Disruptive Innovations transforming  logistics by Abhilasha Satpathy, DCMME Center Graduate Student Assistant

Disruptive innovation and bid data can address many challenges in logistics. Some of them are:

  1. The Last Mile of Shipping Can Be Quickened – The last mile of a supply chain is notoriously inefficient, costing up to 28% of the overall delivery cost of a package.
  2. Reliability Will Be More Transparent – As sensors become more prevalent in transportation vehicles, shipping, and throughout the supply chain, they can provide data enabling greater transparency than has ever been possible.
  3. Routes Will Be Optimized – If you underestimate how many vehicles a particular route or delivery will require, then you run the risk of giving customers a late shipment, which negatively affects your client relationships and brand image. Optimizing saves money and avoids late shipments.
  4.  Sensitive Goods Are Shipped With Higher Quality – Keeping perishables fresh has been a constant challenge for logistics companies. However, big data and the Internet of Things could give delivery drivers and managers a much better idea of how they can prevent costs due to perished goods. A temperature sensor inside the truck could alert the driver, and suggest alternate routes.
  5.  Automation of Warehouses and The Supply Chain – The ability to accurately predict demand in every DC, retailer, and customer is the holy grail of being able to deploy inventory where and when it is needed.
  6.  Better inventory deployment and labor management – For retail store managers, planning shifts to meet customer demand is a sensitive task- overstaffing kills profitability, and understaffing results in angry customers.  Planning has always been done based on history.  One retailer took into account the following additional data:
  • New delivery times
  • Local circumstances and holidays
  • Road construction
  • Weather forecasts

Big data and predictive analytics gives logistics companies the extra edge they need to overcome these obstacles. Sensors on delivery trucks, weather data, road maintenance data, fleet maintenance schedules, real time fleet status indicators, and personnel schedules can all be integrated into a system that looks at the past historical trends and gives advice accordingly.

References:

Swingle, K. (2017, September 25). Disruptive Innovation in Logistics. Retrieved from https://www.spartanwarehouse.com/blog/spartan-logistics-understanding-big-date-and-how-its-revolutionizing-logistics.

Questions:

  1. What challenges can be fixed with big data and disruptive innovations in logistics?
  2. How does big data help in better inventory deployment?
  3. How does big data improve reliability in transportation?

 

Automation in Manufacturing by Abhilasha Satpathy, DCMME Center Graduate Student Assistant

Three types of automation in production can be distinguished: (1) fixed automation, (2) programmable automation, and (3) flexible automation.

Fixed automation, also known as “hard automation,” refers to an automated production facility in which the sequence of processing operations is fixed by the equipment configuration. In effect, the programmed commands are contained in the machines in the form of cams, gears, wiring, and other hardware that is not easily changed over from one product style to another. This form of automation is characterized by high initial investment and high production rates. It is therefore suitable for products that are made in large volumes. Examples of fixed automation include machining transfer lines found in the automotive industry, automatic assembly machines, and certain chemical processes.

Programmable automation is a form of automation for producing products in batches. The products are made in batch quantities ranging from several dozen to several thousand units at a time. For each new batch, the production equipment must be reprogrammed and changed over to accommodate the new product style. This reprogramming and changeover take time to accomplish, and there is a period of nonproductive time followed by a production run for each new batch. Production rates in programmable automation are generally lower than in fixed automation, because the equipment is designed to facilitate product changeover rather than for product specialization. A numerical-control machine tool is a good example of programmable automation. The program is coded in computer memory for each different product style, and the machine tool is controlled by the computer program. Industrial robots are another example.

Flexible automation is an extension of programmable automation. The disadvantage with programmable automation is the time required to reprogram and change over the production equipment for each batch of new product. This is lost production time, which is expensive. In flexible automation, the variety of products is sufficiently limited so that the changeover of the equipment can be done very quickly and automatically. The reprogramming of the equipment in flexible automation is done off-line; that is, the programming is accomplished at a computer terminal without using the production equipment itself. Accordingly, there is no need to group identical products into batches; instead, a mixture of different products can be produced one right after another.

References:

(n.d.). Numerical control. Retrieved from https://www.britannica.com/technology/automation/Numerical-control

Questions:

  1. What are the different forms of automation in manufacturing?
  2. How is flexible automation different from programmable automation?
  3. What is are the disadvantages of programmable automation?

