LEAN PRODUCTION IN A WORLD OF UNCERTAINTY: IMPLICATIONS OF VARIABLE DEMAND

Prepared for Jerry Gabriel Instructor, Engineering Communications Program

Jack Muckstadt Professor, School of Operations Research & Industrial Engineering

Prepared by Diana Coggin Student, School of Operations Research & Industrial Engineering

December 15, 2003

College of Engineering, Cornell University

Ithaca, NY 14850

© 2003 Diana Coggin

Table of Contents

List of Figures ................................................................................................................................ iii 1. Introduction................................................................................................................................. 1 2. Methodology............................................................................................................................... 2 3. Discussion ................................................................................................................................... 2

3.1. Background to Lean Production Systems ............................................................................ 3

3.1.1 Principles........................................................................................................................ 3 3.1.2 Takt ................................................................................................................................ 4 3.1.3 Pull ................................................................................................................................. 5

3.2. Implications of Variable Demand........................................................................................ 7

3.2.1 Common Challenges...................................................................................................... 7 3.2.2 In-Depth Example: Velocity Manufacturing ................................................................. 8

3.3 Proposed Solutions.............................................................................................................. 10

3.3.1 Recommendations to Velocity and Implemented Transformations............................. 10 3.3.2 Volume and Variability Demand Analysis .................................................................. 13 3.3.3 Continuous Improvement - Beyond Factory Walls ..................................................... 17

4. Conclusion ................................................................................................................................ 19 List of References ......................................................................................................................... 20 Glossary ...................................................................................................................................... G-1 Appendices.................................................................................................................................. A-1

Appendix A. Volume-Variability Analysis of Velocity’s Parts ............................................. A-1 Appendix B. Customer Demand Analysis for Velocity Part Number 1 ................................. A-4

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List of Figures

Figure 1: Takt Time at Boeing 4 Figure 2: Push versus Pull System and Resulting Reduction in Inventory 6 Figure 3: New SKU Classification Based on Demand Volume and Variability 14 Figure A1: Velocity Part 1 - High Volume, Low Variability A-1 Figure A2: Velocity Part 7 – High Volume, High Variability A-2 Figure A3: Velocity Part 12 - Medium Volume, Medium Variability A-3 Figure A4: Velocity Part 59 – Low Volume A-3 Figure B1: Velocity Part 1 Demand, Customers 1 A-5 Figure B2: Velocity Part 1 Demand, Customers 1, 2, 4 A-6 Figure B3: Velocity Part 1 Demand, Customers 3 and 5 – 22 A-6

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1. Introduction

American engineer and acclaimed inventor, Charles F. Kettering, once said, “The world hates

change, yet it is the only thing that has brought progress.” Countless changes have taken place in

the business environment during recent decades, including globalization, the technology boom,

and the paramount importance of customer satisfaction in increasingly competitive markets.

Whereas yesterday’s manufacturers could expect to sell massive amounts of standardized

products manufactured at their convenience, customer demands today are much more stringent.

Companies now must meet higher standards on quality, customization, and timely delivery to

customers who are now accustomed to conducting business at Internet speed. Companies must

meet these and other requirements while maintaining the lowest possible cost in order to avoid

losing customers to the competition. Throughout these times of change, however, the goal of any

company has remained the same: to make a profit and to stay in business. Numerous companies

throughout the world have adopted an innovative production philosophy called Lean Production

or World-Class Manufacturing in order to survive in today’s competitive markets.

The goals of Lean Production, or simply Lean, are multifaceted, but its main goal is, “to get one

process to make only what the next process needs when it needs it [and] to link all processes –

from raw material to final consumer” [1]. The principles of Lean are based on continuous

improvement and the minimization of defects, inventory, and non-value added time. If

implemented correctly, the Lean approach provides for increased throughput, return on assets

and, most importantly, profit. The success of Lean implementation, however, requires specific

attributes of the manufacturing environment and the enterprise in which it exists. Primarily,

because Lean attempts to match production rates with marketplace demand and requires

processes engineered accordingly, lean systems are biased towards demand that is consistently

level. However, many industries have demand rates that vary from season to season or even

daily, and such variable demand can have numerous negative consequences on lean systems.

According to Panizzolo, “The lean production system is fundamentally a fragile system, in which

slight perturbations or deviations from the working conditions planned for can seriously affect

system performance” [2]. Many companies that have invested significant amounts of time and

money in Lean have experienced the detrimental effects of such deviations. The purpose of this

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research paper is to present examples of how variable customer demand often undermines the

mission of Lean Production Systems, and to explore potential solutions that may assist

companies with variable demand in sustaining their competitive advantage.

2. Methodology

My first goal in researching this problem was to get a firm understanding of Lean Production

Systems. In order to do so, I have utilized widely cited resources written by experts in Lean and

its implementation, which I obtained through Cornell University Libraries and Yale University

Library. Online journals like the International Journal of Production Economics and Supply

Chain Management Review have been instrumental in my search for recent challenges and

developments in manufacturing and supply chain. I have also utilized Google.com as well as

other public search engines to find information on specific companies and their initiatives.

Furthermore, I have gained a substantial amount of knowledge from my course in design of

manufacturing systems and the discussions I have had with my professors, Jack Muckstadt and

Peter Jackson, both experts in the world of Lean. In this course, we have examined the Velocity

Manufacturing Company, a real company protected by a fictional name, and we have

experienced first-hand the challenges and consequences it has faced through a simulation of its

shop-floor environment. Furthermore, I have worked with a team of peers to serve as a

consulting firm with the goal of redesigning Velocity’s entire business strategy for the next five

years. This class has provided me with an in-depth perspective on one company that has

implemented Lean principles and undergone various challenges due to unpredictable customer

demand.

