Supply Chain Management Improved by Blockchain

Understanding how Blockchain can help solve supply chain issues for a NATO member country. 

Introduction

The North Atlantic Treaty Organization (NATO) is an alliance of 29 countries from Europe and North America. Its purpose is to guarantee the freedom and security of its members through political and military means.

It provides a unique link between these two continents, enabling them to consult and cooperate in the field of defense and security, and conduct multinational crisis-management operations together.  NATO is committed to the principle of collective defense which is, an attack against one or several of its members is considered as an attack against all.

In this document we will look at four of the challenges faced by a NATO member country and discuss how Blockchain and AI (Artificial Intelligence) can be used to resolve these problems.

  • Distribution Planning and Forecasting
  • Supply Chain Sustainment Simulation Tools
  • Predictive Forecasting
  • End-to-End (E2E) Asset Visibility

As we discuss the challenges and our target solutions, we will use the schematic below to bring in focus the specific technology that addresses the challenges.  In our summary section we tie in the benefits of a permissioned Blockchain network to the overall logistics mission of managing people, process, and assets.

Distribution Planning and Forecasting

Problem Statement:

There is a lack of collaborative distribution planning: NATO member requires synchronized planning, forecasting and collaboration capabilities to ensure people, processes and assets are in place to execute planned operations.

Why solve this:

Collaborative distribution planning and forecasting forms an essential component of the supply chain process. It is the driver for almost all supply chain related decisions. While forecasting is undeniably important, it is also one of the most difficult aspects of supply chain planning as demand is often volatile making forecasting both an art and a science.

Forecasting is defined as the process by which the historical data is used to develop an estimate of the expected forecast of customer demand. Forecasting provides an estimate of what is needed at any given time, based on available data, historical data and current statistics.

For accurate forecasting and planning, the accuracy and authenticity of data regarding inventory is important. Many inventory management tools and techniques have been applied over the years. Most inventory management systems run an archaic DBMS and linear data entry system. In today’s environment where the need for accurate and timely information to manage logistics systems, reliance on an archaic system is not practical

Solution:

The solution involves implementing a system that: –

  1. Provides true, trusted and accurate data in real time.
  2. Has high end algorithms that churn the data and generate patterns for internal learning.
  3. Provides a way to input previous planning models and measured outcomes.
  4. Provides the best possible scenario for a given situation that allows the system to learn and correct itself. (Machine Learning)
  5. Provides the opportunity for simulation models to be executed and corrected.
  6. Allows data to flow-in from different organizations with the ability to opt-in/out for simulation.

Recommendation:

For such a system to be designed, it is recommended that a private blockchain based on ETC or IBM Hyperledger that allows accurate tracking of associated assets be implemented.

The data from the blockchain will feed into a simple collaborative performant system built on distributed architecture that is secure and scalable and which is backed by strong Microsoft Technology Platform. Microsoft Platform allows us to build solutions on DoD approved Operating System and application frameworks.  The solution will also utilize commercial-off-the-shelf tools such as Microsoft Teams, Zoom and AWS Chime which allow collaboration.

The solution built on recommendations described above will achieve the goal of distributed planning and forecasting.

Supply Chain Sustainment Simulation

Problem Statement:

There is a need for tools and simulation modelling to do and analyze “What-if” scenarios. Without proper tools to integrate with sustainment flow modeling at the strategic and operational levels (wholesale and Service-level retail).

Why Solve this:

In Supply chain management, planning for what-if scenario’s is of extreme importance,

Understanding the weak links in supply chain, making consistent improvement to the whole process and thereby ensuring a strong supply chain.

We need the ability to answer these questions:

  • What if you could accurately predict how your supply chain will perform in the future?
  • Know how a strategy change will impact your service levels or costs?
  • Predict when you will stock out or be late with a shipment?

Solution:

Toby Brzoznowski, an Executive Vice President at LLamasoft, wrote a good article on supply chain simulation.
https://logisticsviewpoints.com/2014/08/05/use-simulation-planning-predict-future-supply-chain-performance/

Baselining:

Modeling and simulating an existing system using the current documented demand using the most recent transactions is an essential starting point for all other forms of analysis. This forms the baseline.

