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Unlocking the Value of Artificial Intelligence (AI) in Supply Chains and Logistics

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Speed in decision-making. Speed in reducing cycle-times. Speed in operations. And, speed in continuous improvement. The use of Artificial Intelligence in the supply chain is here to stay and will make huge waves in the years to come.

According to Gartner, supply chain organizations expect the level of machine automation in their supply chain processes to double in the next five years. At the same time, global spending on IIoT Platforms is predicted to grow from $1.67B in 2018 to $12.44B in 2024, attaining a 40% compound annual growth rate (CAGR) in seven years.

In today’s connected digital world, maximizing productivity by reducing uncertainties is the top priority across industries. Plus, mounting expectations of supersonic speed and operational efficiencies further underscore the need to leverage the prowess of Artificial Intelligence (AI) in supply chains and logistics.

Accelerating Supply Chain Success with AI in Supply Chains & Logistics

AI in supply chains can deliver the powerful optimization capabilities required for more accurate capacity planning, improved demand forecasting, enhanced productivity, lower supply chain costs, and greater output, all while fostering safer working conditions.

The pandemic and the subsequent disruptions has demonstrated the dramatic impact of uncertainties on supply chains and has established the need for smart contingency plans to help companies deal with these uncertainties in the right way.

But is AI the answer? What can AI mean for companies as they struggle to get their supply chain and logistics back on track? Let’s find out.

ACCURATE INVENTORY MANAGEMENT

Accurate inventory management can ensure the right flow of items in and out of a warehouse. Simply put, it can help prevent overstocking, inadequate stocking and unexpected stock-outs. But the inventory management process involves multiple inventory related variables (order processing, picking and packing) that can make the process both, time consuming and highly prone to errors.

WAREHOUSE EFFICIENCY

An efficient warehouse is an integral part of the supply chain. AI-based automation can assist in the timely retrieval of an item from a warehouse and ensure a smooth journey to the customer. AI systems can also solve several warehouse issues, more quickly and accurately than a human can, and also simplify complex procedures and speed up work. Also, along with saving valuable time, AI-driven automation efforts can significantly reduce the need for, and cost of, warehouse staff.

ENHANCED SAFETY

AI-based automated tools can ensure smarter planning and efficient warehouse management, which can, in turn, enhance worker and material safety. AI can analyze workplace safety data and inform manufacturers about any possible risks. It can record stocking parameters and update operations along with necessary feedback loops and proactive maintenance. This helps companies react swiftly and decisively to keep warehouses secure and compliant with safety standards.

REDUCED OPERATIONS COSTS

Here’s one benefit of AI systems for the supply chain that one simply can’t ignore. From customer service to the warehouse, automated intelligent operations can work error-free for a longer duration, reducing the number of human oversight-led errors and workplace incidents. Additionally, warehouse robots can provide greater speed and accuracy, achieving higher levels of productivity – all of which will reflect in reduced operations costs.

ON-TIME DELIVERY

As we discussed above, AI systems help reduce dependency on manual efforts, thus making the entire process faster, safer and smarter. This helps facilitate timely delivery to the customer as per the commitment. Automated systems accelerate traditional warehouse procedures, removing operational bottlenecks along the value chain with minimal effort to achieve delivery targets.

 

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How AI Can Solve Supply Chain Financial Management Challenges

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Never has the issue of supply chain management been so immense

In particular, Covid-19 has made these challenges all the more prominent, with unprecedented pressure on the supply chain after lockdowns and varying restrictions imposed by different countries around the world. Businesses within the supply chain must be resilient and adaptable as the combination of changes that are underway, such as increased globalisation, digitalisation, and driver and other skill shortages, have increased the industry’s complexity. While Covid-19 restrictions have eased and many countries are learning to live with the virus, the supply chain crisis isn’t going away. Political unrest has hampered the movement of products and services worldwide, notably to and from China and, more recently, Russia.

Artificial intelligence (AI) has been cited as a solution to some of the problems businesses within the supply chain. Over half (53%) of UK supply chain decision-makers believe AI advances are crucial to managing disruption. On the finance side, technologies such as AI are being used by innovative companies to better understand their capital through data analytics and performance insights so they can meet their goals through effective financial management. However, data and the overarching strategy must be in the right state to effectively utilise AI, analytics, and data science.

Top three financial management data challenges

1. Granular financial management

Calculating important metrics such as cost to serve is vital for any supply chain business. Still, it can be difficult without real-time data visibility across your service, costs, and inventory. Platforms for enterprise resource planning (ERP) and supply chain management (SCM) produce information on point of sale, inventory, manufacturing, warehousing, and transportation. You can optimise your supply chain if you know how to analyse this data, spot patterns, identify trends, and produce insights. By implementing a supply chain data strategy, you can eliminate complex supply chain issues by implementing a plan backed up by accurate financial data.

2. Data integration & data silos

The use of multiple essential applications is standard practice in logistics businesses, with typical applications including financial planning and analysis (FP&A), delivery planning, warehouse management (WMS), and order management. There are various leadership roles responsible for channels, territories, and products, although traditional monthly management accounts are aggregated at a level above these operational roles at the company P&L level.

3. Data sharing across the supply chain

Within the supply chain industry, it’s important to share data with third parties, including partners, suppliers, and customers – quickly, in as near real-time as possible – to make decisions fast.

Data and AI in action

AI can be embedded into your data platform – it enables you to use predictive analytics to get better insights into all levels of the supply chain – an improved understanding of demand fluctuations and their effect throughout the supply chain. AI data models can help deliver competitive advantage, improve financials and help businesses gain control across many areas. Implementing a big data platform is critical to get insights in real-time or daily. With so much data at hand, the platform must be scalable to ensure success.