3. Discussion

I will begin by discussing Lean in further detail and examine the reliance on level-loaded

demand. Next, I will examine common consequences of variable demand and give specific

examples of lean companies that have faced such challenges. Finally, I will present potential

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solutions to these challenges, including implemented practices and recently proposed strategies

that may enable companies to meet unpredictable customer demand more successfully.

3.1. Background to Lean Production Systems

As mentioned above, Lean is essentially characterized by doing more with less. However, Lean

is a philosophy that goes beyond merely a method of inventory management and production

control. Lean is based on a set of key principles, and Lean systems utilize a measurement tool

called takt time, and a manufacturing strategy based on the pull system.

3.1.1 Principles

Beginning with the acclaimed Toyota Production System and related operational modes of Just-

In-Time (JIT) delivery and production scheduling, Kaizen (continuous improvement), and

Kanban (visual signals utilized in a pull system), Lean has evolved and essentially embodies the

spirit and approaches of all of these movements [3]. Womack et al. contend that “Lean thinking

can be summarized in five principles: precisely specify value by specific product, identify the

value stream for each product, make value flow without interruptions, let the customer pull value

from the producer, and pursue perfection” [4]. In more detail, Harvard Business Review

describes the implementation of Lean production:

By eliminating unnecessary steps, aligning all steps in an activity in a continuous flow, recombining labor into cross-functional teams dedicated to that activity, and continually striving for improvement, companies can develop, produce, and distribute products with half or less of the human effort, space, tools, time, and overall expense. They can also become vastly more flexible and responsive to customer desires [5].

To further reinforce this idea, it helps to compare and contrast Lean production systems to the

more traditional mass production systems, which focus on labor and production efficiency of

standardized products, low unit manufacturing cost, and quality through inspection. Key

attributes of mass production systems include high volume, long product runs, infinitely

fragmented work, “good enough” product quality, enormous inventories, massive factories [6],

lengthy setup times, and long and variable flow times. In contrast, Lean production systems

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focus on exceeding customer requirements, creating flexible and responsive systems, incurring

the lowest possible total cost, and proactively designing quality processes and products.

3.1.2 Takt

The high quality processes inherent to Lean systems include focused factories with cellular

manufacturing, minimal variation in all processes, minimal setup times, and perhaps most

importantly, short, predictable, and repeatable flow times, which is the time spent within the

system by a single unit including queue time [7]. The use of manufacturing cells focused around

product families are of primary importance in Lean production, and takt, German for rhythm or

beat, is a frequently used term during cell design. “Takt time is the basis for cell design and

represents the rate of consumption by the marketplace… The ratio for takt time has scheduled

production time available as the numerator and designed daily production rate as the

denominator” [8]. Computing the average demand for the next 6 to 12 months based on the

business forecast gives the designed daily production rate, which is increased to create a buffer

by taking into account a subjective and minimal amount of variation. Once takt time is

computed, all operational elements are extensively examined with relation to takt time, and

eventually the associated cell is designed, workloads are determined, and successive steps of lean

implementation are performed.

Boeing is a prime example of a company that has

implemented Lean initiatives, and takt-paced production is at

the heart of their airplane production system. According to

the Boeing website regarding Lean initiatives, “Lean does

not mean doing things faster; it means doing things at the

right pace. Essentially, the customer's rate of demand

establishes the pace, or takt time” [9]. Rather than

maximizing the production rate and factory utilization to

their fullest potential, production rates are determined by

customer demand, ensuring that customer needs can be

Figure 1 [9]. Takt Time at Boeing

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satisfied in a timely and predictable fashion. As shown in Figure 1, Boeing has calculated takt

time based on historical customer demand rate and applied it to the assembly of airplanes.

3.1.3 Pull

Another important principle of the Lean system is in the utilization of a pull system rather than a

push system. In traditional mass production systems, materials are pushed though the

manufacturing process in order to meet predetermined stock levels between processing steps in

the production sequence. The push system results in high levels of inventory, overproduction,

and excessive amounts of obsolete products in finished goods inventory. Pull systems, on the

other hand, attempt to emulate actual demand, and material flows only when pulled by the next

step [4]. As material flows from the back of the shop to the front of the shop, or “downstream,”

to the end customer, information flows in the opposite direction, signaling production only when

needed and keeping Work-in-Process (WIP) inventory levels at a minimum.

Figure 2 compares the push system, shown in the top half of the picture, to the pull system,

illustrated in the bottom half. The figure contains white circles, which represent processes, such

as machining, assembly, welding, or customization, and these process are divided into three

sections, or sectors, by the darker black lines. The black circles represent inventory, mostly WIP,

except for the last black circle before the end customer, which represents finished goods

inventory. Inventory is a prime example of muda, or waste, in working capital, storage space,

holding costs, material handling, risk of damage and obsolescence, and so on [10].

In the push system, excessive amounts of inventory are tied up in queues between each process,

unnecessarily lengthening flow times for parts and lead time of shipments to customers. For the

pull system shown in the bottom half of Figure 2, the need for production is sent in the opposite

direction of material flow beginning with a visual signal at the end of the production line.

Namely, if a customer demands a unit from finished goods inventory, the third sector operator

would be signaled that he must produce a unit to replace it. The final process in sector three

would then pull the needed unit from the preceding upstream process in order to replace the unit

in finished goods inventory. The upstream operator would then pull material from the process

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before her, and so forth until WIP inventory is taken from the end of Sector 2. Similarly, as soon

as the unit is removed from the WIP at the end of Sector 2, the Sector 2 operators would see that

production of another unit is required, and the flow of information and signals for production

continue in this manner. When the pull is felt between processes, one piece at a time, the system

is said to operate with “one-piece flow.”