Quantification:

Simulating a future system using current documented demand can help an analyst to quantify a theoretical change to the supply chain structure or policies before implementing such a change in the real world.

Forecasting:

Simulating the current system using future (forecasted) demand & scenario’s (human, economic, social, climate and political) can help an analyst predict operational challenges such as capacity limitations, people and system movement issues, delayed deployments or shortages for deployments

Innovation:

Modeling new supply chain system using newer strategies, learning from data, results of simulation based on the new projected demand data.

Using Blockchain, as described in Distribution Planning and Forecasting, combined with the power of Artificial Intelligence will create a powerful solution.  Machine learning makes it possible to discover patterns in supply chain data by relying on algorithms that quickly pinpoint the most influential factors to a supply networks’ success, while constantly learning in the process.

The solution involves implementing a system that: –

  1. Provides true, trusted and accurate data in real time.
  2. Can independently vary the parameters that affect the planning
  3. Can accept macroeconomic and geopolitical feeds like political strife in a region, real time news, weather and technology updates
  4. Has high end algorithms that churn the data and generate patterns for internal learning.
  5. Provides a way to input previous forecasting models and measured outcomes.
  6. Provides the best possible scenario for a given situation that allows the system to learn and correct itself. (Machine Learning)
  7. Provides the opportunity for simulation models to be executed and corrected.
  8. Allows data to flow-in from different organizations with the ability to opt-in/out for simulation.
  9. Strong visualization tools that:
    • Shows the data flows from one system to another,
    • Shows the deficiency in system during simulation
    • Uses ML/AI based algorithms to provide suggestions that can be acted upon to fix the issues in the supply chain system.

Recommendation:

Implement a private blockchain, that is secure and scalable, based on ETC or IBM Hyperledger that allows accurate tracking of assets.

The data from the blockchain will feed into an integrated collaborative performant system, using strong messaging system like Apache Kafka which handles real-time data feeds. The solution will also incorporate the Google Tensor Flow ML/AI framework that will be trained using the previous defined models as well as self-learning.

Predictive Forecasting

Problem Statement:

There is a need to more accurately forecast future logistics requirements: there is a   lack in the capability to predict maintenance and logistics requirements to enhance operational needs and optimize the supply chain, both forward and reverse flow.

Why solve this:

The ability to effectively forecast demand is essential for supply chain management decisions. In fact, demand forecasts are used throughout the supply chain including supply chain design, purchasing, operations and inventory management. In large part due to computer processing power, new advances in forecasting and the abundance of new data sources have helped to increase forecast reliability.

Solution:

Using Blockchain, as described in Distribution Planning and Forecasting, combined with the power of Artificial Intelligence will create a powerful solution.  Machine learning makes it possible to discover patterns in supply chain data by relying on algorithms that quickly pinpoint the most influential factors to a supply networks’ success, while constantly learning in the process.

The solution involves implementing a system that: –

  1. Provides true, trusted and accurate data in real time.
  2. Can independently vary the parameters that affect the planning
  3. Can accept macroeconomic and geo political feeds like political strife in a region, real time news, weather and technology updates.
  4. Has high end algorithms that churn the data and generate patterns for internal learning.
  5. Provides a way to input previous forecasting models and measured outcomes.
  6. Provides the best possible scenario for a given situation that allows the system to learn and correct itself. (Machine Learning)
  7. Provides the opportunity for simulation models to be executed and corrected.
  8. Allows data to flow-in from different organizations with the ability to opt-in/out for simulation.

Recommendation:

Implement a private blockchain, that is secure and scalable, based on ETC or IBM Hyperledger that allows accurate tracking of assets.

The data from the blockchain will feed into an integrated collaborative performant system, using strong messaging system like Apache Kafka which handles real-time data feeds. The solution will also incorporate the Google Tensor Flow ML/AI framework that will be trained using the previous defined models as well as self-learning.

 End-To-End (E2E) Asset Visibility

Problem Statement:

There are challenges in the effectiveness and efficiency of data capture, visibility of assets in-theater, and ability to create an enterprise view of the data.