This requires breaking down data silos, joining data across the organisation, and using modern advanced analytics in a performant, scalable, and cost-effective data platform with data governance in place.

 

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The future of supply chain management is AI and Data

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The future of supply chain management is AI and Data

The future of supply chain management is AI and Data

Because enterprises are like organisms in an economic ecosystem, the principles that enable a healthy biological ecosystem are, from a physical, chemical and informational perspective, identical to those that enable a healthy business ecosystem and that ensure the survival of members of that business ecosystem. Value is created by solving problems through the application of information and creativity. By speeding the information flows and reducing inefficiencies, we are equipping our part of the bigger picture to operate effectively, adapt quickly and evolve to meet competitive threats and exploit opportunities in the environment.

Supply chains are a crucial and complex part of the information flowing in this ecosystem. They are an intricately structured and variable system that is highly sensitive, with many possible outcomes based on even minor changes in the initial conditions or components. Supply chains feature a large collection of interacting components that are difficult to understand or examine due to their design and operations. And they represent a system in process, changing and developing over time.

It’s critical to think holistically about the information ecosystem as you prepare the digital representation of various stages of product design and development. Even a product designed in isolation from other systems and groups—whether in a specialized department or in a separate contracting organization—is still part of an information ecosystem. Information that may be inconsequential to the group that is creating the product, such as an obscure material specification that has no immediate value, will likely have value either downstream (perhaps to a distributor or engineering group) or upstream (perhaps to a procurement manager or supply chain manager).

Too often, these unseen dependencies and information relationships are neglected, and the impact of this neglect can be significant. If a piece of data that will be needed when assembling or distributing a future product is not captured, is lost or is incorrectly represented, the cost of remediation is orders of magnitude larger than that of addressing the data need at the source.

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EU Launches Estimated €400M Blockchain, AI Fund to Avoid Lagging US, China

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A new fund has been set up with the aim of preventing the EU falling behind nations like the U.S. and China on blockchain and artificial intelligence (AI) innovation.

The European Investment Fund (EIF) and the European Commission have together put up €100 million (over $110 million) for a dedicated investment scheme that will make capital available to AI and blockchain projects via VC funds or other investors, EIF, an EU agency set up to indirectly fund SMEs, said in a blog post on Wednesday.

With the “cornerstone” funding in place, the EIF said private investors are expected to bring up to €300 million ($331 million) into the fund, while the total could rise further from next year, with national promotional banks being able to co-invest under the scheme.

Sifted reports that the fund could ultimately raise up to €2 billion ($2.2 billion) under the InvestEU Programme.

According to the post, the EU already spends plenty on blockchain (expected spending for 2019 is $674 million), but that is mostly directed toward research and proof-of-concepts.

The U.S. is the biggest spender, with a $1.1 billion expected spend, and China is second with $319 million, according to cited numbers from the International Data Corporation.

The new fund is aimed to address the fact that not so much is spent in the EU on developing “larger scale projects.

“Investing in a portfolio of innovative AI and blockchain companies will help develop a dynamic EU-wide investors community on AI and blockchain. By involving national promotional banks, we can scale up the volume of investments at a national level,” the EIF said.

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Reefknot Investments launches $50 million fund to invest in logistics and supply chain startups

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Reefknot Investments launches $50 million fund to invest in logistics and supply chain startups

Reefknot Investments launches $50 million fund to invest in logistics and supply chain startups

Reefknot Investments, a joint venture between Temasek, Singapore’s sovereign fund, and global logistics company Kuehne + Nagel, announced today the launch of a $50 million fund for logistics and supply chain startups. The firm is based in Singapore, but will look for companies around the world that are raising their Series A or B rounds.

Managing director Marc Dragon tells TechCrunch that Reefknot will serve as a strategic investor in its portfolio companies, providing them with connections to partners that include EDBI, SGInnovate, Atlantic Bridge, Vertex Ventures, PSA unBoXed, Unilever Foundry and NUS Enterprise, in addition to Temasek and Kuehne + Nagel .

Dragon, a veteran of the supply chain and logistics industry, says Reefknot plans to invest in about six to eight startups. It is especially interested in companies that are using AI or deep mind tech, digital logistics and trade finance to solve problems that range from analyzing supply chain data and making forecasts to managing the risk of financing trade transactions. Data from Gartner shows that about half of global supply chain companies will use AI, advanced analytics or the Internet of Things in their operations by 2023.

“There is a high level of expectation from vendors that because of technology, there will be new methods to do analytics and planning, and greater visibility in terms of information and product, materials and goods flowing throughout the supply chain,” says Dragon.

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Scientist Ng brings AI to manufacturing

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Artificial intelligence pioneer Andrew Ng launched a new AI company Landing.ai on Thursday.

On the same day, the company announced a strategic cooperation with electronics contractor Foxconn to develop a program that aims to bring AI and machine learning technologies to the manufacturing industry.

According to Ng’s statement, his company is developing a series of programs to help enterprises transform for the age of AI, including providing new technologies to optimize companies’ organizations structures, train employees, and more. The company’s businesses will start in the manufacturing industry.

Ng said the AI technology is conductive to manufacturing enterprises to improve quality testing process, shorten products’ design cycle, remove bottleneck of supply chain, reduce waste on materials and energy and raise output.

AI will revitalize manufacturing industry and generate jobs in the industry, he said. I In the age of AI, the employees need to accept new skills training to fit jobs that will be more complex than before, Ng added.

Landing.ai will provide solutions to some employees who are likely to be laid off, Ng said. Currently, the company is discussing the training plan with some potential partners including local governments.

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