David R. Nowicki

Six sigma project selection using data envelopment analysis

Purpose – The evolution of six sigma has morphed from a method or set of techniques to a movement focused on business‐process improvement. Business processes are transformed through the successful selection and implementation of competing six sigma projects. However, the efforts to implement a six sigma process improvement initiative alone do not guarantee success. To meet aggressive schedules and tight budget constraints, a successful six sigma project needs to follow the proven define, measure, analyze, improve, and control methodology. Any slip in schedule or cost overrun is likely to offset the potential benefits achieved by implementing six sigma projects. The purpose of this paper is to focus on six sigma projects targeted at improving the overall customer satisfaction called Big Q projects. The aim is to develop a mathematical model to select one or more six sigma projects that result in the maximum benefit to the organization.Design/methodology/approach – This research provides the identification ...

Spares provisioning under performance-based logistics contract: profit-centric approach

Performance-based logistics (PBL) is emerging as a preferred logistic support strategy within the public sector, especially the Department of Defence. Under a PBL strategy, the customer buys performance, such as operational availability, mission readiness and operational reliability, instead of contracting for a specified collection of resources defining the underlying support infrastructure. The literature on PBL is still in its infancy and additional research is required to optimize logistic resources such as spare parts, equipment, facilities, labour etc within a PBL context. In this paper, an optimization model is developed for spares provisioning under a multi-item, multi-echelon scenario. The objective of the optimization model is to maximize the profit to the supplier under a PBL contract.

Explaining the effectiveness of performance‐based logistics: a quantitative examination

Purpose – Performance‐based logistics (PBL) strategies are providing governments and for‐profit organizations with a contractual mechanism that reduces the life cycle costs of their systems. PBL accomplishes this by establishing contracts that focus on the delivery of performance not parts. PBL establishes a metric based governance structure where suppliers make more profit when they invest in logistics process improvements, or system redesign, that reduces total cost of ownership. While work has been done to outline an overall PBL theoretical framework, the underlying theory explaining the enablers that lead to organizational and team‐level, team‐goal alignment associated with the PBL governance structure requires testing. The purpose of this paper is to quantitatively test previously posited relationships between enablers of PBL and PBL effectiveness. An additional objective is to explore any differences in PBL effectiveness between different business sectors.Design/methodology/approach – A multiple reg...

Service-dominant logic and supply chain management: are we there yet?

Purpose – Research suggests that service-dominant logic (SDL) is well suited to support supply chain management (SCM) research and practice. Qualitative research has shown that SDL is particularly consistent with an outcome-based supply chain strategy known as performance-based logistics (PBL). The purpose of this paper is to extend theory and practice by exploring the degree to which SDL is utilized in practice. Specifically, PBL is examined for consistency with the underlying fundamental premises (FPs) of SDL. In doing so, this paper answers the positive question, “what exists”, at the intersection of SDL and SCM. Design/methodology/approach – This study employs a mixed methodological approach. First, the FPs of SDL are operationalized using the language of PBL. The PBL FPs are tested quantitatively through an online survey of 52 supply chain PBL experts. A qualitative analysis is conducted using comments associated with each premise. Findings – The survey results suggest that PBL is consistent with SDL...

A GOAL PROGRAMMING MODEL FOR OPTIMIZING RELIABILITY, MAINTAINABILITY AND SUPPORTABILITY UNDER PERFORMANCE BASED LOGISTICS

Reliability, Maintainability and Supportability (R, M and S) are the main drivers of the system operational effectiveness (SOE). New procurement strategies have been developed by both public and private sectors to focus on the R, M and S characteristics inherent to the design of a system. One such strategy known as Performance Based Logistics (PBL) has gained popularity due to its success in improving the operational effectiveness of the system. In a PBL contract the customer buys performance, typically measured using R, M and S metrics, instead of contracting for a specified collection of resources defining the underlying support infrastructure. In this paper we have developed a mathematical model, using Goal Programming to optimize multiple performance measures of a design. We show how the best design is chosen from competing design alternatives when systems engineering principles are considered in defining the evaluation measures. The proposed mathematical model simultaneously considers multiple system engineering metrics during the design stage of the product development. The engineering metrics considered are a representation of the systems operational availability, reliability, maintainability, supportability and total cost of ownership. The Goal Programming model developed in the paper can be easily solved using software such as LINDO, LINGO and Excel Solver.

Reliability and maintainability allocation to minimize total cost of ownership in a series-parallel system

Allocation of system level requirements is most effective when performed early in the systems design phase. This holds especially true for two critical and fundamental design characteristics: reliability and maintainability. Traditional reliability allocation models are developed to either maximize system reliability under a cost constraint or minimize cost subject to a system-level, target reliability constraint. Cost, in these traditional allocation models, is represented solely by unit cost. Unit cost, by itself, is an inadequate measure of a systems operational effectiveness. In fact, the underlying economic metric used to properly describe the operational effectiveness of a system is total cost of ownership (TCO). TCO includes not only the upstream unit cost but the downstream operations, maintenance, and support costs. In this paper, new allocation models are developed based on TCO that simultaneously allocate both reliability and maintainability for a series-parallel system subject to meeting a system-level availability target. A non-linear representation of a mathematical model is defined that simultaneously allocates both system-level reliability and maintainability targets in a manner that minimizes TCO. This non-linear model is then transformed into a surrogate linear model that can be solved using existing commercial software. Examples are then discussed to illustrate the solution procedure and to show the sensitivity of allocation design decisions to fluctuations in economic factors such as discount rates, and design factors such as the life of the system.