Figure 2 [10]. Push versus Pull System and Resulting Reduction in Inventory

The key to this system lies in the utilization of Kanbans, or visual cards, signaling when WIP

inventory levels are below the predetermined level between each sector, thus notifying upstream

sectors when production is necessary. Implementation of this system minimizes inventory,

prevents finished goods from becoming obsolete, and enables production to mirror demand in the

marketplace.

The fundamental problem lies in the fact that Lean systems that utilize takt-time, pull, and one-

piece flow are based on a level-loaded system which assumes that customer demand does not

vary over time. Feld states that the amount of variation that can be accounted for in calculating

designed daily production rate, for use in calculating takt time, should not exceed 50% of the

average because a cell cannot be designed for infinite capacity [8]. While level-loaded demand

and lean principles are applicable in some industries, such as the automotive industry, not all

companies can be as successful in Lean implementation as the Lean pioneer, Toyota, for

instance. As mentioned above, many industries, such as aerospace or electronics, have demand

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rates that vary from season to season or even daily, and in such cases, companies must adjust

their processes in order to sustain high levels of customer satisfaction.

3.2. Implications of Variable Demand

Any company or value stream, whether lean or not, can experience detrimental effects of a

mismatch between supply and demand. However, the problems caused by variable, unpredictable

demand are amplified in Lean environments, which have a built-in bias for level-loaded demand

and production.

3.2.1 Common Challenges

Management teams frequently bring up the recurring question, “How do we schedule production

and distribution to maximize capacity utilization and minimize inventory levels while achieving

high levels of customer service?” The question is complicated by the fact that demand for the

firm’s products varies widely. For instance, some products with low sales volume experience

significant demand fluctuations, while other products experience the exact opposite, stable

demand with very little variation [11]. Products with variable demand create many headaches for

production controllers and management alike. Unpredictable, variable demand presents several

problems for suppliers within any manufacturing environment. Among the most common are:

• Stock-outs when demand spikes are higher than usual

• Excessive levels of inventory to serve as buffers against changes in marketplace

• Inability to forecast accurately because data may not accurately predict future demand

• Increasing problems with obsolescence caused by short product life cycles

• Inability to meet growing demand for shorter lead times

• Detrimental effects on customer service levels and order fulfillment rates [11].

When lean companies experience these problems, it becomes difficult to even continue to

classify them as lean because lean implies and requires the exact opposite of several of these

points. As mentioned above, lean requires the elimination of muda in the form of excessive

inventory and obsolete finished goods. Furthermore, short lead times and high customer service

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levels are key metrics in any lean system [12], and lean requires level-loaded production based

on forecasted average demand rates.

Lean manufacturing avoids the requirement for robustness by calling for the demand to be stable

through the use of market knowledge and information, and forward planning [13]. By its very

nature, lean production tends to reduce demand variation by optimizing, simplifying, and

streamlining the supply chain [12]. However, end-user demand is beyond the control of the

supply chain, and sudden variations in demand lead to waste either in not producing near

capacity or needing to keep larger buffer stocks, as has recently occurred at Boeing [14]. As

discussed above, Boeing pursued a lean manufacturing strategy, which utilized takt time and

one-piece flow through a pull system [9]. However, they adopted this strategy without taking

into account the variability of demand in the aerospace industry. Boeing has been able to cope

with a doubling of production but their increased efforts still falls far short of the market

demand. Boeing's sole competitor, Airbus Industries, has been able to exploit Boeing’s capacity

constraints and ramped up successfully to take a larger share of the market [12].

3.2.2 In-Depth Example: Velocity Manufacturing

Velocity Manufacturing Company is the subject of our in-depth study in my design of

manufacturing systems course, and it serves as a good example of an industrial company with

challenges due to variable demand. A supplier in the U.S. hydraulic hose and fittings market,

Velocity began experimenting with some of the principles of Lean Production three years ago.

Significant investments were required to implement changes including JIT delivery scheduling,

focused factories, cross-trained operators, and pull manufacturing [7]. Upon reading about the

history of the company and the changes that had been made, it appeared to me that Velocity was

on the right track toward gaining market share from its competitors. However, when the class

simulated Velocity’s shop floor environment based on the current set up of their manufacturing

processes, prototype information system, and actual demand data, the problems Velocity faced

due to variable demand became evident.

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Primarily, a main issue that contributed to Velocity’s problems was the long and unpredictable

flow time, which could be attributed to the setup of the manufacturing floor and the excessive

movement of material. The manufacturing floor was set up in decoupled sectors, each with

unique processes and staff, including operators, materials clerks, and production controllers.

During each shift, operators in each sector would process and inspect the parts released to

production for the day and place them in finished goods inventory for their sector. At the end of

each shift, materials clerks from each sector brought finished units that had been fabricated to a

stockroom, where they would await release for production in the subsequent sector the following

day. Extensive communication would then occur between materials clerks and production

controllers to determine standings on orders and to determine how many units to release for

production the following day. Furthermore, each production controller utilized a computer

system to keep track of inventory levels between sectors, so they constantly had to update

inventory levels and production schedules to keep the information system in sync with the

physical numbers.

Velocity operated on a pull system in the sense that information flowed in the opposite direction

of material. The amount of inventory removed for production in Sector 4, for instance, would

determine how many units would be released for production in Sector 3 the following day.

However, rather than keeping WIP inventory on the floor right between sectors and utilizing

visual cues to signal production in upstream sectors, WIP parts were stored in the stockroom and

numbers were tracked by production controllers, who would type numbers, perhaps incorrectly,

into a computer system.

