Supply Chain 4.0 – the next-generation digital supply chain

Over the last thirty years, logistics has undergone a tremendous change: from a purely operational function that reported to sales or manufacturing and focused on ensuring the supply of production lines and the delivery to customers, to an independent supply chain management function that in some companies is already being led by a CSO – the Chief Supply Chain Officer. The focus of the supply chain management function has shifted to advanced planning processes, such as analytical demand planning or integrated S&OP, which have become established business processes in many companies, while operational logistics has often been outsourced to third-party LSPs. The supply chain function ensures integrated operations from customers to suppliers.

Experts would usually claim that supply chain management is about delivering the right quality at the lowest cost, with the agreed service level, right? Well, not anymore. As the two examples above show, it is also about increasing sales and profits; the supply chain is no longer just about efficiency, working capital reduction and inventory management.


Adidas is the leading sports’ shoe brand in Russia with more than 1,200 stores. As part of its strategy to please customers, Adidas is implementing an omni channel strategy, allowing people to buy in a number of ways.

Initially, Adidas implemented a trial of click and collect in Moscow expecting that just a few consumers would choose this option – to buy on-line and collect the product at a store. They expected around 10 to 20 orders per week, but consumers embraced the idea and orders reached 1,000 per week. Adidas was forced to stop the experiment and build the supply chain infrastructure needed to support such demand. Today, up to 70% of online sales are through click and collect.

For Adidas Russia, the supply chain is no longer about reducing costs: It is – more importantly – about increasing sales. All of this is possible thanks to the technology being used in the supply chain. Most of these technologies belong to Industry 4.0, a high-tech strategy promoting the computerisation of manufacturing.


Digitization brings about a Supply Chain 4.0, which will be

  • Faster. New approaches of product distribution reduce the delivery time of high runners to few hours. The basis for these services is built by advanced forecasting approaches, e.g., predictive analytics of internal (e.g., demand) and external (e.g., market trends, weather, school vacation, construction indices) data as well as machine status data for spare-parts demand, and provides a much more precise forecast of customer demand.
  • more flexible. Ad hoc and real-time planning allows a flexible reaction to changing demand or supply situations. Planning cycles and frozen periods are minimized and planning becomes a continuous process that is able to react dynamically to changing requirements or constraints
  • more granular. The demand of customers for more and more individualized products is continuously increasing. That gives a strong push towards microsegmentation, and mass customization ideas will finally be implemented.



What are the challenges in the implementation of Digital Supply Chain?

What will be the future of supply chains due to the technology trends?




3D Printing Impact on Supply Chain


What is 3D printing


3D printing, also known as additive manufacturing – AM (the terms 3D printing and additive manufacturing have become interchangeable), is an additive technology used for making three dimensional solid objects up in layers from a digital file without the need for a mould or cutting tool. 3D printing uses a computer aided design (CAD) to translate the design into a three-dimensional object. The design is then sliced into several two dimensional plans, which instruct the 3D printer where to deposit the layers of material. Additive process, of depositing successive thin layers of material upon each other, producing a final three dimensional product

Impact of 3D printing on the Supply Chain


The impact of AM technologies on the global setup of supply chains can be very disruptive. The technology has the potential to eliminate the need for both high volume production facilities and low-level assembly workers, thereby drastically reducing supply chain cost. In terms of impact on inventory and logistics, we can print on demand. Meaning we don’t have to have the finished product stacked on shelves or stacked in warehouses anymore. Whenever we need a product, we just make it. And that collapses the supply chain down to its simplest parts, adding new efficiencies to the system. Those efficiencies run the entire supply chain, from the cost of distribution to assembly and carry, all the way to the component itself, all the while reducing scrap, maximising customisation and improving assembly cycle times.

Image result for Metal 3d printers in supply chain

Traditional supply chain vs AM model


The supply chain traditional model is founded on traditional constraints of the industry, efficiencies of mass production, the need for low cost, high volume assembly workers, and so on. But 3D printing bypasses those constraints. 3D printing finds its value in the printing of low volume, customer specific items, items that are capable of much greater complexity than is possible through traditional means. This at once eliminates the need for both high volume production facilities and low level assembly workers, thereby cutting out at least half of the supply chain in a single blow. From that point of view, it is no longer financially efficient to send products across the globe when manufacturing can be done almost anywhere at the same cost or lower. The raw materials today are digital files and the machines that make them are wired and connected, faster and more efficient than ever. And that demands a new model of supply chain . With support local sourcing, the 3D printing technology has the potential to tear established global supply chain structures apart and reassembles it as a new, local system. Furthermore, the technology creates a close relationship between design, manufacturing and marketing. The technology could transform the global supply chain to a globally connected, but totally local supply chain

Image result for Metal 3d printers in supply chain



What is the future of 3d printing?

What are the challenges in using 3 D printing in supply chains?




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.


