What is supply chain optimization?

In an age of increased scrutiny over corporate social responsibility, artificial intelligence can play a role in improving health and safety. The machines that are deemed to be too dangerous for workers can be handled automatically using AI. The AI may be used to report unrecognized safety hazards promptly so that a contingency plan can be activated. Despite the magnificent achievements of artificial intelligence, there is always room for growth.

• This would save the US for-hire trucking industry between $85–125 billion each year.
• Often, most of a company’s data is collected for compliance purposes or use during audits.
• Data analytics can deliver actionable insights incredibly quickly, and even if executives aren’t familiar with techniques, machine learning & predictive analytics can drive insights from data streams within weeks, not years.
• As supply chain companies shift their focus from products to outcomes, traditional business models will become dated and then obsolete altogether, with the bodies and brands of the laggards and losers scattered along the way.
• And to enhance your supply chain visibility, check out our data-driven list of Supply Chain Visibility Software.
• In general, ML & AI help organizations to automate & improve their inventory decision processes at scale, saving you time & money.

A subset of AI, ML represents automated learning of implicit properties or underlying rules of data. ML “trains’” or learns and refines an algorithm to perform a task better until the machine gains a new capability. Large amounts of data -including previously unseen datasets are processed for ‘“inference.”’ ML models trained to infer, are used to analyze trends, spot anomalies, and derive insights. Insights from the self-taught ML intelligence are used to make business decisions and even change strategies if the feedback warrants it. In fact, ML makes authoritative recommendations based on past, present, and future data trends.

AI/ML Applications in SCM

Mosaic built a bespoke solution powered by AI that automatically generates a sales forecast and recommends a production plan to meet this demand for a quick service restaurant managing thousands of locations across the Globe. The proposed application detects people, objects and vehicles using depth-only information, achieving remarkable levels of accuracy and robustness. Our goal is to provide an easily scalable and cost-efficient solution, that can be implemented in a wide variety of real-life scenarios.

AI can help with this process by automating many mundane tasks and leaving room for companies to focus on the bigger picture. As artificial intelligence improves supply chain management, businesses will be able to run safer, more efficient operations and better compete in today’s economy. It also allows businesses to respond intuitively with real-time information about their inventory levels and stock location.

Bad Customer Experiences

Along with rising fuel costs and labor shortages, fleet managers constantly face data overload issues. Managing a large fleet can easily seem like a daunting task more akin to an air traffic controller. If you can’t find the information you need quickly, or properly utilize the data you collect, you may find your data pool quickly turning into an unproductive swamp. AI in supply chain and logistics provides real-time tracking mechanisms to gain timely insights including the optimal times by where, when, and how deliveries must and should be made. Such powerful multi-dimensional data analytics further aides in reducing unplanned fleet downtime, optimizing fuel efficiencies, detecting and avoiding bottlenecks. It provides fleet managers with the intelligent armor to battle against the otherwise unrelenting fleet management issues that occur on a daily basis.

• Companies that can put data at the core of their supply chain and apply AI at scale can create a connected and truly intelligent supply chain network.
• Having a view into when, where, and why bottlenecks occur can transform a company’s workflows and radically improve a supply chain company’s profitability.
• This shift requires designing experiences that merge an understanding of human behavior with large-scale automation, machine learning development and data integration.
• 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.
• “If the data is imprecise or incomplete, the tool will not be able to produce useful results, following the well-known garbage-in garbage-out principle,” Rigonat warns.
• Make data-driven decisions based on data gathered from traffic conditions, weather and other external factors to manage your fleet.

If you can’t compel teams to work together and share important business information as a matter of course, you might not be ready. When faced with a pandemic like COVID-19, establishing a good understanding of the impact on supply chains and contingency plans can help manufacturing companies deal with uncertainties in the right way. Due to the complexity and the multifaceted nature AI Use Cases for Supply Chain Optimization of the supply chains, all of your expectations could hardly be met by a single vendor. So, don’t be afraid to examine what the supply chain technology market has to offer and integrate the optimum offerings into a solution that addresses your specific needs. Another piece of advice is going for a vendor-agnostic integrator, so you prevent technology and solution lock-in.

Use cases of AI in Supply Chain

Rising customer expectations mean supply chains must innovate and optimize every step of the way to meet those needs. Streamlining the supply chain is a priority for organizations that want to continue to meet and exceed customer expectations. There is much more that can be achieved through the strategic use of artificial intelligence.

• The pandemic has pushed risk management to the top of every corporate agenda.
• In sum, this assessment requires a combination of meticulous planning at the personnel and application levels, and big-picture thinking about the state of the entire enterprise.
• With relevant input, fleet managers have accurate data insights to pick the most optimal routes to get fleets to their destinations on time.
• This can impact business efficiency as supply chain partners will need to work with the AI providers to create a training solution that is impactful yet affordable during the integration phase.
• Autonomous delivery robots and drones are being used for last-mile delivery, slashing costs, reducing the traffic burden on roads and improving delivery times.
• Despite recognizing the power and value of data and AI, companies will likely continue to find it difficult to leverage their investments more broadly.

Gramener is a leading provider of AI-powered supply chain solutions that revolutionize the way businesses work. These insights can come from the data generated through supply chain operations. It can also help predict where demand will be highest and ensure that no single location gets overloaded with shipments or runs out of stock for too long. A digital supply chain is a complex, interconnected web of business activities, which is automated and managed by several stakeholders.

AI for cost-saving and revenue boost in supply chain

Robots that are driven by artificial intelligence are often used to overcome known health and safety traps. AI and advanced analytics can process massive and diverse data sets from all functions to provide better visibility across the supply chain. But with more data sources, more computational power and more server capacity will be needed. With thecloud, a company can connect this data to create one single and trusted source of truth.

It is no secret that reactive pricing has peaked, and we are now officially in the age of predictive commerce. Organizations are expected to help their consumers (B2B and B2C!) find products in their precise moment of need, and potentially even before they perceive that need. This shift requires designing experiences that merge an understanding of human behavior with large-scale automation, machine learning development and data integration.