Utilizing people, analytics, and AI for decision making in the digitalized retail supply chain

[1]  John A. Aloysius,et al.  Demand planning for the digital supply chain: How to integrate human judgment and predictive analytics , 2023, Journal of Operations Management.

[2]  M. Klumpp,et al.  Artificial intelligence, robotics, and logistics employment: The human factor in digital logistics , 2022, Journal of Business Logistics.

[3]  Weihua Liu,et al.  Supplier participation in digital transformation of a two-echelon supply chain: Monetary and symbolic incentives , 2022, Transportation Research Part E: Logistics and Transportation Review.

[4]  Terry L. Esper,et al.  In artificial intelligence (AI) we trust: A qualitative investigation of AI technology acceptance , 2022, Journal of Business Logistics.

[5]  Brian S. Fugate,et al.  Supply chain plasticity during a global disruption: Effects of CEO and supply chain networks on operational repurposing , 2021, Journal of Business Logistics.

[6]  Spyros Makridakis,et al.  The M5 competition: Background, organization, and implementation , 2021, International Journal of Forecasting.

[7]  Sachin Modgil,et al.  Artificial intelligence for supply chain resilience: learning from Covid-19 , 2021, The International Journal of Logistics Management.

[8]  Rafay Ishfaq,et al.  Digital supply chains in omnichannel retail: A conceptual framework , 2021 .

[9]  R. Hoek.  Larger, counter-intuitive and lasting – The PSM role in responding to the COVID-19 pandemic, exploring opportunities for theoretical and actionable advances , 2021 .

[10]  Vahid Sohrabpour,et al.  Artificial intelligence in supply chain management: A systematic literature review , 2021, Journal of Business Research.

[11]  B. Gibson,et al.  Talent Management For a Post‐COVID‐19 Supply Chain—The Critical Role for Managers , 2020, Journal of Business Logistics.

[12]  E. Hartmann,et al.  Digitalization and its Impact on the Future Role of SCM Executives in Talent Management – An International Cross‐Industry Delphi Study , 2020, Journal of Business Logistics.

[13]  Z. Shen,et al.  When Triple‐A Supply Chains Meet Digitalization: The Case of JD.com's C2M Model , 2020, Production and Operations Management.

[14]  Hau L. Lee,et al.  Retailing with 3D Printing , 2020, Production and Operations Management.

[15]  Saravanan Kesavan,et al.  Field Experiment on the Profit Implications of Merchants' Discretionary Power to Override Data-Driven Decision-Making Tools , 2020, Manag. Sci..

[16]  Jordan D. Tong,et al.  Eliciting Human Judgment for Prediction Algorithms , 2020, Manag. Sci..

[17]  David G. Sirmon,et al.  Humans and Technology: Forms of Conjoined Agency in Organizations , 2020, Academy of Management Review.

[18]  Dara G. Schniederjans,et al.  Supply chain digitisation trends: An integration of knowledge management , 2020 .

[19]  Tina Blegind Jensen,et al.  A systematic review of algorithm aversion in augmented decision making , 2019, Journal of Behavioral Decision Making.

[20]  Tonya Boone,et al.  Sustainable Supply Chains in the Age of AI and Digitization: Research Challenges and Opportunities , 2019, Journal of Business Logistics.

[21]  Anníbal C. Sodero,et al.  The social process of Big Data and predictive analytics use for logistics and supply chain management , 2019, International Journal of Physical Distribution & Logistics Management.

[22]  Enno Siemsen,et al.  Integrating human judgement into quantitative forecasting methods: A review , 2019, Omega.

[23]  Stanley E. Griffis,et al.  A Simulation Model Of Multi‐Echelon Retail Inventory With Cross‐Channel Product Returns , 2019, Journal of Business Logistics.

[24]  Behnam Fahimnia,et al.  The human factor in supply chain forecasting: A systematic review , 2019, Eur. J. Oper. Res..

[25]  Jessica L. Darby,et al.  Interpretive Research , 2019, Approaches and Processes of Social Science Research.

[26]  Zach G. Zacharia,et al.  Defining Supply Chain Management: In the Past, Present, and Future , 2019, Journal of Business Logistics.

[27]  Morgan Swink,et al.  An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective , 2018 .

[28]  Alexandre Dolgui,et al.  The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics , 2018, Int. J. Prod. Res..

[29]  N. Sanders,et al.  Big Data in Supply Chain Management , 2018, Production and Operations Management.

[30]  David Simchi-Levi,et al.  Powering retailers’ digitization through analytics and automation , 2018, Int. J. Prod. Res..