  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




Blockchain a Game Changer for Supply Chain Management Transparency


What is Blockchain?
Blockchain is a distributed database that holds records of digital data or events in a way that makes them tamper-resistant. While many users may access, inspect, or add to the data, they can’t change or delete it. The original information stays put, leaving a permanent and public information trail, or chain, of transactions

If the entire blockchain were the history of banking transactions, an individual bank statement would be a single “block” in the chain. Unlike most banking systems, however, there is no single organisation that controls these transactions. It can only be updated through the consensus of a majority of participants in the system

How Will Blockchain Technology Affect the Supply Chain?
If blockchain technology allows us to more securely and transparently track all types of transactions, imagine the possibilities it presents across the supply chain.

Every time a product changes hands, the transaction could be documented, creating a permanent history of a product, from manufacture to sale. This could dramatically reduce time delays, added costs, and human error that plague transactions today.

Some supply chains are already using the technology, and experts suggest blockchain could become a universal “supply chain operating system” before long. Consider how this technology could improve the following tasks:

  • Recording the quantity and transfer of assets – like pallets, trailers, containers, etc. – as they move between supply chain nodes
  • Tracking purchase orders, change orders, receipts, shipment notifications, or other trade-related documents
  • Assigning or verifying certifications or certain properties of physical products; for example determining if a food product is organic or fair trade
  • Linking physical goods to serial numbers, bar codes, digital tags like RFID, etc.
  • Sharing information about manufacturing process, assembly, delivery, and maintenance of products with suppliers and vendors


Benefits in a Nutshell
Regardless of the application, blockchain offers shippers the following advantages:

  • Enhanced Transparency Documenting a product’s journey across the supply chain reveals its true origin and touch points, which increases trust and helps eliminate the bias found in today’s opaque supply chains. Manufacturers can also reduce recalls by sharing logs with OEMs and regulators
  • Greater Scalability Virtually any number of participants, accessing from any number of touch points, is possible
  • Better Security A shared, indelible ledger with codified rules could potentially eliminate the audits required by internal systems and processes
  • Increased Innovation Opportunities abound to create new, specialised uses for the technology as a result of the decentralised architecture.


  • What is Blockchain?
  • What are the applications of blockchain in supply chain?
  • What are the risks of Blockchain technology?


Advanced Analytics in Supply Chain

The sole role of analytics is to support decision making. Through Advanced Analytics, a supply chain can leverage more insights with more accuracy. This empowers to take decisions better, faster and/or with more confidence. Specific use-cases include the following:

Create inventory visibility and visualise which products rotate at which speed through your warehouse and why (decreased sales, increased returns). Use the available data to segment your products in high- and low-rotating units and provide this as input to your warehouse manager to relocate goods and alter safety stock levels.

Derive root-causes of delivery promise failures such as vendors who deliver to late, fulfilment partners who exceed average delivery times – and identify supply-chain improvement initiatives.

Get smarter into product development by leveraging data-driven insights on your customer- and order base: what are my customers segments, how did they grow over time, how are they in one region vs. another region, what are their shared preferences, which products features do they like

Reduce lead time by understanding when which drivers impact lead-time at what impact: which parts increase the risk of production delay, which parts require a strategic inventory? With no IT investment, a solid data-mining exercise through your supply chain order-, production- and delivery data can likely already identify low hanging fruit opportunities.

Optimise inventory space and value by forecasting demand with accuracy. Do you overestimate, you will likely overproduce and stack up inventory; do you underestimate, you’ll miss sales. Through analytics we can analyse your historical sales data and assess patterns driven by seasonality, partner activity, marketing activeness, offline sales agents, weather or even country-specific GDP. Turning these patterns into inputs, we predict sales and thus prescribe needed inventory levels.

Locate geographical growth opportunities by visualising all order, delivery- and customer-locations and deriving sweet spots for new sales hubs, production sites or warehousing depots. Assess supply-chain merger potential by visualising overlapping supply-chain networks, assessing overlap and thus assessing strategic added value.

Assess failure patterns of production machines to understand which drivers are recurrently causing failure (volumes, #batch switches, temperature, speed, operator). Then translate these drivers into inputs building an early-warning-trigger tool/model to pre-empt failure (first steps of predictive maintenance).

Deep learning for smart manufacturing: Methods and applications:


Smart manufacturing refers to using advanced data analytics to complement physical science for improving system performance and decision making. With the widespread deployment of sensors and Internet of Things, there is an increasing need of handling big manufacturing data characterised by high volume, high velocity, and high variety. Deep learning provides advanced analytics tools for processing and analysing big manufacturing data.

