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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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5 Ways Analytics Are Disrupting Supply Chain Management

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5 Ways Analytics Are Disrupting Supply Chain Management

5 Ways Analytics Are Disrupting Supply Chain Management

Harmonizing supply chain management analytics will put organizations on a path to automating their operations.

The evolution of infotech increased customer expectations, economic behavior, and other competitive priorities have caused firms to modify themselves in the current business landscape. Supply chains globally are becoming more complex, thanks to globalization and the consistently changing dynamics of demand and supply. As per a forecast by Gartner, the global supply chain management market was valued at USD 15.85 billion in 2020 and is expected to reach almost USD 31 billion by 2026.

Businesses are channeling the power of big data analytics to disrupt transformations at all levels of supply chain management. Data started as a fundamental component of digital transformation and is now a revolutionary concept. It is the key to achieving breakthroughs in supply chain management systems, and more organizations are integrating data analytics to mine data for proactive insights and accelerate intelligent decision-making.

Big data implementation in supply chain management addresses several issues, from strategic to operational to tactical. It includes everything from building efficient communication between suppliers and manufacturers to boosting delivery times. Decision-makers can utilize analytics reports to increase operational efficiency and boost productivity by closely monitoring the system’s performance at each level.

What is Big Supply Chain Analytics, and How Does it Work?

Integrating big data analytics with the supply chain makes big supply chain analytics enable business executives to compute better growth decisions for all possible maneuvers by combining data and quantitative methodologies. Notably, it adds two features.

First, it broadens the dataset for analysis beyond internal data stored in existing SCM and ERP systems. Second, it uses advanced statistical techniques to analyze the new and existing data. This generates new insights that help make better decisions for improving front-line operations and strategic decisions like implementing the best supply chain models.

Here are five ways big data and analytics are disrupting supply chain management:

1) Improved demand forecasting

Demand forecasting is one of the crucial steps in building a successful supply chain strategy. With data science and analytics in play, businesses experience automated demand forecasting. This assists them in quickly responding to fluctuations in the market and streamlining the optimal stock levels every time.

2) Enhanced production efficiency

Data science and analytics play a significant role in gauging organizational performance. Accurate application of big data analytics can help organizations track, analyze, and share employee performance metrics in real time. You can identify excellent employees who are struggling to maintain a consistent performance. This could be quickly done with IoT-enabled work badges, which exchange information with sensors installed in production line units.

3) Better sourcing and supplier management

Supply chain management systems have empowered organizations to collate data on multiple suppliers. Using data science solutions, you can leverage this data to gain insights into the historical record of any supplier. With this, you can gauge based on crucial metrics such as compliance, location, reviews, feedback, services, etc.

4) Better warehouse management

Warehouses are acquiring modern technology and have started installing sensors to collect data on the inventory flow. This helps you build an extensive database containing information based on the weight and dimensions of the packages. With sensors installed in your warehouse, you can identify bottlenecks that obstruct the flow and can be easily resolved at the earliest with the big data-fueled systems.

5) Improved distribution and logistics

Order fulfillment and traceability are essential for business productivity and customer satisfaction. Logistics have traditionally been cost-focused and effectively look for ways that provide them competitive advantages. Data science solutions enable logistic providers to leverage data analytics to improve their operations. For instance, they use fuel consumption analytics to improve driving efficiency. With GPS technology, they can track real-time routing of deliveries and reduce long waiting times by allocating nearby warehouses.

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DHL Supply Chain introduces first digital twin of warehouse in Asia for Tetra Pak

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The market leader in contract logistics, DHL Supply Chain, is introducing its first digital twin of a warehouse in the Asia-Pacific region for Tetra Pak with one goal in mind: optimised, agile and cost-efficient supply chains.

The warehouse is one of the biggest Tetra Pak warehouses worldwide and remains the first smart warehouse for DHL in the Asia-Pacific region that exists as a digital twin.

Having launched an integrated supply chain for Tetra Pak in Singapore, the digital twin is supplied with real-time data on a consistent basis from the physical warehouse in Singapore and makes changes consistently in real-time.

“The joint implementation of such a digital solution to improve Tetra Pak’s warehousing and transport activities is an excellent example of the smart warehouses of the future,” said Jerome Gillet, CEO, DHL Supply Chain Singapore, Malaysia, Philippines. “This enables agile, cost-effective and scalable supply chain operations.”

DHL Supply Chain is focusing on technologies and processes such as physical objects like industrial trucks kitted out with IoT technology. The DHL Control Tower tracks incoming and outgoing goods to ensure all goods are stored in the correct way within 30 minutes of receipt.