Big Data Technology: Impact on supply chains

Sources

https://www.supplychaindive.com/news/what-Big-Data-supply-chain-application-primer/435865/

https://www.mckinsey.com/business-functions/operations/our-insights/big-data-and-the-supply-chain-the-big-supply-chain-analytics-landscape-part-1

 

What is Big Data

Big Data as a concept requires three distinct layers before application: more data, processing systems, and analytics. If Big Data only recently entered the supply chain management spotlight, then, it may be because the technology only recently reached the last layer to deliver insights.

Information processing

Businesses are no stranger to data; supply chain managers have been producing reports, tracking trends and forecasting for decades. So when data exploded to become Big Data, companies were quick to rise to the challenge of collecting it for future use.

“What the CIOs and IT organizations were asked to do, early part of this decade – probably even the latter half of the last decade – was ‘hey there’s a lot of value in data, let’s actually keep on collecting data,’” Suresh Acharya, Head of JDA Labs told Supply Chain Dive.

But even if a pedometer generates bits and bytes each second, the information created remains unpalatable unless it is stored with previous data to be analyzed over time.

Therein came the need for information processing systems more powerful than spreadsheets.  Many of these are now known by their three letter acronyms (e.g. ERP, CRM, TMS or WMS), but their purpose is similar: to store, collect and simplify information for the average user. Such processors became so ubiquitous, it is now common for a company to boast nine or ten distinct systems supporting supply chain management in a single plant.

Insight and decision-making: The next frontier

There’s a new wave of data processors on the market promising to reap the benefits of Big Data for supply chains.

Supply chain solutions companies often offer to integrate the various systems from the previous generation, allowing companies to visualize data sets at each corporate level for the increased granularity and analytical capacity desired from Big Data.

Yet, Big Data is not only the ability to process more information, but the ability to innovate, automate and use data for enhanced decision-making. The toolkit is meant to be applied, not simply possessed.

A look back at our pedometer example may help illustrate the difference between having a software solution and actively unpacking Big Data. At first, the pedometer could only track information – making it a data generator. If connected to the Cloud and transmitting to a data processor, the device could be considered as helping to generate Big Data. But it was never a Big Data device because it never actively helped a user make decisions.

Meanwhile, the Fitbit – which tracks steps, heart rates and other biometrics – can analyze and apply the data it collects to guide the wearer to better health habits; for example, it alerts the user when they have been sitting too long and reminds them to go take a walk.

 

 

Big Data applications in supply chain

Manufacturing

Big data and analytics can already help improve manufacturing. For example, energy-intensive production runs can be scheduled to take advantage of fluctuating electricity prices. Data on manufacturing parameters, like the forces used in assembly operations or dimensional differences between parts, can be archived and analyzed to support the root-cause analysis of defects, even if they occur years later. Agricultural seed processors and manufacturers analyze the quality of their products with different types of cameras in real-time to get the quality assessments for each individual seed.

The Internet of Things, with its networks of cameras and sensors on millions of devices, may enable other manufacturing opportunities in the future. Ultimately, live information on a machine’s condition could trigger production of a 3D-printed spare part that is then shipped by a drone to the plant to meet an engineer, who may use augmented reality glasses for guidance while replacing the part.

Warehousing

Logistics has traditionally been very cost-focused, and companies have happily invested in technologies that provide competitive advantage. Warehousing in particular has seen many advances using available ERP data. One example are “chaotic” storage approaches that enable the efficient use of warehouse space and minimize travel distances for personnel. Another are high-rack bay warehouses that can automatically reshuffle pallets at night to optimize schedules for the next day. Companies can track the performance of pickers in different picking areas to optimize future staff allocation.

New technologies, data sources and analytical techniques are also creating new opportunities in warehousing. A leading forklift provider is looking into how the forklift truck can act as a big data hub that collects all sorts of data in real time, which can then be blended with ERP and Warehouse Management System (WMS) data to identify additional waste in the warehouse process. For example, the analysis of video images collected by automated guided vehicles, along with sensor inputs including temperature, shelf weight, and the weight on the forklift, can be used to monitor picking accuracy, warehouse productivity and inventory accuracy in real time. Similarly forklift driving behavior and route choices can be assessed and dynamically optimized to drive picking productivity. The data can also be used to conduct root-cause analysis of picking errors by shape, color, or weight, to help to make processes more robust.

