Researchers have developed a new way to model how inventory behaves when both customer demand and supplier deliveries are unpredictable, and when missed sales cannot be recovered. The approach provides more accurate estimates than common industry rules-of-thumb, and so might help businesses avoid costly overstocking and damaging shortages.

The work, discussed in the International Journal of Integrated Supply Management, builds on the Economic Order Quantity (EOQ) model. This is a well-known tool for helping stock controllers decide how much to order. The standard EOQ assumes that demand is steady and deliveries arrive on time. The new model is more realistic and recognises that demand and supply can vary from day to day.

In their model, each day's demand and supply are treated as a series of simple "yes or no" events: a unit is either sold or not, and a unit is either delivered or not. In probability terms, these are called Bernoulli trials. When combined over several days, familiar statistical patterns emerge in the distribution for demand and the geometric distribution for delivery times. This approach allows the model to capture both steady daily sales and highly irregular, demand.

The researchers were able to calculate an exact "steady-state" picture of how much stock a business is likely to have on hand in the long run, after short-term fluctuations even out. To do this, they used a Markov chain, a type of mathematical model in which the next step depends only on the current state, not the full history.

From this steady-state analysis, the model gives precise numbers for important measures: the average inventory level, how long an inventory cycle lasts, how often the business runs out of stock, and the "fill rate". Fill rate is the share of customer demand that can be met immediately from stock. One key result is a new formula for average inventory that has been shown to work better than the current estimation tools, especially when demand is patchy and deliveries are unreliable.

The researchers explain that this matters in many real-world situations where lost sales are permanent. A supermarket cannot sell yesterday's spoiled fruit, a retailer cannot ship a promotional item after the promotion ends, and a factory may lose an urgent order for spare parts if they are not in stock at the moment they are needed. In all such cases, even small errors in estimating average inventory can have significant financial and reputational costs.

Gebennini, E., Grassi, A. and Santillo, L.C. (2025) 'Discrete-time EOQ with lost sales, binomial demand, and geometric lead time: inventory level distribution and performance analysis', Int. J. Integrated Supply Management, Vol. 18, No. 5, pp.1–35.
DOI: 10.1504/IJISM.2025.148011

Research in the International Journal of Hydrology Science and Technology has shown that conventional approaches to measuring water storage across Europe's complex river systems may significantly under-represent the scale and severity of changes linked to climate change.

The Earth's gravitational field is not uniform, it changes slightly depending on the presence of mountains, where the oceans, are even levels of groundwater. Indeed, when large amounts of water move through heavy rainfall, melting glaciers, or groundwater depletion, they change the local gravitational field by a small amount. Conventionally, these changes have been detected by a technique known as satellite gravimetry. NASA's GRACE (Gravity Recovery and Climate Experiment) and GRACE-FO (Follow-On, its successor), use satellites flying in tandem to measure the tiny gravitational changes. In turn, the technique can then be used to monitor changes in natural water storage in a region, such as Europe.

However, the new research has compared traditional and data-driven filtering techniques used to interpret data from GRACE-FO. The researchers found that newer, model-independent approaches offer more accurate results, particularly in capturing droughts and floods in Europe's complex system of rivers.

The main issue with the old approach is that the data from the satellites has low spatial resolution and is affected by signal interference from neighbouring regions. This latter issue, known as leakage, makes interpreting the raw data a technical challenge. To reduce noise and clear up the signals, researchers apply mathematical filters, such as Gaussian smoothing. The new study shows that this conventional method can introduce errors in regions with irregular geography, such as coastlines and densely interlaced river basins, characteristics common across the continent of Europe.

The researchers have evaluated two data-driven techniques: the "method of scale" and the "method of deviation," which use only the satellite data rather than putatively biased external hydrological models. They showed that method of scale reduce measurement uncertainty to less than 15%.

This improvement has practical implications for understanding three European river basins, the Rhone in France, the Neman on the Belarus-Lithuania border, and the Vuoksi-Neva in Finland and Russia. The new approach provides a clearer and more precise view of how water levels respond to extreme weather events.

