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Demand forecasting has long been an imperative tenet in production planning especially in a make-to-order environment where a typical manufacturer has to balance the issues of holding excessive safety stocks and experiencing possible stockout. Many studies provide pragmatic paradigms to generate demand forecasts (mainly based on smoothing forecasting models.) At the same time, artificial neural networks (ANNs) have been emerging as alternatives. In this chapter, we propose a two-stage forecasting approach, which combines the strengths of a neural network with a more conventional exponential smoothing model. In the first stage of this approach, a smoothing model estimates the series of demand forecasts. In the second stage, general regression neural network (GRNN) is applied to learn and then correct the errors of estimates. Our empirical study evaluates the use of different static and dynamic smoothing models and calibrates their synergies with GRNN. Various statistical tests are performed to compare the performances of the two-stage models (with error correction by neural network) and those of the original single-stage models (without error-correction by neural network). Comparisons with the single-stage GRNN are also included. Statistical results show that neural network correction leads to improvements to the forecasts made by all examined smoothing models and can outperform the single-stage GRNN in most cases. Relative performances at different levels of demand lumpiness are also examined.

Product success is contingent on forecasting when a product is needed and how it should be offered. Forecasting accuracy is contingent on the correct forecasting technique. Using supermarket data across two product categories, this chapter shows that using a bevy of forecasting methods improves forecasting accuracy. Accuracy is measured by the mean absolute percentage error. The optimal methods for one consumer goods product may be different than for another. The best model varied from sophisticated, most such as autoregressive integrated moving average (ARIMA) and Holt–Winters to a random walk model. Forecasters must be proficient in multiple statistical techniques since the best technique varies within a categories, variety, and product size.

This chapter assesses the operating units within electronic shopping stores with regard to their productivity. The methodology used to measure the productivity is data envelopment analysis (DEA). Two different linear programming model formulations of the DEA model are used. In the first linear programming model, the weights are applied to the inputs, with the outputs remaining the same. In the second model, weights are applied to the inputs, but the outputs are different.

Purpose: This research investigates the effects of the coronavirus pandemic on Portuguese and Brazilian consumers’ vulnerability in contrasting age groups. It seeks to establish the relationship between fear of COVID-19 and the pandemic’s impact on customer’s vulnerability to help companies design strategies to cope with this new market context and be prepared to address these vulnerabilities in a future international health crisis.

Design/Methodology/Approach: This study employs a quantitative research method to assess the different impacts of the COVID-19 pandemic on consumer vulnerability. Based on Portuguese and Brazilian residents’ surveys, an age/country-segmented cross-cultural multi-group analysis was performed to understand the differences in vulnerability.

Findings: Outcomes proved how the pandemic aggravates distinctively the vulnerability dimensions of consumers living in Portugal and Brazil. Besides, results highlight significant differences in consumers’ vulnerability during the pandemic given their age group. A positive correlation between age and fear of COVID-19 was also verified.

Value: Results were obtained based on consumers’ perceptions and scores rather than postulated behaviors. The findings highlight the need for health prevention measures to avoid neglecting existing vulnerable groups, whilst verifying how COVID-19 has managed to proliferate consumers’ vulnerability. Suggestions are drawn for both firms and governments based on obtained results and existing literature. Exemplar business strategies to avoid these vulnerabilities are put forward and discussed. The potential business advantages of firms shaping their activity according to their customers’ current vulnerabilities, during international pandemics, are also pointed.

• Customers expect retail companies to adapt their strategies to their behaviour, so they should use different channels and devices in interaction;

• Multichannel and omnichannel strategies represent two distinct approaches to managing relationships with the customers;

• The key to developing a successful omnichannel strategy is to integrate channels and touch points that will create a frictionless experience for the customer;

• Introducing omnichannel interaction requires organizational and IT changes within the company.

Customers expect retail companies to adapt their strategies to their behaviour, so they should use different channels and devices in interaction;

Multichannel and omnichannel strategies represent two distinct approaches to managing relationships with the customers;

The key to developing a successful omnichannel strategy is to integrate channels and touch points that will create a frictionless experience for the customer;

Introducing omnichannel interaction requires organizational and IT changes within the company.

With the holistic approaches of Industry 4.0, products, services, standards, and application techniques have been improved. This digitalization era has not only impacted the production and service dynamics, but also added advanced dimensions to logistics and supply chain management. According to the current world standards, consumer behavior makes the logistics and supply chain processes more challenging. Especially during the COVID-19 outbreak, logistics and supply chain operations became more crucial for the firms, as most consumers have tended toward online shopping while they are in lockdown. Therefore, the competitive environment today enables firms to adapt the technologies and approaches of Logistics 4.0 and smart/digital supply chain, as they must respond to consumers' demands quickly. Moreover, firms need to have strong relations with their supply chain partners via these technologies. The technologies such as the Internet of Things (IOT), cyber-physical system, Big Data, and cloud computing help to change the fundamentals of logistics and supply chain and improve processes for all industries. This study aims to analyze the transformation of traditional logistics and supply chain activities into Logistics 4.0 and smart/digital supply chain. Primarily, we hope to analyze the existing studies by investigating the concept of Logistics 4.0 within Industry 4.0 dynamics. As firms develop their logistics operations, their supply chain processes will be shaped by the technologies and applications, and this situation also leads us to find out the importance of smart or digital supply chain operations. Discussing the potentials of smart or digital supply chain also lets us to reveal how companies handle their logistics operations during the COVID-19 period.

Any career is marked by luck, both good and bad, as well as by hard work interspersed by times of uncertainty, fits and starts, and learning from one's mistakes and successes. But beyond these outcomes and actions, I owe an enormous debt to people who have shaped me and made life the challenging and rewarding journey it is. My family of origin and extended family were incredibly supportive in personal and functional ways. So many mentors and teachers influenced what I know and who I am. Many students, colleagues, secretaries, computer and library staff, and group chairs and deans provided the help, inspiration, and friendship guiding my career behind the scenes. My wife, son, and daughter sustained me through times of tears and joy, as did my community of faith. All these relationships were foundational to any contributions I may have made to attitudes, social action, and theory of mind; methodology, statistics, and philosophical foundations of research; sales force, organization, and health behaviors; emotions, ethics, and moral behavior; and marketing and managerial practice. For me, my career contributions are secondary to the relationships within which I was fortunate to engage.