Artificial Intelligence (AI) in Supply Chain Management (SCM) Market 2019-2024 by Technology …

Artificial Intelligence (AI) in Supply Chain Management (SCM) Market: AI in SCM by Technology, Solution, Management Function (Automation, …

DUBLIN–(BUSINESS WIRE)–The “Artificial Intelligence (AI) in Supply Chain Management (SCM) Market: AI in SCM by Technology, Solution, Management Function (Automation, Planning and Logistics, Inventory, Fleet, Freight, Risk), and Region 2019-2024” report has been added to’s offering.

This research evaluates how AI is revamping the operational process and facilitating cost-effective supply chain solutions. It provides analysis of leading companies and solutions that are leveraging AI in their supply chains and those they manage on behalf of others, with evaluation of key strengths and weaknesses of these solutions. The report also provides a view into the future of AI in supply chain management.

Modern supply chains represent complex systems of organizations, people, activities, information, and resources involved in moving a product or service from supplier to customer. Supply Chain Management (SCM) solutions are typically manifest in software architecture and systems that facilitate the flow of information among different functions within and between enterprise organizations.

Leading SCM solutions catalyze information sharing across organizational units and geographical locations, enabling decision-makers to have an enterprise-wide view of the information needed in a timely, reliable and consistent fashion. Various forms of Artificial Intelligence (AI) are being integrated into SCM solution to improve everything from process automation to providing greater visibility into static and real-time data as well as related management information systems.

In addition to fully automated decision making, AI systems are also leveraging various forms of cognitive computing to optimize the combined efforts of artificial and human intelligence. For example, AI in SCM is enabling improved supply chain automation through the use of virtual assistants, which are used both internally (within a given enterprise) as well as between supply chain members (e.g. customer-supplier chains).

Select Report Findings:

  • The Global AI in SCM Market by AI-as-a-Service will reach $1.3 Billion by 2024
  • AI in SCM for the healthcare industry in North America will reach $183B USD by 2024
  • Combination of AI and IoT in supply chains will improve operational flow and efficiency
  • AI supported supply chains become 45% more effective at on-time delivery with fewer errors

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction

2.1 Supply Chain Management

2.1.1 Challenges

2.1.2 Opportunities

2.2 AI in SCM

2.2.1 Key AI Technologies for SCM

2.2.2 AI and Technology Integration

3.0 AI in SCM Challenges and Opportunities

3.1 Market Dynamics

3.1.1 Companies with Complex Supply Chains

3.1.2 Logistics Management Companies

3.1.3 SCM Software Solution Companies

3.2 Technology and Solution Opportunities

3.2.1 Leverage Artificial Intelligence (AI) Integrate AI with Existing Processes Integrate AI with Existing Systems

3.2.2 Integrate AI with Internet of Things (IoT) Leverage AIoT Platforms, Software, and Services Leverage Data as a Service Providers

3.3 Implementation Challenges

3.3.1 Management Friction

3.3.2 Legacy Processes and Procedures

3.3.3 Outsource AI SCM Solution vs. Integrate with Existing

4.0 Supply Chain Ecosystem Company Analysis

4.1 Vendor Market Share

4.2 3M

4.3 Adidas

4.4 Amazon

4.5 Arvato SCM Solutions

4.6 BASF

4.7 Basware

4.8 BMW

4.9 C.H. Robinson

4.10 Cainiao Network (Alibaba)

4.11 Cisco Systems

4.12 ClearMetal

4.13 Coca-Cola Co.

4.14 Colgate-Palmolive

4.15 Coupa Software

4.16 Descartes Systems Group

4.17 Diageo

4.18 E2open

4.19 Epicor Software Corporation

4.20 FedEx

4.21 Fraight AI

4.22 H&M

4.23 HighJump

4.24 Home Depot

4.25 HP Inc.

4.26 IBM

4.27 Inditex

4.28 Infor Global Solutions

4.29 Intel

4.30 JDA

4.31 Johnson & Johnson

4.32 Kimberly-Clark

4.33 L’Oreal

4.34 LLamasoft Inc.

4.35 Logility

4.36 Manhattan Associates

4.37 Micron Technology

4.38 Microsoft

4.39 Nestlé

4.40 Nike

4.41 Novo Nordisk

4.42 NVidia

4.43 Oracle

4.44 PepsiCo

4.45 Presenso

4.46 Relex Solution

4.47 Sage

4.48 Samsung Electronics

4.49 SAP

4.50 Schneider Electric

4.51 SCM Solutions Corp.

4.52 Splice Machine

4.53 Starbucks

4.54 Teknowlogi

4.55 Unilever

4.56 Walmart

4.57 Xilinx

5.0 AI in SCM Market Analysis and Forecasts 2019 – 2024

5.1 AI in SCM Market 2019 – 2024

5.2 AI in SCM by Solution 2019 – 2024

5.2.1 Platforms

5.2.2 Software

5.2.3 AI as a Service

5.3 AI in SCM by Solution Components 2019 – 2024

5.3.1 Hardware Non-IoT Device IoT Embedded Device Security Devices Surveillance Robots and Drone Networking Devices Smart Appliances Medical and Healthcare Device Smart Grid Devices In-Vehicle Devices Energy Management Device Components Wearable and Embedded Components Real Time Location System (RTLS) Barcode Barcode Scanner Barcode Stickers RFID RFID Tags Sensor Processors

