Basics of qualitative research: Grounded theory procedures and techniques

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

计量经济分析 = Econometric analysis

The 2008 crash has left all the established economic doctrines - equilibrium models, real business cycles, disequilibria models - in disarray. Part of the problem is due to Smith’s "veil of ignorance": individuals unknowingly pursue society’s interest and, as a result, have no clue as to the macroeconomic effects of their actions: witness the Keynes and Leontief multipliers, the concept of value added, fiat money, Engel’s law and technical progress, to name but a few of the macrofoundations of microeconomics. A good viewpoint to take bearings anew lies in comparing the post-Great Depression institutions with those emerging from Thatcher and Reagan’s economic policies: deregulation, exogenous vs. endoge- nous money, shadow banking vs. Volcker’s Rule. Very simply, the banks, whose lending determined deposits after Roosevelt, and were a public service became private enterprises whose deposits determine lending. These underlay the great moderation preceding 2006, and the subsequent crash.

The Economic Institutions of Capitalism

http://files.ferraz8.webnode.com/200000065-e27a6e46de/Artigo2.pdf

Lean manufacturing: context, practice bundles, and performance

The contemporary quality management (QM) literature prescribes various quality improvement strategies. However, it lacks scientifically developed and tested constructs that represent an integrative QM philosophy. Moreover, an impact of the prescribed QM strategies on a firm's product quality has not been analyzed. Through a detailed analysis of the literature, this research identifies 12 constructs of integrated QM strategies. Using a survey of 371 manufacturing firms, the constructs are then empirically tested and validated. LISREL 7 is used for this purpose. Finally, a framework to examine the effects of integrated QM strategies on a firm's product quality is suggested. Comparisons between this and two other comprehensive scales of TQM are made.

Development and Validation of TQM Implementation Constructs

We illuminate the myriad of opportunities for research where supply chain management intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of a SCM data scientist. Such knowledge is crucial to developing future supply chain leaders. We propose definitions of data science and predictive analytics as applied to supply chain management. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of supply chain management and DPB.

Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management

We illuminate the myriad of opportunities for research where supply chain management (SCM) intersects with data science, predictive analytics, and big data, collectively referred to as DPB. We show that these terms are not only becoming popular but are also relevant to supply chain research and education. Data science requires both domain knowledge and a broad set of quantitative skills, but there is a dearth of literature on the topic and many questions. We call for research on skills that are needed by SCM data scientists and discuss how such skills and domain knowledge affect the effectiveness of an SCM data scientist. Such knowledge is crucial to develop future supply chain leaders. We propose definitions of data science and predictive analytics as applied to SCM. We examine possible applications of DPB in practice and provide examples of research questions from these applications, as well as examples of research questions employing DPB that stem from management theories. Finally, we propose specific steps interested researchers can take to respond to our call for research on the intersection of SCM and DPB.

Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management

Latent variables in business logistics research: scale development and validation /

Vendor Managed Inventory (VMI) involves the vendor making the replenishment decision for products supplied to a customer based on various inventory and supply chain policies. Information sharing between supply chain members is required in VMI. Sometimes VMI decisions are delayed and/or the information shared is inaccurate. This research examines the effects of information delay and accuracy, and the sharing of sales and forecast information in a VMI environment facing stationary and nonstationary demand. The simulation experiments show the impact of information delay, information inaccuracy, and information sharing on a variety of performance measures, including inventory levels and fill rates.

Matthew A. Waller

Papers

Development and Validation of TQM Implementation Constructs

Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management

Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management

Latent variables in business logistics research: scale development and validation /

Supply chain information sharing in a vendor managed inventory partnership