REFLECTIVE PRACTICE

Achieving customer serviceexcellence using Lean Pull

ReplenishmentSameer Kumar

Opus College of Business, University of St. Thomas,St. Paul, Minnesota, USA, and

David Choe and Shiv VenkataramaniTE Connectivity, Shakopee, Minnesota, USA

Abstract

Purpose – The purpose of this study is to highlight a key strategic initiative within the former ADCCompany (now part of TE Connectivity) called “Lean Pull Replenishment”, designed and implementedto achieve Six Sigma customer service excellence. This case study would also help facilitateproblem-based learning pedagogy.Design/methodology/approach – The study showcases implementation of the Lean PullReplenishment approach using the define, measure, analyze, improve and control (DMAIC)framework. Key input variables were analyzed that contributed to historically inconsistent andunsatisfactory customer delivery performance. Analysis resulted in improving the allocative efficiencyof critical input variables through pilot programs on strategic value streams by deploying dozens ofkaizen events, and sustaining the gains through leveraging best practices and effective changemanagement principles.Findings – The study presents a strong case for the team work and the cultural transformation thatoccurred during the course of implementing this initiative across ADC supply chain. The paper alsosummarizes the improvement in customer service metrics and financials of the company.Originality/value – Through this study, it has been established that with consistency of purpose,using the right tools for solving problems and through teaching Lean principles, remarkable resultscan be achieved, which can be sustained for the long-term and become a self-sustaining businessphilosophy.

Keywords Six Sigma, DMAIC, Lean Pull Replenishment, Process capability, Process improvement,Quality assurance, Telecom, Customer service, Customer service management

Paper type Case study

IntroductionBusiness organizations continue to be involved in Lean and Six Sigma implementationto improve their operations. Real life industrial case studies providing detailedimplementations of Lean Six Sigma using define, measure, analyze, improve, andcontrol (DMAIC) methodology would markedly facilitate problem-based learningpedagogy (e.g. see Johnson et al., 2006; Rasis et al., 2002a, b). There are, however, fewcomprehensive real-world case studies available on this topic (Allen, 2010). Six Sigmais a multi-method approach and has been described as a data-driven method forprocess improvement (De Mast, 2007; Linderman et al., 2003).

The study described in this paper shows how a team of highly engaged employeesexecuted a large business transformation christened “Lean Sigma Pull Replenishment”in a leading US global telecommunication products company from the bottom-up. Byreaching across functional, geographic, and organizational boundaries; focussing on a

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International Journal of Productivityand Performance Management

Vol. 62 No. 1, 2013pp. 85-109

r Emerald Group Publishing Limited1741-0401

DOI 10.1108/17410401311285318

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common vision; and working together, they overcame years of stagnation and cynicismand demonstrated a real cultural change in less than one year. This is also a story ofhow the same employees integrated Lean and Six Sigma methodology very quicklyand applied these to every step of critical supply chain, order management, factoryfloor execution, and logistics operations to drastically improve inventory turns, costperformance, and more importantly reduce lead time to meet the customers’ demand,while reducing overall inventory at the same time. In particular, this case studyprovides an example of major implementation efforts to streamline business operationsand supply chain of a company with a focus on improving quality of deliveryperformance to customers.

Literature reviewThe purpose of this literature review is to provide a brief overview of Lean thinking,Six Sigma, and Lean Pull Replenishment methodology to identify the major challengesthat will face any business organization in implementing these practices, and examinetheir use in similar organizations.

Lean thinkingLean thinking originated within the Japanese automobile industry following SecondWorld War and is principally based on the Toyota Production System (TPS), whichwas developed by a production executive named Taiichi Ohno and was used toimprove the quality and productivity within Toyota Motor Company (Ohno, 1988).Lean thinking later increased in popularity in the 1990s, after the publication of thebestselling book, The Machine that Changed the World: The Story of Lean Production,which chronicled how organizations could transform their operations by adopting theLean approach developed at Toyota (Womack et al., 1991). Lean has since beenwidely accepted and adopted across every industry ranging from automobiles toelectronics and in the recent years, is being increasingly applied to a wide rangeof service organizations, including health insurance companies, hospitals, clinics,retail stores, etc.

Lean is an integrated system of principles, practices, tools, and techniques that arefocussed on reducing waste, synchronizing work flows, and managing productionflows (de Koning et al., 2006). The reduction of waste is the fundamental philosophy ofthe Lean approach. In Lean, waste is also referred to as non-value-added activities.Value-added activities are those for which the customer values and is willing to pay.Other activities are considered non-value-added activities and should be eliminated.The elimination of these non-value-added activities reduces cycle time and costs,which results in more competitive, agile, and customer-responsive organizations(Alukal, 2003). The level of competition to capture customers in both domestic andinternational markets demands that organizations be quick, agile, and flexible tocompete effectively (LaLonde, 1997; Fliedner and Vokurka, 1997; Shenchuk and Colin,2000; Wang et al., 2005).

Six SigmaSix Sigma was developed in the early 1980s at the Motorola Corporation and waspopularized in the late 1990s by former General Electric CEO, Jack Welch (Furtererand Elshennawy, 2005; de Koning and de Mast, 2006). Six Sigma’s foundation was inthe statistical analysis of data, and this is reflected in its name, which refers to astatistical measure of process performance (Maguad, 2006). Besides Motorola and

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General Electric, other major corporations have embraced Six Sigma includingAlliedSignal, DuPont, Honeywell, Lockheed Martin, Polaroid, 3M, Samsung, and TexasInstruments (Hahn et al., 1999; Yang et al., 2007). The reported advantages toimplementing Six Sigma include increased market share and higher profit margins(Harry, 1998). While Six Sigma originated within manufacturing in the electronicsindustry, it has since been adopted across many other industries and has spread intothe service sector, including a few government organizations. Six Sigma refers to thephilosophy, tools, and methods used to seek, find, and eliminate the causes of defects ormistakes in business processes by focussing on the outputs that are important to thecustomers (Anthony and Banuelas, 2004). Six Sigma represents a highly disciplinedand statistically based approach to quality (Hahn et al., 1999). Six Sigma methodicallyanalyzes underlying data and identifies the root causes of problems as opposed tousing subjective opinions. Since every step in a process represents an opportunity for adefect to occur, Six Sigma seeks to reduce the variation in these steps, which results inthe occurrence of fewer defects and the production of higher quality goods and services.By controlling this variation, Six Sigma prevents defects from occurring rather thansimply detecting and correcting them.

