Dematic Sets the Benchmark With Landmark Group's Premier DC
Dematic seems to be leading the way in warehouse automation, with the announcement of the completion of an automated distribution centre for , one of the largest retail and hospitality organisations in the Middle East, Africa and India. Dematic is an intralogistics innovation-forward company that designs, builds and supports intelligent automated solutions for warehouse and distribution centres and manufacturing facilities. In automating Landmark Group’s Premier Distribution Center, Dematic sets a logistical benchmark for the Middle East market.
The distribution centre is located in Dubai, UAE, and allows the multinational retailer to consolidate its logistics for part of its five existing manual distribution sites.
Located in the Jebel Ali Free Zone (JAFZA), the distribution centre is near one of the largest container ports in the world, DP WORLD Jebel Ali Port. The Landmark Group uses the location to store and distribute such items as furniture, garments, toys and small goods to nearly 1,400 of its retail stores and onto end consumers.
From this location, the Landmark Group stores and distributes garments, furniture, toys, small goods and more to nearly 1,400 of its retail stores and thereafter directly to end consumers.
The 265,000 sqm site holds a 43-meter high pallet warehouse with a silo design and contains up to 36,000 storage locations for received goods. The high-bay warehouse is climate-regulated to store temperature-sensitive items and can even hold highly flammable goods thanks to its fire prevention system with an oxygen-reduced environment.
The Dematic Multishuttle system handles faster-moving goods with a patented Inter-Aisle Transfer feature that maximises space with aisle-spanning exchanges and double-deep storage. With several lifts per aisle and conductor rail-controlled shuttles for high-performance acceleration and speed, one shuttle can serve between 700 and 800 storage locations. As a result, up to 15,000 totes per hour can be transported to the picking stations, making it the largest and fastest Dematic has ever installed.
Using the Dematic GOH system, the facility can accommodate up to 2 million garments and is capable of achieving a throughput rate of up to 250,000 items per day. An 11-kilometre Dematic conveyor system for containers and pallets and a Dematic sortation system completes the operation.
What They Have to Say About It
"The automation solution designed by Dematic allows our supply chain network to now operate with enhanced efficiency, productivity and transparency. With this investment, we are advancing technological progress and taking a pioneering position in our region," said Mihin Shah, Chief Supply Chain Officer of Landmark Group.
"Landmark presented us with an opportunity to go beyond consolidating their fulfilment operations to becoming a partner in transforming their business," said Hasan Dandashly, Dematic President and CEO. "They have experienced remarkable growth in a short amount of time to become one of the largest retailers in the Middle East, Africa, and India, supplying over 2,300 businesses in 24 countries. We take pride in being the kind of resource that Landmark would trust to streamline their operations both to meet current demand and prepare for future success."
"Automation offers scalability and speed while at the same time improving work safety," Shah said.
Landmark Group says that as a result of the consolidated and automated operation, Landmark's B2B and B2C customers can now depend on even better service.
For organisations that stand under the unrelenting pressure to increase efficiencies and meet the high demands of today’s customers, Dematics successful completion of Landmark’s Distribution Center stands as a shining example.
5 Minutes With PwC on AI and Big Data in Manufacturing
Please could you define what artificial intelligence is, and what Big Data is?
AI is the ability of a machine to perceive its environment and perform tasks that normally require human intelligence, and it’s a whole field of different technologies, techniques and applications.
Big data is a set of tools and capabilities for working with, for processing, extremely large sets of data.
How does AI and Big Data work together?
Big data is just one of the enablers of AI, though as we see increasing volumes of data, it’s one of the most important
How can this be applied to a manufacturing setting?
Broadly speaking, there are many benefits of AI, and the use of data, which include reducing costs, minimising human error, and increasing productivity and efficiency. The important thing to consider is any setting - for the use of any technology - is what is the problem you are trying to solve? Be it merely automating repetitive tasks or to reinventing the nature of work in factories by having humans and machines collaborate in order to make better and faster decisions.
Why should manufacturers use AI and Big Data when adopting smart manufacturing capabilities, what is the value for manufacturers?
One view is, again, the economic benefits of AI, which come in manufacturing as a result of:
1. Productivity gains from automating processes and augmenting the work of existing labour forces with various applications of AI technologies.
2. Increased consumer demand due to the increased ability to personalise and tailor manufactured products, along with higher-quality digital and AI-enhanced products and services.
