Getting Lean Six Sigma Embedded Into Organizations Research Proposal

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Logistics & Transportation Managers Benefit

From the Use of Lean Six Sigma

Given the continual economic turbulence and uncertainty surrounding nearly every industry, the need for stabilizing, securing and growing supply chains has become critically important for the long-term viability of many manufacturing and services-based industries. Lean Six Sigma continues to gain widespread adoption as enterprises look to better manage their supply chains by reducing risk, costs and increasing speed, accuracy and responsiveness to customers' rapidly changing demands (Huehn-Brown, Murray, 2010). The DMAIC Define, Measure, Analyze, Improve and Control) methodology that is foundational to Lean Six Sigma continues to be relied on for bringing the Voice of the Customer (VoC) into new product development initiatives, manufacturing, customer service and order process re-engineering initiatives as well (Found, Harrison, 2012). Lean Six Sigma is critically important for any enterprise looking to streamline its processes and gain greater levels of customer-driven improvements into existing processes while continually striving to deliver greater value and benefit in the products and services produced and delivered (Huehn-Brown, Murray, 2010). Models exist for integrating lean Six Sigma into manufacturing (Kroslid, 2001), logistics (Rahman, Rahman, 2012), and throughout entire supplier enablement systems that are global in scope including the Toyota Production System (Udoka, 2004) (Timans, Antony, Ahaus, Van Solingen, 2012). DMAIC originally began with the orientation towards streamlining internal processes to reflect the expectations, needs, requirements and wants of customers (Byrne, Lubowe, Blitz, 2007). Today the benchmark of any successful Six Sigma program is predicated on how well the DMAIC methodology is implemented, how well defined the variances and sigma analyses and differences in values to benchmarked data from customers' histories and product quality metrics (Found, Harrison, 2012) to the long-term orientation of creating and continually improving customer-centric processes and programs (Huehn-Brown, Murray, 2010). All of these factors however have in the past been left in a more compartmentalized and often myopically-defined state that eventually leads to their lessening in value in organizations (Laureani, Antony, 2012). Organizations may be staffed with literally dozens of Six Sigma Black Belts but unless they can successfully integrate the lessons learned and knowledge into the organizations they serve, lean Six Sigma fails to deliver the value it is capable of. Giving lean manufacturing and Six Sigma professionals the opportunity to make a broader contribution starts by defining and continually improving frameworks that allow for greater accountability of performance and most of all, greater success rates at change management by concentrating on the most proven critical success factors (Laureani, Antony, 2012).

Creating A Framework For Logistics & Transportation Managers To Benefit From The Use of Lean Six Sigma

With the methodologies proven with regard to Six Sigma's value across a myriad of industries and the impact of its deliver innovations quantified in dollar and business model terms (Byrne, Lubowe, Blitz, 2007), what's needed is a framework that can provide prescriptive guidance to logistics and transportation managers using analytics, key performance indicators (KPIs) and metrics of performance that guide each phase of the DMAIC process to successful implementation. This component of Six Sigma intelligence has yet to be fully integrated across the entire value chains of manufacturing companies however, which is the industry most in need of this depth of prescriptive analysis and sophistication level of analytics, KPI and metrics. The void left by not having a uniform set of analytics, KPIs and metrics across a Lean Six Sigma supply chain minimizes the performance of complex supply chains that need to stay within tolerance to quality audit metrics first and process performance guidelines as well (Huehn-Brown, Murray, 2010).

The effects of this gap in analytics, KPIs and metrics in complex supply chains most closely approximates the deceleration of supplier velocity due to a lack of adequate and timely information (Rahman, Rahman, 2012). The information gap that is dominating the most complex manufacturing supply chains for example is leading to a drastic reduction accuracy and longer cycle times even for the most rudimentary or simplistic assemble-to-stock products (Timans, Antony, Ahaus, Van Solingen, 2012). This finding illustrates how the value of a DMAIC-based series of analytics, KPIs and metrics can deliver useful insights not available today on a real-time basis in the major8ity of complex manufacturing supply chains. Further, the lack of accuracy and alacrity of data being delivered is hindered by the nascent and in some cases, complete lack of KPIs that are mapped to the specific business model inflexion decision points and trade-offs needed to keep an entire value chain synchronized (Udoka, 2004).