Fig. 1

With new technologies (e.g. IoT, big data) embraced in smart manufacturing, smart facilities focus on creating manufacturing intelligence that can have a positive impact across the entire organisation. The manufacturing today is experiencing an unprecedented increase in available sensory data comprised of different formats, semantics, and structures. Sensory data was collected from different aspects across the manufacturing enterprise, including product line, manufacturing equipment, manufacturing process, labour activity, and environmental conditions. Data modelling and analysis are the essential part of smart manufacturing to handling increased high volume data, as well as supporting real-time data processing

From sensory data to manufacturing intelligence, deep learning has attracted much attention as a breakthrough of computational intelligence. By mining knowledge from aggregated data, deep learning techniques play a key role in automatically learning from data, identifying patterns, and making decisions. Different levels of data analytics can be produced including descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. Descriptive analytics aims to summarise what happens by capturing the product’s conditions, environment and operational parameters. When the product performance is reduced or the equipment failure happens, diagnostic analytics examine the root cause and report the reason it happens. Predictive analytics utilises statistical models to make predictions about the possibility of future production or equipment degradation with available historical data. Prescriptive analytics goes beyond by recommending one or more courses of action. Measures can be identified to improve production outcomes or correct the problems, showing the likely outcome of each decision.

With the advanced analytics provided by deep learning, manufacturing is transformed into highly optimised smart facilities. The benefits include reducing operating costs, keeping up with changing consumer demand, improving productivity and reducing downtime, gaining better visibility and extracting more value from the operations for globally competitiveness.


Fig. 3



Applications to smart manufacturing

Computational intelligence is an essential part of smart manufacturing to enable accurate insights for better decision making. Machine learning has been widely investigated in different stages of manufacturing lifecycle covering concept, design, evaluation, production, operation, and sustainment. The applications of data mining in manufacturing engineering are reviewed in, covering different categories of production processes, operations, fault detection, maintenance, decision support, and product quality improvement. The evolution and future of manufacturing are reviewed in, emphasising the importance of data modelling and analysis in manufacturing intelligence. The application schemes of machine learning in manufacturing are identified as summarised in. Smart manufacturing also requires prognostics and health management (PHM) capabilities to meet the current and future needs for efficient and reconfigurable production.

Fig. 8




  • How does smart manufacturing help companies?
  • How will smart manufacturing evolve in the future?


NextGen Supply Chain: The emerging role for drones

drone delivery package future

Five years is a lifetime in the world of technology, but maybe you remember how Jeff Bezos turned the supply chain world upside down in December 2013.

That’s when he dropped a bombshell on 60 minutes, walked correspondent Charlie Rose and his production team “into a mystery room at the Amazon offices and revealed a secret R&D project: ‘Octocopter’ drones that will fly packages directly to your doorstep in 30 minutes.”

Faster than you can say “same day delivery,” drones were the buzz word du jour at supply chain conferences, much like RFID following the infamous Walmart mandate about a decade earlier.


Organisations are beginning to adopt drones in the first phase of supply chain management: obtaining raw materials. Drones are also used in mining, prospecting, and land surveying applications. In farming and agriculture, UAVs are used to inspect plant health, photo-log plant growth, and map crop yields. Drones are also testing soil to help optimise water content and fertiliser usage, with the intent of improving crop yields.

Drone use in manufacturing, warehousing, and distribution facilities is expected to rise in the next five years, aiding the work-in-process inventory stage of supply chain management. UAVs promise to enhance safety and security as well as promote overall efficiency. For example, drones with cameras can “walk the perimeter” of facilities, seeing areas an ordinary security camera might not reach. Inside of facilities, drones can perform safety inspections, perform maintenance and repair functions like fixing a leaky roof, or fly across a campus to retrieve a forgotten tool – all of which could potentially reduce work hours.7

In warehousing, developers at the Massachusetts Institute of Technology say UAVs are the best new way of tracking inventory anonymously. Using RFID, QR-codes, and IoT, drones can take physical inventory. Walmart Stores, for example, is currently testing drones for that purpose. Drones can move small items quickly, reducing the need for forklifts or possibly replacing the conveyor systems often used to transport boxes around distribution centers. Outside of the warehouse, drones may also be used for supply chain deliveries. For example, UAVs could ship inventory between production facilities and distribution centers, potentially expediting order fulfilment.


In terms of barriers to adoption, there’s the usual suspects in terms of privacy, safety, security and legal barriers for the industry to overcome. According to Goldman Sachs, some of the biggest obstacles to commercial adoption are regulations imposed by the FAA stating that drone flight is limited to a pilot’s line of sight, cannot fly over 400 feet, cannot operate over people or crowds, and must be flown by someone with a remote pilot certificate.

To unlock commercial demand, drones need to be able to operate beyond line of sight, above 400 feet, autonomously, as well as over populated areas. Of course, in densely populated areas, there’s many more hurdles to cross – particularly for drone package delivery, which is expected to be much further out in terms of market adoption.


  • What are the challenges in the adoption of the Drone technology?
  • What are the applications of drones in supply chain?
  • Will drones lead to a reduction in jobs?
  • Is the investment in drones worth it for businesses?