Stakeholders throughout the deployment and distribution process require the ability to determine shipment status from the beginning of the movement through to the destination/point of need.

Further, there is an interest in partnering with other organizations to provide solutions to overcome challenges including integration of asset visibility data into appropriate business processes and system(s).

Why Solve this:

The availability of this information contributes to informed decision making, confidence in the supply chain, and improved overall performance of the logistics processes.

Solution:

To create an effective solution, we will utilize the benefits of Blockchain technology coupled with the power of Visualization tools.

The convergence of manufacturing digitization, accurate tracking of data via Blockchain and Cloud-enabled technologies makes for a powerful combination for driving end-to-end supply chain visibility. It is possible to integrate all the technologies to build real-time system that offers greater flexibility, scalability and often, more affordability.

The solution involves implementing a system that

  1. Provides true, trusted and accurate data in real time.
  2. Utilizes a read fast NO SQL DB that allows us to store data needed for visualization
  3. Has a messaging system that allows flow of data in real-time.
  4. Has a visual element that allows users to create complex visualization diagram with ease.
  5. Utilizes start of art visualization tools like Graphana and Tablaeu. 

Recommendation:

It is recommended that a permissioned blockchain that is secure and scalable based on ETC or IBM Hyperledger that allows accurate tracking of assets be implemented.

The data from the blockchain will feed in to a collaborative performant system using a strong messaging tool like Apache Kafka for processing real-time feeds. The solution will also use SaaS system that allows visualization of assets and their utilization.

BLOCKCHAIN AND AI BRINGS PEACE OF MIND IN SUPPLY CHAIN

Supply chain industry being a pivotal entity in the world goods movement is striving to implement new technology that can overcome inherent flaws.

Especially with an effectively implemented blockchain Inventory management system, businesses look to streamline the work flow and maintain a hassle free and accurate record keeping system. Blockchain bridges the chasm in this sector and additionally offers benefits that businesses have never attained.  Using a distributed ledger delivers on security and transparency; it promises to resolve the existing problems in the inventory management part of the supply chain sector.

Supply chain planning is a crucial activity within supply chain management strategy. Having intelligent work tools for building concrete plans is a must in today’s business world.  Machine Learning (ML), applied within supply chain planning could help with forecasting within inventory, demand and supply. If applied correctly through SCM work tools, ML could revolutionize the agility and optimization of supply chain decision-making.

By utilizing ML technology, supply chain management professionals would be given best possible scenarios based upon intelligent algorithms and machine-to-machine analysis of big data sets. This kind of capability could optimize the delivery of goods while balancing supply and demand, and wouldn’t require human analysis, but rather action setting for parameters of success.

Blockchain & AI Brings true data into problem solving and helps build solutions with comprehensive supply chain visibility.

What blockchain introduces, fundamentally, is greater trust across the ecosystem. With blockchain, participants have everything they need to know about their assets and they know the information is reliable because of the security of the distributed ledgers. In terms of the challenges and the needs of the transportation industry, blockchain can become a transformative tool.

With increased research on AI and developing efficient machine learning algorithms, systems can and are being developed to where in the Asset Tracking & Ownership are being stored as immutable entries in ledger and are being used for tracking assets, determining state, location and owners of the asset and also be involved in transfer of ownership or retirement of assets. With increased visibility of assets, the procurement process becomes easy.

AI and efficient machine routing algorithms allow us to see the total volume regardless of who directed the purchase activity — without each user having to share its operational data with the others and systems can be provide the best mechanism to procure, route and manage the assets.

As we add more data and more transactions, these sets of data become the backbone of delivering decision by analytics methodology. With better data we get better outcomes. The oldest phrase in computing is “garbage in, garbage out” — and nowhere does that apply more strongly and more expensively than in supply chain management.

Our professionals have worked with various industries to deliver solutions using AI and Data analytics as a backbone. With the experience of developing prototype distributed apps (DApps), we are well positioned to assist our clients create solutions that are not only transparent but also performant, secure and scalable.