Predicting Data Quality Success - The Bullwhip Effect in Data Quality

Over the last years many data quality initiatives and suggestions report how to improve and sustain data quality. However, almost all data quality projects and suggestions focus on the assessment and one-time quality improvement, especially, suggestions rarely include how to sustain the continuous data quality improvement. Inspired by the work related to variability in supply chains, also known as the Bullwhip effect, this paper aims to suggest how to sustain data quality improvements and investigate the effects of delays in reporting data quality indicators. Furthermore, we propose that a data quality prediction model can be used as one of countermeasures to reduce the Data Quality Bullwhip Effect. Based on a real-world case study, this paper makes an attempt to show how to reduce this effect. Our results indicate that data quality success is a critical practice, and predicting data quality improvements can be used to decrease the variability of the data quality index in a long run

Think Exogenous to Excel: Alternative Supply Chain Data to Improve Transparency and Decisions

Efficient decisions along the supply chain have traditionally demanded sophisticated information sharing processes. Even with decades of research on theoretical and practical developments on integrating systems and stakeholders, in practice, we still seem to struggle to achieve full transparency and mitigate inefficiency challenges. We explore the emerging sentiment analysis technique to augment sales and operations planning (S&OP) with currently unavailable exogenous information. Even though sentiment analysis has gained traction, a comprehensive application in supply chains has not yet been attempted. Relevant topics are reviewed to allow an examination of the key relationships in a process framework, grounded in dual-process and bullwhip effect theory. Our proposed conceptual framework extends our conception of sentiment analysis integration to improve supply chain decisions and performance. The framework addresses managers interested in developing additional analytical capabilities and researchers to initiate further empirical research on the potential held by sentiment analysis in supply chain research

Strategies to Minimize the Bullwhip Effect in the Electronic Component Supply Chain

Supply chain leaders in the information technology industry face challenges regarding their ability to mitigate amplified demand and supply variability in a supply chain network--the bullwhip effect--and reduce adverse implications on their component supply chain networks. The purpose of this multiple case study was to explore the strategies supply chain leaders in the United States used to reduce the bullwhip effect. Bullwhip effect theory served as the conceptual framework. Participants in the study were 5 purposefully selected supply chain leaders in the state of Texas who successfully implemented strategies to reduce the bullwhip effect on their networks. Data were collected from semistructured interviews and analysis of documents from the participants\u27 websites. The data were analyzed using the 5 data analysis steps consistent with Yin\u27s approach: collection, stratification, reassembly, interpretation, and conclusion. Four themes emerged from data analysis: (a) collaboration strategy, (b) communication strategy, (c) component shortage reduction strategy, and (d) resource management strategy. Supply chain leaders might use the findings of this study to reduce the bullwhip effect within their networks and improve their profitability. The implications for positive social change include the potential for leaders to improve environmental sustainability by using effective supply chain strategies to reduce the accumulation of excess inventories, reduce transportation fuel usage, and lessen the consumption of natural resources

A Mediated Impacts Model of Demand Volatility on Inventory Flow Integration in Supply Chains

We develop a theoretical model about how organizations cope with the bullwhip effect created by consumer demand uncertainty through product modularity and information sharing across the supply chain. Unpredictability of consumer demand is likely to accentuate inventory flows in the supply chain. Information sharing and product modularity can be used by organizations to mediate the impact of uncertain product demand on inventory flow integration. An organization’s success in coping with the bullwhip effect is reflected in the degree to which inventory flows are integrated across the supply chain. Our results suggest that (1) information sharing is essential for achieving integration of inventory flows irrespective of the demand environment, and (2) the strategy of modular product design can help organizations enhance inventory flows under conditions of consumer demand uncertainty

Risk assessment and relationship management: practical approach to supply chain risk management

The literature suggests the need for incorporating the risk construct into the measurement of organisational performance, although few examples are available as to how this might be undertaken in relation to supply chains. A conceptual framework for the development of performance and risk management within the supply chain is evolved from the literature and empirical evidence. The twin levels of dyadic performance/risk management and the management of a portfolio of performance/risks is addressed, employing Agency Theory to guide the analysis. The empirical evidence relates to the downstream management of dealerships by a large multinational organisation. Propositions are derived from the analysis relating to the issues and mechanisms that may be employed to effectively manage a portfolio of supply chain performance and risks

Expanding Sales and Operations Planning using Sentiment Analysis: Demand and Sales Clarity from Social Media

