Towards Configurable Data Collection for Sustainable Supply Chain Communication

These days, companies in the automotive and electronics sector are forced by legal regulations and customer needs to collect a myriad of different indicators regarding sustainability of their products. However, in today's supply chains, these products are often the result of the collaboration of a large number of companies. Thus, these companies have to apply complex, cross-organizational, and potentially long-running data collection processes to gather their sustainability data. Comprising a great number of manual and automated tasks for different partners, these processes imply great variability. To support such complex data collection, we have designed a lightweight, automated approach for contextual process configuration

Towards Simple and Robust Automation of Sustainable Supply Chain Communication

In supply chains, companies are forced to produce in a more sustainable way. Amongst others, this involves the reporting of various sustainability indicators to legal entities. To gather necessary data from their suppliers, companies must employ long-running, cross-organizational data collection processes. Being dependent on many com- panies and contextual factors such processes imply great variability, are manually managed, and tend to be tedious and error prone. This paper proposes an approach for automated contextual process configuration that also supports humans with lightweight, correct modeling and exe- cution of configurable processes

Towards Process-based Composition of Activities for Collecting Data in Supply Chains

Manufacturing companies more and more face the challenge of ensuring sustainable production. In particular, they continuously need to report sustainability data about their products and manufacturing processes that is categorized by various sustainability indicators. However, in a supply chain, such data collection also involves the companies suppliers. Thus, companies must issue cross-organizational data collection processes with potentially high numbers of responders. Due to the heterogeneity in a supply chain and the necessary involvement of services from external sustainability service providers, such processes are often long-running and error-prone. In response to that, we propose an approach for automatically and contextually assembling the required activities and services and managing them by an explicitly specified and enacted process

Leveraging Cloud Computing and Software-as-a-Service to Build Sustainable and Resilient Supply Chains

Building sustainable and resilient supply chains has emerged as a strategic priority for organizations to improve their environmental, social, and governance performance while deal with unexpected disruptions, such as the COVID-19 pandemic. However, clear guidance for practitioners and analysis of the practices serving both purposes are missing. Leveraging cloud computing benefits, Software-as-a-Service (SaaS) products feature commercial and technical characteristics that help organizations resolve certain sustainable and resilient supply chain challenges by strengthening key capabilities, such as transparency, collaboration, and agility. We apply an affordance lens and a theory-generation case research design to define these challenges and identify how SaaS solutions can respond to them, using empirical qualitative data. We formulate SaaS affordances promoting the concepts of community, standard, update, data, applications, communication, and governance. We determine and illustrate the potential of SaaS solutions for sustainability and resilience for supply chain practitioners and software providers to help them unleash it

Optimization Of Strategic Planning Processes For Configurable Products: Considerations For Global Supply, Demand, And Sustainability Issues

The assortment planning problem is to decide on the set of products that a retailer or manufacturer will offer to its customers to maximize profitability. While assortment planning research has been expanding in recent years, the current models are inadequate for the needs of a configurable product manufacturer. In particular, we address assortment planning for an automobile manufacturer. We develop models to integrate assortment planning and supply chain management, designed for use by a large automaker in its strategic planning phase. Our model utilizes a multinomial logit model transformed into a mixed integer linear program through the Charnes-Cooper transformation. It is able to scale to problems that contain thousands of configurations to possibly be offered, a necessity given the number of possible configurations an automaker can build. In addition, most research in assortment planning contains simplified costs associated with product complexity. We model a full supply chain and give a rich treatment of the complexity associated with product complexity. We believe that our model can significantly aid automotive manufacturers to balance their product complexity with supply chain complexity, thus increasing profitability. In addition, we study the effect of packaging on the assortment and supply chain of an automaker. We develop a new model for mathematically expressing the effect that packaging has on the way in which customers choose products. Packaging significantly complicates the search space of the assortment planning problem. We introduce a heuristic method based on our packaging model that speeds up the solve times of the models while finding reasonably good solutions. Finally, we extend our initial model to study the effects of sustainability requirements on an automaker\u27s assortment and supply chain. We introduce constraints on the vehicle program average fuel economy, greenhouse gas emissions in the supply chain, and greenhouse gas emissions in the product use phase. We dive deep into each case to glean insights about how automakers can change their decision-making process to balance making their companies more sustainable with profit maximization. While all the examples discussed are from the automotive industry, the models developed can be adapted to address assortment planning for other types of configurable products (e.g., computers, printers, phones)

Cloud Computing for Efficient Data Storage and Processing in Maritime Logistics

Considering that the maritime logistics is by volume and financial value the largest logistics sector enabling global trade, the number of supply chains is constantly growing, as well as the number of supply chain stakeholders. Due to the large number of stakeholders in the supply chains, the volume of generated data is enormous. Therefore, it is necessary to implement modern and intelligent solutions for processing and storing the data. One possible solution for processing and storing the data in maritime logistics is cloud computing. There are different approaches to implementing cloud computing in maritime logistics. In this paper, the authors present a detailed structure of the maritime cloud and compare it with the legacy systems. Finally, the advantages and challenges arising from the implementation of cloud computing in maritime logistics are analyzed