Europe is already experiencing more frequent and severe droughts, with disproportionate effects on its smaller river basins. These catchments are limited in water storage and often highly sensitive to climatic fluctuations, but nevertheless form the backbone of many regional water systems. More accurate monitoring is critical to policymakers attempting to respond to changes.

Lenczuk, A. (2025) 'Impact of spatial filtering to GRACE-FO-derived TWS changes: a case study of European river basins', Int. J. Hydrology Science and Technology, Vol. 20, No. 5, pp.1-30.
DOI: 10.1504/IJHST.2025.147953

Research in the International Journal of Shipping and Transport Logistics has analysed four decades of freight transportation data across the USA and shows how different modes of transport contribute to carbon dioxide emissions. The work reveals a complex picture wherein transportation is indeed a major source of greenhouse gases, alongside electricity generation, but air, rail, road, water, and pipeline make different contributions to the problem.

The researchers used an Autoregressive Distributed Lag model to consider both short-term and long-term relationships between transportation activity and emissions. This allowed them to smooth out the information even when the underlying data are uneven. Such a level of detail is uncommon in climate and transport studies, which often focus on a narrower time frame or fewer transport categories.

Surprisingly, a negative relationship between both air and pipeline transport and carbon emissions over the period 1980 to 2022 showed that even as these two forms of freight activity increased, emissions actually declined slightly: by 0.03% for every 1% rise in air transport activity, and by 0.06% for pipeline transport. Such figures seem small, but they could be statistically meaningful when scaled across the vast transport systems of the USA and over several decades. The findings suggest that adoption of cleaner technologies, especially in aviation, is having an effect. Innovation in the road and rail sectors could reap similar rewards, the research suggests.

The various interdependencies suggest that emissions, energy consumption, and transport activity are all so closely intertwined that coordinated policy responses is now essential rather than isolated reforms if we are to achieve net-zero.

The researchers suggest that by breaking down the environmental impact of individual transport modes, it is possible to develop more targeted climate strategies. For example, expanding pipeline infrastructure or accelerating the rollout of sustainable aviation technology may deliver greater emissions reductions than blanket policies applied across all transport sectors.

Ergen, H., Aslan, A. and Ayvaz, E.E. (2025) 'Can air transportation reach to zero carbon emissions: comparative econometric analysis between transportation modes in the USA', Int. J. Shipping and Transport Logistics, Vol. 21, No. 1, pp.71–99.
DOI: 10.1504/IJSTL.2025.147885

Research conducted at a Chinese university and reported in the International Journal of Computational Systems Engineering has looked at how machine learning and big data techniques might be used to identify the most influential factors in English language learning for non-English majors. The researchers analysed the academic progress of 1,805 students using a refined machine learning model and found that student motivation was the single most important driver of improvement, outweighing even the teaching methods or mode of instruction.

The findings emerged from an analysis using an advanced decision tree algorithm, an enhanced CHAID (Chi-squared Automatic Interaction Detector) decision tree coupled with a genetic algorithm that filters out irrelevant data and has improved predictive accuracy.

A decision tree is a machine learning model that maps the relationships between variables in a branching format. The CHAID variant is particularly suited to education research, as it handles categorical variables well, including learning environments and teaching styles. By enhancing the CHAID algorithm with genetic programming, the researchers were able to evolve the decision model iteratively, improving its ability to identify key patterns in the student data.

The primary metric analysed was the percentage of students making notable progress. Just under 20 percent of students in the sample, 352 individuals, met this threshold. The model was then tasked with identifying what differentiated these students from the majority.

The answer, the researchers found, lay first and foremost in student motivation. Whether driven by career ambitions, academic goals, or personal interest, a student's reason for studying English had the strongest correlation with measurable improvement. Teaching methods, ranging from interactive approaches to more traditional lecture formats, ranked second in influence, followed by the mode of instruction, whether face-to-face or online learning.

English continues to serve as a bridge language in academia, commerce, and international dialogue, so effective English instruction is a priority in educational institutions around the world. The study provides a new insight into how teaching might be improved, specifically in China, but perhaps elsewhere too.

