Using the Quick Scan Audit Methodology (QSAM) as ...

International Journal of Lean Six Sigma Using the Quick Scan Audit Methodology (QSAM) as a precursor towards successful Lean Six Sigma implementation Andrew Thomas Richard Barton

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To cite this document: Andrew Thomas Richard Barton, (2011),"Using the Quick Scan Audit Methodology (QSAM) as a precursor towards successful Lean Six Sigma implementation", International Journal of Lean Six Sigma, Vol. 2 Iss 1 pp. 41 - 54 Permanent link to this document: http://dx.doi.org/10.1108/20401461111119440 Downloaded on: 20 June 2015, At: 02:31 (PT) References: this document contains references to 20 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 589 times since 2011*

Users who downloaded this article also downloaded: P. Childerhouse, D.R. Towill, (2011),"Effective supply chain research via the quick scan audit methodology", Supply Chain Management: An International Journal, Vol. 16 Iss 1 pp. 5-10 http:// dx.doi.org/10.1108/13598541111103458 Lawrence M. Corbett, (2011),"Lean Six Sigma: the contribution to business excellence", International Journal of Lean Six Sigma, Vol. 2 Iss 2 pp. 118-131 http://dx.doi.org/10.1108/20401461111135019 Nurul Fadly Habidin, Sha'ri Mohd Yusof, (2013),"Critical success factors of Lean Six Sigma for the Malaysian automotive industry", International Journal of Lean Six Sigma, Vol. 4 Iss 1 pp. 60-82 http:// dx.doi.org/10.1108/20401461311310526

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Using the Quick Scan Audit Methodology (QSAM) as a precursor towards successful Lean Six Sigma implementation

Using QSAM

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Andrew Thomas Graig Campus, Coleg Sir Gar, Llanelli, UK, and

Richard Barton Downloaded by OPEN UNIVERSITY At 02:31 20 June 2015 (PT)

OCDGB Ltd, Bridgend, UK Abstract Purpose – This paper seeks to propose the use of and analyses the effectiveness of a supply chain auditing methodology called Quick Scan (QS) as a precursor to the implementation of Lean Six Sigma (LSS) projects in small and medium manufacturing enterprises (SMEs). The purpose of this paper is to identify how effective the use of such an approach will be on an organization aiming to implement LSS. The effectiveness of QS Audit Methodology (QSAM) is measured in qualitative terms through its ability to assist LSS project teams in providing the required focus to develop correctly strategically aligned projects which ensure maximum benefits are obtained from LSS projects. Design/methodology/approach – Through the development of a case study approach, the paper initially and briefly describes the results of the implementation of LSS projects into ten manufacturing SMEs. The paper then, through a case study, describes the implementation of the QSAM into the design, development and early stage implementation of an LSS project with an SME and analyses its effectiveness in being able to develop a more robust approach to planning, measuring and defining the early stages of LSS project implementation in companies. Findings – The paper initially identifies that most LSS project champions identified the project definition stage as being particularly problematic since it was at this stage that future LSS success hinged. The paper then goes on to show how the effective development and implementation of QSAM into the front end of LSS project implementation brought benefit to a subject company and how the approach has led to improvements in project performance for that company. Practical implications – The design, development and implementation of an effective QSAM/LSS approach provide a simple yet effective method to achieving improvements in systems performance. Whilst the authors accept that QSAM does not provide a panacea for project definition, the paper offers practicing process managers and engineers as well as industry-focussed academics a strategic framework for increasing the likelihood of identifying strategically focussed projects for future LSS implementation. Originality/value – The proposed QSAM/LSS strategy contributes to the existing knowledge based on supply chain and quality systems and subsequently disseminates this information in order to provide impetus, guidance and support towards increasing the development of companies in an attempt to move the UK manufacturing sector towards improved manufacturing performance. Keywords Six Sigma, Small to medium-sized enterprises Paper type Case study

1. Introduction Over recent years, UK manufacturing industry has faced increasing competition from low-labour cost countries. Alongside this, the sustained global downturn has further threatened the sustainability of many manufacturing industries. In response,

International Journal of Lean Six Sigma Vol. 2 No. 1, 2011 pp. 41-54 q Emerald Group Publishing Limited 2040-4166 DOI 10.1108/20401461111119440