New 3D modelling technologies can also help to optimize warehouse design and simulate new configurations of existing warehouse space to further improve storage efficiency and picking productivity. German company Logivations, for example, offers a cloud-based 3D warehouse layout planning and optimization tool.

Transportation

Truck companies already make use of analytics to improve their operations. For example, they use fuel consumption analytics to improve driving efficiency; and they use GPS technologies to reduce waiting times by allocating warehouse bays in real time.

Courier companies have started real-time routing of deliveries to customers based on their truck’s geo-location and traffic data. UPS, for example has spent ten years developing its On-Road Integrated Optimization and Navigation system (Orion) to optimize the 55,000 routes in the network. The company’s CEO David Abney says the new system will save the company $300 million to $400 million a year3.

Big analytics will also enable logistics providers to deliver parcels with fewer delivery attempts, by allowing them to mine their data to predict when a particular customer is more likely to be at home. On a more strategic basis, companies can cut costs and carbon emissions by selecting the right transport modes. A major CPG player is investing in analytics that will help it to understand when goods need be shipped rapidly by truck or when there is time for slower barge or train delivery.

Point of Sale

Brick and mortar retailers—often under heavy pressure from online competitors that have mastered analytics—have understood how data driven optimization can provide them with competitive advantages. These techniques are being used today for activities like shelf-space optimization and mark-down pricing. Advanced analytics can also help retailers decide which products to put in high value locations, like aisle ends, and how long to keep them there. It can also enable them to explore the sales benefits achieved by clustering related products together.

Search engine giant Google has acquired Skybox, a provider of high resolution satellite imagery, that can be used to track cars in the car park in order to anticipate in-store demand. Others have explored the use of drones equipped with cameras to monitor on-shelf inventory levels.

Questions:

Q) What role can Big Data play in the optimization of Supply Chains across industries?

Q) What are the cost implications in companies leveraging big data to organize their supply chains?

Q) How is Big data different from data analytics?

 

 

How disruptive technologies are improving food supply chains by Abhilasha Satpathy, DCMME Center Graduate Student Assistant

One of the lectures in my Logistics class, got my interest in understanding how we as professionals interested in the supply chain industry can do our bit to improve the efficiencies in the food supply chain area and I decided to do some reading on the same. I decided that since it’s the need of the hour, maybe I can share it with others too.

IOT enabling better decisions

Internet of Things (IoT) or sensors can continuously capture large amounts of relevant information, while the decreasing cost of storing data in cloud solutions, and the increased possibilities of analysing these big amounts of data, creates new insights and the basis for better decisions. For example, the sensors can capture data in biological processes, such as aquaculture. Advanced analytics on these data may create new insights and better decisions. They may contribute to improved fish health and fish welfare, reduced mortality rates, improved feed efficiency and a more sustainable seafood production.Moreover, IoT enables the entire food and beverage industry to monitor raw goods and products all the way through the value chain, and use the information to ensure safe and sustainable products at the consumers’ tables.

Use of blockchain

Blockchain and other digital technologies will enable the communication of information from sensors directly to the consumer at the purchasing moment. Digital assurance may contribute to making the story true and trustable and an effective defence against counterfeiting and food fraud.For example, the food service industry may log and blockchain temperature information of products throughout the supply chain, from the ready meal producer to the consumer in the convenience store. In addition to the value of this information to the consumer, this may also contribute to longer shelf lives, improved cooling chain performance and reduced food waste. The flip side of making this information fully transparent to the consumer, is of course that the consumer will also know if the cooling chain was disrupted.

Shorter value chains

Thirdly, the platform economy may disrupt the supply chain and impact the retailers by connecting the consumers more directly to the food producers, as short value chains or direct purchase become consumer values in themselves. The decrease in transaction cost and the growing e-business in the food market, may increase the power of consumers, as a larger variety of products and producers may be made available at a lower cost. In addition to deep customer insight, platforms and social media creates open innovation opportunities, by involving customers directly in product development. Through engagement, sense of belonging and loyalty your customers may increasingly become part of your brand.

