Technological Advancements Of Order Fulfillment With The Utilization Of Business Process Management: A Business Practicum Of Lowe’s Companies, Inc.

This thesis reflects the evolution of order fulfillment at Lowe’s Companies, Inc. with the aid of business process management from an intern’s perspective. In retail, the process of order fulfillment changes almost daily, which makes it very difficult to keep information systems that manage these operations up to date. However, until the recent CEO of Lowe’s Companies, Inc. was brought in, the company had failed to update its technological systems for order fulfillment in an effective way. The study presents managerial insights into the technological advancements being implemented at Lowe’s Companies, Inc., with an emphasis on the new order management system (OMS) that they are creating to support their order fulfillment. Further, recommendations are given for ways the company can continue to succeed

Enhancing in-store picking for e-grocery: an empirical-based model

Purpose: This paper identifies, configures and analyses a solution aimed at increasing the efficiency of in-store picking for e-grocers and combining the traditional store-based option with a warehouse-based logic (creating a back area dedicated to the most required online items). Design/methodology/approach: The adopted methodology is a multi-method approach combining analytical modelling and interviews with practitioners. Interviews were performed with managers, whose collaboration allowed the development and application of an empirically-grounded model, aimed to estimate the performances of the proposed picking solution in its different configurations. Various scenarios are modelled and different policies are evaluated. Findings: The proposed solution entails time benefits compared to traditional store-based picking for three main reasons: lower travel time (due to the absence of offline customers), lower retrieval time (tied to the more efficient product allocation in the back) and lower time to manage stock-outs (since there are no missing items in the back). Considering the batching policies, order picking is always outperformed by batch and zone picking, as they allow for the reduction of the average travelled distance per order. Conversely, zone picking is more efficient than batch picking when demand volumes are high. Originality/value: From an academic perspective, this work proposes a picking solution that combines the store-based and warehouse-based logics (traditionally seen as opposite/alternative choices). From a managerial perspective, it may support the definition of the picking process for traditional grocers that are offering – or aim to offer – e-commerce services to their customers

Challenges at the marketing–operations interface in omni-channel retail environments

To compete in today’s omni-channel business context, it is essential for firms to co-ordinate their activities across channels and across different stages of the customer journey and the product flow. This requires firms to adopt an integrative approach, addressing each omni-channel design decision from a dual demand-side (marketing) and supply-side (operations) perspective. However, both in practice and in academic research, such an integrative approach is still in an immature stage. In this article, a framework is developed with the following key decision areas: (i) assortment and inventory, (ii) distribution and delivery and (iii) returns. These affect both the customer journey and the product flow. As a consequence of the resulting interdependencies between the firm’s functions, addressing the issues that arise in the three decision areas requires an integrated marketing and operations perspective. For each of the areas, the key decisions that affect or involve both the customer journey and product flow are identified first. Next, for each decision, the marketing and operational goals and the tensions that arise when these goals are not perfectly aligned are described. The opportunities for relieving these tensions are also discussed and possible directions for future research aimed at addressing these tensions and opportunities are presented

Abordagem preditiva e adaptativa de gestão operacional aplicada à cadeia de suprimentos do varejo Omni-channel

