Lean Production in a World of Uncertainty: Implications of Variable Demand

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

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

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

iii

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

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

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

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 Figure 1 [9]. Takt Time at Boeing customer demand, ensuring that customer needs can be

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

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

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

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.

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

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

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

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.

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.

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

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

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 µ σ σ/ µ Category Number 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.

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

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

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.

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.

[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

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].

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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].

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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 Order Quantity

Days in the Past

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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 Order Quantity

Days in the Past

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.

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

Days in the Past

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 Order Quantity

Days in the Past

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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. Average Average Total Number of Percentage of Cumulative Rank Customer Demand/ Daily Demand Orders Total Percentage Order Demand 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

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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 Order Quantity

Days in the Past

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.

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

y uantit Q Order

Days in the Past

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 y uantit Q Order

Days in the Past

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