AI Chatbots for Banking

Gramener is a design-led data science company that solves complex business problems with compelling data stories using insights and a low-code platform, Gramex. We helped a German manufacturing company integrate a robust data roadmap aligned with its business objectives by identifying analytics investment opportunities & performing data maturity assessments, saving up to $30 Mn. This means you will be able to reduce fuel costs, streamline routes, and get products out faster so that they arrive at their destination sooner with less wear on the vehicles themselves. When customers receive their orders sooner than expected, they are more likely to give positive reviews and recommend your brand to others. Another disadvantage of AI in the supply chain is that it can’t always account for human error or unpredictability.

You can follow the below-discussed practices on AI and analytics to minimize the supply chain disruption and make the most out of your business. Improving the efficiency of the supply chain plays a crucial role in any enterprise. Operating their businesses within tough profit margins, any kind of process improvements can have a great impact on the AI Use Cases for Supply Chain Optimization bottom line profit. Machine Learning techniques have allowed the company to build a seamlessly integrated supply chain system enabling them to capture data in a real-time and analyse the same. Further, the company’s robust supply chain utilises proactive and early warning systems to assist them in mitigating the risk and quick query resolution.

Supply Chain Trends to Watch for in 2022

Those that have embraced artificial intelligence have reported reduced costs, improved productivity, and more controllable margins or error. In the logistics industry, even a minute saved can accrue significant cumulative benefits. Hence, the need to understand the operational use of artificial intelligence for competitiveness. Cloud computing and big data offer opportunities for many businesses across the globe. The challenge is in identifying those aspects of operations that could benefit from artificial intelligence.

Chances are good that you’ll need to bring in personnel to fill new roles in your organization, so you’ll need a plan for identifying and recruiting those people. You may also need to train existing employees and ensure they understand how their responsibilities and workflows will change during and after implementation. Artificial intelligence is becoming essential to bringing these strategic transformations to life. As a matter of fact, 95% of the highest-performing organizations see AI as a cornerstone of their supply chain success.

Machine Learning in Supply Chain Case Study

In Mendix, Aiden created a pipeline that collects data via SAP connectors and more simple REST APIs and performs different data transformation steps. As a result, the different data sources are automatically grouped together and each new entry in the data is prepared for further use by the optimization model without manual interference. It may vary depending on for example the number of trucks, ordered goods and loading stations. The analytics model we implemented must be able optimize the total loading time of all trucks through the warehouse. Ecosystem partners such as technology vendors and consulting firms also can be great sources of important skills, supplying talent who can augment a company’s existing employees where needed.

Even the proliferation of technology in business is only in the earliest stages. Warehouse workers of the future will be increasingly equipped with augmented reality tools, such as smart glasses that enable hands-free order picking. This also has enormous potential for improving warehouse efficiency, as illustrated in a recent pilot developed by Ricoh and DHL. If you want to learn more about how to build Mendix applications powered by analytics code or machine learning, please reach out to us. In this case we used Mendix to rapidly unlock data from the source systems, transform it and complement it.

The future of intelligent, self-driving supply chain networks

As the corporate world becomes increasingly more globalized, it is not uncommon for a company to move a product through multiple locations before it lands in a customer’s hand. Connect your ecommerce shopping cart or upload your own sales data to NetworkVu for a free network analysis, delivered to your inbox within minutes. Demand planning and scenario mapping are more important than ever for companies looking to build a more resilient supply chain. According to McKinsey, we can expect a disruption to the supply chain lasting more than one month every 3.7 years. Provide permissioned trading partners with an immutable shared record of real-time, security-rich digital transactions powered by IBM Blockchain.

• With AI driven decision making, business can gain unprecedented speed and scale its business amid the continuous market shifts.
• The supply chain is a diverse and complex domain and manufacturing industries must align with its workflow to remain competitive.
• Algorithms predicting demand and supply after studying various factors enable early planning and stocking accordingly.
• For example, forecasting the decline and end-of-life of a product accurately on a sales channel, along with the growth of the market introduction of a new product, is easily achievable.
• Most business leaders know this, and they assume that they don’t have enough data to make an AI investment worthwhile.
• Artificial Intelligence enables a machine to respond in real-time to a challenge, request, or question in the way that a human would.

Innovative technologies like machine learning makes it easier to deal with challenges of volatility and forecasting demand accurately in global supply chains. Gartner predicts that at least 50% of global companies in supply chain operations would be using AI and ML related transformational technologies by 2023. This is a testament to the growing popularity of machine learning in supply chain industry. Machine learning models and workflows do this by analysing historical data from varied sources followed by discovering interconnections between the processes along the supply value chain.

How is AI Used in Supply Chain?

Machine learning in supply chain can also be used to detect issues in the supply chain even before they disrupt the business. Make data-driven decisions based on data gathered from traffic conditions, weather and other external factors to manage your fleet. With relevant input, fleet managers have accurate data insights to pick the most optimal routes to get fleets to their destinations on time. Combining ML with data collected by IoT devices and sensors onboard fleets, fleet operators have the ability to make changes to routes in real-time.