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis, 2020.A evolução tecnológica e a digitalização possibilitam a comercialização de produtos através de múltiplos canais e plataformas de forma integrada, propiciando a gestão de varejo omnichannel. Esse processo contínuo de integração das tecnologias digitais/virtuais aos processos gerenciais físicos dos diversos canais influencia na interação das organizações com os clientes. O comportamento de consumo dos clientes é influenciado em decorrência do aumento da conveniência, tornando, contudo, a gestão operacional das cadeias de suprimentos do varejo mais complexa. Para a gestão da cadeia de suprimentos de varejo omni-channel a complexidade reside na incerteza, oscilações no volume de vendas e incompatibilidade entre oferta e demanda. Para lidar com essa complexidade é necessária a adoção de abordagens inovadoras relacionadas a tecnologias de informação e métodos de decisão inteligentes, destacados pela indústria 4.0. No entanto, ainda faltam pesquisas sobre a conexão entre os mundos digital e real, principalmente quando se trata de cadeias de suprimentos de varejo omni-channel, que se baseiam na integração de fluxos e atividades multicanais para melhor atender ao consumidor. Neste contexto, esta pesquisa tem como objetivo propor uma abordagem preditiva e adaptativa para a gestão operacional combinando aprendizado de máquina para minimizar a incerteza, e otimização baseada em simulação para lidar com a sincronização entre oferta e demanda, aplicada à cadeia de suprimentos do varejo omni-channel. Para isso foram identificados os métodos de aprendizado de máquina, de simulação e de otimização aplicados à cadeia de suprimentos e a indústria 4.0 com o intuito de apoiar a escolha do método de redes neurais e da otimização baseada em simulação por meio do algoritmo genético. O método de redes neurais e a otimização baseada em simulação foram analisados por meio de aplicação de um caso teste, visando identificar a aplicabilidade do método levantado na literatura, na gestão operacional da cadeia de suprimentos varejista omni-channel. Em seguida, a abordagem preditiva e adaptativa é aplicada a uma empresa varejista brasileira e como resultado um modelo de gerenciamento operacional de demanda e suprimentos é proposto para a cadeia de suprimentos varejista omnichannel. Os resultados da aplicação do modelo evidenciaram uma redução dos custos da cadeia de suprimentos, do tempo de entrega dos produtos e da quantidade de pedidos provenientes da incompatibilidade de oferta-demanda. Dessa forma, a tese possibilitou a redução das incertezas proveniente da previsão de demanda, redução da falta de produtos na cadeia, e consequentemente um melhor gerenciamento da distribuição da cadeia de suprimentos.Abstract: Technological evolution and digitalization enable the commercialization of products through multiple channels and platforms in an integrated way, providing omni-channel retail management. This ongoing process of integrating digital / virtual technologies into the physical management processes of the various channels influences the interaction of organizations with customers. Customer consumption behavior is influenced by the increase in convenience, however, making the operational management of retail supply chains more complex. For the management of the omni-channel retail supply chain the complexity lies in uncertainty, fluctuations in sales volume and incompatibility between supply and demand. To address this complexity, it is necessary to adopt innovative approaches related to information technologies and intelligent decision methods, highlighted by industry 4.0. However, there is still a lack of research on the connection between the digital and real worlds, especially when it comes to omni-channel retail supply chains, which are based on the integration of multi-channel flows and activities to better serve the consumer. In this context, this research aims to propose a predictive and adaptive approach to operational management combining machine learning to minimize uncertainty, and simulation-based optimization to deal with synchronization between supply and demand, applied to the omni-channel retail supply chain. For this, the machine learning, simulation and optimization methods applied to the supply chain and industry 4.0 were identified in order to support the choice of neural networks method and simulation-based optimization through the genetic algorithm. The neural networks method and the simulationbased optimization were analyzed by applying a test case, aiming to identify the applicability of the method raised in the literature, in the operational management of the omni-channel retail supply chain. The predictive and adaptive approach is then applied to a Brazilian retail company and as a result an operational demand and supply management model is proposed for the omnichannel retail supply chain. The results of the model application showed a reduction in the supply chain costs, in the products fulfillment time and in the quantity of orders resulting from the incompatibility of supply and demand. In this way, the thesis allowed reduce uncertainties arising from demand forecasting, reduce product shortages in the chain, and thereby better manage supply chain distribution

Relationship Between Assets, Liabilities, Earnings Before Interest and Taxes, and Financial Distress