Pull Replenishment methodologyThe Lean Pull Replenishment (or kanban) system is a compromise between the ideal ofone-piece flow and the traditional large-batch “push” business (Liker, 2004). A kanbansystem is a means to achieve just-in-time production. It works on the basis that eachprocess on a production line pulls just the number and type of components that processrequires, at just the right time. The mechanism used is a kanban card. This is usually aphysical card but other devices can be used.

In the 1940s, Toyota began studying US supermarkets to understand and analyzehow they managed to anticipate, plan, stock, and replenish goods based on customerdemand. Supermarkets, fresh meat, and produce in particular, are great case studiesfor Lean management methods. A profitable supermarket only stocks what it can selland supermarket customers only buy what they need when they need it because theyare confident in an uninterrupted supply of goods and produce (Liker, 2004).

In the 1950s, Toyota began to create a type of supermarket for parts and materialswithin its factories. Toyota purchased a strategic inventory of parts and suppliesbased on its calculations of Takt rate or customer demand. Employees at eachworkstation withdrew the parts and supplies they required as needed on-demandto maintain continuity and efficiency throughout the vehicle assembly process. In aPull/Replenishment system a very small strategic level of inventory is built andmaintained in bins at selected points in the production process. When a downstreamcustomer takes away specific items they are replenished. If a customer does not usean item, it sits in a bin but is not replenished. When the strategic inventory is depleted,the downstream customer uses a simple card or “kanban” as a signal to order theupstream supplier to refill the bin with a specific number of parts or send back a cardwith detailed information regarding the part and its location (Martin, 2007).

As this system of consumption-driven replenishment proves its applicabilityacross tiers of distribution and supplier networks, it is becoming an increasinglycompelling alternative to more traditional material requirements planning (MRP) or“push” style systems, which rely on forecasts to determine what and how much toproduce (Lakham, 2008). Although forecasts have proven useful in predicting overalldemand, they can be poor indicators of exactly which products will be needed and

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when. Multi-tiered supply chains push manufacturers further and further from theircustomers, and forecasts further and further from reality. As a result, information isdiluted by each layer in the distribution system, and excessive inventory and costlylast-minute changes orders ripple through the supply chain. In addition to these issues,manufacturers often complain that they incur large inventory-carrying costs with anMRP system, yet can still run out of key parts. These stock-outs stop production, delaycustomer shipments, increase premium freight charges, and disrupt plant operationsby forcing unnecessary and expensive changeovers. In the current MRP world there isoften no clear record of how many times stock-outs occur or which parts repeatedlystock-out. This could be due to configuration of ERP systems where the records of theprevious day could be over-written the next day and thereby the system loses track ofhistorical stock-outs, needed for corrective actions. Another drawback of this system isre-configuration could be very expensive and time consuming. Stock-outs also can leadto an overreaction of parts buying, followed by substantial excess inventory, which isoften carried for months afterwards. Keeping expensive inventory is a waste ofresources, including working capital, storage space, and the manpower needed foradditional handling. Unlike MRP forecast-driven replenishment, a kanban systemre-orders based on actual consumption at the point of use. However, it is important tonote that the telecommunication products company uses collaborative forecastingprocesses such as sales and operations planning that drives internal companycollaboration and collaborative planning forecasting and replenishment that involvescustomers and suppliers. The simplest version of this is the “two-bin” method. In thiscase, an operator has two bins of material, one being consumed and the other, full.When the first bin is empty, the operator continues working using the second bin.The empty bin is sent to the producing station, an obvious signal to replenish. Theamount of material per bin is set so a full one returns before the operator runs out.

Another example of the kanban process uses the manual kanban card, which travelswith its inventory and contains information such as the description of the item or partnumber, and its location. Each card has a number and is used to trigger an order forreplenishment when an item is consumed. However, manual kanban’s benefits areseverely limited when an external supplier enters the supply chain. In an electronickanban system, the card information is translated into a barcode that is scanned andelectronically communicated at each stage of the replenishment cycle (consumption,shipping, receiving, etc.). In this way, electronic kanban dramatically increases theeffectiveness of kanban throughout the supply chain. While MRP systems pushmaterial throughout the supply chain, pull-based manufacturing strives to synchronizeproduction with consumption in real time, which increases on-time deliveryperformance, reduces stock-outs, and cuts down on costly last-minute changes toorders. As orders arrive, material is pulled from the end of the final assembly line,which instantly sends an order to final assembly to produce more (Liker, 2004).

Implementation of Pull Replenishment methodology at a leading UStelecommunication products companyThe company provides the connections for wireline, wireless, cable, broadcast, andenterprise networks around the world. The company’s innovative networkinfrastructure equipment and professional services enable high-speed internet,data, video, and voice services to residential, business, and mobile subscribers. Withsales more than 130 countries, the company’s innovative, high-performance fiberconnectivity and wireless coverage and capacity solutions support a broad range of

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network applications that help its customers provide new and advanced services tomeet their own customers’ unrelenting demand for bandwidth.

