Technology remains a strategic imperative for supply chain organizations. In a recent Gartner survey, 61% of respondents say technology is a source of competitive advantage. Many also identify several emerging technologies as critical investment areas, with 20% investing in robotics.
Many application vendors now offer AA and AI capabilities embedded within their applications and continue to expand these areas. Large megavendors and supply chain suites vendors have an advantage due to their size, which allows for more investments, but smaller vendors are catching up.
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As supply chain complexity and volatility increases, supply chain organizations must become more agile. This means that traditional applications, built around aging architectures, are not fit for the job anymore. One way to future-proof the technological base of the supply chain is to switch to microservices-based and composable application architectures.
Both digital supply chain twins and control towers help unlock quality insights needed from technology investments. Although relatively unknown, these two initiatives are closely related and should be merged. Prepare for significant value loss from underperformance and missed opportunities if these initiatives are kept separate.
If kept separate, neither initiative operates at peak performance. The digital supply chain twin is too far removed (breadth but not depth), resulting in lower-quality, end-to-end supply chain optimization. In comparison, control towers are too granular and lack the bigger picture (breadth). Their focus is then to solve the lower-quality decisions that are being executed. The result is underperformance and missed opportunities.
Streamlining the supply chain means managing multiple complex factors all at once, in real time: optimizing inventory, streamlining transportation, and delivering just in time, every time. Analysts and domain experts need to be able to make smarter decisions faster and with more confidence. Tableau Business Science is a new class of AI-powered analytics that brings the powerful capabilities of AI into the hands of business people. As part of this new toolkit, the latest Tableau extension, Einstein Discovery, allows users to leverage machine learning (ML) to build advanced analytics models via Tableau Prep or Tableau Desktop and share their insights within the secure, governed framework of the Tableau ecosystem. Join Razvan Nistor, PhD, and Adam Mico from Tableau Gold Partner, Keyrus, to learn how Business Science can help you:
Few events have brought the issue of supply chain to life like the COVID-19 pandemic. The pandemic exacerbated supply chain challenges in industries ranging from life sciences to manufacturing, and consumers had to wait longer to receive everything from computers to cars. But the pandemic is far from the only factor causing supply chain disruption in recent years. As geopolitical conflicts, labor shortages, and other disruptors continue to pop up, companies must find effective ways to respond to rapid change. Supply chain automation tools give companies new ways to cope with uncertainty and keep business processes and products moving.
Supply chain automation refers to the use of technology to handle supply chain tasks without direct human intervention. Automation comes in many forms and uses several types of technologies. For example, manufacturing plants may use physical machines and Internet of Things (IoT) devices to automate physical tasks on the shop floor. Technologies like robotic process automation (RPA) and intelligent document processing (IDP) can automate software-driven processes on the shop floor and in the back office.
Automation can also help improve supply chain agility. When a company sets up automated alerts to monitor demand signals from customers, it gains a chance to help teams adapt faster. For example, the procurement team can start ordering products and materials sooner to prevent shortages. Companies can also develop more resilient supply chains by automating notifications when suppliers may run behind schedule or lack manufacturing capacity. In these situations, the purchasing company can find alternate, temporary suppliers or choose to purchase premium freight.
Supply chain automation does not replace human workers but augments them. For instance, in the absence of widespread autonomous vehicles, the world still needs truck drivers, airline pilots, and ship crews to continue routing deliveries. Automation tools can make teams working across the supply chain more effective.
In the digital transformation age, speed and agility have become true competitive differentiators. Supply chain automation tools and strategies can help you respond efficiently to the next disruption. Ready to craft your new supply chain strategy? Keep reading to discover emerging supply chain trends, examples of what can be automated, and business benefits.
Manufacturing companies have long adopted automation tools such as industrial IoT devices to improve production. For those in the manufacturing link of a supply chain, automated technology has revolutionized shop floor operations:
Finally, expect the focus on environmental, social, and governance (ESG) to accelerate. Companies across the supply chain will need to automate collection of their carbon emissions and other environmental data so they can easily report this information to regulators and suppliers. As businesses face requirements to report on their scope-3 carbon emissions to investors and regulators, those who cannot easily report their ESG data may lose out on important deals. Organizations can get ahead of this trend by automating carbon data capture now.
During the presentation, we discussed RF-SMART's end-to end mobile supply chain solutions for Oracle Healthcare, including Healthcare Package Delivery Tracking. Using our Delivery solution for healthcare supplies deliveries, UWHealth and other RF-SMART customers are able to log each and every package as soon as it arrives on the dock and then track that package through every step of the process: logging, PO receipt, staging, and ultimately delivery. Upon delivery, photos or recipient information can be captured all with the goal of providing near real time visibility. As we discussed with attendees during the presentation, the efficiencies created with a streamline mobile delivery solution have greatly increased productivity. Data visibility in the form of the Delivery Console has also given greater range of decision making for day-to-day operations.
Abstract:From the last decade, pharmaceutical companies are facing difficulties in tracking their products during the supply chain process, allowing the counterfeiters to add their fake medicines into the market. Counterfeit drugs are analyzed as a very big challenge for the pharmaceutical industry worldwide. As indicated by the statistics, yearly business loss of around $200 billion is reported by US pharmaceutical companies due to these counterfeit drugs. These drugs may not help the patients to recover the disease but have many other dangerous side effects. According to the World Health Organization (WHO) survey report, in under-developed countries every 10th drug use by the consumers is counterfeit and has low quality. Hence, a system that can trace and track drug delivery at every phase is needed to solve the counterfeiting problem. The blockchain has the full potential to handle and track the supply chain process very efficiently. In this paper, we have proposed and implemented a novel blockchain and machine learning-based drug supply chain management and recommendation system (DSCMR). Our proposed system consists of two main modules: blockchain-based drug supply chain management and machine learning-based drug recommendation system for consumers. In the first module, the drug supply chain management system is deployed using Hyperledger fabrics which is capable of continuously monitor and track the drug delivery process in the smart pharmaceutical industry. On the other hand, the N-gram, LightGBM models are used in the machine learning module to recommend the top-rated or best medicines to the customers of the pharmaceutical industry. These models have trained on well known publicly available drug reviews dataset provided by the UCI: an open-source machine learning repository. Moreover, the machine learning module is integrated with this blockchain system with the help of the REST API. Finally, we also perform several tests to check the efficiency and usability of our proposed system.Keywords: blockchain; machine learning; drug supply chain; healthcare; smart contract; hyperledger fabrics
Abbas, K.; Afaq, M.; Ahmed Khan, T.; Song, W.-C. A Blockchain and Machine Learning-Based Drug Supply Chain Management and Recommendation System for Smart Pharmaceutical Industry. Electronics 2020, 9, 852.
Abbas K, Afaq M, Ahmed Khan T, Song W-C. A Blockchain and Machine Learning-Based Drug Supply Chain Management and Recommendation System for Smart Pharmaceutical Industry. Electronics. 2020; 9(5):852.
Abbas, Khizar, Muhammad Afaq, Talha Ahmed Khan, and Wang-Cheol Song. 2020. "A Blockchain and Machine Learning-Based Drug Supply Chain Management and Recommendation System for Smart Pharmaceutical Industry" Electronics 9, no. 5: 852.
Proper supply chain risk management enables businesses of all shapes and sizes to take advantage of tried-and-true strategies that mitigate risk and set them up for success. In order to develop your own risk management strategy, it helps to first understand what supply chain risks you might face. 2ff7e9595c
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