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The article addresses the optimization of safety stock service levels for parts in a repair kit. The work was undertaken to assist a public transit entity that stores thousands of parts used to repair equipment acquired over many decades. Demand is intermittent, procurement lead times are long, and the total inventory investment is significant.

Demand exists for repair kits, and a repair cannot start until all required parts are available. The cost model includes holding cost to carry the part being modeled as well as shortage cost that consists of the holding cost to carry all other repair kit parts for the duration of the part’s lead time. The model combines deterministic and stochastic approaches by assuming a fixed ordering cycle with Poisson demand.

The results show that optimal service levels vary as a function of repair demand rate, part lead time, and cost of the part as a percentage of the total part cost for the repair kit. Optimal service levels are higher for inexpensive parts and lower for expensive parts, although the precise levels are impacted by repair demand and part lead time.

The proposed model can impact society by improving the operational performance and efficiency of public transit systems, by ensuring that home repair technicians will be prepared for repair tasks, and by reducing the environmental impact of electronic waste consistent with the right-to-repair movement.

The optimization model is unique because (1) it quantifies shortage cost as the cost of unnecessary holding other parts in the repair kit during the shortage time, and (2) it determines a unique service level for each part in a repair kit bases on its lead time, its unit cost, and the total cost of all parts in the repair kit. Results will be counter-intuitive for many inventory managers who would assume that more critical parts should have higher service levels.

A conflict exists between the way statistics is practiced in contemporary business environments and the way statistics is taught in schools of management. While businesses are embracing programs, such as six sigma and TQM, that bring statistical methods to the forefront of management decision making, students do not graduate with the skills to apply these methods effectively. Based on the concept of process thinking, it is argued that evolutionary rather than revolutionary changes should be made in the way statistics is taught. The difference between the process thinking approach and the classic statistical approach is illustrated using several business‐related examples.

This paper aims to use Lean Six Sigma (LSS) and consumption mapping concepts to develop a disciplined methodology for a self-directed investor so that adverse decision-making behaviors are avoided. Classical financial theories assume that individuals maximize expected utility by arriving at financial decisions in a rational manner. But, over time, investor performance has lagged behind corresponding market performance. Despite these results and research on their causes, investors continue to repeat systematic mistakes leading to suboptimal financial outcomes.

Consumption maps are developed based on behavioral finance research that shows why investors make predictable and costly errors in their decision making. The authors show that the contemporary methodologies within LSS, used successfully in the manufacturing and service sectors, can be used to enforce rationality in investing.

The approach proposed herein provides a new framework that researchers should be able to test in practice. By applying a structured, disciplined approach based on the Design-Measure-Analyze-Improve-Control (DMAIC) methodology of LSS, it is posited that the gap between financial theory and actual results can be bridged.

Rather than hoping to avoid irrational behavior through self-awareness of behavioral biases, the DMAIC approach will standardize self-directed investor decision-making so that discipline is enforced.

The purpose of this paper is to identify upcoming challenges related to the implementation of Lean Six Sigma (LSS) principles and techniques. Particular emphasis is placed on the changes needed to ensure that LSS continues to support an organization's competitiveness.

The methodology begins with a review of the projections of business experts as they relate to the practice of LSS. Then, the ASQ Six Sigma Body of Knowledge forms the basis of a reconciliation of these changes and the methods currently employed by LSS practitioners. Finally, four main modifications to standard LSS are proposed.

In total, four implications to LSS practice are defined (rigorous value definition, vigorous accounting for risk, global workforce considerations, and international regulatory concerns); specifics are offered that explain how LSS practitioners need to respond. A construct is presented illustrating how the complexity of a LSS project will be affected by the four implications.

Changes that supplement or replace LSS tools and techniques are suggested, including; a more robust methodology for value definition, expansion of the importance of risk assessment, appreciation for international regulations, and understanding how to motivate workers in various jobs, locations, and cultures. Coverage of manufacturing issues should not be sacrificed for the incorporation of these concepts.

The construct developed can be effective in a number of ways, including assisting project teams with understanding the scale of the effort needed, the key skills necessary for project team members, and the selection among a number of candidate projects.