[31]  Dimitris Bertsimas,et al.  Inventory Management in the Era of Big Data , 2016 .

[32]  Robert Glenn Richey,et al.  A global exploration of Big Data in the supply chain , 2016 .

[33]  Konstantinos Nikolopoulos,et al.  Supply chain forecasting: Theory, practice, their gap and the future , 2016, Eur. J. Oper. Res..

[34]  E. Brynjolfsson,et al.  The Rapid Adoption of Data-Driven Decision-Making , 2016 .

[35]  Fotios Petropoulos,et al.  Do ‘big losses’ in judgmental adjustments to statistical forecasts affect experts’ behaviour? , 2016 .

[36]  Robert Fildes,et al.  Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra- and inter-category promotional information , 2016, Eur. J. Oper. Res..

[37]  David Simchi-Levi,et al.  Analytics for an Online Retailer: Demand Forecasting and Price Optimization , 2016, Manuf. Serv. Oper. Manag..

[38]  Morgan Swink,et al.  How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management , 2015, J. Manag. Inf. Syst..

[39]  Kenneth K. Boyer,et al.  A Dyadic Investigation of Collaborative Competence, Social Capital, and Performance in Buyer–Supplier Relationships , 2015 .

[40]  T. Schoenherr,et al.  Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential , 2015 .

[41]  Terry L. Esper,et al.  Attribution Effects of Time Pressure in Retail Supply Chain Relationships: Moving From “What” to “Why” , 2014 .

[42]  Enno Siemsen,et al.  Judgmental Forecasting: Cognitive Reflection and Decision Speed , 2014 .

[43]  Dimitris Bertsimas,et al.  From Predictive to Prescriptive Analytics , 2014, Manag. Sci..

[44]  S. Fawcett,et al.  Data Science, Predictive Analytics, and Big Data: A Revolution that Will Transform Supply Chain Design and Management , 2013 .

[45]  Kevin G. Corley,et al.  Seeking Qualitative Rigor in Inductive Research , 2013 .

[46]  Lorin M. Hitt,et al.  Strength in Numbers: How Does Data-Driven Decisionmaking Affect Firm Performance? , 2011, ICIS 2011.

[47]  Robert Fildes,et al.  Incorporating demand uncertainty and forecast error in supply chain planning models , 2011, J. Oper. Res. Soc..

[48]  Özalp Özer,et al.  Trust in Forecast Information Sharing , 2009, Manag. Sci..

[49]  Daniel J. Flint,et al.  A REFINED VIEW OF GROUNDED THEORY AND ITS APPLICATION TO LOGISTICS RESEARCH , 2009 .

[50]  D. Whetten.  An Examination of the Interface between Context and Theory Applied to the Study of Chinese Organizations , 2009, Management and Organization Review.

[51]  Kenneth K. Boyer,et al.  Empirical Elephants—Why Multiple Methods are Essential to Quality Research in Operations and Supply Chain Management , 2008 .

[52]  M. Alvesson,et al.  Constructing mystery: Empirical matters in theory development , 2007 .

[53]  Larry P. Ritzman,et al.  Integrating judgmental and quantitative forecasts: methodologies for pooling marketing and operations information , 2004 .

[54]  Yossi Aviv,et al.  The Effect of Collaborative Forecasting on Supply Chain Performance , 2001, Manag. Sci..

[55]  Zach G. Zacharia,et al.  DEFINING SUPPLY CHAIN MANAGEMENT , 2001 .

[56]  A. Strauss,et al.  Grounded theory research: Procedures, canons, and evaluative criteria , 1990 .

[57]  Robert C. Blattberg,et al.  Database Models And Managerial Intuition: 50% Model + 50% Manager , 1990 .

[58]  D. Whetten.  What Constitutes a Theoretical Contribution , 1989 .

[59]  M. Swink,et al.  Digital supply chain transformation : visualizing the possibilities , 2019 .

[60]  Thomas H. Davenport,et al.  THE AI ADVANTAGE , 2018 .

[61]  C. Gray,et al.  Misconceptions, Missteps, and Bad Practices in S&OP , 2017 .

[62]  Kenneth K. Boyer,et al.  Triggers and patterns of integration initiatives in successful buyer-supplier relationships , 2014 .

[63]  Joe Peppard,et al.  Why IT Fumbles Analytics , 2013 .

[64]  R. Fildes,et al.  Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning , 2009 .

[65]  Tina Berger.  Getting back to business , 1990, Nature.