In 2008, this company was a $1.5 B company, yet it sold more stock keeping units(SKU’s) than target corporation, a $60 B giant. The SKU proliferation created a literalgridlock in its supply chain. Since the company was operating under a build-to-forecastmodel, no matter how brilliant the SAP software system expert master schedulerswere, they invariably incorrectly guessed product mix. It may be noted the companyhas been using SAP enterprise software to manage business operations andcustomer relations. This software is developed by SAP AG, a market leader inenterprise resource planning. When the real orders came in, operations and saleswere at odds just to ship even basic regularly ordered products. The operations literallylooked like the I-405 in Los Angeles, 12 lanes of gridlock. There was so muchcongestion that the company started inventing “sales alerts,” phantom orders that“might” turn into real orders. If it could just start earlier, it might ship on time. It didnot work (Choe, 2011).

After four years of false starts and over $10 M spent on consultants, software, andFTE’s, every “transformational” initiative the company tried failed. Cynicism grewrampant about the “flavor of the month” initiative. Employees felt that managementwas totally disconnected from reality. However, the consultants were not wrong.The underlying operating system was falling apart. Without massive change thesystem would crash, meaning, the company would be unable to profitably serve itscustomers and compete in the twenty-first century. Something had to change(Choe, 2011).

A new operations management team with deep and broad expertise in Lean, SixSigma, and Lean Pull Replenishment methodology, was assembled from other Fortune500 companies. Its purpose was to create efficient operation, eliminating waste andreducing variation, superior customer service with minimal inventory. This teambrought to bear Lean and Six Sigma methodologies, including Lean Pull Replenishmentand process capability thinking. Process capability thinking was important since thecompany’s sales force was setting delivery expectations with the customers withoutunderstanding what the capability of operational and associated transactionalprocesses. Through the application of the time-tested DMAIC methodology, was ableto energize the company’s team to transform the fundamental approach tomanufacturing: “Push-” based MRP planning to a “Pull-” based replenishment model.

In most companies, the traditional manner of implementing Lean and Six Sigmawould be through extensive Lean and Six Sigma tool training, black belts, and kaizensenseis converging with metrics like: the number of green belts and practitionerstrained, number of kaizens facilitated and so on. Also, these kaizens and improvementactivities are focussed on the shop floor of the manufacturing or assembly plant for acouple of reasons:

(1) Lean is primarily associated with manufacturing due to its association with theTPS; and

(2) one can touch and feel the products flowing through the production floor, aswell as, easily observe the physical flow of material, machine workers, etc.

This approach results in improvement in production efficiencies, labor and materialproductivity, etc, but in customer service there is no visible improvement. Thefundamental reason is that teams and initiatives only focus on what they touch and feel

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and end up optimizing parts and not the whole process end-to-end. The company’soperations management team recognized this due to their experiences from othercompanies and organizations. The team decided to investigate the entire customerorder-to-delivery process for a few part numbers, instead of taking on the entireproduct portfolio of 60,000þ finished goods part numbers.

Step 1: define the problemThe team decided to map out what it took to book and ship an order. In the history ofthe company, this had never been done. After four days and hundreds of post-it notes,the research team was able to show all the hand-offs and steps on a 12� 8 ft mural.It was so large that it could not be taped on the wall. The centerpiece of this processwas what was dubbed “the circle of death.”

“The circle of death” was the product of the company’s build-to-forecast mess.Since customers could not get products on time, they started giving the request datesof “today” and even “January 1, 1900” in the hopes that their order would get bumpedforward in the queue. With over 30,000 orders per month, this phenomenon turned intowhite noise. The only orders being worked on was where the salesperson andcustomers were yelling the loudest. Back and forth the e-mails and phone calls wouldgo. The process was essentially a negotiation. The customer would ask for deliverytoday on products where we had no forecast visibility. Operations would immediatelyreply with a “90-day” lead time. Customer service would hit the “expedite” button.Operations would then reply with a “50-day” lead time. The sales person wouldyell and scream. Operations would then start looking at capacity, scheduling, andmaterials and then commit to a “45-day” lead time. This process would go backand forth for a week until operations gave their best and final: “40-days.” Theexhausting circle-of-death aggravated customers, infuriated sales people, and woredown everyone from customer service through supply chain. But, a ship-to-promisemetric was created that stated order completions were 97 percent on time to thatfinal commit date!

This was also validated by “voice of the customer” (VOC) data that the companyhad obtained and following are some of the quotes from our customers andsalespeople:

The first confirm date is ridiculous.

It’s taking me so long just to get a delivery date.

Why am I spending so much time chasing down orders?

Our lead-times aren’t competitive.

It’s difficult to do business with the company.

Ship-promise does not reflect customer satisfaction.

Our inconsistency is costing our customers money.

People don’t trust our ability to ship product.

We are all over the place in lead-times.

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At this point in time it was concluded that the MRP based-push system was a failure inthis company and the company would have to implement a Lean Pull Replenishmentsystem instead of a build-to-forecast model, but the task seemed impossible.

Step 2: measureUsing the MRP-based push system, the company was trying to cram too much demandinto the supply chain without truly understanding lead time requirements of customersand cycle times of not only the manufacturing processes, but also the transactionalprocesses which created chaos in the system. In order to get ahead, sales, productmanagement, and other functional groups designed and created tools like “expedites,”“sales alerts,” “hot orders,” and the like. These functional groups had no confidence inthe company’s processes and were setting false expectations with customers.Sales and product management were signing contracts with customers with unrealisticlead times. In short, no one in the company understood what the “true processcapability” was of the order creation to delivery process of different product lines andsegments.

When the newly created Lean Pull Replenishment team, led by the authors, tried tomeasure process capability of the end-to-end process of one of the product lines, it wasimpossible to do so, because the true process capability was masked by the abovementioned transactional noises such as expedites and sales alerts, etc.