Retail sector investors who do not interpret Altman\u27s Z´´-score accurately can underestimate a company\u27s economic viability and ability to secure debt and equity financing. Grounded in agency theory, the purpose of this quantitative correlational study was to examine the relationship between assets, liabilities, earnings before interest and taxes (EBIT), and financial distress. The data were based on financial statements from 101 U.S. public retail sector companies (U.S. Securities and Exchange Commission Standard Industrial Codes 5200 through 5990). Multiple linear regression (MLR) analysis indicated a statistically significant relationship between assets, liabilities, EBIT, and Altman\u27s Z´´-score for financial statements prepared under ASC 840, F(3,100) = 8.165, p \u3c .001, R2 = .202, and for financial statements prepared under ASC 842, F(3,100) = 3.682, p = .015, R2 = .102. A key recommendation is for business managers to apply the MLR equations\u27 coefficients to optimize their asset acquisition or earnings strategies. The implications for positive social change include the potential to enhance the financial literacy of individual investors by showing how using Altman\u27s Z´´-score can help them decide the levels of investment risk they might be willing to take

Model and management indicators in industrial omnichannel (B2B)

The COVID-19 pandemic has driven increases in the provision of services through digital channels, even by more traditional companies. An Omnichannel model of service provision poses new management challenges for companies. This research reviews the literature on Omnichannel Management by companies whose clients are other companies (B2B) and classifies the different areas of research to date. The principal finding is that, despite considerable academic interest in Omnichannel management, there have been few studies of Omnichannel in the B2B field. This emphasizes a significant research gap to address. We have also outlined the Research Agenda to highlight future lines of research

Verso lo smart retailing. Uno studio esplorativo tra domanda e offerta nel contesto italiano

Il lavoro indaga, adottando una prospettiva esplorativa, il tema dello smart retailing con particolare riferimento al contesto italiano. Lo smart retailing rappresenta oggi per le aziende la modalità principale con cui è possibile rendere le strutture aziendali omnicanali, ossia giungere alla creazione di un sistema di canali e di punti di contatto con i consumatori che siano tra di essi perfettamente integrati. Lo smart retailing consente alle aziende di conseguire questo nuovo obiettivo strategico attraverso lo strumento delle cosiddette in-store technologies, ossia tecnologie “smart” applicate nei canali fisici (punti vendita) delle aziende che consentono a queste ultime di poter interagire in maniera costante (seamless) e “ubiqua” (online e offline) con i propri consumatori. Data la novità del tema e la scarsa applicazione, ad oggi, delle in-store technologies nei punti vendita dei retailer italiani, il lavoro è stato volto ad esplorare le opinioni sia della domanda che dell’offerta in merito agli effetti dell’applicazione di in-store technologies nei punti vendita dei retailer italiani. Nel primo caso, i consumatori intervistati sono stati 46 giovani millennials in merito all’applicazione delle in-store technologies nei punti vendita monomarca dei retailer di fast fashion. Lo studio, volto a chiarire l’impatto di questi medium tecnologici sulla in-store experience e sulla store loyalty dei millennials, dimostra che le in-store technologies possono conferire numerosi benefici alle esperienze in store dei millennials, sia da un punto di vista utilitaristico (es. aumentando la facilità di reperire informazioni sui prodotti), che emozionale (es. aumentando il brand engagement) e sociale (es. invogliando i consumatori a recarsi con accompagnatori nel punto vendita per condividere la nuova esperienza di shopping “smart”). Le in-store technologies possono altresì rafforzare la fedeltà verso le marche preferite dai millennials, mentre non si rileva un effetto altrettanto forte nel caso in cui ad adottare queste tecnologie fossero retailer di cui i millennials non apprezzino il sistema d’offerta. Infine, con riferimento al lato dell’offerta, sono stati intervistati 13 esperti e consulenti di in-store technologies per il fashion retailing al fine di chiarire: 1) l’atteggiamento complessivo dei retailer di fast fashion italiani verso le in-store technologies; 2) le modifiche che dovrà subire il punto vendita in futuro per poter diventare “fattivamente” uno smart store; 3) quale possono essere le modalità di creazione di maggiore brand engagement nei millennials qualora un retailer decidesse di adottare queste tecnologie. I risultati mostrano una tendenziale resistenza dei retailer a valutare, oggi, come di valore strategico l’investimento in in-store technologies, che viene invece visto dal management in ottica per lo più tattica, di breve periodo e finalizzato al mero aumento del fatturato. In secondo luogo, la struttura organizzativa, assieme alla sua cultura, necessita di profondi ripensamenti a partire dal top management, il quale dovrà ripensare alcune figure manageriali (in particolare quelle legate all’IT e alla relazione con il cliente), introdurne di nuove (es. CDO) che consentano all’intera organizzazione di comprendere l’importanza dell’orientamento allo smart retailing e l’investimento in nuove competenze, in particolare nell’assunzione di data scientist. Il punto vendita “del futuro” sarà sottoposto a significativi cambiamenti che riguarderanno tanto i suoi aspetti strutturali (es. ridimensionamento della superficie calpestabile) quanto strategici (es. rendendolo il principale canale per svolgere comunicazione integrata di marketing). Infine, gli esperti convengono che le tecnologie mostrino un forte potenziale di creazione di maggiore coinvolgimento verso la marca nei millennials grazie alle nuove opportunità di intrattenimento, di personalizzazione dell’esperienza e dell’accresciuto livello di integrazione online-offline tra i canali e i customer touchpoints

