The use of AI for reverse logistics management enables your business to address issues related to waste management and environmental sustainability. Reverse logistics is a part of a bigger concept known as the circular economy, which seeks to tackle problems such as waste reduction, pollution, biodiversity loss and climate change. For building sustainability, governing bodies around the world create product take-back laws, which hold producers financially accountable for factors related to waste recycling, handling of perishable goods and the production of circular products. Managing these activities effectively offers a far greater challenge in reverse logistics for your business. The use of new approaches and tools like AI for reverse logistics can help in overcoming the challenges associated with reverse logistics.

Big data has many applications in the digital marketing field. Jas Saran, the CEO of G Web Pro Marketing Inc., has reported on some of the numerous benefits of using big data in digital marketing. Every business owner and marketer will agree that their primary goal is to reach more customers. More customers means more sales, and more sales means business growth and success. Big data has become incredibly necessary for reaching these goals.

We consider an NP-hard selective and periodic inventory routing problem (SPIRP) in a waste vegetable oil collection environment. This SPIRP arises in the context of reverse logistics where a biodiesel company has daily requirements of oil to be used as raw material in its production process. These requirements can be fulfilled by using the available inventory, collecting waste vegetable oil or purchasing virgin oil. The problem consists in determining a period (cyclic) planning for the collection and purchasing of oil such that the total collection, inventory and purchasing costs are minimized, while meeting the company's oil requirements and all the operational constraints. We propose a MIP-based heuristic which solves a relaxed model without routing, constructs routes taking into account the relaxation's solution and then improves these routes by solving the capacitated vehicle routing problem associated to each period. Following this approach, an a posteriori performance guarantee is ensured, as the approach provides both a lower bound and a feasible solution. The performed computational experiments show that the MIP-based heuristic is very fast and effective as it is able to encounter near optimal solutions with low gaps within seconds, improving several of the best known results using just a fraction of the time spent by a state-of-the-art heuristic. A remarkable fact is that the proposed MIP-based heuristic improves over the best known results for all the large instances available in the literature.

Soft Robotics Inc., a pioneer in robotic grasping, announced today that it has raised $23 million in an oversubscribed Series B funding round. The round was co-led by Calibrate Ventures and Material Impact and included existing investors Honeywell, Hyperplane, Scale, Tekfen Ventures, and Yamaha. FANUC Corp., the world's largest industrial robot manufacturer, joined this round as a new investor in Soft Robotics. Soft Robotics previously announced a strategic partnership with FANUC to integrate Soft Robotics' mGrip adaptable gripper system with any FANUC robot through the deployment of a new controller. The combined product was introduced at IREX in Tokyo in December 2019.

According to Forrester Research, the adoption of e-commerce solutions is accelerating, driven by the advantages offered by omnichannel strategies. So much, in fact, that Forrester is recommending business-to-business (B2B) and business-to-consumer (B2C) businesses embrace an omnichannel strategy -- one that offers several significant benefits to both retailers and wholesalers alike. In essence, omnichannel adoption can tear down silos and reduce operational overhead while bringing more opportunities to businesses looking to sell products and services. However, omnichannel solutions are still plagued by supply chain inefficiencies that can reduce value and hamper productivity. Take, for example, the returns management process, where defined methodologies for handling customer returns have become high-touch processes that can introduce delays, inefficiencies and mistakes -- all of which can impact the bottom line while also affecting the relationship with the customer.