Why the company cannot use inventory as a buffer against forecast errors?When the fallacy of this MRP-based approach to other stakeholders like sales andproduct management was discussed, they said that the company could achievesuperior lead times and customer service with finished goods inventory as a buffer.This intuitively made sense if a company sells a few SKUs, but it was known thatit would not work for 60,000þ active SKUs. To prove our hypothesis, we did aregression analysis of lead time vs finished goods inventory of 35 part numbers(all high runners) sold to this company’s number 1 customer for fiscal year 2008(see Figure 1).

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Figure 1 shows that for the 35 part numbers, there is no correlation (R2 value¼ 0percent) between quantity in stock and lead times. In other words, if we had a quantityof 100 at any given time, it could take us anywhere from two days (if we happen to havethe SKU in stock) to 90 days (if even one of the component is in short supply and notavailable for use). Imagine the scenario for 60,000þ finished SKUs, each consistingof hundreds of components and sub-assemblies in its bill of material (BOM structure).We have performed similar analysis on numerous parts for numerous customersand the resulting analysis does not look very different than the above chart. This typeof analysis conclusively proves that in this line of business (telecom), it is impossible toforecast every SKU accurately and maintain an inventory buffer. A group forecastingapproach was tried and led to higher inventory levels of components resulting ingreater excess and obsolete inventory. This approach was abandoned in favor of a Pullsystem (Replenishment, sequential, and hybrid Pull).

In the telecom business, there has been a rapid evolution of technologies due to theadvent of smart phones, smart TVs, data centers, social networking sites, etc., thatconsume tremendous amount of bandwidth. The technologies become obsolete veryquickly and therefore finished goods that we keep in inventory, could become obsoletewithin months leading to excessive dead and excess inventory, and a drain on thecompany’s financials and infrastructure resources.

This problem alone behooves us to look at models such as Make-to-Order and LeanPull Replenishment, where no finished goods are manufactured or assembled unlessa purchase order (PO) is received from the customer. In some cases, Pull systemsmaintain a small amount of finished goods inventory that gets replenished only onconsumption and not to a forecast.

The second reason why it makes stocking finished goods to forecast impossibleis SKU proliferation, which commonly happens in many organizations and thetelecommunications company was no exception. Figure 2 shows the Pareto chartof the company’s revenue in North America by SKU.

Figure 2 speaks for itself in how skewed the Pareto looks in terms of the 80/20 rule.In fact 7 percent of SKUs make up 80 percent of the revenue. The “tail” as we call it isamazingly skewed. A total of 40 percent of the revenue in the tail generates only0.5 percent of the overall revenue. So, as a team, it was decided not to put efforts into

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Figure 2.Telecommunicationscompany revenue Paretoof all SKUs in Americas

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implementing Lean Pull Replenishment on the “tail” SKUs. Instead, the team focussedonly on the top 7 percent in waves 1 and 2 before considering the “middle” section ofthe Pareto chart.

The skewness of the Pareto was not the only problem. The issue was also that therewas little differentiation in pricing and lead-time between high-runners and low-runners. There was no incentive for customers to make choices so they chose morecustomization. In other words, at this telecommunications company, if one ordereda custom cabinet, you got the same lead time as an off the shelf cabinet. Why would thecustomer not opt for the custom cabinet most of the time?

All of the above issues resulted in varying lead times for the customers for thesame product. In other words, customers could not rely on this telecommunicationscompany to give them the same product at the same time every time. The teamanalyzed historical delivery times for several products and most of them had theprofile as shown in Figure 3 (actual delivery times for a typical high-turning SKU).

Figure 3 is an individual chart (X-Y plot) that shows all lead times for the salesorder for one product SKU. In some cases, the product was delivered in one day and insome cases it took 200-250 days to deliver the same product. This variation in leadtime was also independent of delivered quantity. Due to the inconsistency, customerscould not rely on the telecommunications company to deliver products on scheduleto the project site, be it a carrier installation or an enterprise data center. In thisbusiness, if a component or a product does not get delivered to the installation siteand the installation contractors are already on-site, the customer (carriers/installationcontractors) still have to pay the contractors for their time, and this creates a badexperience for the customer. Therefore, some customers hedge their bets by having thetelecommunications company sales people expedite orders, placing sales alerts(phantom orders) in the system creating “false” demands on the system, drivingmaterial and taking away manufacturing capacity meant for actual orders.

All of the above resulted in an overall customer service level of 65 percent, whichmeans only 65 percent of the line items were shipped on time. However, the companyreported a metric called ship-to-promise and showed an on time shipment of 98 percent(Figure 4).

Ship-to-promise was an internal company metric that basically said, we will deliverthe goods when we promise them, which of course was hardly the first promiseddate. In fact, most of the time, it was the delivery date that we promised the customerafter several expedites, phone calls, e-mails, and the like with several people involved –sales, product management, master schedulers, planners, buyers, and so on. However,

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as the voice of the customer was analyzed, this was not the case at all. Customers weremeasuring the company by how reliable it is with the lead times written in thecontracts (ship-to-target) or when they requested the product (ship-to-request). Thesemetrics were more meaningful than ship-to-promise.

The value stream and process capability approachIn the measure phase, “true process capability” was needed to be measured using awell-known tool in the Six Sigma tool kit – process capability analysis. From the VOCdata and interviewing customers as to what would be a reasonable, predictable leadtime they could accept, the answer invariably came back as two to three calendarweeks if the company can predictably deliver the products to their distribution centersor installation sites (barring emergencies, of course). In the customers’ world, whenparts are ordered to service new installation sites or turning up or upgradingcustomers’ sites, customers typically use their own technicians or outside contractors.If the products are not delivered to the site at the agreed upon dates, the contractorscannot finish the installation and still have to be paid expensive rates, not to mentiondisappointing the end consumer or business person. Armed with this information, thetelecommunications company decided to measure process capability of its productstreams (value stream performance) using process capability tools (Cpk and Ppk).For example, for one of the strategic, high running product families (DS-3), Ppk wasmeasured as �0.04 for the past 12 months’ performance to 14 days as the upperspecification limit (please refer to Figure 5). In this case, Ppk was measured as the teamwanted to understand long-term process performance vs short-term processperformance, usually measured as Cpk.