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companies are continuing to focus on the optimization of their operational activities through reducing waste, increasing their value proposition whilst simultaneously trying to break into new markets by offering increased levels of service and new and improved products. These manufacturing priorities have necessitated an increased level of control of production and supply chain systems and engineering resources. This paper will primarily concentrate upon how companies employ and develop Lean Six Sigma (LSS) as a business process improvement (BPI) strategy which is capable of improving the internal operating structure of companies by making them leaner and more robust to external influences. It will briefly outline the application and implementation of LSS into ten manufacturing companies and will then describe the issues which emerged from the implementation process. The paper then goes on describe the application of the Quick Scan Audit Methodology (QSAM) into the initial stages of an LSS project undertaken in a subject company. The paper then goes on to analyse the effectiveness of the QSAM approach towards enabling companies to define strategically relevant LSS projects for future development in organisations. This case study highlights the usefulness of the QSAM process in identifying key business process areas for enhancement through LSS implementation. 2. LSS – an evolutionary perspective Lean has been widely considered as a BPI strategy which has facilitated high levels of sustainable growth in many manufacturing industries throughout the world. Lean is a strategy for achieving continuous improvements in business performance through identifying a company’s value stream, adding value to a company’s operations and then systematically removing waste – anything that does not add value to a company’s operations (Hines and Rich, 1997). The application of “Agility” was introduced as a sub-set of the Lean paradigm into companies some years later in order to deal with the issues of volatile markets and irregular demand patterns. In this case, high levels of responsiveness and flexibility are required in order to cope with these demands (Christopher and Towill, 2000; Ramasesh, 2001). Academics and practitioners have attempted to integrate the two approaches to form a hybrid concept of “Leagility” (Childerhouse and Towill, 2000; Mason-Jones et al., 2000) and later “Agilean” (Cox et al., 2007) since it was suggested that an integrated system for creating a Lean yet highly responsive manufacturing system was beneficial to a range of companies dealing with the increased threat of globalization and low-labour cost competition. Over the past ten years or so, Six Sigma has been hailed as a key business-improvement approach, that is capable of achieving significant improvements in business process performance (Breyfogle, 1999; Anthony, 1999; Bohte, 1991). Companies such as Motorola and GE have based their BPI strategy around the Six Sigma concept. The core of the Six Sigma concept is the application of the DMAIC methodology and its use of rigorous statistical analysis to improve processes (Pande and Holpp, 2002). DMAIC provides a structured approach to tackling specific business-process problems in order to reach Six Sigma levels of performance. These DMAIC stages are shown in Figure 1. As companies have continued to seek ways of delivering greater business performance at lower cost, the concept known at LSS has come to the forefront. The application and development of LSS enables companies to achieve the benefits of waste reduction and responsive manufacturing offered by Lean with developing robust, error free and fault tolerant production offered by Six Sigma. Early developers of the LSS

Using QSAM Create perfection

Identify value

Control

Pull on demand

Improve

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Define Lean Six Sigma

Measure

Measure value stream

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Analyse Create flow Source: Thomas et al. (2009), republished with the permission of Interscience Publisher

approach (George, 2002; Pande and Holpp, 2002) seemed to develop a simple linear connection between Lean and Six Sigma suggesting that a company should “Lean up” the business first and then introduce Six Sigma as a mechanism to reduce variation and improve quality. Others (Breyfogle, 1999) proposed that the “Lean” part of LSS could be brought in at the Improve stage of the Six Sigma DMAIC process thus effectively demoting Lean to a secondary process. More modern views of LSS, however, provide a departure from a traditional linear application of Lean followed by Six Sigma in that it has at its heart an integrated and simultaneous approach to “leaning” company operations as well as driving variation reduction forward through Six Sigma implementation. This “double-helix” approach creates a unique environment for the development of a hybrid implementation strategy. The work of Thomas et al. (2009) shows how both the Lean and Six Sigma methodologies integrate where the DMAIC stages of Six Sigma can be aligned with the key stages of the Lean manufacturing continuous improvement principles as shown in Figure 1. 3. The LSS project and results In order to test the effectiveness of the LSS approach in companies, a regional project was established where ten companies were invited to take part in an LSS implementation programme. The medium manufacturing enterprises operated in a range of differing manufacturing sectors from automotive to aerospace to instrument making. The project was multi-staged with the major stages being: . initial Six Sigma and Lean principles training; . project identification and initiation; . work in conjunction with the university to establish an LSS activity programme; . guided work to measure and analyse data; . company-based work to solve and execute the LSS-based improvements; and

Figure 1. Alignment of DMAIC and Lean principles

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.

collaborative assessment of actions required to embed the solutions into the company, and the effectiveness of the process. Hence, review the tools utilised in light of the company culture.