Cai, H. (2025) 'A study on the optimisation of university English teaching based on an enhanced decision tree model in the context of big data', Int. J. Computational Systems Engineering, Vol. 9, No. 12, pp.1–11.
DOI: 10.1504/IJCSYSE.2025.147788

Research in the International Journal of Continuing Engineering Education and Life-Long Learning has looked at why some students persist with Massive Open Online Courses (MOOCs) while many others quietly drop out. It is an issue that has long dogged the promise of online education.

MOOCs have been with us since the late 2000s. Initially, they were touted as the means to democratize education by making high-quality university-level content available globally, often for free. They have to some extent fulfilled that early promise and countless MOOCs have since given access to millions of learners, often in remote, underserved, or economically constrained contexts. Fundamentally, MOOCs allow students to learn at their own pace and on their own terms. Yet despite the obvious putative reach and their appeal, MOOCs continue to suffer from notably low retention and completion rates. There is a gap between the initial enthusiasm of new students and their ongoing engagement or otherwise.

In the current work, the team has turned to a statistical technique known as Fuzzy Set Qualitative Comparative Analysis (FSQCA) to look for multiple causes of MOOC dropouts. FSQCA can be used to examine how combinations of conditions work to produce a given outcome. In the case of MOOCs, the approach has looked at how different complex and context-dependent factors lead to students persisting or failing to complete a course.

Five interrelated factors are thought to affect outcomes. First, the quality of the online system, its reliability and usability. Second, the content quality of the course itself, the learner's engagement, referred to as learning presence. Third, the influence of other students or mentors. Fifth, self-efficacy, the students' belief in their ability to succeed. No single factor could be used to reliably predict whether a student would stick with a course or drop out.

The FSQCA approach found three distinct configurations, or "recipes" in the jargon of the system, that affected outcomes. For example, one recipe recognised combined strong content quality, high self-efficacy, and positive social influence. Another emphasized system usability and learner engagement. Each recipe suggests that course retention depends on various factors, either in or out of balance.

The findings have implications for how MOOCs are developed and improved. Platform providers, universities, and instructional designers must think more holistically about their potential students. Improving technical infrastructure or offering well-produced video lectures alone will not lead to better retention. Instead, interventions must be multifaceted, they must build technically user-friendly platforms, but also foster support communities, enhance instructional design, and assist students in building confidence over time.

Chen, J. and Chen, G. (2025) 'College students' willingness to continue using MOOC platform: configuration analysis based on FSQCA method', Int. J. Continuing Engineering Education and Life-Long Learning, Vol. 35, No. 7, pp.1–18.
DOI: 10.1504/IJCEELL.2025.147807

A new approach to sniffing out user behaviour on social networks could improve how service providers understand and respond to their users' needs. The work discussed in the International Journal of Computational Systems Engineering offers a more accurate way to track "browsing trajectories", the path a user takes through a sequence of pages and content. Critically, the approach cleans up this data by filtering out the digital noise that has always been the bane of such analyses.

Social networks generate huge amounts of data, much of it ambiguous, inconsistent or irrelevant. For researchers and developers trying to spot patterns in this activity, for example, those running content-recommendation systems or monitoring user wellbeing, this is a significant problem. Current systems struggle to distinguish between signal and noise, which means any analysis leads to less effective personalisation and an increased risk of information overload for users.

The new approach integrates several computational tools, including "fuzzy logic", which can handle uncertainty in a way that binary logic cannot. Fuzzy logic can thus model ambiguity in human behaviour online. A second tool is the use of a "random forest learning algorithm", which can handle large and complex datasets and offer several decision trees by combining outputs to improve how well the system predicts behaviour. The third tool is "matched filtering", a technique borrowed from signal processing, that detects patterns within noisy data.

When combined, these tools allowed the researchers to isolate meaningful user behaviour among irrelevant or redundant data and so boost the signal-to-noise ratio. In subsequent simulations, the team was able to achieve up to 93 percent behaviour-prediction accuracy. Such precise analysis of browsing trajectories might help social platforms serve content that more closely matches what users are actually looking for, increasing satisfaction while reducing exposure to irrelevant or disruptive material.