We outline the use of sentiment analysis as a tool for demand planning in sales and operations planning (S&OP). First, we explain how S&OP functions and the reliance on cooperation or collaboration with other firms to gain information. We introduce sentiment analysis and show its value in determining marketplace-changes which feed into supply chains. We show how sentiment analysis supports data acquisition independent of other firms in the supply chain; incorporated into S&OP, these data can support preparation for changing requirements. While demonstrated in marketing, this concept remains unproven in supply chain research. We believe this is the first assertion and examination of how sentiment analysis can support effective S&OP but further empirical research is required to validate this concept

Advances in Supply Chain Management: Potential to Improve Forecasting Accuracy

Forecasting is a necessity almost in any operation. However, the tools of forecasting are still primitive in view of the great strides made by research and the increasing abundance of data made possible by automatic identification technologies, such as, radio frequency identification (RFID). The relationship of various parameters that may change and impact decisions are so abundant that any credible attempt to drive meaningful associations are in demand to deliver the value from acquired data. This paper proposes some modifications to adapt an advanced forecasting technique (GARCH) with the aim to develop it as a decision support tool applicable to a wide variety of operations including supply chain management. We have made an attempt to coalesce a few different ideas toward a “solutions” approach aimed to model volatility and in the process, perhaps, better manage risk. It is possible that industry, governments, corporations, businesses, security organizations, consulting firms and academics with deep knowledge in one or more fields, may spend the next few decades striving to synthesize one or more models of effective modus operandi to combine these ideas with other emerging concepts, tools, technologies and standards to collectively better understand, analyze and respond to uncertainty. However, the inclination to reject deep rooted ideas based on inconclusive results from pilot projects is a detrimental trend and begs to ask the question whether one can aspire to build an elephant using mouse as a model

Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews

This study aims to investigate the contributions of online promotional marketing and online reviews as predictors of consumer product demands. Using electronic data from Amazon.com, we attempt to predict if online review variables such as valence and volume of reviews, the number of positive and negative reviews, and online promotional marketing variables such as discounts and free deliveries, can influence the demand of electronic products in Amazon.com. A Big Data architecture was developed and Node.JS agents were deployed for scraping the Amazon.com pages using asynchronous Input/Output calls. The completed Web crawling and scraping data-sets were then preprocessed for Neural Network analysis. Our results showed that variables from both online reviews and promotional marketing strategies are important predictors of product demands. Variables in online reviews in general were better predictors as compared to online marketing promotional variables. This study provides important implications for practitioners as they can better understand how online reviews and online promotional marketing can influence product demands. Our empirical contributions include the design of a Big Data architecture that incorporate Neural Network analysis which can used as a platform for future researchers to investigate how Big Data can be used to understand and predict online consumer product demands

Data Science in Supply Chain Management: Data-Related Influences on Demand Planning

Data-driven decisions have become an important aspect of supply chain management. Demand planners are tasked with analyzing volumes of data that are being collected at a torrential pace from myriad sources in order to translate them into actionable business intelligence. In particular, demand volatilities and planning are vital for effective and efficient decisions. Yet, the accuracy of these metrics is dependent on the proper specification and parameterization of models and measurements. Thus, demand planners need to step away from a black box approach to supply chain data science. Utilizing paired weekly point-of-sale (POS) and order data collected at retail distribution centers, this dissertation attempts to resolve three conflicts in supply chain data science. First, a hierarchical linear model is used to empirically investigate the conflicting observation of the magnitude and prevalence of demand distortion in supply chains. Results corroborate with the theoretical literature and find that data aggregation obscure the true underlying magnitude of demand distortion while seasonality dampens it. Second, a quasi-experiment in forecasting is performed to analyze the effect of temporal aggregation on forecast accuracy using two different sources of demand signals. Results suggest that while temporal aggregation can be used to mitigate demand distortion\u27s harmful effect on forecast accuracy in lieu of shared downstream demand signal, its overall effect is governed by the autocorrelation factor of the forecast input. Lastly, a demand forecast competition is used to investigate the complex interaction among demand distortion, signal and characteristics on seasonal forecasting model selection as well as accuracy. The third essay finds that demand distortion and demand characteristics are important drivers for both signal and model selection. In particular, contrary to conventional wisdom, the multiplicative seasonal model is often outperformed by the additive model. Altogether, this dissertation advances both theory and practice in data science in supply chain management by peeking into the black box to identify several levers that managers may control to improve demand planning. Having greater awareness over model and parameter specifications offers greater control over their influence on statistical outcomes and data-driven decision