Of the ten companies which took part in the pilot project: . two withdrew from the programme stating that they were unable to identify suitable projects; . three completed the LSS programme but felt that they did not yield the expected benefits. This was primarily because the projects selected either lost impetus along the way or that they had limited capacity to return the benefits to the company. In other words, the projects selected by the respective teams were not challenging enough; and . five companies completed and felt that the work had yielded the expected benefits but in all cases, the companies did not know if they could have achieved more by way of benefits had they employed a different approach or selected a different project. Following the elicitation of the project results, the authors undertook in-depth interviews with the project champions within each company with a view to identify the problematic areas of the LSS cycle. The aim was to identify methods of improving the delivery and training mechanism of LSS implementation. From this analysis, it was found that the over-riding concern to them was that they felt that the LSS project had failed to deliver the expected results by way of performance, bottom-line improvements. Further in-depth post-project discussions with the relevant project champions revealed that they believed that they had set themselves unrealistic expectations at the start of the project and that this may have tainted their view of what had transpired in practice. However, on further reflection and by asking the project champions what they would do differently in future LSS project implementations, the response in all cases was that insufficient rigor had been applied to the project-selection stage even though most project champions were content with the fact that the Define stage had been carried effectively in so much as it allowed the LSS teams to define the problem areas but did not necessarily enable them to select the correct project for implementation. It was felt in general that the projects failed to deliver true bottom-line results since they were often not linked to improving the key strategically important areas of the company. On the basis of these results, the authors felt that it may have been possible to provide a much more robust front-end analysis mechanism which could have been used to allow teams to focus on more strategically important projects from which the LSS projects may have yielded greater benefits. As a result, the authors proposed the application of the QSAM as a pre-cursor to the LSS projects in an attempt to investigate and later validate its effectiveness in enabling LSS teams to align their improvement work with the strategic areas of their respective organizations. The QSAM study would also identify the key operational issues within companies so as to allow engineers and managers to identify and prioritize correctly a range of LSS projects for future execution. The QSAM would be introduced into the pre-DMAIC stage with the intention that it would then provide clear guidance and support through improved and focused activity at the Define, Measure and Analyse stages of the LSS projects as they were subsequently implemented. The introduction of QSAM would then enable a company to devise its own focused LSS projects through ensuring that the supply chain system as a whole

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is considered and not just a specific element of the business process. A description of the QSAM is highlighted in the next section. 4. Supply chain system auditing The QSAM was developed by Cardiff University’s Logistics Systems Dynamics Group. The details of the audit methodology are explained in the work by Naim et al. (2002). The structure of QSAM is based primarily on control systems theory and can be traced back to the work of Paranaby (1979). The QSAM involves an in-depth analysis of the company’s supply chain system by a specialist team made up of university staff and their industrial partners who have the knowledge of the supply chain under scrutiny. Once a suitable supply chain has been identified and commitment from the company has been achieved, on-site attendance by the QSAM team is required for a period of between three and four days where semi-structured interviews and questionnaire completion with key staff members of the company are undertaken. The complete audit can normally be undertaken in a ten-day period. A detailed walk-through of company operations is also undertaken in order to contextualize the questionnaire information and interviews. This method of data gathering allows the QSAM team to triangulate the data which further validates the information obtained and provide a firm foundation for further analysis of the supply chain effectiveness to be made. The QSAM diagnostic is based upon four sources of data: (1) attitudinal and qualitative questionnaires; (2) process maps; (3) semi-structured interviews; and (4) archival information. Up to 11 qualitative attitudinal questionnaires are completed during the QS Audit. These questionnaires are undertaken with key members of the company ranging from: managing director, production manager, purchasing, supply chain managers, quality manager, HR manager, etc. The second format of data collection is process mapping, which provides a detailed understanding of the material and information flows for the business processes. The third type of data that is collected during the QSAM is from semi-structured interviews. These are conducted with a cross-section of the senior and middle management from all functions and include similar coverage of all the questionnaires as well as the process mapping. The final type of data collected during the QSAM is archival data. The archival data are segmented into four categories utilising a generic uncertainty circle (UC) model (Mason-Jones and Towill, 1998) shown in Figure 2. The UC model is a convenient way to categorise the disturbances that may be encountered by a business in the supply chain. Thus, for example, a business may find uncertainties associated with: erratic, frequent and problematic downtime of its machinery and equipment thus affecting process performance, changing customer schedules due to poor voice of the customer (VoC) data capture and erratic demand profiling which leads to increased demand disturbance in a system, poor supplier delivery performance which affects adversely supplier performance and inaccurate and distorted production plans, wrong and inaccurate process control features which inhibits the company’s ability to control its manufacturing operations in the way it ideally should. By understanding how each