In addition to the excessive handling of material and the unnecessary amount of time spent in the

stockroom, the target inventory between sectors for most parts was very low. Minimal inventory

is a key principle in any Lean system, but Velocity’s variable demand resulted in many

challenges. When demand became higher than the minimal positive variation deemed normal for

certain parts, production controllers tried to expedite manufacturing of the highly demanded

parts by pushing them through the system. This disrupted the flow of planned production,

causing setups to be performed out of order and pushing back the production of other parts,

further lengthening their flow times. Furthermore, the target inventory levels were the same

across the board for all products, although each product had its own average demand rate and

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variability. Unreliable scrap rates throughout the line also had detrimental effects on Velocity’s

efficiency, especially when defective products managed to get past the last sector tester and into

the customer’s hands. Both scrap and expedited orders cause inventory levels to fluctuate,

resulting in shortages of particular raw material components often needed on high priority items.

Finally, in some circumstances, customer orders were received by production controllers,

preventing them from planning production based on actual demand numbers. In such cases, the

production controllers would simply guess at how many units should be produced based on

memory and gut feelings. Such hasty decisions often led to Velocity having finished goods

inventory of the wrong parts. All of the problems mentioned above led to Velocity experiencing

a disappointing customer order fill rate of only 66%. Without a rapid and drastic change,

Velocity would suffer tremendous consequences of loss in market share and profit.

3.3 Proposed Solutions Lean companies in industries with variable demand often face challenges similar to those of

Velocity. By examining the solutions recommended and implemented by Velocity, perhaps other

companies could adapt them to their own businesses. Furthermore, a newly proposed technique

based on volume-variability demand analysis could provide help in further understanding a

product line and demand for individual parts. However, it is important to look even further to

understand why demand occurs in certain ways, and manufacturers must look beyond their

factory walls to their direct customers and throughout the supply chain in order to overcome the

challenges of variable demand.

3.3.1 Recommendations to Velocity and Implemented Transformations

Several recommendations were made to Velocity in order to improve its processes, increase the

order fill rate to 98%, and increase market share. While Velocity had clearly tried to implement

Lean techniques such as a pull system and low inventory levels, adjustments were necessary to

solve the problems due to variable demand. When considering that machine operators are the

only people who add value to the parts, it was interesting to see that Velocity had so many

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employees working in back-room operations handling material and releasing work orders to the

shop floor. The entire class acted fast during the simulation exercise to redesign the layout of the

manufacturing floor and to remove superfluous steps from the production process. Namely, by

removing the backroom operations and the persistent movement of parts in and out of the

stockroom, parts were able to move directly from one sector to the next on an as-needed basis.

In addition to reducing material movement, some process steps were eliminated. For instance,

inspection at the end of some sectors was eliminated, in order to reduce flow time as much as

possible. These changes streamlined the process and greatly reduced non-value added time and

overall flow time significantly, which are of paramount importance, as short flow times are

instrumental in maintaining responsiveness to variable customer demand. Furthermore, Velocity

purchased new equipment in order to increase the overall factory capacity, enabling it to meet

more customer demand and further reduce overall flow time.

Eliminating the need for the computer system to keep track of WIP inventory and switching to a

visual pull system also helped to make the process more efficient. Sector 2 operators, for

instance, could then see when parts were pulled to Sector 3, and continue production on an as-

needed basis. While depending on the information technology (IT) system to track inventory

between sectors was unnecessary, it was recommended that Velocity should consider utilizing an

IT system to track historic customer demand and to measure trends and variability. By doing so,

Velocity’s production controllers could avoid making decisions based on memory and feelings

when demand data are unavailable, and rather, make decisions based on hard data, while taking

both average demand rates as well as variability into account.

Other potential solutions regard Velocity’s use of inventory. For instance, Velocity had been

operating with target inventory levels between each sector. However holding inventory between

each sector did not assist Velocity in meeting customer demand; rather, it hurt Velocity’s order

fill rate by increasing the time spent in queue by each unit. By joining sectors two through four

to operate with pieces flowing directly through production, flow time was reduced significantly.

Machining, which took place in Sector 1, was the bottleneck of the entire production process, so

Velocity decided to keep Sector 1 separate from the remaining processes, which would now

operate on one-piece flow, and they divided Sector 1 from the rest of the line with a decoupling

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point. This involved setting target inventory levels for components of fast moving parts, and

when operators in assembly (Sector 2) removed units from the WIP inventory at the end of

Sector 1, kanban cards would signal operators in Sector 1 to replenish those components by the

end of the following day. Velocity called this strategy a two-sector pull system.

Furthermore, Velocity’s finished goods inventory levels with two days of inventory were

insufficient. This level was based on average demand rates with only a minimal allowance for

variation, which could not be successful in an industry with such variable demand. Simulation

software is often used in industry in order to determine appropriate inventory levels. In

Velocity’s case, we utilized a simulation model that had been programmed with historical

demand data and appropriate statistical distributions in order to predict the resulting customer fill

rate that would be achieved. After performing sensitivity analysis based on different inventory

levels, we were able to determine target inventory levels that should result in 98% order fill rates,

significantly increasing customer satisfaction.

Finally, other adjustments were required to improve the manufacturing and inspection equipment

in order to ensure that customers would not receive parts with defects. Releasing defective

products to the customer is unacceptable, and high scrap rates significantly increase the

investment required in material and labor to meet customer demand. Furthermore, quality

problems result in inefficient capital and capacity utilization. Other proposed solutions for

Velocity involved reducing lead-time variability as much as possible by switching to raw

material suppliers with more predictable delivery times and higher quality products.

By reducing overall flow time, investing in new equipment and capital modifications,

restructuring inventory, IT, and production strategies, and utilizing simulation to determine

appropriate inventory levels, Velocity has been able to make significant improvements in terms

of meeting variable customer demand, and their customer order fill rate has increased

significantly, leading to more satisfied customers and growth in market share. Several of the

strategies implemented by Velocity may be applied to other companies with similar challenges,

keeping in mind unique circumstances and business models.

