATT, E. M.; SCHRAGENHEIM, E. & PTAK, C. Necesary but not sufficient. Great

Barrington: North River Press, 2000.

HOLMBERG, S. A system perspective on supply chain measurement. International Journal

of Physical Distribution & Logistics Management, v. 30, p. 847-868, 2000.

KENDALL, G.I. Viable Vision: transforming total sales into net profits. Boca Raton: Ross

Publishing, 2005.

MABIN, V.J.; BALDERSTONE, S.J. The performance of the theory of constraints

methodology: Analysis and discussion of successful TOC applications. International

Journal of Operations & Production Management, v. 23, n. 6, 2003.

NEELY, A.; GREGORY, M.; PLATTES, K. Performance measurement system design: a

literature review and research agenda. International Journal of Operations & Production

Management, v. 15, n. 4, 1995.

NOREEN, E.; SMITH, D.; MACKEY, J. T. Theory of Constraints and its implications for

management accounting. Great Barrington: North River Press, 1994.

PTAK, C. ERP, tools, techniques and applications for integrating the supply chain. New

York: St. Lucie Press, 2004.

SMITH, D.W. The measurement nightmare. Boca Raton: Lucie Press, 2000.

SCHRAGENHEIM, A. Managing distribution according to TOC principles. Available at

http://www.inherentsimplicity.com/. Accessed on December 15, 2007.

SCHRAGENHEIM, E. Make-to-stock under Drum-Buffer-Rope and Buffer Management

methodology. International Conference Proceedings – The Educational Society for

Resource Management (APICS), 2002.

SCHRAGENHEIM, E.; BURKHARD, R. Drum Buffer Rope and Buffer Management in a

make-to-stock environment. Available at http://www.inherentsimplicity.com/. Accessed on

December 15, 2007.

SCHRAGENHEIM, E. M.; DETTMER, H.W. Manufacturing at Warp Speed. Boca Raton:

St. Lucie Press, 2001.

SIMATUPANG, T.M.; WRIGHT, A.C.; SRIDHARAN, R. Applying the theory of constraints

to supply chain collaboration. Supply Chain Management: An International Journal, v. 9,

n.1, p. 57-70, 2004.

TAYLOR, D. A. Supply Chains: a manager’s guide. Pearson Education, 2004.

UMBLE, M. M.; SRIKANTH, M. L. Synchronous manufacturing: principles for world

class excellence. Cincinnati: South Western, 1990.

UMBLE, E. J.; UMBLE, M. Integrating the Theory of Constraints into Supply Chain

Management. Proceedings of the 33rd annual Decision Sciences Conference, p. 479-484,

2002.

WATSON, K.J; BLACKSTONE, J.H.; GARDINER, S.C. The evolution of a management

philosophy: The theory of constraints. Journal of Operations Management, v. 25, p. 387-

402, 2007.

YUAN, K; CHANG, S; LI, R. Enhancement of Theory of Constraints replenishment using a

novel generic buffer management procedure. International Journal of Production

Research, v. 41, n. 4, p. 725-740, 2003.

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