Many organizations include benchmarking as a component of their performance management system. Often, a performance index is used to quantify the ability of an organizational entity to operate successfully. Benchmarking a performance index is done inappropriately when statistical methods are employed that ignore sample size effects or use aggregate performance data over a period during which changes occurred within the organization. Benchmarking will also be ineffective when invalid targets are employed. When benchmarking is done incorrectly, customer satisfaction may actually decline due to gaming and poor morale among employees. Based on the philosophy of W. Edwards Deming, the techniques of statistical process control (SPC), and basic undergraduate statistics, a system is described for effectively benchmarking a performance index. Examples are presented to illustrate the pitfalls that exist in many performance management systems and to explain the system presented for effective benchmarking.

To eliminate many misconceptions regarding Six Sigma and lean management by describing each system and the key concepts and techniques that underlie their implementation. This discussion is followed by a description of what lean organizations can gain from Six Sigma and what Six Sigma organizations can gain from lean management.

Comparative study of Six Sigma and lean management using available literature, critical analysis, and knowledge and professional experience of the authors.

The joint implementation of the programs will result in a lean, Six Sigma (LSS) organization, overcoming the limitations of each program when implemented in isolation. A thorough analysis of the two programs provides some likely reasons why the programs alone may fail to achieve absolute perfection.

A lean, Six Sigma (LSS) organization would capitalize on the strengths of both lean management and Six Sigma. An LSS organization would include three primary tenets of lean management, and the LSS organization would include three primary tenets of Six Sigma.

Suggestions are made regarding concepts and methods that would constitute a lean, Six Sigma organization. Figures summarize the nature of improvements that may occur in organizations that practice lean management or Six Sigma, and the corresponding improvements that an integrated program could offer.

The purpose of this paper is to attempt to provide insight into the management of an internal service system derived from a perspective of Lean management. These internal service systems would exist within professional service units of development, engineering, information technology, human resources, and consumer affairs.

A meta‐analysis of approximately 60 internal service systems was performed. The objectives of the meta‐analysis were: identification of structural similarities; categorization of wasteful activities; tabulation of typical problems; and synthesis and exploration.

Internal service systems have numerous common structural characteristics, including the importance of information, process flows across functions, many hand‐offs of information, hidden costs and benefits, and no explicit motivation for urgency. The wasteful activities can be classified into seven groups: delays, reviews, mistakes, duplication, movement, processing inefficiencies, and resource inefficiencies. The most common problems included a lack of standard procedures, long service times, communication breakdowns, and poor personnel management.

Critical insights valuable to a manager of an internal service system are: it is likely that the main service provided is information; it is likely that cross‐functional coordination is required; and it is likely that people play a critical role in the system's performance. These insights can form the basis of cross‐function cooperative improvement efforts.

The paper presents the first known large‐scale assessment of problems and opportunities in applying Lean principles to internal service systems.

A comprehensive approach to quality goal setting is presented that is consistent with Motorola's six‐sigma metric development. In particular, process mean shifts are determined and incorporated into the goal setting process. Methodology includes basic probability theory, statistical control charts, and capability indices. Data for an aircraft part consisting of hundreds of laser‐drilled holes are used to illustrate the procedures. As a result of applying the methods, quality goals are customized based on the number of opportunities for defect, the target end‐product quality level, and the mean shift shown to exist for the processes involved in the product's manufacture. The methods described overcome the mistakes implicit in blindly conforming to the standard six‐sigma goal of 3.4 defects per million.

A practical model based on basic probability theory is developed to evaluate the operational and financial performance of mammography systems. The model is intended to be used by decision makers to evaluate overall sensitivity, overall specificity, positive and negative predictive values, and expected cost. As an illustration, computer aided detection (CAD) systems that support a radiologist's diagnosis are compared with standard mammography to determine conditions that would support their use. The model's input parameters include the operational performance of mammography (with and without CAD), the age of the patient, the cost of administering the mammogram and the expected costs associated with false positive and false negative outcomes. Sensitivity analyses are presented that show the CAD system projecting financial benefit over ranges of uncertainty associated with each model parameter.