Cai, Q. (2025) 'Social network user browsing trajectory detection based on soft computing to promote a healthy environment', Int. J. Computational Systems Engineering, Vol. 9, No. 12, pp.12–21.
DOI: 10.1504/IJCSYSE.2025.147790

As international companies face increasingly volatile conditions around the world especially in the face of rapid technological change, a study in the European Journal of International Management has looked at how they might systematically understand the way in which digital technologies are reshaping business strategies.

The research introduces the concept of Digital International Value Creation, a model that explains how businesses can use their digital capabilities to create added value in their international operations. Far beyond improving back-end efficiency, digitalisation, defined here as the transformation of an organisation's products, services, and processes into internet-compatible formats, is now an integral component of corporate activity.

Information and communication technology has, of course, supported international firms for many years by enabling more coordinated and efficient global operations. However, the current study argues that recent digital advances represent a structural shift. The new tools are creating new modes of international expansion, particularly for firms that were once limited by their geographic or organisational constraints.

The researchers outline three pathways already being taken by companies. The first involves enhancing existing operations with digital tools. The second considers how digital-native firms have been expanding globally from the outset. This involves historically parochial firms now using digital platforms to enter global markets for the first time. The research shows that whichever path is being taken, digital adoption alone is insufficient for ongoing global success. Rather, success depends heavily on how well firms align their technological strategies with their internal structures and the external environments in which they operate. The researchers add that political stability, legal frameworks, and cultural compatibility can all affect how well digital tools perform in different regions.

The team suggests that companies need to re-imagine digitalisation not as a supporting function but as a central component of their international strategy. This perspective is especially pertinent for small and medium-sized enterprises (SMEs), which often face significant barriers to digital adoption but also stand to benefit from the agility and reach these technologies offer.

Halpern, C. and Fernández-Méndez, L. (2025) 'The role of digitalisation in firms' international value creation: an integrative conceptual framework and a research agenda', European J. International Management, Vol. 27, No. 5, pp.1–42.
DOI: 10.1504/EJIM.2025.147769

Research in the International Journal of Value Chain Management sheds light on a blind spot in how society is tackling complex problems such as climate change, inequality, and food insecurity. There is, the work found, an underrepresentation of small and medium-sized enterprises (SMEs) in public innovation strategies. The researchers focused on the Uppsala region of Sweden and investigated why SMEs, which are often key sources of innovation and adaptability, struggle to engage with the kind of mission-orientated innovation that could help us address the big challenges we face.

Such challenges are large-scale, interlinked societal issues that cannot be solved by a single actor or sector. So, mission-oriented innovation, a framework being used increasingly by governments and institutions worldwide, breaks up the big challenges into smaller measurable missions. Rather than pursuing incremental technological development or narrowly defined market goals, mission-oriented innovation brings together public authorities, the private sector, and civil society in pursuit of transformative objectives. Such missions might include achieving carbon neutrality in city transport or eliminating food waste in national supply chains.

This research suggests that SMEs have all but been forgotten in defining mission-oriented innovation and yet the participation of SMEs, because of their size and flexibility, could be massively beneficial. Through interviews with the owners of SMEs and public sector representatives, the researchers found that there are three structural barriers to the inclusion of SMEs in mission-oriented innovation.

The first is a lack of awareness among many SMEs. This could be address through more effective communication from government bodies and coordinating agencies. Secondly, SMEs face constraints related to resource allocation. With a smaller workforce and tight budgets, these businesses often cannot afford the time nor the investment to participate in longer-term innovation missions. Public actors could solve this by prioritizing appropriate funding mechanisms or institutional support. Thirdly, there are domain differences. These are cultural and operational mismatches between the public and private sectors. Public institutions and SMEs often approach problems with different timelines, risk tolerances, and definitions of success, making sustained cooperation difficult.

As such, there is an urgent need to put in place mechanisms to bridge the gap between SMEs and public missions so that governments can bring a part of the economy into plans for the systemic change needed to address big issues.