Sharing Economy Last Mile Delivery: Three Essays Addressing Operational Challenges, Customer Expectations, and Supply Uncertainty

Last mile delivery has become a critical competitive dimension facing retail supply chains. At the same time, the emergence of sharing economy platforms has introduced unique operational challenges and benefits that enable and inhibit retailers’ last mile delivery goals. This dissertation investigates key challenges faced by crowdshipping platforms used in last mile delivery related to crowdsourced delivery drivers, driver-customer interaction, and customer expectations. We investigate the research questions of this dissertation through a multi-method design approach, complementing a rich archival dataset comprised of several million orders retrieved from a Fortune 100 retail crowdshipping platform, with scenario-based experiments. Specifically, the first study analyzes the impact of delivery task remuneration and operational characteristics that impact drivers’ pre-task, task, and post-task behaviors. We found that monetary incentives are not the sole factor influencing drivers’ behaviors. Drivers also consider the operational characteristics of the task when accepting, performing, and evaluating a delivery task. The second study examines a driver’s learning experience relative to a delivery task and the context where it takes place. Results show the positive impact of driver familiarity on delivery time performance, and that learning enhances the positive effect. Finally, the third study focuses on how delivery performance shape customers’ experience and future engagement with the retailer, examining important contingency factors in these relationships. Findings support the notion that consumers time-related expectations on the last mile delivery service influence their perceptions of the delivery performance, and their repurchase behaviors. Overall, this dissertation provides new insights in this emerging field that advance theory and practice

Sharing Economy Last Mile Delivery: Three Essays Addressing Operational Challenges, Customer Expectations, and Supply Uncertainty

Last mile delivery has become a critical competitive dimension facing retail supply chains. At the same time, the emergence of sharing economy platforms has introduced unique operational challenges and benefits that enable and inhibit retailers’ last mile delivery goals. This dissertation investigates key challenges faced by crowdshipping platforms used in last mile delivery related to crowdsourced delivery drivers, driver-customer interaction, and customer expectations. We investigate the research questions of this dissertation through a multi-method design approach, complementing a rich archival dataset comprised of several million orders retrieved from a Fortune 100 retail crowdshipping platform, with scenario-based experiments. Specifically, the first study analyzes the impact of delivery task remuneration and operational characteristics that impact drivers’ pre-task, task, and post-task behaviors. We found that monetary incentives are not the sole factor influencing drivers’ behaviors. Drivers also consider the operational characteristics of the task when accepting, performing, and evaluating a delivery task. The second study examines a driver’s learning experience relative to a delivery task and the context where it takes place. Results show the positive impact of driver familiarity on delivery time performance, and that learning enhances the positive effect. Finally, the third study focuses on how delivery performance shape customers’ experience and future engagement with the retailer, examining important contingency factors in these relationships. Findings support the notion that consumers time-related expectations on the last mile delivery service influence their perceptions of the delivery performance, and their repurchase behaviors. Overall, this dissertation provides new insights in this emerging field that advance theory and practice