5.3.2 Software

5.3.3 Services Professional Services

5.4 AI in SCM by Management Function 2019 – 2024

5.4.1 Automation

5.4.2 Planning and Logistics

5.4.3 Inventory Management

5.4.4 Fleet Management

5.4.5 Virtual Assistance

5.4.6 Freight Brokerage

5.4.7 Risk Management and Dispute Resolution

5.5 AI in SCM by Technology 2019 – 2024

5.5.1 Cognitive Computing

5.5.2 Computer Vision

5.5.3 Context-aware Computing

5.5.4 Natural Language Processing

5.5.5 Predictive Analytics

5.5.6 Machine Learning Reinforcement Learning Supervised Learning Unsupervised Learning Deep Learning

5.6 AI in SCM by Industry Vertical 2019 – 2024

5.6.1 Aerospace and Government

5.6.2 Automotive and Transportation

5.6.3 Retail and Consumer Electronics

5.6.4 Consumer Goods

5.6.5 Healthcare and Medical

5.6.6 Manufacturing

5.6.7 Building and Construction

5.6.8 Others

5.7 AI in SCM by Deployment 2019 – 2024

5.7.1 Cloud Deployment

5.8 AI in SCM by AI System 2019 – 2024

5.9 AI in SCM by AI Type

5.10 AI in SCM by Connectivity 2019 – 2024

5.10.1 Non-Telecom Connectivity

5.10.2 Telecom Connectivity

5.10.3 Connectivity Standard

5.10.4 Enterprise

5.11 AI in SCM Market by IoT Edge Network 2019 – 2024

5.12 AI in SCM Analytics Market 2019 – 2024

5.13 AI in SCM Market by Intent Based Networking 2019 – 2024

5.14 AI in SCM Market by Virtualization 2019 – 2024

5.15 AI in SCM Market by 5G Network 2019 – 2024

5.16 AI in SCM Market by Blockchain Network 2019 – 2024

5.17 AI in SCM by Region 2019 – 2024

5.17.1 North America

5.17.2 Asia Pacific

5.17.3 Europe

5.17.4 Middle East and Africa

5.17.5 Latin America

5.18 AI in SCM by Country 2019 – 2024

5.18.1 Top Ten Country Market Share

5.18.2 USA

5.18.3 China

5.18.4 Canada

5.18.5 Mexico

5.18.6 Japan

5.18.7 UK

5.18.8 Germany

5.18.9 South Korea

5.18.10 France

5.18.11 Russia

6.0 Summary and Recommendations

6.1 Artificial Intelligence Providers

6.2 Automation System Providers

6.3 Communication Service Providers

6.4 Computing Companies

6.5 Data Analytics Providers

6.6 Enterprise and Government

6.7 Immersive Technology (AR, VR, and MR) Providers

6.8 IoT Suppliers and Service Providers

6.9 Logistics Management Companies

6.10 Semiconductor Companies

For more information about this report visit

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Supply Chain Big Data Analytics Market – Key Futuristic Trends and Competitive Landscape 2025

According to Verified Market Research, the Global Supply Chain Big Data Analytics Market was valued at USD 2.12 Billion in 2018 and is projected to …

Global Supply Chain Big Data Analytics Market Analysis

According to Verified Market Research, the Global Supply Chain Big Data Analytics Market was valued at USD 2.12 Billion in 2018 and is projected to reach USD 7.53 Billion by 2026, growing at a CAGR of 17.2% from 2019 to 2026.

What is Supply Chain Big Data Analytics?

Supply chain Big Data analytics is defined as a software program that is helpful for organizations in order to automate the processes involved in the material purchase and inventory maintenance. Supply Chain Big Data Analytics is necessary for optimizing business performance as well as proper alignment of supply chains with business strategy. Management of supply chain is required in order to ensure the competitiveness of an organization. It has application in different sectors such as retail, healthcare, transportation & logistics and manufacturing.

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Supply Chain Big Data Analytics

Global Supply Chain Big Data Analytics Market Outlook

In the report, the market outlook section mainly encompasses fundamental dynamics of the market which include drivers, restraints, opportunities, and challenges faced by the industry. Drivers and Restraints are intrinsic factors whereas opportunities and challenges are extrinsic factors of the market.