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3.3.2 Volume and Variability Demand Analysis

Companies facing similar problems due to variable demand may also consider an innovative

approach that addresses the trade-off between inventory and customer service levels. While

debate exists regarding the capability and practicality of this technique in solving variable

demand problems, it may help companies that have not performed similar analysis to gain more

insight into their product line.

This framework relies on two principles:

1) Both the volume and the variability of demand must be taken into account by

using what is called volume-variability demand profiling.

2) Product manufacturing and distribution must be aligned with this profile through

a mix of build-to-stock, build-to-order, and make-to-order strategies [11].

Aside from using simple safety-stock inventory planning concepts, companies rarely consider

both volume and variability of demand in their planning and execution processes, and they often

apply a "one-size-fits-all" strategy for different stock keeping units (SKUs), regardless of volume

and variability levels. "As such, variable SKUs with erratic demand often get treated the same as

those units with predictable patterns, such as high-volume SKUs with a consistent level of

demand" [11].

Velocity Manufacturing and other companies using lean methodologies exemplify the use of

volume-based demand analysis in designing manufacturing cells without taking into account the

dimension of variability. Consequently, companies end up manufacturing products and holding

inventories in the right quantities but in the wrong mix of products. This problem is exacerbated

by what is known as the bullwhip effect, which occurs when slight perturbations in demand

downstream of the supply chain result in huge inventory build-ups upstream at the supplier

levels.

Vitasek et al. argue that by adhering to the principles outlined above, companies may be able to

move in the right direction towards solving their supply-demand problems. Implementing this

framework begins with an in-depth analysis of the product line, and categorization of products

into four categories based on volume and variability, as shown in Figure 3.

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Figure 3 [11]. New SKU Classification Based on Demand Volume and Variability

"A" SKUs represent medium-to-high volume products with predictable demand. "B" SKUs

represent products with medium volume and low-to-medium variability. "C" SKUs represent all

low-volume products, and "D" SKUs have both medium-to-high volume and high variability. By

categorizing products accordingly, management can better understand the effects that each

product has on their operations and more effectively administer the manufacturing and

distribution of the different SKUs.

After stratifying the product mix into these four categories, the second principle suggests that

manufacturing and distribution strategies should be applied to each distinctive group of products,

rather than adopting one manufacturing strategy for the entire product line. Vitasek et al. propose

that the following techniques chosen for each product category represent the most efficient

techniques to drive high customer service levels while minimizing inventory on hand.

"A" SKUs are most effectively run in traditional assembly line, make-to-stock environments,

which are the most labor-, time-, and cost-effective methods to manufacture and distribute large

quantities of goods [11]. However if lean systems are already in place, "A" SKUs can also be

produced in rate-based manufacturing cells, since the predictable nature of the demand for these

SKUs is suitable for level-loaded production. Finished goods inventory should be on hand for

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these high volume SKUs, serving as safety stock to buffer variable demand and to prevent stock-

outs. Because the variability in demand for “A” SKUs is relatively low, companies can rest

assured that the products will be sold and not become obsolete.

"B" SKUs are most effective under a kanban or a JIT model because these approaches provide

for optimal service levels and minimal inventory for this product group [11]. Cellular

manufacturing and lean methodologies are most effective with low volume goods rather than

mass production or assembly line models because efficiency is lost in the more old-fashioned

models when frequent setups must be performed between production runs of different SKUs.

Finished goods inventory should also be kept for "B" SKUs unless short lead times are available.

"C" SKUs are most effective in cellular manufacturing with a make-to-order manufacturing

strategy. By not holding finished goods inventory of these low-volume products, companies can

prevent goods from becoming obsolete when demand levels drop and can also save on inventory

holding costs. Because these products are low volume, companies can expect to produce all of

the units needed to meet an order within a relatively short amount of time without utilizing all

available production capacity.

Finally, "D" SKUs are most effectively produced make-to-order in assembly lines because they

have the most potential to affect operations and service levels. When one or more customers

place unexpected orders for large quantities of goods, stock-outs often occur not only in that

finished SKU, but in component parts used in other products as well. However, it would be

detrimental to hold excessive inventory of these units because demand levels could drop

erratically, resulting in high inventory holding costs and finished goods becoming obsolete. If a

manufacturing-on-demand option with a quick cycle time is not available for these SKUs,

companies can increase service levels by informing their customers of a maximum order quantity

and maintaining that amount of finished goods inventory on hand. Customers should be warned

that if orders are placed for more units than the maximum order quantity, these units will be

made-to-order, and a longer lead-time should be allowed.

By performing volume-variability analysis of historical demand data from Velocity

Manufacturing, it can be shown that Velocity’s product line consists of SKUs that fall into each

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of the categories listed above. Some of Velocity’s parts are listed and categorized in Table 1.

Specific part numbers are listed in the first column, average daily demand, µ, for each part is

listed the second column, the standard deviation of each part’s daily demand, σ, is shown in the

third column, and the coefficient of variation, σ/ µ, is shown in the fourth column. The

coefficient of variation is useful in quantifying the variability of a part’s demand in relation to its

average demand.

Part Number µ σ σ/ µ Category

1 8.50 6.94 0.82 A 12 0.20 0.69 3.50 B 59 0.02 0.18 11.05 C 7 1.59 2.50 1.57 D

Table 1. Velocity Parts Categorized by Volume-Variability

Part 1 is clearly a high-volume part, with an average of 8.5 units demanded per day. Though its

standard deviation of demand is high at 6.94 units, in relation to average demand, the variability

is relatively low in comparison to other parts. Thus, this part would fall into the “A” category

with high-volume, low-variability SKUs. According to the suggested strategies, finished goods

inventory of Part 1 should be kept in stock. Part 12 can be classified as a “B” SKU because it has

medium demand volume, with an average of 0.2 units per day, and medium demand variability.