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of the four areas contributes to supply chain systems performance, a business may prioritise its resources adequately and focus on improving the areas which provide the highest scope for business performance improvement. These four areas of uncertainty identified as Process, Demand, Supply and Control are shown in Figure 2 in the form of an “UC”. The UC method is based on the control of a company’s internal process in responding to the effects of customer demand and in turn the ability of the company’s internal processes to place orders on to their suppliers (Towill, 2006). Uncertainty within the supply system can be significantly affected by the company’s own process not yielding the required products on time. Supplier interface uncertainty results from the supply chain’s inability to cope with the requested demand patterns (frequency and quantity of order profiles). Demand interface uncertainty is compounded by lack of accurate tracking of customer requirements by way of product features, volumes, frequency of delivery, etc. Finally, further uncertainty is induced by poor system controls based on the wrong decision rules and stale, noisy or incomplete information. As various business improvement programmes are successfully implemented into supply chains, it is expected that the corresponding uncertainty will reduce in size and the result can be a more effective and focused supply chain capable of moving towards a seamless supply performance (Childerhouse et al., 2004). In seeking to establish the degree of uncertainty in an individual value stream, complex material flow is a primary lead indicator. Consequently, it is possible to produce checklists for supply chain analysts to use when monitoring the behaviour of their existing systems based around four groups of complex material flow symptoms. These groups are termed: (1) dynamic behaviour; (2) physical situation; (3) operational characteristics; and (4) organisational characteristics. Table I shows the groups and the elements contained in each group. The presence of uncertainty will result in a high frequency of these complex material flow symptoms occurring. This grouping of symptoms allows the analyst to work through individual checklists via a combination of data modeling, activity sampling, process mapping and structured questionnaires in order to clearly identify the symptoms and to attempt

Control

Supply

Figure 2. The uncertainty circle and the contribution of module variability to overall uncertainty

Process

Interfaces to be monitored / re-engineered’

Demand

Material flow Information flow

Class of symptoms

Symptoms observed in complex material flow

Dynamic behaviour

Systems-induced behaviour observed in demand patterns System behaviour often unexpected and counterintuitive Causal relationships often separated geographically Excessive demand amplifications as orders are passed upstream Rogue orders induced by system “players” Poor and variable customer service levels Large and increasing number of products per pound turnover High labour content Multiple production and distribution points Large pools of inventory throughout the system Complicated material flow patterns Poor stores control Shop floor decisions based on batch-and-queue “Interference” between competing value streams Causal relationships often well separated in time Failure to synchronize all orders and acquisitions Failure to compress lead times Variable performance in response to similar order patterns Decision making by functional groups Excessive quality inspection Multiple independent information systems Overheads and indirect costs allocated across product groups and not by activity Excessive layers of management between the CEO and the shop floor Bureaucratic and lengthy decision-making process

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Physical situation

Operational characteristics

Organizational characteristics

Sources: Towill (1999); this table has been republished with the permission of the Institute of Operations Management

to provide solutions to these problems via the identification of a series of short-, mediumand long-term improvement opportunities. Codification via Likert scales allows the analysts to evaluate the statistical relationship between these four groups of uncertainty symptoms and the four UC segments (Childerhouse and Towill, 2003). A smooth well-controlled material flow system lies at the heart of best supply chain management design and practice (Childerhouse and Towill, 2003) and is crucial if a company is to avoid the adverse impact of the bullwhip effect. By identifying the shortfall in smooth material flow, it is possible to highlight those areas most in need of reengineering to obtain significant performance improvement. To this end, a set of 12 rules shown in Table II has been devised. Together, these rules point the way forward to smoothing material flow throughout the chain. Simplified material flow is a highly desirable feature of supply chain operations and can be achieved via innovative and thorough application of the 12 simplicity rules. Furthermore, these rules when correctly applied during business process reengineering programmes produce a significant impact on “bottom-line” performance metrics. Furthermore, if material flow is over complex, then numerous symptoms become clearly visible and result in ineffective product delivery process performance. Towill (1999) identifies 24 detailed symptoms that can be categorized into dynamic, physical, organizational and process characteristics. Therefore, the 12 simplicity rules have been

