Solving the last mile delivery challenge by Abhilasha Satpathy, DCMME Center Graduate Student Assistant

  1. Crowdsourcing

This model allows retailers and logistics partners to connect with local couriers who use their own transportation to make deliveries. In this gig economy, crowdsourcing is a great way to ensure customers get faster delivery and it also eliminates the possibility of repeat attempt deliveries by providing the option of on-demand and scheduled deliveries to customers.

  1. Brick-and-Mortar Distribution Centers

Some retailers are using their storefront as a solution to the quick delivery problem. They have transformed their stores into distribution centers so that options such as same-day delivery are available to the customers.

  1. Smart Technology

The advancements in technology have inspired solutions that are cost-effective and convenient for both the retailer, as well as the customer. They make use of smart technology like sensors to provide retailers information regarding temperature variation in packaging, weather conditions for route planning, etc.

  1. Data Analytics

Advanced analytics (such as machine learning) help retailers optimize their last mile delivery operations. Data analytics can inform the company (or logistics partners) regarding customer-specific delivery constraints. Studying GPS traces along with relevant insights into the availability of local infrastructures such as roads and parking spaces can help make the entire process more efficient.

  1. Futuristic Delivery

Many startups, retailers and logistics services, are discovering new ways to tackle last mile delivery. Drone delivery, for instance, can not only shorten the time spent on delivery but also reduce the expensive human workforce. This workforce can then be directed towards more complex tasks. Autonomous self-driving vehicles with lockers are predicted to be the most dominant form of last mile delivery in the future.

Reference:

6 Last Mile Delivery Challenges and Solutions in Today’s Market. (2018, December 28). Retrieved from https://volttech.io/last-mile-delivery-market/.

 Questions:

  1. What are the different ways to tackle the last mile delivery problem?
  2. How do brick and mortar stores help in solving the last mile delivery problems?
  3. What are the futuristic delivery options to solve the last mile delivery problems?

 

How drones will benefit supply chains by Abhilasha Satpathy, DCMME Center Graduate Student Assistant

Drones speed up operations and decrease delivery times for the end user while cutting down on supply chain costs.

Asset Management

Manually monitoring inventory can require workers to spend extraordinary amounts of time and resources to count products on shelves. Keeping track and monitoring inventory levels can be an exhaustive process when done periodically, especially during high order demand periods such as the holidays. The use of drones to scan and check inventory anywhere in the warehouse using OCR, RFID, and barcode readers can offer better inventory management, especially when the drone can move from warehouse to warehouse on the property in moments and deliver this information instantaneously to integrated warehouse management software.

Speeding up Deliveries Between Commercial Buildings

Raw materials can be moved from warehouse to manufacturing floor with the use of drones. Drones can also move finished products from warehouse shelves to store shelves, or place products on pallets for shipping to end users and retailers.

Monitoring Supply Chain Delivery Routes

Additional capabilities involve monitoring supply chain routes for disruptions that could impact truck deliveries. These drones can monitor road conditions, construction slow-downs and other hazards while reporting the information to logistics managers who can quickly select alternative shipping routes.

Reference:

Drones and Supply Chain: How they May Impact the Process. (n.d.). Retrieved from https://www.ecsourcinggroup.com/drones-and-supply-chain-how-they-may-impact-the-process/.

Questions:

  1. How do drones impact supply chain?
  2. How will drones help cut supply chain costs?
  3. How will drones help in asset management?

 

 

How to streamline procurement in the future of supply chains by Abhilasha Satpathy, DCMME Center Graduate Student Assistant

Here are some recommendations of how companies can thrive with all the disruptions taking place in the field of supply chain and change their procurement strategies to keep up.

  1. How to plan for impacts of automation and migration of workforce

Mass migration on a large scale, along with forecasts that significant numbers of workers will be displaced by automation, will increase volatility in supply chain labor dynamics. This volatility could be mitigated through responsible and inclusive labor practices. Companies with supply chains that expect significant uptake of automation through 2025 could insist that key suppliers develop clear plans to support a sustainable workforce transition.

