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?

 

 

How Augmented Reality is disrupting supply chains. – Abhilasha Satpathy

With over one billion AR enabled smartphones and tablets already in use, companies don’t have to wait for low-cost augmented reality glasses to start reaping the benefits of augmented reality. Here are five ways that AR is transforming the supply chain into a nimble tool for global distribution:

1) Pick-and-Pack Services

Augmented reality is being used in warehouses to more efficiently locate products and pack them in outgoing boxes. One of the costliest parts of running a “pick and pack” service is training new workers to navigate a large warehouse and find the one product they are searching for. AR glasses can paint an imaginary line on the warehouse floor to simplify the searching and training. During the peak holiday season, temporary workers need to be on-boarded quickly. AR shortens the learning curve by providing new hires with constant feedback on their glasses about how they are doing and what can be improved. Field tests of AR pick-and-pack systems have reduced errors by as much as 40%.

2) Collaborative Robotics

Robots are the ultimate human augmentation. Workers sitting comfortably at their desks can wear AR glasses that let them see what a robot in the warehouse sees. AR glasses can now chart the paths of robots through warehouses and use their strength to lift and move heavy cargo. Dangerous or repetitive tasks, such as loading a truck, can be delegated to robots that operate with human guidance when it comes to how to best load the items to achieve the maximum load. Additionally, logistics robots are able to scan each product for damage, check its weight, and abide by any package shipping instructions. By connecting robots with managers, customers can be automatically alerted if any products that aren’t available before the truck even leaves the warehouse.

3) Maintenance

Fixing a problem before it happens is the most cost-effective form of maintenance. With many aircraft engines now transmitting usage data via Wi-Fi when they are on the ground, augmented reality is assisting maintenance crews in reducing engine downtime by comparing engine data with the past history of other similar aircraft with avionics systems. These algorithms then suggest maintenance before a problem is likely to occur. For planes that spend most of their ground time at distant locations, AR can also enable more experienced maintenance teams at the airline’s hub to see what local technicians are dealing with and provide timely live support.

4) Last Mile Delivery

In logistics, the last-mile of delivery to customers is the most expensive. AR can save money by cutting the time spent on last-mile delivery nearly in half. According to a DHL report, drivers spend 40% to 60% of their day searching inside their own truck for the correct boxes to deliver next. Instead of having to remember how their truck was loaded that morning, augmented reality is used to identify, tag, sequence, and locate every parcel. Combined with artificial intelligence, AR glasses can also navigate the driver to the proper door or building gate for delivery. These systems will record each and every delivery so that new drivers will benefit from past driver experiences. In the near future, every driver will be given a graphic overlay of each building they encounter.

5) Procurement

The distributed ledger capability of blockchain is being combined with augmented reality to bring transparency and traceability to procurement. The entire supply chain falls apart when customers can’t be assured of a product’s origin or authenticity. Each year, billions of dollars’ worth of counterfeit pharmaceuticals are distributed to patients, and tens of thousands are dying. Using AR to identify and track each shipment from manufacturer to end user is a way to help solve this deadly problem. Recording each transfer of ownership on a blockchain can also assist in tracing the origin of fish or the source of harvested crops.

Big data drives the decision making behind the world’s distribution of products throughout the supply chain. Augmented reality is now poised to exponentially increase the speed at which data can be analyzed and acted on. The insights augmented reality bring to the supply chain can be used to power the next generation of the supply chain, which will feature autonomous vehicles and delivery drones.

References:

“5 Ways Augmented Reality Is Disrupting the Supply Chain.” Fortune, fortune.com/2018/03/01/5-ways-augmented-reality-is-disrupting-the-supply-chain/.

Questions:

  1. How does augmented reality help in reducing costs in supply chain?
  2. How is blockchain is being combined with augmented reality to bring transparency and traceability to procurement?
  3. How does augmented reality help in last-mile delivery?

Disruptive Innovations and their applications in Supply Chain Management – by Abhilasha Satpathy, DCMME Center Graduate Student Assistant

Procurement and supply chain are at the cusp of a disruption with AI, IoT and blockchain technology. A digital transformation is ensuing with the promise of greater efficiency in business processes, operations, transparency and security.

Spend analysis

Spend analysis used in strategic sourcing, needs a shift from the traditional descriptive analytics model to more predictive and prescriptive analytics. Organizations can develop tools to enhance their spend analysis with public domain data — from social media, weather data, demographics, suppliers, competition and logistics to name a few — to help uncover insights that can save money and improve supply chain.

 

Supplier lifecycle management

The traditional supplier lifecycle management platform, when augmented by big data from the public domain, can offer meaningful information on suppliers and supply chain risks. An IoT solution can be employed to track the quality of the product at various stages of the supply chain thus improving the efficiency in the process and providing the metrics for supplier evaluation.

 

Strategic sourcing

Supplier bids are collected using online sourcing events, but a large part of the sourcing evaluation and award process is manual in nature. Using blockchain for through all steps of the process — proposals, quotes and bids — or auction, can offer greater efficiency and transparency.