This shows that the entire value stream (order entry to delivery) is not even capableto perform at 14 days, yet the sales people and product managers were givingcustomers arbitrary or SAP generated artificial lead times that we were only met bytaking material meant for different orders, expensive expediting, air shipment fromsuppliers, or simply, the times were not met at all. It took an army of people to scrambleand hope to meet the customers’ dates. It was not surprising to find out that otherproduct streams performed similarly or even worse, especially if they had a few long-lead time components. When the research team mapped the order managementprocess, it was obvious that the process was very complicated – it would take

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Figure 4.Ship-to-promisepercentage forLSX-9000 productline during FY 2009

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anywhere between two and 21 days, just to process an order. The high-level processmap summary is shown in Figure 6.

Moreover, it was very difficult to measure “true” manufacturing process capabilitybecause of the existence of the “expedite” tool within SAP. In other words, if aproduction order is released to floor and the planner has started to build to that order,an expedite generated by a product manager, master scheduler, or any other person upand down the chain, could “steal” materials meant for the first order, thereby stoppingthe order mid-way and re-starting only when material becomes available again. Since40 percent of the line items were expedited or escalated, it was very difficult tounderstand “true” manufacturing process capability.

Another culprit for inconsistent delivery performance is the tool called “salesalerts.” This was a glorified forecast, where the sales person submitted a sales alert inthe system without a PO from the customer, believing that the customer would sendthe PO soon. Our SAP system immediately converted the sales alert into a production

Expedite

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Figure 6.“Current state” process

map of the ordermanagement process

before Lean PullReplenishment was

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Process data Overall capability

Exp. overall performancePPM < LSL

571,396.50571,396.50

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Figure 5.Process capability

analysis of total processlead time (sales order

create to finish process) forDS-3 product line before

Pull Replenishmentimplementation

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order (phantom order) and built inventory of finished goods, hoping that the customerwould send in the PO shortly. In 40 percent of the instances, customers would cancelthe sales alert or postpone the PO, which results in finished goods sitting in thewarehouse, leading to excess and obsolete inventory.

Step 3: analyzeAfter analyzing the delivery lead times against lead times specified in customercontracts, and brainstorming all the reasons as to why the company ships 40 percentof the line items late, it was obvious that the main problem was not in themanufacturing operations. As shown in Figure 7, for DS1 product line, themedian end-to-end lead time was 27 days. As shown, it takes on an average, sixdays of transactional activities (mostly non-value added) from the time a salesorder is created in the system to when the production order is created on the factoryfloor. This represents approximately 25 percent of the total lead time. Also, as canbe seen, it takes 13 days from the time a production order is created to when it isactually released to factory floor. The reason for this long lead time is that themanufacturing line is waiting for a raw material or a sub-component from a supplieror an internal plant. Once the production order is released to the factory floor,it takes only three days on an average for the product to be manufactured in thefactory.

Similar process lead time profiles exist for most of the products made in thecompany. In most instances, the customer demands a lead time of one to two weeks.This shows that the process is not capable of meeting the customers lead timespecifications.

Based on the above data, the team decided to focus on the following three criticalinput factors as shown in Figure 8:

(1) raw material and sub-assembly availability;

(2) transactional processes like expedites; and

(3) sales alerts.

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Figure 7.Breakdown of totalprocess lead time (salesorder created to ship fromdistribution center) forDS-1 product line

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To analyze “true process capability” and “true” lead times, we decided to do an“experiment” with 36 part numbers out of the value stream. The BOM of each of the36 part numbers was exploded and, based on historical demands and future forecast,an SAP’s kanban calculator was used to fill up the “bins” with raw materialcomponents by working with both external suppliers and internal plants. This tooktwo months, and in the meantime, sales and product management shut off expeditesand sales alerts in the system for 45 days to help the company understand true processcapability. Sales and product management responded by saying that they do not wishto expedite since they spend 40 percent of their time expediting orders internallyand not closing deals with customers. The expedite button was “grayed out” in SAP forthe 36 part numbers and called them “optimized” part numbers. This was the indicatorthat the particular part number was on a “Lean Pull Replenishment” mode.

Step 4: improve: implementing Pull ReplenishmentThe key to making Pull Replenishment work is by establishing “supermarkets.”A manufacturing supermarket (or market location) is, for a factory process, what aretail supermarket is for the customer. The customers draw products from the“shelves” as needed and this can be detected by the supplier who then initiatesa replenishment of that item. It was the observation that this “way of working” couldbe transferred from retail to manufacturing which is one of the cornerstones ofthe TPS (Liker, 2004). In a manufacturing supermarket, processes consume what theyneed when they need it. Since the system is self-service, the materials managementis reduced. The shelves are refilled as parts are withdrawn on the assumption thatwhat has been consumed will be consumed again, which makes it easy to see howmuch has been used and to avoid overstocking. The most important feature of asupermarket system is that stocking is triggered by actual demand, as shown inFigure 9, which is a standard two-bin system utilized in Pull manufacturing. Usuallymanufacturing supermarkets are in clearly marked bins right on the shop floor witheasy access to the manufacturing cell.