Transportation Automation

Transportation planners are on the frontlines of the latest supply chain disruption — and they’re making significant progress in more ways than one. Although many think of autonomous vehicles when it comes to the next generation of transportation, supply chain managers have a myriad of applications for advanced robotics and automated systems:

  • Smart Traffic Management: The city of Nanjing, China recently introduced a traffic flow management system that incorporates real-time data as well as predictive analytics and forecasts to help travelers plan their routes on a day-to-day basis. Such a system is easily extrapolated to the supply chain by providing information on traffic delays, detours and even weather conditions.
  • Enhanced Safety Mechanisms: While some are concerned with the safety issues presented by autonomous and driverless vehicles, others focus on human drivers. New systems can estimate a driver’s fatigue by monitoring various vital signs to help avoid accidents on the road.
  • Aerial Drone Delivery: Remote-controlled aerial drones are already popular among consumers, so it makes sense that they’re being considered for product deliveries and shipments.

 

References:

https://www2.deloitte.com/content/dam/Deloitte/ie/Documents/ConsumerBusiness/2015-Deloitte-Ireland-Food_Value_Chain.pdf

(n.d.). How Are Digital Technologies Transforming Food Value Chains? Retrieved from https://www.mygfsi.com/news-resources/news/news-blog/1330-how-are-digital-technologies-transforming-food-value-chains.html

Nichols, M. R. (2018, April 25). 5 Technologies Disrupting the Supply Chain. Retrieved from https://www.manufacturing.net/article/2018/04/5-technologies-disrupting-supply-chain

Questions:

  1. How is IOT changing the food supply chains as we know it?
  2. How can transportation automation help improve the efficiency of food supply chains?
  3. How will shorter value chains enhance the efficiencies of food supply chains world over?

 

 

Digital twins in supply chain

What is a digital twin

A digital twin is a virtual image/software representation of a real product, process, asset or service. For example, a digital visualization of an organ used to simulate an upcoming operation. A digital copy can be used for monitoring and control, as well as for planning and forecasting the outcomes of various scenarios. This makes it possible to understand, predict and optimize performance.

For the digital twin to be truly a twin, all available information/data must be linked one-to-one. In some cases, individual digital twins are connected to each other in order to reproduce and optimize twins of an entire construct.

Advantages of digital twins

he benefits that digital twin technologies offer your business include:

  • Increased reliability of equipment and production lines
  • Improved OEE through reduced downtime and improved performance
  • Improved productivity
  • Reduced risk in various areas including product availability, marketplace reputation, and more
  • Lower maintenance costs by predicting maintenance issues before breakdowns occur
  • Faster production times
  • New business opportunities such as mass customisation, mixed manufacturing, small-batch manufacturing, and more
  • Improved customer service as customers can remotely configuring customised products
  • Improved product quality, and enhanced insight into the performance of your products, in multiple real-time applications and environments
  • More efficient supply and delivery chains
  • All the above combined will result in the ultimate benefit of improved profits

 

How do digital twins work and where can they be used?

Digital twins act as a bridge between the physical and digital world. With the help of intelligent sensors integrated into physical elements, all necessary data can be captured and transmitted. In conjunction with relevant business data, this data is then analyzed and, in the best case scenario, can uncover opportunities that may otherwise have gone undetected.

NASA has been using digital twins for many years. This is mainly because the systems it needs to monitor are located at an unattainable distance. John Vickers, NASA’s leading manufacturing expert and manager of the NASA National Center for Advanced Manufacturing, describes NASA’S vision to be able to create, test and build their equipment in a virtual world in the future.

Installing IoT sensors can not only help the company itself, but also increase the efficiency of  its supply chain partners and prevent possible disruptions. But where can digital twins be used in practice? Practically everywhere: You can recognize existing customer requirements, simulate the effects of corresponding trends and thus obtain a comprehensive view of the broad spectrum of customers. In production, current state analyses can be carried out, adjustments can be made if necessary and untapped potential can be identified. In logistics, digital twins can be used to optimize stocks and to track and monitor them through geolocation. Digital twins enable companies to meet their supply chain partners’ requirements to the best of their abilities .

What does the future look like?

Forbes describes the current state of digital twin technology as the threshold to a digital explosion in which significantly more companies will develop and introduce their own digital twins in the future based on success stories of others. The number of digital representatives of physical objects is estimated to be in the billions, which simultaneously opens up the opportunity for cooperation between product experts and data scientists. Gartner predicts that by 2021, half of the major industrial companies will be using digital twins, resulting in an average efficiency increase of 10%.

In addition, experts predict that future developments will be much more likely to involve the combination of individual twins than at present. The fact that the use of digital twins can open up new business fields and models arouses curiosity about the unknown potential of innovative solutions in the future.