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Table I. Four classes of symptoms observed in complex material flow

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Rule Rule Rule Rule Rule

Rule 6 Rule 7

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Rule Rule Rule Rule Rule Table II. The 12 simplicity rules

1 2 3 4 5

8 9 10 11 12

Only make products that can be quickly despatched and invoiced to customers Only make in one time bucket those components needed for assembly in the next Streamline material flow and minimize throughput time Reduce information lead times via the use of the shortest planning periods Only take deliveries from suppliers in small batches as and when needed for processing and assembly Synchronization of time buckets throughout the chain Form natural clusters of products and designing processes appropriate to each value stream so that the requirements of diverse customer requirements can be best served Eliminate all uncertainties in all processes Develop a structured approach to change (understand, document, simplify, optimize) Highly visible and streamlined information flows Use proven and robust decision support systems in the management of the supply chain The operational target of the seamless supply chain needs to be commonly accepted and shared by all members of the change team

Sources: Towill (1999); this table has been republished with the permission of the Institute of Operations Management

designed as a complete set of guidelines by for practitioners to simplify their material flow, in the sense of developing the supply chain. The following is a summary of each of the 12 rules that highlights why they have been included and when they are likely to be of particular importance. Table II shows the 12 simplicity rules. Through the rigorous analysis of the complex material flow characteristics and assessing the company’s adherence to the simplicity rules including further observed and contextual evidence the QSAM team can then accurately rate the company’s UC by scoring each element of the circle with a grade from 1 to 4 (1 being low uncertainty and 4 being highly uncertain with a tendency to be unstable and erratic). The case study and Table III shows the uncertainty score profiling and its use in defining strategic points for LSS implementation projects. The scope of the QSAM can vary from company to company depending upon the supply chain issues encountered by each company and the relative complexity and size of their operations and associated supply chain capabilities, which reflects the variation in tools and techniques that can be drawn from the DMAIC process. 4.1 Impact of utilizing QSAM into the LSS approach By considering the use of QSAM as a precursor to the LSS improvement project, a significant amount of rich contextual data can be developed and processes characterized so that this information can then be used as a key input to the Define stage of the

Questions asked of each value stream

Table III. QSAM uncertainty score profile

The value-added process generates low system uncertainty The supplier side generates low system uncertainty The demand side generates low system uncertainty The system controls do not generate uncertainty

Strongly agree 1 1 1 1

Rating by QS team Weakly Weakly agree disagree 2 2 2 2

3 3 3 3

Strongly disagree 4 4 4 4

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LSS methodology. The QSAM outputs will help describe in some detail the requirement for improvement, to what level the process must be improved and, what symptoms can be monitored to understand the degree of improvement to be realised. In terms of managing variation induced in the system (from external uncertainties in supply, demand and well as internal uncertainties from the processes and the control placed upon them), this can be broken down to reducing variation (increasing sigma quality levels) in each of the four uncertainty areas, graphically represented as a normal distribution of variability at each of the elements shown in Figure 2. Note that the normal distribution curves shown in Figure 2 are examples and are not actual calculations of process variability at each element in that particular supply chain system. 5. Case study The application of QSAM and its connection to LSS is described in this section through the analysis of its implementation into a subject-manufacturing company. The subject company is a small engineering company which manufactures musical instruments to a world market. Established in 2003, the company has grown significantly over the years but as with many companies, the recent global recession has forced the company to revisit its internal manufacturing processes with a view to reducing system uncertainty and increasing business performance. The company has for a number of years employed the LSS approach in order to assist in its journey towards business performance improvement. The authors discussed the implementation of QSAM with the company and were given permission to undertake a ten-day QSAM analysis of the company’s systems as outlined in the previous section of this paper. The aim of QSAM implementation would be to identify a range of strategically important business improvement projects which could then be implemented by LSS. A team was set up within the company which consisted of the authors and the LSS team from the company. Full training in the implementation of QSAM was given to each member and each team members was assigned a specific responsibility for conducting the audit and undertaking the subsequent analysis following the audit stage. A consensus of opinion was achieved by the team as to the scores given to each stage of the QSAM analysis. These scores were then moderated by plugging the results into a QSAM database to ensure that the scoring system was commensurate with the type, size and structure of company. The authors undertook an initial study of the company operations by completing the QSAM questionnaire with the management team of the company as well as the tier 1 suppliers to the company. Once the questionnaire work had been undertaken, the team proceeded to meet up with the management team from within the company as well as the tier 1 suppliers to discuss the business process and/or supply chain connectivity in greater depth and to draw out archival data which could be used to back up any claims made by the respective management teams. The team then completed the survey with a detailed walk through of the manufacturing and supply end operations. By the end of the data-gathering stage, the team had successfully triangulated its data-gathering systems which allowed them to proceed to the analysis stage of the QSAM process. 5.1 The business process – analysis stage The company reacts to an order placed by the customer in a traditional pull format. The order is placed by the sales team as a response from the customer. The sales team process the order through a card-based system which informs the shop floor (CNC and