Another key ingredient for a Lean Pull Replenishment system to work efficiently isthe role of “water spider” or “Mizusumahi.” Water spider is a person who manages all

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the logistical work of bringing components, raw materials, etc., in small quantities toworkstations to minimize work-in-process inventories. This allows machines to beplaced closer together, and spares the operator from having to interrupt his/her cycletime, thus minimizing transportation muda (waste). Water spiders usually areexperienced workers. They know where needed parts or raw materials are stored, andserve several workstations.

The supermarkets on the shop floor are generally a set of “bins,” with each bincontaining a finite set of parts. For example, for a certain raw material or sub-assembly,we could have two bins of that part, each containing 1,000 pieces. When one bin isemptied due to consumption and the “re-order point” is sent to 1,000, the system or thewater spider will signal the need for another bin, either from an external supplier orfrom an internal plant or a different process. During the lead time it takes to fill theempty bin, the process is consuming from the second bin. By the time the second bin isempty, the first bin is stocked with parts. Generally, the re-order point is establishedbased on manufacturing lead time of the part, transportation lead times, etc. SAP andother ERP systems have re-order point calculation algorithms based on demandpatterns of the particular part. Supermarkets work very efficiently when there arerobust signaling systems between the supermarkets and their suppliers. Mostcommonly used signals, or kanbans, are faxes, ERP signaling, visual index cards(kanban cards), web cams, PO, etc.

When the company combines the availability of raw materials and elimination ofexpedites with classic Lean tools on the production floor (like 5S, Takt time analysis,standard work, cell design, operator balancing and line balancing, visual management,cross-training of operators, etc., all implemented using a bottom-up kaizen approach)there was also immediate improvement in the delivery performance. Delivery

Supplier Telecommunicationscompany

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Figure 9.Schematic showingthe mechanics ofPull Replenishmentwhere the entire supplychain is aligned tocustomer demand

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performance (ship-to-target) increased from an average of 76-100 percent for these 36part numbers over a period of 45 days. In other words, the telecommunicationscompany delivered the 36 SKUs to its customers (hundreds of line items) at or before14 days. In summary, here are the steps in the Pull Replenishment implantation process:

(1) choose a select number of SKUs representing high volume (80-20 rule);

(2) maintain 100 percent availability of components through a kanban-bin system;

(3) actual orders only trigger a production order;

(4) production orders deplete kanban bins;

(5) when component inventory falls below a reorder point, it automaticallytriggers replenishment in the supply-chain; and

(6) suppliers are aligned to a 14-day replenishment lead time.

The results from this experiment gave the company confidence and it extended theexperiment ( July experiment) to 250 part numbers over three value streams, as shownin Figure 10.

When an order for any of the 250 SKUs came in from a customer, the order wasimmediately acknowledged with a delivery estimate of 14 days (predictability).Through the elimination of expedites and sales alerts and having a kanban-replenishment approach instead of a guesstimating-forecast approach, and optimizingthe manufacturing floor through kaizens and continuous improvement, the 100 percentship-to-target results were extended to the 250 SKUs that were in scope over hundredsof line items and over a period of a month.

Figure 11 shows the comparison of actual delivery time (in days) for the 250 partnumbers that were placed on Pull during the month of July to that of the same

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250 part numbers prior to July (three months’ data). As can be seen, the Pull Replenishmentmethodology ( July experiment) is very consistent with a mean of five days and 95percent confidence interval (CI) of 11 days. This means that 95 percent of the time, thecompany will deliver the products within 11 days. Using the MRP methodology(pre-July), there was too much variation (anywhere between one day and 150 days)with a mean of 18 days and 95 percent CI of 50 days.

These results were truly astounding and customers started to take notice andasked the telecommunications company to accelerate the Lean Pull Replenishmentmethodology to more SKUs and by October, 750 more SKUs (20 percent of Americas’revenue and line items) were added to the Pull systems. The results were even moreimpressive as shown in Figure 12.

As shown in Figure 12, ship-to-target performance was still 495 percent comparedto 65-70 percent prior to implementing Lean Pull Replenishment. This clearly provedthat the methodology is highly scalable and extendable to at least 75-80 percent of therevenue or line items shipped. If the telecommunications company was able achievethis kind of results, the company could end up delighting the customer and as acompany, and could win on superior and predictable delivery times vs win on price,a strategy very difficult to sustain as a large public corporation.

Manufacturing process improvementsOnce the transactional process improvements were made and sustained, the teamturned its attention to improving the manufacturing and assembly processes usingtraditional Lean and Six Sigma tools such as 5S, operator balance charts, cell design,material replenishment, kaizens, standard work and QCPC/turnbacks, visualmanagement, etc.

In one of the manufacturing lines, for example, the company used operator andline balancing using Takt time analysis (Takt time¼ time available for operations/customer demand). Before any improvements were done, the line was not balancedand had too many operators as shown in Figure 13.

Figure 13 shows the cycle time of each operator to perform their respectivetasks with respect to a Takt time (red line) of approximately 165 seconds (Takt timeis defined as the time available for operations divided by customer demand). Theassembly line in this case was a straight line and it was difficult for the operatorsto carry out multiple tasks in order for the line to be balanced. It is obvious that the line

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could be balanced better using a cell, in this case a U-shaped cell, that would allowthe operators to move within the cell to execute multiple repetitive tasks. Uponre-balancing the line and the operators, the cycle times for the operators areshown in Figure 14.

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Figure 14 shows that operations 1-5 have been combined and allocated to oneoperator and operations 6-8 have been allocated to the second operator and so on. Eachbar represents an operator and thus we can see that ten operators can perform thetasks of 30 operators and still meet Takt time of 165 seconds (red line) and thus meetcustomer demand. This means that the efficiency of the production line has increasedthree times for the same required output.