  1. How to build responsible regional sourcing hubs

Growth in new markets and demographics and meeting customer demands for customized, on-demand goods and services will require understanding and meeting new consumption patterns and preferences, as well as providing goods and services in new locations and formats. In response, supply chain leaders will have an opportunity to develop nimble, regional supplier networks that can meet both commercial expectations and sustainability aspirations.

  1. Digitalize Supplier Assessment and Engagement

With more data about supply chains produced and disseminated than ever before, supply chain leaders have the opportunity to rethink how they collect and interpret supply chain information. Practitioners will need to hone in on the supply chain information that is decision-useful in a sea of available data and dashboards and will need to reconsider which data they need to commission and how it is collected.

  1. Strengthen Supply Chain Transparency and Disclosure

The regulations that shape mandatory corporate disclosures about sourcing practices, futuristic supply chain leaders can prepare for a variety of possible future scenarios through enhancing both visibility into supply chain practices and disclosures about those practices. Enhanced transparency will support supply chain leaders in the case that global trade is transformed by political shifts toward economic nationalism.

  1. Embed Climate-Smart Supply Chain Planning

To prepare for the environmental changes and other supply chain risks that come with it, companies will need to take into account climate risk and preparedness into supply chain planning models, seek alternative materials and resources where necessary, and look for new ways to secure supply and minimize disruptions in their supply chains.This would also mean working with suppliers that share a commitment towards climate awareness and action.

Reference:

Future of Supply Chains 2025: Blog. (n.d.). Retrieved from https://www.bsr.org/en/our-insights/primers/future-of-supply-chains-2025.

 Questions:

  1. How can companies plan for shifts in procurement trends?
  2. How will climate-smart supply chains be useful in the future?
  3. How will regional sourcing hubs be useful?

 

Autonomous Vehicles transforming supply chains by Abhilasha Satpathy, DCMME Center Graduate Student Assistant

Last Mile Delivery and Distribution Center Implications

The final mile of delivery is usually a bottleneck in the delivery process, both to suppliers and distributors alike. They result in delays frequently, even with the close proximity of the product to the end consumer. Thus, companies have begun experimenting with autonomous vehicles, that could deliver goods to the end costumer without the presence of a driver within the vehicle. Self-driven vehicles seem to affect coordination by decreasing costs and delays. They may to incredibly affect distribution and production centers as well. A common hone has been to construct these in cheaper areas, where good roads and human resources were available. With a move in customer prerequisites that presently call for speedier deliveries, these huge centers will have to be built closer to the end buyer. These centers will also have to be smaller in size, since companies want to be present near the end consumer at various places rather than being present in limited or central locations. This would increase the cost of real estate, warehouse costs and operational costs. However, these costs can be offset by the reduction in costs due to the implementation of these autonomous vehicles for the last mile deliveries. These vehicles can operate for longer hours, are less prone to accidents due to human errors, thus increasing operational efficiencies.

No drivers for long hauls

It is most likely that these autonomous vehicles will see their implementation in long distance travel first. Since driving on highways is more predictable than on city roads, it requires for lesser skills to navigate. Currently, a large chunk of the transportation costs arise from having to pay drivers. Also, drivers can only drive for a certain number of hours at a stretch and then need to rest. Thus, the vehicle lies idle for that duration. Hence, driverless vehicles would reduce these costs and improve efficiencies.

Corporations are also looking into “platooning”,  in which a group of trucks would travel together over long distances.  The lead vehicle would fix a speed and direction and the following vehicles would just have to follow it.During the last leg of the travel, or the last miles, these vehicles would go in their separate directions respectively. This would not only reduce the costs of having drivers, but also reduce the risk accidents and fuel costs.

Reference:

Impact of Autonomous Vehicles in Your Supply Chain – Bâton Global. (n.d.). Retrieved from https://www.batonglobal.com/post/impact-of-autonomous-vehicles-in-your-supply-chain.

Questions:

  1. How will autonomous vehicles change supply chain as we know?
  2. How will driverless vehicles solve the last mile delivery issue?
  3. How can driverless vehicles be used to reduce transportation costs?

 

 

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?