Strömqvist, J., Hilletofth, P., Muhos, M., Saarela, M. and Virkkala, P. (2025) 'Barriers of SME engagement in mission-oriented innovation', Int. J. Value Chain Management, Vol. 15, No. 5, pp.1–23.
DOI: 10.1504/IJVCM.2025.147750

As digital technologies continue to transform the logistics industry, research in the International Journal of Logistics Systems and Management has looked at their impact on the wellbeing of professional drivers, namely truckers. The researchers conducted in-depth interviews and drew in survey data in Germany to look at his digital tools are reshaping the technical aspects of the job but also the lived experience of those behind the wheel.

Drivers report that digitalisation, ranging from GPS tracking to automated route planning, can reduce physical demands, streamline communications, and even contribute to improved safety. Systems designed to optimise routes and schedules, for example, reduce stress by minimising delays and helping drivers make more informed decisions in real time. In some cases, these innovations enhance the driver's sense of control, contributing to greater overall job satisfaction.

However, the research also shows that there are some drawbacks. Many drivers express unease about the level of surveillance enabled by digital tools. There is constant monitoring, whether through location tracking, in-cab cameras, or real-time performance metrics, and they often feel this to be intrusive and to erode their sense of independence. Given that independence is often central to the identity of professional drivers, this is not a positive outcome of the adoption of digital technologies. Indeed, some interviewees suggested that it was akin to being "micromanaged by a machine" and this was detrimental to their job satisfaction and psychological wellbeing.

The study identifies driver perceptions as a crucial determinant of how digitalisation is experienced. Those who see digital tools as supportive tend to report greater benefits, such as feeling safer or more efficient. In contrast, drivers who are more sceptical of these technologies are likely to experience them as a source of pressure or even alienation. This divide underscores the importance of the human factor in digital transitions: technological change is not experienced uniformly, and its impact depends heavily on individual attitudes and workplace culture.

For logistics companies, the implications are particularly will be difficult to navigate. Their drivers play a critical role in keeping supply chains functioning, yet their voices are often underrepresented in discussions about innovation. The research argues for a more inclusive approach to digital transformation, one that takes into account not just productivity and profit, but also encompasses driver wellbeing.

Straub, S.M. and Ruiner, C. (2025) 'For better or for worse? How the use of digital technologies affects truck drivers' wellbeing', Int. J. Logistics Systems and Management, Vol. 51, No. 3, pp.420–449.
DOI: 10.1504/IJLSM.2025.147556

As carbon neutrality become an increasingly urgent issue, the challenge of managing increasingly complex and renewable-heavy power systems remains. Work in the International Journal of Reasoning-based Intelligent Systems looks at power system optimization and presents a new approach to reducing both emissions and operating costs using artificial intelligence, AI.

The context for this innovation is the dual carbon goal, a policy framework adopted by many countries aiming to reach carbon neutrality sooner, rather than later. As electricity grids evolve from centralized, fossil-fuel-based structures to more decentralized systems dominated by intermittent renewable sources like wind and solar, they become harder to control in real-time. Grid operators must now balance not only power supply and demand, but also the economic cost of power generation and its environmental impact.

The researchers have used a refined version of a computational technique known as Particle Swarm Optimization (PSO). Originally inspired by the coordinated movement of animals, PSO seeks out optimal solutions from complex systems by simulating swarming behaviour. The researchers have extended PSO with a more advanced variant called Multi-Strategy Adaptive PSO (MAPSO), which incorporates a dynamic "reward mechanism" to improve how the AI searches for optimal outcomes. The researchers have then taken this yet another step forward, with MOMAPSO (Multi-Objective MAPSO). It operates within a dual-objective framework that simultaneously minimizes fuel costs and pollutant emissions, while respecting essential operational constraints such as power balance and generation limits.

Traditional mathematical optimization methods, while theoretically accurate, often struggle under the weight of modern grid complexity. They tend to be computationally demanding and less effective at navigating trade-offs between competing objectives. Heuristic algorithms like PSO and the extended version MAPSO, by contrast, offer a more flexible and efficient alternative.

Yao, L. (2025) 'Optimisation of operation algorithms based on artificial intelligence in power system control', Int. J. Reasoning-based Intelligent Systems, Vol. 17, No. 9, pp.34–43.
DOI: 10.1504/IJRIS.2025.147656