Tetra Pak has developed a smart storage solution that tracks and simulates the physical condition and individual stock levels in real-time, allows smooth non-stop coordination of operations, makes faults visible as well as improves safety and productivity in the warehouse.

DHL Supply Chain Singapore has in-depth expertise in the region in achieving individual customer needs, the firm provides Third-Party Logistics (3PL) solutions in which customers can outsource their logistics management and operations.

“We expect the partnership with DHL Supply Chain to further increase our productivity and maintain high standards in our supply chains,” commented Devraj Kumar, Director, Integrated Logistics, South Asia, East Asia & Oceania, Tetra Pak.

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How Humans and Robots Will Work Side-by-Side in the Supply Chain

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Humans and robots can work in harmony to create a safer, more efficient working world, here’s what that world might look like.

Robots and Humans Working Together
In Robots in the Supply Chain: The Perfect Employee? Merril Douglas paints a picture of a time in the near future when robots and humans will work side-by-side to help companies gain speed, increase accuracy, cut costs, and handle the grunt work.

“We’re sitting in the middle of a perfect storm for robots in the supply chain. E-commerce sales continue to climb, forcing retailers to pick up the pace in their fulfillment and distribution centers,” Douglas writes.

“But these days, it’s hard to find workers to keep product moving in any kind of warehouse e-commerce or otherwise.”

We’re already seeing examples of robots being designed to take over the supply chain’s least attractive tasks. “In some cases, robotic systems do this work entirely on their own, freeing humans for more complex functions,” Douglas points out.

“In other instances, bots collaborate with humans. Whatever the scenario, proponents say that these automated solutions provide a big productivity boost.”

Some companies are deploying robots to perform repetitive, simple job tasks and allowing human laborers to focus on tasks that require deeper thinking and strategizing.

The new term for this collaboration, “cobot,” allows each type of worker to focus on the tasks they do best.

For example, bots can be used to deliver products from place-to-place in the warehouse, DC, or yard; autonomous drones can perform mundane and repetitive inventory management tasks (as well as tasks that are dangerous for humans, such as flying up to view inventory on high shelves); and robots can lift shelving units from densely-packed storage areas and then transport those goods to a picking station.

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One-Page Data Warehouse Development Steps

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Data warehouse is the basis of Business Intelligence (BI). It not only provides the data storage of your production data but also provides the basis of the business intelligence you need. Almost all of the books today have very elaborated and detailed steps to develop a data warehouse. However, none of them is able to address the steps in a single page. Here, based on my experience in data warehouse and BI, I summarize these steps in a page. These steps give you a clear road map and a very easy plan to follow to develop your data warehouse.

Step 1. De-Normalization. Extract an area of your production data into a “staging” table containing all data you need for future reporting and analytics. This step includes the standard ETL (extraction, transformation, and loading) process.

Step 2. Normalization. Normalize the staging table into “dimension” and “fact” tables. The data in the staging table can be disposed after this step. The resulting “dimension” and “fact” tables would form the basis of the “star” schema in your data warehouse. These data would support your basic reporting and analytics.

Step 3. Aggregation. Aggregate the fact tables into advanced fact tables with statistics and summarized data for advanced reporting and analytics. The data in the basic fact table can then be purged, if they are older than a year.

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Data Lake vs Data Warehouse: Key Differences

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Some of us have been hearing more about the data lake, especially during the last six months. There are those that tell us the data lake is just a reincarnation of the data warehouse—in the spirit of “been there, done that.” Others have focused on how much better this “shiny, new” data lake is, while others are standing on the shoreline screaming, “Don’t go in! It’s not a lake—it’s a swamp!”

All kidding aside, the commonality I see between the two is that they are both data storage repositories. That’s it. But I’m getting ahead of myself. Let’s first define data lake to make sure we’re all on the same page. James Dixon, the founder and CTO of Pentaho, has been credited with coming up with the term. This is how he describes a data lake:

“If you think of a datamart as a store of bottled water – cleansed and packaged and structured for easy consumption – the data lake is a large body of water in a more natural state. The contents of the data lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.”

And earlier this year, my colleague, Anne Buff, and I participated in an online debate about the data lake. My rally cry was #GOdatalakeGO, while Anne insisted on #NOdatalakeNO. Here’s the definition we used during our debate:

“A data lake is a storage repository that holds a vast amount of raw data in its native format, including structured, semi-structured, and unstructured data. The data structure and requirements are not defined until the data is needed.”

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