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Teams given the responsibility of providing a high degree of accuracy and clarity throughout the production process are today limited in what they can accomplish and how quickly the can accomplish it based on this lack of clarity around Lean Six Sigma metrics of performance. The reality is that many manufacturing companies today are doing only the minimum they need from a change management standpoint, including getting C-level executives to adopt and buy into a Lean Six Sigma mindset (Laureani, Antony, 2012). Only getting tacit approval and not having a C-level executive being a strident, enthusiastic supporter of Sig Sigma methodologies across the entire manufacturing value chains of their businesses including creating a lean mindset in the organization are only partially delivering the value they could from a transformational leadership standpoint (Byrne, Lubowe, Blitz, 2007).

Moving Beyond the Supply-chain Operations Reference Model (SCOR)

The ubiquity of the Supply Chain Operations Reference Model (SCOR) and its six management processes of Plan, Source, Make, Deliver, Return and Enable have often been relied on as a means to further accelerate the adoption of lean Six Sigma throughout enterprises since the model's inception (Rahman, Rahman, 2012). The SCOR model's approach to defining the Plan, Source, Make, Deliver, Return and Enable aspects of a supply chain's lifecycle and performance has become standardized in complex manufacturing and throughout industries that rely on rapid inventory turns. What's been missing from the SCOR model as it relates to lean Six Sigma is the ability to integrate the two into a unified analytics and reporting platform and then scale the insights into a global supply chain and production environment (Kroslid, 2001). The accuracy of supply chain performance and its ability to scale operations globally is predicated on having real-time, accurate data on supplier quality performance, delivery performance and the ability to plan for new products quickly without impacting existing supply chain operations. The lack of strategic insight into supply chain performance often limits their ability to become more engrained across the more intricate value chains of an industry, and inefficiencies result (Huehn-Brown, Murray, 2010). The SCOR model takes on only the first phase of a much broader level of analytics maturity needed through global supply chains (Kroslid, 2001).

To make lean Six Sigma permeate an entire organization there needs to be a more strategic set of analytics, KPIs and metrics that provide insights into every aspect of operational and financial performance. The lack of existing frameworks to unify production floor performance with financial performance is limiting the innovation of supply chains as a result (Byrne, Lubowe, Blitz, 2007). Creating a link between financial performance and operational metrics, while also providing a multidimensional view of supply chain operations and performance is an elusive goal of supply chain management today (Udoka, 2004). Lean Six Sigma adoption lags in organizations that have this gap in analytics and strategic insight, failing to achieve the full potential they have to transform the organizations that rely on them.

The Hierarchy of Supply Chain Metrics' approach to a unified model that illustrates the operational, functional and strategic role of analytics, KPIs and metrics brings lean Six Sigma closer to quantification in complex supply chains. The Hierarchy of Supply Chain Metrics takes the position that Demand Forecast Accuracy, Perfect Order Performance and Supply Chain Management (SCM) costs must act to unify the overall metrics structure of supply chains (Hofman, 2004). In aggregate, the entire set of analytics, KPIs and metrics when used longitudinally can become the foundation for an organization to continually pursue innovative redesigns of their processes as well (Byrne, Lubowe, Blitz, 2007). The model also provides a useful prioritization of supply chain-centric analytics, KPIs and metrics as well yet stops short of what's needed to make lean Six Sigma an integral process in organizations attaining consistently high levels of supply chain accuracy and performance. Figure 1, the Hierarchy of Supply Chain Performance, provides an overview of the analytics, KPIs and metrics that comprise the model.

Figure 1: The Hierarchy of Supply Chain Performance

Source: (Hofman, 2004)

The Hierarchy of Supply Chain Metrics provides a useful framework for organizations beginning their journey towards unifying lean Six Sigma and manufacturing performance by taking into account the integral nature of supply chains and their relative performance as well (Kroslid, 2001). The model's hierarchical nature does not however provide a time dimension for the performance of supply chains. As every supply chain operates.....

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