Step 5: control: sustaining the gainsThe key to sustaining any process improvements is the control phase. To make surethat the gains made during improve phase are sustained and further improved toachieve and exceed the customer delivery performance goal of 95 percent every month,the initiative used many of the tools in the Lean/Six Sigma tool kit.

The key metrics used to monitor the performance of the end-to-end value streamsare ship-to-target and process capability (Ppk) to 14 days (market lead time specifiedby our customers). Also used are the CI concepts to statistically show to sales peopleand customers what the process is capable of and what would be the lead time (in days)for the service level to be 95 percent. For example, Figures 15 and 16 show the

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Figure 15.Histogram and statisticalsummary of delivery leadtimes (sales order creationto completion) before LeanPull Replenishment

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histograms of delivery performance and key statistical measures before and after thevalue stream transformation using Lean Pull Replenishment methodology.

Operations lead times before Lean Pull Replenishment implementation averaged16 days. The SD between these points was 9.2 days. The CI was 31.2 days. This meansthat 95 percent of the time, the product will be built in o31.2 calendar days.

Operations lead times after Lean Pull Replenishment transformation had a mean(average) of 4.8 days, with the median being four days. The SD between lead timeswas 3.9 days, with a 95 percent CI of nine days. This means that 95 percent of the timethe product will be built in less than nine calendar days.

Process capability analysisFigures 17 and 18 show the process capability analysis of the value stream beforeand after Lean Pull Replenishment deployment in the value stream.

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Figure 17.Process capability of

delivery lead times (salesorder creation to

completion) before LeanPull Replenishment

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Actual manufacturing build time in average is 2.43 days. That means when the wasteand non-value-added time were taken out and optimized the manufacturing line, valuestream performance has attained performance (Ppk¼ 2.31) is Six Sigma or world classlevel!

The above is an example of just one value stream that has been transformed fromno process capability to 14 days to one that performs at Six Sigma level. Figure 19shows ship-to-target performance of the value stream before and after thetransformation.

As can be seen in Figure 19 chart, ship-to-target performance of this value streamhas steadily shown improvements and has been sustained for Six Sigma deliveryperformance of 14 days and is in control over six months after improvements havebeen carried out in the transactional processes and in the manufacturing line.

The average ship-to-target performance has increased from 64 to 89 percent. Thetelecommunications company’s consistency has improved so much that the 95 percentCI is ten days. It has been communicated to the sales force that the company can deliver

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this product in ten days if it is able to gain more market share. Consistent use of thismethodology across the strategic value streams in the company can help gain marketshare, defend price position with customers, improve inventory turns, and maintain acompetitive advantage.

Many cross-functional teams are working on multiple value streams and we expectto achieve similar results like the above “model” value stream. The new “current state”process map of the order management process as a result of all the improvements isillustrated in Figure 20.

With the implementation of the new Lean Pull Replenishment system, customerservice levels (ship-to-target) increased from 64 to 89 percent and operations lead timereduced from an average of 16 to 5 days. The telecommunications companytransformed from MRP-Push system to Lean Pull Replenishment system using visualmanagement tools. This included transforming the product line to a build-to-ordermodel; cell redesigning for optimal operator/work balance; eliminated kitting andmoving 100 percent to supermarkets on the line; and reducing the number of operatorsfrom 52 to 34 in the work center. As a result, labor productivity is three times forcritical sub-assemblies in the work cell. Additionally, no finished goods inventory inthe system implies better cash flow.

Lessons learnedThe most fundamental lesson from designing and executing the Lean PullReplenishment initiative at the telecommunications company has been that it ispossible to achieve customer service excellence and improve financial results at thesame time! Although this initiative may sound like an operations initiative, it has beenvery much a customer-centric and a business focussed strategy. It is necessary toengage with all facets of the business – sales, product management, operations, supplychain, logistics, IT, HR, and engineering for a successful outcome. If it involved onlythe plants, success would have been very limited and the business would have beensub-optimized.

Consistency of purpose among senior management was important for success.They stayed true to their commitment in allocating necessary resources for deployingand leading this initiative. Since the company started this initiative from scratch andwas completely homegrown, senior management gave the core team members the time,space, and tools to learn new methodologies and skills, deploy them for a successfuloutcome.

Senior management recognized that the Lean Six Sigma tools are not verycomplicated. It takes strong leadership and a change in culture, which is “bottom-up”driven to transform a company from decades-old MRP-based Push system to aradically new philosophy of a Pull system.

Quote14 days

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Figure 20.Simplified order

management process afterLean Pull Replenishment

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Tight scoping of projects and value streams was important to build confidenceand success in the company’s changes. “Boil the ocean” types of projects did not leadthe company anywhere. Throughout the deployment, the team followed the principleof “start small, show success and replicate.” This helped the teams take on meaningfulprojects, were able to learn the tools as applied to their projects and show quick wins.As was observed, success breeds success and soon, many people working on smallprojects were able to make a huge difference in delivering positive results to the overallinitiative.

Successful projects can also make the champions and sponsors to move onto thenext project without taking the time to recognize extraordinary efforts and resultsby the initiative and project teams. Upon completion of every value stream, at the endof every kaizen and achievement of sustainable results, the management team madesure that the teams, from supervisors to operators were recognized consistent withlocal culture and budget. These small celebrations of victories energized our people toachieve more results. Recognition was a key part of the initiative, events very muchlooked forward to by the teams, as well as, senior management.

Managerial implicationsUsually Lean and Six Sigma efforts are viewed primarily as “operational efficiency”projects. While a significant source of productivity improvements, many high-levelmanagers, especially in sales, do not demonstrate support other than perfunctoryverbal commitments. What made the Pull Replenishment initiative unique was the factthat the sales team participated in and supported us from Day 1. Pull was positionedand sold as a sales enablement initiative. This organizational alignment and supportultimately created the momentum necessary to improve customer delivery, employeeengagement, and shareholder value.