Part 59 is a very low volume part, with an average of only 0.02 units ordered per day. In fact, this

part was only ordered on one occasion out of 120 days. Thus, Part 59 should be categorized as a

“C” SKU. Finished goods inventory should not be maintained of Parts 12 and 59 because they

are usually demanded in small enough quantities to enable Velocity to fulfill orders relatively

easily. Finally, Part 7 is a high volume part with an average of 1.59 units demanded per day, and

it has high variability with a standard deviation of 2.5 units. Part 7 should thus be classified as a

“D” SKU. Since Velocity’s manufacturing system is set up in a lean manufacturing cell with a

much shorter flow time, All “B”, “C”, and “D” units should be made-to-order, with varying

factory capacity utilization requirements for products with different demand rates. For

illustrations of time-series demand trends of Velocity’s parts listed above, please refer to

Appendix A.

16

SKUs can be placed into different product categories at different times. For instance, when

special promotions go on for certain products, demand rates are likely to change. Furthermore,

companies should mix and match different strategies to best satisfy their specific needs based on

availability and suitability of different strategies. "The key element of this approach is the use of

different operational models to buffer demand volatility in the supply chain" [11]. This approach

attempts to mitigate the risk of inaccurate forecasts for products with highly variable demand,

while attempting to keep inventory low and customer service levels high. While only a few

companies have successfully implemented this innovative technique thus far, companies in any

industry can benefit from performing volume-variability demand profiling to get a better

understanding of their products and the manufacturing and distribution strategies that would best

assist them in solving the problems they face due to variable demand.

3.3.3 Continuous Improvement - Beyond Factory Walls

The findings I have presented thus far have dealt with solutions from an operations management

perspective within the manufacturing environment. However, operations managers must also

look beyond the four walls of their plant and extend their efforts throughout the supply chain.

"Too many companies fail to adequately recognize that the supply chain extends far forward to

customers, and back, to suppliers and their suppliers… Our study showed only 7% going outside

their four walls to track the performance of supply-chain activities at their vendors, logistics

providers, distributors, and customers" [15]. Panizzolo argues, "for a full implementation of lean

production principles, the most critical factor appears to be the management of external

relationships rather than internal operations… [T]he focus must move from operations

management to relationships management [2]."

While the volume-variability technique mentioned above may be useful in understanding overall

demand of products, companies should perform further analysis to determine exactly which

customers are ordering each part, especially for high volume, lower variability parts requiring

safety-stock, and thereby tying up capital and generating excessive inventory holding costs. By

identifying key customers for such parts and examining the volume and variability of their orders

on an individual basis, many important insights can be revealed. More than likely, manufactures

17

may discover that the overall demand variability for a part may be attributed primarily to key

customers with highly variable demand. Furthermore, examining the trends of order placement

by an individual customer can provide insight on the manufacturing and ordering strategies on

which the customer operates, assuming the customer is an original equipment manufacturer or

somewhere else along the supply chain before the end user. This information not only informs

the supplier of the customer’s daily operations, but it also provides a better understanding of how

their ordering policies ultimately affect supplier operations.

Beyond analysis of data, relationships must be improved in order to initiate more open

communication with key customers with variable demand. Ideally, suppliers should aim for full

visibility into such a customer’s operations in order to see when orders will be placed and for

what quantities. That way, the supplier can schedule production to match the actual rate of usage

by the customer, enabling preparing the supplier to fill large orders immediately when they are

placed. Assuming the remaining customers of a certain part demand the part in low volume and

lower variability, suppliers would no longer require excessive and constant amounts of safety-

stock in anticipation of large spikes in demand. They would simply prepare for the spikes before

they even occur, by adjusting production and target inventory levels as necessary. More

communication with customers regarding order estimates lead to reductions in inventory and

overtime, and consequently, increased cost savings, while meeting customer demands and

maintaining high order fill rates. Please refer to Appendix B for an example of customer demand

analysis for a high volume, low variability part using data from Velocity.

Another school of research has evolved in support of a new concept called "leagility," which

integrates the lean and agile manufacturing paradigms within the total supply chain. Agile

manufacturers use market knowledge and a virtual corporation to exploit profitable opportunities

in a volatile market place. Proponents of leagility argue that the tendency to view lean production

and agile manufacturing in a progression and in isolation is too simplistic a view [12]. The main

idea of leagility is to strategically position the decoupling point in such a way that will best

satisfy customer requirements. The decoupling point should separate the downstream part of the

supply chain that responds directly to the end-user from the upstream part of the supply chain

that uses forward planning and a strategic stock to buffer against the variability in the demand of

the supply chain [12]. Velocity used a decoupling point within their factory to meet similar

18

goals, while leagility applies a broader use for the decoupling point within the supply chain.

While an in-depth analysis of leagility would be out of the scope of this paper, further research of

this newly developing solution might also prove helpful for lean companies who are currently

battling the challenges of variable demand.

4. Conclusion

Since Toyota pioneered lean production, it has essentially changed the world of manufacturing.

It started as a buzzword and eventually became considered a deciding factor between life and

death in today’s competitive markets. Many companies saw the results that lean companies were

achieving and feared being left behind. This may have caused several of them to adopt lean

practices in such a way that may not have been suitable to the nature of their industries. Boeing

and Velocity are two such companies presented in this paper that have suffered detrimental

effects of uncertain demand. While suppliers do not have control over customer demand,

operations managers can implement solutions to try to cope with sudden surges or reductions in

demand, and overcome the challenges associated with such uncertainty.