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woodworking areas) that an order is on its way. The sales department then informs purchasing of the order and purchasing subsequently orders the materials for internal manufacture and triggers off the specialist machinists via an order and a CAD drawing to produce the specialist parts required for production. The sub-contracted products and raw materials are delivered to the company where the stores department will match the products with the sales order. From here, a production card is raised and the order is placed into the woodworking and assembly route for production as shown in Figure 3. When the order enters production it will go initially to the woodworking department and then onto the assembly and finishing areas. Thereafter, all work is pushed through the system until it reaches either the stores area where customer can arrange to pick up the product (and sound test before leaving) or, the product goes straight to the customer (depending upon the requirements of the customer at the time). At the time of product dispatch, the sales team will obtain a confirmation order from the production manager which shows that the particular item has been produced. The team conducted a value stream mapping exercise of the process operations of a key product. This was achieved by undertaking a detailed walk through the product delivery and realization process from the start of operations right through to the finish (Figure 3). A detailed analysis of the value stream calculated that 78 percent of the total time undertaken for product manufacture was considered to be non-value adding (NVA). Subsequent analysis of the complete process identified that the major area contributing to NVA activities was across both the CNC and woodworking areas by means of delays between operations and the production of an excess over-make quantities due to incorrect order elicitation. Following the value stream mapping exercise, an analysis of each of the main elements of the UC was undertaken. This analysis was undertaken using the standard QSAM approach as outlined in the work of Towill (1999), Childerhouse and Towill (2003) and Naim et al. (2002) and included detailed causal loop analysis of each element

Purchasing

Mat’l suppliers Raw materials (Monthly)

Wax (Monthly) Wax suppliers

Stores

Production lists

Sales

Customer Products (Weekly)

Production control

Prod mgr

Stores

Figure 3. VSM of the production process Key:

WIP

CNC routing

CNC milling

CNC turning

Deburring

Wood cutting

Stage inspection

Neck shaping

Veneering

Sub-assy

Box shaping

Neck drilling

Engraving

Assembly

Cut and adjust

Spray and finish

Final inspection

Finished goods

Materials

Packing and despatch

Stores

Information

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of the UC. As a result of this analysis the uncertainty profile was created and is shown in Table III. As stated previously, the scores allocated to the profile were developed through team consensus and moderation against previous QSAM scores for similar companies. Through subsequent detailed analysis of each element of the UC, the profile identified that there was a high level of uncertainty present in the supply and demand sides of the supply chain system. Further analysis by the team so as to identify the root causes of poor performance quickly highlighted poor supply chain relationships as well as the company’s inability to successfully track the customer voice meant that order profiling was poor and the company reacted to order sub-mission rather than developed any form of predictive capabilities. This meant that the supply chain system reacted slowly and ineffectively due to demand amplification being present. Also, the analysis suggested that the resulting system controls were weak which further exacerbated the demand/supply synchronization issue. As an example, the team observed a number of instances where data control loops remained open thus limiting feedback to the production control department. This then caused problems in that the production manager did not have real-time data and so resulting production decisions were made based on significant data lag being present. Further discussion with the production management team revealed that the systems controls were deliberately left loose so as to be able to respond to the uneven demand volumes. The observation led the QSAM team to prioritize the firming up of the supply and demand chain aspects first before systematically tightening up the production control systems in order to ensure that production output is maintained. It was also highlighted through the QSAM that the internal process operations although showing some level of uncertainty, performed relatively better than the other elements of the system. However, this was down to the efforts of the process staff which undertook many NVA functions in order to enable production targets to be met. So, whilst production outputs were being met, a deeper analysis of the process performance highlighted a number of key inefficiencies. Further analysis of the demand side highlighted the lack of an effective VoC strategy and a lack of an effective approach towards synchronizing customer demand with the product design options offered to the customer at the point of purchase. The company then suffers from a disruption at the demand end which amplifies itself as it moves through the process stage to the supply end. The team also observed that at certain periods during the month, the disturbance seen at the supply end settled down significantly. Further investigation revealed that this was due in part to the company buying and holding significant levels of raw material stocks when the financial situation allowed them to do so in an attempt to limit the effect of an erratic demand side. Whilst this allowed the company to resolve short-term demand issues, the major impact of this approach was seen by the suppliers where a lumpy demand signal from the company did not assist them in achieving level scheduling and thus becoming leaner and more efficient. Therefore, the QSAM revealed that the company classically has attempted to solve the symptoms of the process problem rather than tackle the root cause of the issue. Therefore, the QSAM process allowed the company to focus initially on the demand-side issues and attempt to resolve these problems after which it was expected

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that the company could reduce high inventory levels at the supply side as the system settles and is less reactive to the fuzzy demand-side issues. As a consequence, the company employed a robust VoC approach in the first phase of the LSS strategy deployment within the company. This subsequently saw an improvement in the demand-side uncertainty which impacted positively on the other elements of the supply chain system. Whilst the prescribed first-stage action above is not seen as hugely innovative, what the QSAM approach was able to do was draw the company into applying the DMAIC approach more strategically and into the non-technical/non-manufacturing process-based areas in order to achieve business performance improvement. Following on from this project, the QSAM action plan highlighted a number of LSS projects aimed at improving the supply chain interface which enabled the supply companies to produce more effective lot sizes at more regular intervals thus allowing the company to someway improve the synchronization of a more settled demand end with a more effective supply end. As the supply and demand-end functions improved then the process elements were required to improve in order to allow the system as a whole to function with improved performance. Further, process-based improvements were identified in an attempt to improve the reliability and performance of the manufacturing process but these would be undertaken after demand and supply improvements were made. 6. Conclusions and further work The success of LSS implementation depends on the maturity of the company’s process reengineering culture. Cycling through the continuous improvement process shown in Figure 1 will provide a team with suitable knowledge achieve greater success over time, however, the demands of today’s marketplace and economy are demanding more rapid results. Hence, the QSAM is proposed as an effective initial platform from which companies with little existing knowledge or experience in the field of LSS implementation can identify projects which will maximise the return on project investment. One important fact being that the most significant project may lie in any of the aspects of the supply chain/UC. Of course, QSAM is not a replacement for knowledge/experience in the practice of performance improvement, and is not a panacea for every company or supply chain. But if a company is only now starting on the road of business reengineering then the QSAM can provide an important role in the planning phase of an LSS implementation project. Tackling the UC issues in a systematic way and ensuring that each element in the supply chain system (demand side, supply side, process and controls) is improved in a balanced way enables the complete system to improve simultaneously. As the system is subsequently improved, however, there will be a further requirement to undertake further QSAMs to ensure that the project list is still relevant (interactions between sources of uncertainty may subsequently change project priorities when one particular issue is resolved). In order to substantiate the claim that QSAM/LSS will play such an important role, it will be important to repeat a comparable project-based exercise with a number of test bed companies, and ideally to extend each project with QS audits after the first “round” of production improvements to identify the relative investments and cost benefits. The second phase of the LSS process concentrated on the supply side where the company embarked upon a major supplier development programme aimed at improving

