Abstract
Purpose This paper investigates the effect of the fit between supply and demand uncertainty (SDU) and supply chain responsiveness (SCR) (SC fit) on business and innovation performance in Brazilian companies. Design/methodology/approach The study presented an analysis carried out on an empirical study based on a sample of 150 manufacturing companies. Business and innovation performance of companies with different types of SC fit ( high-high and low-low fits) and misfit (positive and negative) are compared and discussed. Findings The results indicated that SC fit had a positive effect on both business and innovation performance. Further analyses suggested that companies with SC fit present similar business performance, independent of the level of SDU that characterizes the environment where they compete, while companies in environments with higher levels of uncertainty tend to present superior innovation performance. Companies with positive and negative misfit present similar performance. Originality/value An analysis of the literature showed that there is no consensus when it comes to the definitions and measurements of SC fit. The paper investigates the effects of SC fit on business and innovation performance, while previous empirical studies have mainly addressed its impact on financial performance. Moreover, this study compares the effects of two types of fit and two types of misfit and assesses SC fit in Brazilian manufacturing companies, analyzing the context of an under-researched reality.

Abstract
Omnichannel retailers are increasingly introducing subscription-based delivery services. By subscribing to this service and paying fees upfront, customers are entitled to have orders delivered to their home for a given period without paying any extra delivery charge. We analyze the resulting changes in customer behavior from two perspectives:(i) ordering behavior and (ii) delivery preferences. The model is estimated from the online transactional data of a grocery retailer and combines matching and difference in-differences approaches. We confirm that subscription customers spend more per month and purchase more frequently online than customers without subscriptions. However, this outcome is compromised by shifts towards narrower time slots in the mornings and at night, where slots are requested with less advance notice. When weighing the increased revenue and higher operational costs, we show that subscriptions have a negative impact on a retailer's incremental profit. This remains valid for a wide range of assumptions about (i) the cannibalisation of sales from the retailer's offline business, (ii) picking cost and (iii) delivery cost. To mitigate the impact of subscriptions on retailer profits, we develop a data-driven algorithm that predicts whether certain customers should receive promotions for the subscription plan, rather than it being advertised to all customers. As an extension, we also study whether the addition of a minimum order threshold to subscription plans changes consumer behaviour. We find that this introduction encourages customers to seek more variety and increase their basket size, but does not reduce their order frequency, a phenomena which may be ascribed to cross-selling.

Abstract
In the current global market, managing supply is not a straightforward process and it becomes even more complex as uncertainty and disruptions occur. In order to mitigate their impact, the selection of suppliers of strategic items should have a more holistic view of the operations in the supply chain. We propose an integrated model for supplier selection, considering inventory management and inbound transportation. We approach this problem, incorporating stochastic demand and suppliers' imperfect quality. Imperfect quality triggers additional costs, including external failure and holding costs. Supply disruptions also affect the suppliers' lead time, resulting in delivery delays. We develop a methodology to address this challenge with simulation-optimisation. A genetic algorithm determines supplier selection decisions, while inventory decisions are computed analytically. Discrete-event simulation is used to evaluate the overall performance, as well as to update the lead time dynamically, according to the disruptions. Finally, sensitivity analysis providing managerial insights reveals that criteria in supplier selection should be given a different priority depending on the characteristics of the items, and the effectiveness of disruption mitigation strategies depends on the disruption characteristics.

Abstract
More and more organizations are seeking to adopt organization and management models oriented to their key processes rather than the traditional functional orientation. However, as organizations are seeking to become more process-oriented, numerous gaps and difficulties are recognized at the level of analysis, modelling, management and improvement of processes. The issues surrounding processes are not properly understood and internalised, leading to increased difficulties in implementation and management by processes. There is thus a clear need for expertise in this area of knowledge. In response to this growing demand, in last year's we identify several universities and engineering schools incorporating specific curricular units in their teaching offer. This paper presents some education courses and specialized programs of the Faculty of Engineering of the University of Porto, specifically oriented to analysis, modelling, management and improvement of processes (engineering and business processes). Firstly, the concept of process and process thinking is presented. It will then present the approach followed in some curricular units incorporated in three Master of Science programs and also provides the design of a specialized program oriented to more experienced participants.

Abstract
When making long-term plans for their asset portfolios, decision-makers have to define a priori a maintenance budget that is to be shared among the several assets and managed throughout the planning period. During the planning period, the a priori budget is then allocated by managers to different operation and maintenance interventions ensuring the overall performance of the system. Because asset degradation is stochastic, a considerable amount of uncertainty is associated with this problem. Hence, to define a robust budget, it is essential to account for several degradation scenarios pertaining to the individual condition of each asset. This paper presents a novel mathematical formulation to tackle this problem in a heterogeneous multiasset portfolio. The proposed mathematical model was formulated as a mixed-integer programming two-stage stochastic optimization model with mean-variance constraints to minimize the number of scenarios with an insufficient budget. A Gamma process was used to model the condition of each individual asset while taking into consideration different technological features and operating conditions. We compared the solutions obtained with our model to alternative practices in a set of generated instances covering different types of multi-asset portfolios. This comparison allowed us to explore the value of modeling uncertainty and how it affects the generated solutions. The proposed approach led to gains in performance of up to 50% depending on the level of uncertainty. Furthermore, the model was validated using real-world data from a utility company working with portfolios of power transformers. The results obtained showed that the company could reduce costs by as much as 40%. Further conclusions showed that the cost-saving potential was higher in asset portfolios in worse condition and that defining a priori operation and maintenance interventions led to worse results. Finally, the results showcased how different decision-maker risk-levels affect the value of taking uncertainty into account.

Abstract
Mobility and urban planning are among the main problems that cities face in order to achieve sustainable development. The significant economic and social changes in emerging cities, as well as the urgency of environment protection, make sustainable urban mobility planning a very critical issue. Although sustainability is difficult to be measured directly, it can be assessed through a system of parameters that attempt to reflect its multiple aspects. The definition of adequate indicators constitutes an essential part of this process, even if the literature is already abundant in available metrics. This research proposes a set of indicators, grouped in different dimensions, as a tool for the assessment of sustainability in mobility systems, for the specific context of emerging cities. These dimensions and indicators are based on a comprehensive literature review on sustainable urban mobility indicators, subsequently subjected to validation with experts through surveys, semi-structured interviews and statistical analysis, in order to identify the indicators significance and their priority within the selected dimensions. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.