However, it is important to note that if companies have learned anything from blindly adopting

principles, which appeared to be foolproof, they should know that a solution that works perfectly

for one company might not apply to their particular situation, business model, or industry.

Companies must strive to understand fully their own businesses, products, and customers before

they can find the best solutions to the challenges they face due to variable demand. Only then can

any company implement the right actions to solve their problems, and maintain a sustainable

competitive advantage to carry them into a bright and successful future.

19

List of References

[1] Rother, M., Shook, J. Learning to See: Value stream mapping to add value and eliminate

muda. Brookline, MA: The Lean Enterprise Institute, 1998. p. 39.

[2] Panizollo, R. “Applying the lessons learned from 27 lean manufacturers: The relevance of

relationships management.” International Journal of Production Economics, 1998. p.224

[3] Reary, Bob. “Getting Lean for Optimal Return.” ACSET Volume 5. PeopleSoft, Inc., 2003.

http://www.ascet.com/documents.asp?grID=143&d_ID=1999

[4] Womack, J. P., Jones, D. T. Lean Thinking: Banish waste and create wealth in your

corporation. New York: Simon & Schuster, 1996.

[5] Harvard Business Review on Managing the Value Chain. Harvard Business School Press,

2000. p. 222-230.

[6] Womack, J. P., Jones, D. T. The Machine that Changed the World: Based on the

Massachusetts Institute of Technology 5-million dollar 5-year study on the future of the

automobile. New York: Rawson Associates, c1990.

[7] Muckstadt, Jack, Jackson, Peter. ORIE 416 Design of Manufacturing Systems, Course Packet

of Required Readings, 2003.

[8] Feld, W. M. Lean Manufacturing: Tools, Techniques, and How to Use Them. Boca Raton,

FL: St. Lucie Press, 2000.

[9] Boeing Company Website. Lean Enterprise: Key Lean Initiatives.

http://www.boeing.com/commercial/initiatives/lean/key.html#

[10] Blakemore, J. S. “Maximising Profit with Short Production Runs… Lean Systems

Thinking.” http://www.blakemore.com.au/papers/R571-AGSEI-LM.pdf

[11] Vitasek, Kate L., Manrodt, Karl B., Kelly, Mark. “Solving the Supply-Demand Mismatch.”

Supply Chain Management Review, October/September 2003. Reed Elsevier, Inc.

[12] Naylor, J. Ben, Naim, Mohamed M. “Leagility: Integrating the lean and agile manufacturing

paradigms in the total supply chain.” International Journal of Production Economics,

1999. pp. 106-118.

[13] A. Harrison. ‘The impact of schedule stability on supplier responsiveness: A comparative

study.” Second International Symposium on Logistics, 11-12 July 1995, Nottingham, UK,

pp. 217-224.

20

[14] Anon. “The Jumbo Stumbles.” The Economist, 11th October 1997, p. 116.

[15] Cook, M. “Why Companies Flunk Supply-Chain 101.” Bain & Company, 2003.

www.bain.com

21

Glossary

Cells – The layout of machines of different types performing different operations in a tight

sequence, typically in a U-shape, to permit single-piece flow and flexible deployment of

human effort by means of multi-machine working [4].

Cycle time – The time required to complete one cycle of an operation. If cycle time for every

operation in a complete process can be reduced to equal takt time, products can be made

in single-piece flow [4].

Flow – The progressive achievement of tasks along the value stream so that a product proceeds

from design to launch, order to delivery, and raw materials into the hands of the customer

with no stoppages, scrap, or backflows [4].

Flow time – The total time required for a single unit to move through the entire production

sequence. This includes queue time and setup time.

Just-in-Time – A system for producing and delivering the right items at the right time in just the

right amounts [4].

Kaizen – Continuous, incremental improvement of an activity to create more value with less

muda [4].

Kanban – A small card attached to boxes of parts that regulates pull in the Toyota Production

System by signaling upstream production and delivery [4].

Lead Time – The total time a customer must wait to receive a product after placing an order [4].

Muda – Any activity that consumes resources but creates no value [4].

Operation – An activity or activities performed on a product by a single machine [4].

Perfection – The complete elimination of muda so that all activities along a value stream create

value [4].

Process – A series of operations required to create a design, completed order, or product [4].

Pull – A system of cascading production and delivery instructions from downstream to upstream

activities in which nothing is produced by the upstream supplier until the downstream

customer signals a need; the opposite of push [4].

Queue time – The time a product spends in a line awaiting the next design, order-processing, or

fabrication step [4].

G - 1

Make-to-order – A manufacturing strategy in which production occurs when products are

demanded by the customer.

Make-to-stock – A manufacturing strategy in which production is performed to meet/replenish

predetermined inventory levels.

One-piece flow – A situation in which products proceed, one complete product at a time,

through various operations in design, order-taking, and production, without interruptions,

backflows, or scrap. Contrast with batch-and-queue [4].

Safety Stock – Raw material and/or finished goods inventory maintained as a buffer to prevent

stock-outs when demand is highly variable; constant target inventory levels.

Stock-out – Occurs when products desired by customer(s) are not available.

Takt time – The available production time divided by the rate of customer demand. Sets the pace

of production to match the rate of customer demand and becomes the heartbeat of any

lean system [4].

Value – A capability provided to a customer at the right time at an appropriate price, as defined

in each case by the customer [4].

Value stream – The specific activities required to design, order, and provide a specific product,

from concept to launch, order to delivery, and raw materials into the hands of the

customer [4].