In the authors’ collective experience, there has never been sales involvement inany Lean Six Sigma rollout prior to the telecommunications company’s PullReplenishment. Generally, there is reluctance on the part of management and theorganization as a whole to involve Sales. This is understandable. Sales should spendtheir time closing new business with customers. Lean Six Sigma is viewed as internallyfocussed, and thus not meriting sales time.

Additionally, operations and Lean Sigma leaders traditionally do not have the kindof working relationships necessary to persuade their sales counterparts to participatein Lean Six Sigma. Neither do most operational-types possess the kind of vocabularyto excite and engage sales. Consider a typical improvement: “We improved cycle timeby 33% and reduced our costs by $1 M.” Someone in sales would be unmoved by suchefforts and possibly think that such improvements are par for the course.

What did this case study do differently? It engaged in a voice-of-the-customerprocess from Day 1. By listening to sales’ frustrations with delivery, authors were ableto analyze the data and correlate sales’ own actions to the problems they wereexperiencing. Expediting, it turned out, consumed 40 percent of sales’ time, yetproduced statistically insignificant improvements. When the research team showedthat data to sales leadership and told them, “We’ll give you 40% of your time back tofocus on closing the business,” they reacted enthusiastically. Sales turned out to be theproject’s biggest cheerleader.

In the pecking order of organizational influence, sales tends to exertdisproportionate influence. Without sales’ engagement and approval, most large-scale initiatives wither from organizational inertia. However, with sales’ backing, the

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telecommunications company was able to execute significant process changes thatresulted in an overall business transformation. The experience was synergistic.The team improved customer delivery from 50 to 14 days (95 percent CI) and thusdemonstrated customer relevance. This made sales excited and positive, which createda positive ripple effect in the rest of the organization.

Senior executives would do well to externalize their large-scale transformationalinitiatives, meaning, “Make it meaningful to the customer!” To put it bluntly, veryfew people care enough about pure productivity improvements to create theorganizational alignment necessary to generate the required changes. Breakingdown organizational silos and mapping enterprise processes end-to-end areprerequisites to success. This project focussed its efforts on selling to sales. Theseefforts paid off and the company was able to demonstrate significant statistical processcapability improvements. Such improvements yielded considerable cost savingsand customer delivery improvements. All the while, the entire organization wasinvolved from sales through supply chain, which resulted in a high level of employeeengagement that generated a real transformational breakthrough.

Recommendations for future workAuthors experience implementing Lean Pull Replenishment highlights a few keyinsights: one, that the vast majority of the positive results stem from replacing“hedging” behavior with “trust.” Trust, in our case, manifested itself in a simple 14-dayreplenishment script that everyone followed. Two, that simplicity is absolutelynecessary. Without simplicity, executing a large-scale initiative down to the operatorlevel is impossible. The multiple grand failures we saw in attempting corporatetransformation initiatives highlight the execution risk created by complexundertakings. Third, and apropos of future recommendations, we believe ourtemplate works across the entire value chain from supplier through customer. Thegreatest value yet to be captured will come from Value-Chain Pull Replenishment.

The company has already begun this journey with several suppliers, value-addedresellers, and distributors. The data confirms that across the value-chain the inabilityto forecast accurately creates massive bullwhip effects. The bullwhip effect (orwhiplash effect) is an observed phenomenon in forecast-driven distribution channels.It refers to a trend of larger swings in inventory in response to changes in demand,as one looks at firms further back in the supply chain for a product. The concept firstappeared in Jay Forrester’s Industrial Dynamics (1961) and thus it is also knownas the Forrester effect. Since the oscillating demand magnification upstream a supplychain is reminiscent of a cracking whip, it became known as the bullwhip effect. MITcreated an elegant simulation called “the Beer Game” to illustrate the bullwhipbehaviors (Kumar et al., 2007). Indeed, even with our internal operations on PullReplenishment, we continue to experience tremendous operational and supply-chaindisruptions from demand spikes created from our channel partners. The cost to theentire value chain is enormous. In the company’s Juarez facility, the company hiresand fires hundreds of workers in an attempt to keep up with the bullwhip. Company’spurchasers order multiples of what is actually required from suppliers. Suppliers forthe company “choke” on the massive orders resulting in costly delays. When thecompany superimposes end-customer demand on top of channel PO, it is observed thatmost of the expediting, overtime, and over-purchasing behaviors are largely unneeded.Every player in the value chain is reacting to each other thus exacerbating theproblems.

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Authors believe the solution is Lean Pull Replenishment based on customer usage.By replenishing company’s channel partners in 14 days with weekly shipments,authors have modeled that 50 percent of excess inventory can be removed in the supplychain and reduce operating costs by 10-20 percent. The company is aggressivelypursuing kaizen events with channel partners, who have responded with openness andenthusiasm. Nevertheless, creating trust when it comes to implementing acrossorganizational boundaries will continue to be the key factor in execution. Trust is theelusive “x-factor” which enables timely and successful execution. Indeed, authorscould write an entire future paper on this topic. Suffice it to say, company’slongstanding relationships with its external partners have facilitated company’scurrent foray into Value-Chain Pull Replenishment. Authors look forward to sharing theresults in the near future.

References

Allen, T.T. (2010), Introduction to Engineering Statistics and Lean Sigma: Statistical QualityControl and Design of Experiments and Systems, 2nd ed., Springer Verlag, London.

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Further reading

Allen, T.T., Tseng, S.-H., Swanson, K. and McClay, M.A. (2010), “Improving the hospitaldischarge process with Six Sigma methods”, Quality Engineering, Vol. 22 No. 1, pp. 13-20.

Corresponding authorSameer Kumar can be contacted at: skumar@stthomas.edu

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