Improving the computational efficiency of metric-based spares algorithms

We propose a new heuristic algorithm to improve the computational efficiency of the general class of Multi-Echelon Technique for Recoverable Item Control (METRIC) problems. The objective of a METRIC-based decision problem is to systematically determine the location and quantity of spares that either maximizes the operational availability of a system subject to a budget constraint or minimizes its cost subject to an operational availability target. This type of sparing analysis has proven essential when analyzing the sustainment policies of large-scale, complex repairable systems such as those prevalent in the defense and aerospace industries. Additionally, the frequency of these sparing studies has recently increased as the adoption of performance-based logistics (PBL) has increased. PBL represents a class of business strategies that converts the recurring cost associated with maintenance, repair, and overhaul (MRO) into cost avoidance streams. Central to a PBL contract is a requirement to perform a business case analysis (BCA) and central to a BCA is the frequent need to use METRIC-based approaches to evaluate how a supplier and customer will engage in a performance based logistics arrangement where spares decisions are critical. Due to the size and frequency of the problem there exists a need to improve the efficiency of the computationally intensive METRIC-based solutions. We develop and validate a practical algorithm for improving the computational efficiency of a METRIC-based approach. The accuracy and effectiveness of the proposed algorithm are analyzed through a numerical study. The algorithm shows a 94% improvement in computational efficiency while maintaining 99.9% accuracy.

A framework for performance based logistics: A system of systems approach

Performance Based Logistics (PBL) represents a highly successful supply chain strategy. This success comes from PBLs ability to strategically align cross-functional and inter-organizational processes of multiple firms, customers, and bill payers. This is accomplished through use of multi-year contracts, performance metrics, and incentive structures that converts the year-to-year cost streams associated with the spare and repair of the traditional return on sales, return to specification, approach to post-production support, into cost avoidance pools to be harvested under a return on investment, reliability improvement post-production support strategy. Academic research into PBL is just emerging, and is very promising. As an emerging research area there is no overall, definitive framework that succinctly describes the essence of PBL, and lays a foundation that guides an overall PBL research agenda. This paper uses a system of systems (SoS) approach, informed by a grounded theory analysis, to inductively derive such a framework. The investigation involved an extensive review of both academic and practitioner literature dealing with PBL, a review of Government Accountability Office (GAO) Reports pertaining to PBL, involvement in PBL conferences and workshops, and 60+ recorded and transcribed interviews with practitioners actively engaged in managing the sustainment of complex and high cost systems. This research presents an overall framework that encapsulates and guide PBL research and practice.

Converting knowledge into value: Gaining insights from service dominant logic and neuroeconomics

Purpose – The purpose of this paper is to describe the conversion of knowledge into value by examining the confluence of service-dominant logic (S-D logic), supply chain management (SCM), human resource management (HRM), and neuroeconomics. S-D logic suggests that knowledge is the raw material of value creation. SCM provides an organized foundation to study the conversion of raw materials into value. HRM recognizes the centrality of human decisions in the process of converting knowledge into value. Neuroscience gives insight into the efficiency and effectiveness of the human decisions processes. Global SCM provides more than markets and raw materials – global SCM provides the human resources central to value creation. Design/methodology/approach – This paper combines literature review with interviews from members of supply chain teams engaged in performance-based logistics (PBL) to develop a model of the S-D logic knowledge conversion process. Findings – The model describes individual-based decision const...

Managing Vulnerabilities of Tactical Wireless RF Network Systems: A Case Study

Organisations and individuals benefit when wireless networks are protected. After assessing the risks associated with wireless technologies, organisations can reduce the risks by applying countermeasures to address specific threats and vulnerabilities. These countermeasures include management, operational and technical controls. While these countermeasures will not prevent all penetrations and adverse events, they can be effective in reducing many of the common risks associated with wireless RF networks. Among engineers dealing with different scaled and interconnected engineering systems, such as tactical wireless RF communication systems, there is a growing need for a means of analysing complex adaptive systems. We propose a methodology based on the systematic resolution of complex issues to manage the vulnerabilities of tactical wireless RF systems. There are is a need to assemble and balance the results of any successful measure, showing how well each solution meets the systems objectives. The uncertain arguments used and other test results are combined using a form of mathematical theory for their analysis. Systems engineering thinking supports design decisions and enables decision-makers to manage and assess the support for each solution. In these circumstances, complexity management arises from the many interacting and conflicting requirements of an increasing range of possible parameters. There may not be a single ‘right’ solution, only a satisfactory set of resolutions which this system helps to facilitate. Smart and innovative performance matrixes are introduced using a mathematical Bayesian network to manage, model, calculate and analyse all the potential vulnerability paths in wireless RF networks.