 

Contract management

A blockchain platform and its smart contract framework coupled with IoT and AI, can help facilitate greater efficiency in compliance and obligation management. AI can help develop smart wizards to build contracts based on responses to specific questions and can further be enabled for pattern recognition to identify changes to standard clauses or introduction of non-standard clauses.

Order management

The traditional order management system is internal to any organization and facilitates the fulfillment process. Blockchain platform powered with AI and IoT can drive greater efficiency in orchestrating and streamlining purchase orders, shipment details, trade documents, goods receipts, quality assurance documents, returns and accounting.

Logistics

The logistics industry is an early adopter of AI, IoT and Blockchain, and is already reaping great business benefits. IoT in the logistics ecosystem can provide great insights on inventory management, shelf life, storage temperature, delivery routes, real-time tracking of freight and more

 

Reference:

https://www.ibm.com/blogs/blockchain/2018/04/digital-transformation-next-gen-procurement-and-supply-chain/

 

Questions:

  1. How are AI, IOT and blockchain transforming the logistics industry?
  2. How is blockchain helping in order management?
  3. How can AI help in contract management ?

How Analytics is Transforming Supply Chain Management

 

 

Supply chain management is a field where Big Data and analytics have obvious applications. Until recently, however, businesses have been less quick to implement big data analytics in supply chain management than in other areas of operation such as marketing or manufacturing.

Of course supply chains have for a long time now been driven by statistics and quantifiable performance indicators. But the sort of analytics which are really revolutionising industry today – real time analytics of huge, rapidly growing and very messy unstructured datasets – were largely absent.

This was clearly a situation that couldn’t last. Many factors can clearly impact on supply chain management – from weather to the condition of vehicles and machinery, and so recently executives in the field have thought long and hard about how this could be harnessed to drive efficiencies

Image result for supply chain analytics

 

Why is it so Important?

Relying on traditional supply chain execution systems is becoming increasingly more difficult, with a mix of global operating systems, pricing pressures and ever increasing customer expectations. There are also recent economic impacts such as rising fuel costs, the global recession, supplier bases that have shrunk or moved off-shore, as well as increased competition from low-cost outsourcers. All of these challenges potentially create waste in your supply chain. That’s where data analytics comes in.

Data analytics is the science of examining raw data to help draw conclusions about information. It is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify (or disprove) existing models or theories.

All businesses with a supply chain devote a fair amount of time to making sure it adds value, but these new advanced analytic tools and disciplines make it possible to dig deeper into supply chain data in search of savings and efficiencies.

The supply chain is a great place to use analytic tools to look for a competitive advantage, because of its complexity and also because of the prominent role supply chain plays in a company’s cost structure and profitability. Supply chains can appear simple compared to other parts of a business, even though they are not. If we keep an open mind, we can always do better by digging deeper into data as well as by thinking about a predictive instead of reactive view of the data.

 

https://www.industryweek.com/blog/supply-chain-analytics-what-it-and-why-it-so-important

https://www.forbes.com/sites/bernardmarr/2016/04/22/how-big-data-and-analytics-are-transforming-supply-chain-management/#3a01760339ad

Questions

  1. Q) What are the applications of analytics in supply chain?
  2. Q) What are some of the pain points in supply chain addressed by analytics

 

 

 

How to Survive the Overwhelming Tide of Data

With the increase in accessibility to production and quality data from the use of automation, the Internet of Things, and handheld devices manufacturers are finally able to gather and analyze data to improve their processes at a level hereto unseen before. However, with this seemingly limitless access data comes a new problem: having too much data. More and more companies are falling into the trap of collecting data for the sake of collecting data just because they can and this can actually be harmful to a business. As Douglas Fair states in his article “Drowning in Quality Data: How to Rise Above”, “the insight gleaned from data that is what actually benefits the business”. This means that along with optimizing their processes and machines on the manufacturing floor, manufacturers now also have to think about optimizing how they collect their data so that they are getting the most benefit from it.

When optimization the data collection process, it is important to ask these five simple questions when assessing whether or not they need to be collecting certain pieces of data.

  1. Why do we need to gather this data? What is the improvement we are trying to make with this data we are collecting?
  2. How will we use the data after collection? What are we going to do with it after we have collated it?
  3. Who will evaluate the data? Will it be automated or will we be dedicating personnel to it? Do we have the labor available right now to handle it?
  4. What is a reasonable amount of data to collect? Can we defend why we need as much as we do or could we do the same thing with less?
  5. How frequently do we need to collect the data? How often are we analyzing and using the data to make decisions? Do these coincide with each other well?

At the end of the day, the only sure fire way to make sure you don’t fall into “data gluttony” is to check yourself and ensure that you are collecting data for specific purposes, using all the data you collect, and acting on the insights gained from the data to improve your bottom-line.

 

Source: https://www.manufacturing.net/article/2019/01/drowning-quality-data-how-rise-above

 

Questions:

  1. With data becoming so centric to operations now-a-days, are we going to start seeing roles dedicated to data analysis on site at plants? How will this affect the way plants are run?
  2. What are the costs associated with “data gluttony”? Is it really as big a problem as Fair makes it out to be?
  3. How long does the process of optimizing data collection take? How often should companies review their data collection process to ensure they aren’t collecting useless data?