G - 2

Appendices

Appendix A. Volume-Variability Analysis of Velocity’s Parts

Time-Series Analysis of Demand

Special software was made available to examine Velocity’s demand data and to simulate

performance on varying inventory levels. The following four graphs generated by this software

show historical demand trends for specified Velocity parts in terms of quantity demanded on a

daily basis through 120 days in the past. By looking at these graphs and analyzing values for

average daily demand and standard deviation of daily demand, one can categorize parts by

demand volume and variability.

As illustrated in Figure A1, the time series graph of demand for Part 1 is very spiky; however,

the spikes on either side of the mean, shown by the horizontal yellow line, stay similar in size.

No extremely large spikes exist in comparison to the other spikes, implying that while this part

has variable demand, the variability is relatively low in comparison to other parts. Furthermore,

the order quantity scale exemplifies that Part 1 is a high volume part, with daily demand rates

ranging form 0 to 25 units, with a mean of roughly 8 units per day. Thus, Part 1 is a high volume,

low variability part.

Figure A1. Velocity Part 1 – High Volume, Low Variability

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A - 1

Figure A2 is the time series demand graph for Velocity Part 7. This part is also a relatively high

volume part, with daily demand ranging from 0 to 14 units and a mean of roughly 2 units per

day. Unlike Part 1, however, the spikes in demand for Part 7 are highly variable and

unpredictable, changing size dramatically throughout the time span. Thus, Part 7 is a high

volume, high variability SKU.

Figure A2. Velocity Part 7 – High Volume, High Variability

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As illustrated in Figure A3, Velocity Part 12 has lower demand volume than Parts 1 and 7, with

daily demand rates ranging from 0 to 4 units and an average daily demand rate of only 0.2 units.

The spikes each represent orders placed, and for the most part the order quantities remain

constant, except for a few larger orders towards the end. The timing of the orders is still

unpredictable though, so this part should be classified as a medium volume, medium variability

SKU.

A - 2

Figure A3. Velocity Part 12 - Medium Volume, Medium Variability

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Finally, Figure A4 illustrates the time series of demand for Velocity Part 59. Only one order was

placed for this particular part, with an order quantity of two units. Thus this product is simply

classified as a low volume part.

Figure A4. Velocity Part 59 – Low Volume

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A - 3

Appendix B. Customer Demand Analysis for Velocity Part Number 1 Because Part 1 has demand in high volumes and low variability, it would normally require safety

stock to buffer variations in demand. However, in depth analysis of the customers who order this

part can lead to subsequent reductions in safety stock requirements while maintaining excellent

levels of customer satisfaction.

Pareto analysis should be used to identify key customers who order Part 1 in the highest

volumes. These customers have the greatest impact on operations and service levels with respect

to this part. As shown in Table 2, Customers 1, 2, and 4 demand 90.55% of the Part 1 units

produced, clearly showing that they are the key customers.

Rank Customer Total Demand

Number of Orders

Average Demand/

Order

Average Daily

Demand

Percentage of Total

Cumulative Percentage

1 1 504 42 12 4.13 48.60% 48.60%2 2 282 66 4.27 2.31 27.19% 75.80%3 4 153 20 7.65 1.25 14.75% 90.55%4 7 23 9 2.56 0.19 2.22% 92.77%5 38 7 1 7 0.06 0.68% 93.44%6 33 6 2 3 0.05 0.58% 94.02%7 37 6 1 6 0.05 0.58% 94.60%8 73 6 1 6 0.05 0.58% 95.18%9 18 5 1 5 0.04 0.48% 95.66%

10 23 5 2 2.5 0.04 0.48% 96.14%11 34 5 1 5 0.04 0.48% 96.62%12 88 5 2 2.5 0.04 0.48% 97.11%13 25 3 1 3 0.02 0.29% 97.40%14 66 3 1 3 0.02 0.29% 97.69%15 75 3 1 3 0.02 0.29% 97.97%16 59 3 1 3 0.02 0.29% 98.26%17 10 2 1 2 0.02 0.19% 98.46%18 15 2 1 2 0.02 0.19% 98.65%19 52 2 1 2 0.02 0.19% 98.84%20 24 2 1 2 0.02 0.19% 99.04%21 62 2 1 2 0.02 0.19% 99.23%22 80 2 1 2 0.02 0.19% 99.42%23 83 2 1 2 0.02 0.19% 99.61%24 91 2 1 2 0.02 0.19% 99.81%25 26 2 1 2 0.02 0.19% 100.00%

Table 2. Pareto Analysis of Customer Demand for Velocity Part Number 1

A - 4

Customer 1, which has the highest demand volume for Part 1, has highly variable demand as

shown in Figure 8. The intermittent spikes illustrate that days pass with no units demanded, and

then huge spikes of demand occur at variable intervals. This leads one to believe that Customer 1

operates on an s-S ordering policy, which means no orders for components are placed until

inventory falls to a predetermined level, s, and at that point units are ordered to replenish the

stock to another predetermined level, S.

Figure B1. Velocity Part 1 Demand, Customers 1

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Furthermore, time-series analysis of orders placed by Customers 1, 2, and 4 reveals that these

customers have very variable demand in aggregate, as shown in Figure 9, with total order

quantities from these customers on a given day ranging from 0 to over 20 units.

A - 5

Figure B2. Velocity Part 1 Demand, Customers 1, 2, 4

In contrast, the remaining 19 customers who order Part 1 have much less variable demand in

aggregate, as shown in Figure 10. The total quantity demanded by these customers on a given

day only ranges from 0 to 5 units of Part 1. Thus with good relationships with just Customers 1,

2, and 4, and an ability to get estimates of order quantities before the orders are placed, Velocity

could overcome a lot of the challenges caused by demand variability for Part 1, by eliminating

the need for safety-stock.

Figure B3. Velocity Part 1 Demand, Customers 3 and 5 – 22

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Days in the Past

Days in the Past

A - 6