Manufacturing (and construction industries) are by nature capital intensive, and in our 2018 report, “The potential impact of AI in the Middle East,” we estimated that the adoption of AI applications could increase the sectors’ contribution to GDP gains by more than 12.4% by 2030.
How can AI and Big Data help manufacturers to evolve in the Industry 4.0 revolution? What about those already looking at Industry 5.0?
It’s really about the investment you make now, in order to futureproof your business.
We typically see two broad strategies or approaches to the adoption of AI. There are things that we can do immediately, without any recourse to Big Data - which is to adopt technologies we describe as Sensing, those involving computer vision, for example. There are plenty of use cases where these can be used immediately in manufacturing, such as for automatic fault detection. However, there is a longer term play which requires investing in data - getting the right collection mechanisms in place, storage, data governance, Big Data capabilities etc - in order to develop increasingly valuable machine learning driven AI use cases. This is absolutely necessary for long term adoption success.
What is the best strategy for organisations looking to realise the value of AI and Big Data in manufacturing?
AI and Big Data are only one part of a successful smart factory. The organisations that lead on AI adoption are those who have already made the most progress in digitising core business processes. In order get ahead in using AI solutions at scale, there are a number of technology investments and organisational decisions to be made, including:
1. Digitising processes ultimately leads to improved ability to generate data, and in the manufacturing setting - with many 100s of sensors generating 1000s of measurements in real time, the result is Big Data. Data is key to building AI so reliable and accurate data acquisition, management and governance are key. The production line and factories play a critical and direct role in the data-acquisition process.
2. AI strategy, both long and short term, begins with the use cases, the business applications. Manufacturers need to ask where they want to use AI and gather these use cases together and prioritising projects based on a balance of expected impact and complexity of implementation.
Of course, in addition to technology and business processes, people are at the heart of any successful technology adoption. AI teams need to be composed not only of data scientists, also data engineers and solution architects to enable their work, data stewards to ensure accuracy, and increasingly so call “Analytics/AI translators” who are able to communicate with business leaders and technology experts. Culture is also key, and manufacturers need to enable a data and AI-driven culture, building trust in data and algorithms by educating their workforce about AI and its capabilities, how best to extract value. It’s not just the positive of course, but also the risks and limitations, as these when encountered without expectations having been set, can significantly impact willingness to invest.
What are the challenges when it comes to adopting AI and Big Data in manufacturing?
has shown that one of the major challenges to implementing AI is uncertainty around return on investment (ROI). As I said, there is significant investment required for a long term data and AI strategy to be successful, and expectations around the time to see tangible returns must be set realistically.
Many companies also struggle with the data side: collecting and supplying the data that an AI system needs to operate, and ensuring that it is accurate. Again, this speaks to the bigger investments required in digitisation.
Some of the main challenges for manufacturing companies with implementing AI at a scale from our research include:
- 40% → Technologies not mature
- 40% → Workforce lacks skills to implement and manage AI
- 36% → Uncertain of return on investment
- 33% → Data is not mature yet
- 32% → lack of transparency and trust
- 24% → Work councils and labour unions
- 22% → Regulatory hurdles in home & important markets
One element highlighted here, particularly around lack of trust, and labour unions, is that AI is typically misrepresented in the media as “replacing” workers, and taking jobs. Yes, there are efficiency gains to be made from automation, as there have been since the first industrial revolution. But we believe that Data and AI are at their most valuable when they are used to augment workers, enhancing their abilities and the products being manufactured.
Another challenge we’re starting to see emerge is cyberattacks increasingly targeting interconnected equipment and machinery in smart factories. PwC recently hosted a webcast, in cooperation with the National Association of Manufacturers in the US and Microsoft to discuss this.
What are the current trends in AI and Big Data in manufacturing?
- We see companies putting slightly more focus on adding AI solutions to core production processes such as the engineering, and assembly and quality testing
- Safety is of significant importance, with techniques adopted in protocol adherence capabilities (for example maintaining safe distance from specific machinery) being adopted in more every day scenarios for COVID-19 protocol adherence
- There is considerable interest in predictive maintenance for large machinery involved in manufacturing processes, and also supply-chain optimisation
What do you see happening in the AI and Big Data industry in manufacturing in the next 12-18 months?
Honestly, I think we’ll see a continuance of where we’ve already been going for the last 12- 18 months. AI and data are already being used in manufacturing but this use doesn’t get as much attention in the media as, say, healthcare, but the success stories are there, and they will continue as operations continue their digital journeys.