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supplier response times, delivery reliability. Finally, the company applied an LSS project on the value-adding process in order to remove the variability in the castings section which proved to be a bottleneck once the demand and supply side issues had been eradicated by the Lean programmes employed. References Antony, J. (1999), “Spotting the key variables using Shainin’s variables search technique”, Journal of Logistics and Information Management, Vol. 12 No. 4, pp. 325-31. Breyfogle, F.W. (1999), Implementing Six Sigma, Smarter Solutions – Using Statistical Methods, Wiley, London. Childerhouse, P. and Towill, D. (2000), “Engineering supply chains to match customer requirements”, Logistics Information Management, Vol. 13 No. 6, pp. 337-45. Childerhouse, P. and Towill, D.R. (2003), “Simplified material flow holds the key to supply chain integration”, OMEGA, The International Journal of Management Science, Vol. 31, pp. 17-27. Childerhouse, P., Disney, S.M. and Towill, D.R. (2004), “Tailored toolkit to enable seamless supply chains”, International Journal of Production Research, Vol. 42 No. 17, pp. 3627-46. Christopher, M. and Towill, D. (2000), “Supply chain migration from Lean and functional to agile and customized”, Supply Chain Management: An International Journal, Vol. 5 No. 4, pp. 206-13. Cox, A., Chicksand, D. and Tong, Y. (2007), “The proactive alignment of sourcing with marketing and branding strategies: a food service case”, Supply Chain Management: An International Journal, Vol. 12 No. 5, pp. 321-33. George, M.L. (2002), Lean Six Sigma – Combining Six Sigma Quality with Lean Speed, McGraw-Hill, Emeryville, CA. Hines, P. and Rich, N. (1997), “The seven value stream mapping tools”, International Journal of Operations & Production Management, Vol. 17 No. 1, pp. 46-64. Mason-Jones, R. and Towill, D.R. (1998), “Shrinking the supply chain uncertainty circle”, IOM Control, September, pp. 17-22. Mason-Jones, R., Naylor, B. and Towill, D.R. (2000), “Engineering the leagile supply chain”, International Journal of Agile Management Systems, Vol. 2 No. 1, pp. 54-61. Naim, M.M., Childerhouse, P., Disney, S. and Towill, D. (2002), “A supply chain diagnostic methodology: determining the vector of change”, Computers & Industrial Engineering, Vol. 42, pp. 135-47. Pande, P. and Holpp, L. (2002), What is Six Sigma, McGraw-Hill, Columbus, OH. Parnaby, J. (1979), “Concept of a manufacturing system”, International Journal of Production Research, Vol. 17 No. 2, pp. 123-35. Ramasesh, R., Kulkarni, S. and Jayakumar, M. (2001), “Agility in manufacturing systems: an exploratory modeling framework and simulation”, Integrated Manufacturing Systems, Vol. 12 No. 7, pp. 534-48. Thomas, A., Rowlands, H., Byard, P. and Rowland-Jones, R. (2009), “Lean Six Sigma: an integrated strategy for manufacturing sustainability”, International Journal of Six Sigma and Competitive Advantage, Vol. 4 No. 4, pp. 333-54. Towill, D.R. (1999), “Simplicity wins: twelve rules for designing effective supply chains”, Control the Journal of the Institute of Operations Management, Vol. 25, pp. 9-13. Towill, D.R. (2006), “Fadotomy – anatomy of the transformation of a fad into a management paradigm”, Journal of Management History, Vol. 12 No. 3, pp. 319-38.

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Further reading Bhote, K.R. (1991), World Class Quality, American Management Association, New York, NY. Thomas, A.J. and Webb, D. (2003), “Quality systems implementation in Welsh small- to medium-sized enterprises: a global comparison and a model for change”, Proceedings of the I Mech E: Journal of Engineering Manufacture, Vol. 217 No. 4, pp. 573-9. About the authors Andrew Thomas is the Director of Faculty and Quality at Coleg Sir Gar. He was previously the Associate Dean of Newport Business School, University of Wales Newport. Prior to entering academia, he held posts within the Royal Air Force as well as a number of manufacturing companies. He was previously a Lecturer in Operations Management and Supply Chain Systems at Cardiff University and has published over 130 journal and conference papers in the area of BPI. Andrew Thomas is the corresponding author and can be contacted at: [email protected] Richard Barton is a Continuous Improvement Manager at Ortho Clinical Diagnostics GB Ltd in Bridgend, Wales. Prior to that, he was the Industrial Manager based at the Logistics and Operations Management Division within Cardiff Business School. A Mechanical Engineering graduate, Richard Barton undertook a teaching company scheme upon graduating and went on to hold roles in process and production engineering before returning to academia. He later went on to gain his MPhil and is currently completing his PhD in Supply Chain Systems.

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