Identifying ways to reduce waste and improve resource allocation in complex supply chains and operations is a growing challenge. Innovative technologies, such as solutions using machine learning and optimization algorithms, can help connect the dots and then identify the optimal resource management approaches that are otherwise not apparent. N-SIDE advanced analytics solutions address complicated industry challenges and help to decrease waste and allocate resources wisely.
Our world is facing increasing challenges today: our needs are steadily growing, while our resources are becoming scarcer. Our society needs to address unprecedented changes that are urgently requiring better resource management. We need to consider the limited resources our world has, and use them in the best possible way. General awareness of the scarcity of resources is growing, and daily operation or supply chain management teams across diverse industries are impacted by and need to respond to these profound changes.
In order to address these challenges, companies need to shift from a “business as usual” mindset to a more agile and innovative approach. This starts with the identification of the sources of waste and inefficiencies that have accumulated along some industrial processes in order to get rid of it and aim for a healthier, leaner, optimized resource management.
But how can we tackle this challenge? What would be agile? How can we take a holistic approach?
While the human brain is able to comprehend and process a lot of information, capturing all of the complex interactions between many relevant elements is not always easy. That’s where state-of-the-art optimization and machine learning techniques can help us face and address this challenging situation. Using mathematical tools and models can help us make links that we do not commonly see, and conceive solutions to these complex issues, which could not have been obtained otherwise.
At N-SIDE we aim to support waste reduction through advanced decision-making tools that use the latest knowledge in machine learning, stochastic simulations and optimization algorithms. The purpose of these tools is to optimize decisions for better management and allocation of resources in specific aspects of the supply chain or daily operations, including the risk assessment required in uncertain/dynamic environments.
N-SIDE is providing solutions to the seemingly different worlds of energy management and pharmaceuticals. Both of these domains actually face similar challenges of unstable supply and demand while having an intrinsic social responsibility because of the scarcity or unpredictability of the given resources.
- Managing a clinical trial supply chain is not an easy task, essentially due to the very high variability in the patient treatment demand. In addition, the recent shift towards bio-tech drugs, which are more expensive and face a shorter validity shelf life, is reinforcing the need for an optimized supply chain in order to minimize the drug waste. Efficient stocks and shipment management therefore become crucial. On the other hand, while trying to minimize the waste associated with these clinical trials, having the right amount of kits to cover patient treatment demand remains of highest concern, with zero tolerance for missing a patient dispensing visit. Risk assessment and mitigation is therefore at the core of N-SIDE’s clinical trials optimization solution: the N-SIDE Suite for Clinical Trials. Based on several stochastic mathematical methods and simulations, the N-SIDE Suite can help clinical trial supply chain managers to take the best decisions in order to connect the dots throughout the end-to-end R&D supply chain and optimize the clinical trial supply chain while maintaining the lowest risks for patients.
- The energy domain is also facing considerable evolutions, with the rise of unpredictable renewable energies which bring an increasing uncertainty in the power production and demand balance. Since electricity cannot be stored on a large scale, any difference between total generation and consumption affects the balance of the power grid, resulting in frequency deviations. Situations of high imbalance and large frequency deviations induce the protective disconnection of generation units and loads, which can in the worst case lead to a system black-out.Efficiently balancing power systems while considering the increasing uncertainty driven by this renewable generation is therefore an important challenge that needs to be addressed. More precisely, there is a need to predict system imbalance risks linked to wind and photovoltaic, as well as risks of unavailability linked to power plant or transmission line outage.N-SIDE therefore develops advanced “industrial” artificial intelligence (AI) solutions to predict and cope with the inherent uncertainty of power systems. Specifically, these techniques have been used in the recent development of a dynamic method to dimension the required reserve capacity, used to keep the power grid in balance.In this project, artificial intelligence / machine learning algorithms have been developed to forecast the system’s uncertain future behavior, thereby allowing the power system to better address these evolutions. This leads to an increased efficiency, reducing average reserve capacity, without decreasing reliability by ensuring adequate reserve capacity during high needs.
These advanced methods can thus enhance the sustainability by facilitating the integration of renewable energy.
Eventually, these seemingly different applications have a lot in common: both situations have high uncertainty and risk, which require advanced analytics algorithms to address them.
By leveraging its experience in the energy and pharmaceutical domains, N-SIDE has been able to help its clients be ahead of their game by taking optimal decisions. With advanced analytics solutions developed in several industries, N-SIDE makes it possible to decrease waste and optimize resource allocation even in the most complex supply chain and daily operations cases.
Interested to know more about N-SIDE and its solutions for resource management ?