Factors such as growing retail sector, rapidly increasing volumes of business data being generated across various industries, such as manufacturing, transportation, retail & consumer, and healthcare along with growing awareness about benefits offered by supply chain analytics have been driving the Global Supply Chain Big Data Analytics Market. On the other hand, factor such as lacking skilled workers might hinder the overall market at a global level.

Verified Market Research narrows down the available data using primary sources to validate the data and use it in compiling a full-fledged market research study. The report contains a quantitative and qualitative estimation of market elements which interests the client. The “Global Supply Chain Big Data Analytics Market” is mainly bifurcated into sub-segments which can provide classified data regarding the latest trends in the market. This can be of great use in gaining knowledge about the cutting-edge technologies in the market.

Global Supply Chain Big Data Analytics Market Competitive Landscape

The “Global Supply Chain Big Data Analytics Market” study report will provide a valuable insight with an emphasis on global market including some of the major players such as SAS Institute Inc., SAP SE, IBM Corporation, Oracle Corporation, Sage Clarity Systems, Kinaxis Inc., MicroStrategy Inc., Genpact Ltd., Capgemini Group, Birst, Inc., and Tableau. Our market analysis also entails a section solely dedicated for such major players wherein our analysts provide an insight to the financial statements of all the major players, along with its product benchmarking and SWOT analysis. The competitive landscape section also includes key development strategies, market share and market ranking analysis of the above-mentioned players globally.

Global Supply Chain Big Data Analytics Market Segmentation, by Type

• Solution

o Supply Chain Procurement & Planning Tool

o Sales & Operations Planning

o Manufacturing Analytics

o Transportation & Logistics Analytics

o Other

• Service

o Professional Services

o Support And Maintenance Services

Global Supply Chain Big Data Analytics Market Segmentation, by Deployment

• On-premise

• On-cloud

Global Supply Chain Big Data Analytics Market Segmentation, by End-user

• Retail

• Healthcare

• Transportation & logistics

• Manufacturing

• Others

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Global Supply Chain Big Data Analytics Market Geographic Scope

• North America

o U.S.

o Canada

o Mexico

• Europe

o Germany

o UK

o France

o Rest of Europe

• Asia Pacific

o China

o Japan

o India

o Rest of Asia Pacific

• Latin America

o Brazil

• Rest of the World

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Scout RFP Receives Awards from Unitywater and Palo Alto Networks Reflecting Extraordinary Impact

Scout is headquartered in San Francisco, and funded by Workday Ventures, Salesforce Ventures, New View Capital, GV, Menlo Ventures, and Scale …

SAN FRANCISCO–(BUSINESS WIRE)–Apr 10, 2019–Scout RFP, the Sourcing and supplier engagement platform, today announced it has received two awards from customers Unitywater and Palo Alto Networks, reflecting the extraordinary impact Scout has made on their organizations.

Unitywater, the progressive water and sewage utility based in Queensland, Australia, and Scout’s first of a growing number of Australian customers, presented Scout with the 2019 Supply Chain Operations Award following Scout SPARK 2019, Scout’s annual customer knowledge and insight forum. The award required executive board approval, as well as vetting across the leadership and governance board of Unitywater, underscoring its unique value and meaning.

“We are presenting this award to Scout because Scout has given Unitywater much stronger capabilities to serve our customers better,” said Namejs Kins, Branch Manager Procurement, Unitywater. “It is very important to emphasize that Scout’s platform isn’t just administrative software; it’s a real value driver .

Additionally, Palo Alto Networks presented Scout with the 2019 overall award for Supplier Appreciation Business Productivity Tool at its recent Supplier Day event. The network security company noted four factors that made Scout especially impactful for them: extraordinary customer support and success, ease of use within the platform, enhanced efficiency and the glowing feedback from their internal team.

“We are absolutely honored to receive these awards. They reinforce Scout’s chief value of obsessing over the customer and the dedication to customer success that is foundational to our culture,” said Allison Yount, Director of Customer Success at Scout. “To see our technological innovations and product advancements empower our customers is what we strive for every day at Scout.”

Scout has more than 190 customers worldwide in manufacturing, financial services, energy and utilities, retail, food and beverage, and healthcare industries, including major enterprises like Levis, Owens Corning, Starbucks, Verizon Media, and Zebra Technologies. For more information on Scout and its customers, visit

About Scout RFP

Scout is the sourcing and supplier engagement platform trusted by procurement teams to streamline their process, manage a unified pipeline of projects, and collaborate with stakeholders and suppliers to achieve greater business impact. Over 190 global brands like Adobe, Biogen, easyJet, Workday, Salesforce, ServiceNow, and Zappos trust Scout’s automated sourcing and auction platform to deliver greater impact through seamless business engagement. Managing over $23 billion in project spend, Scout’s powerful platform makes enterprise commerce faster and more transparent, reduces risk, and drives profitability. Scout is headquartered in San Francisco, and funded by Workday Ventures, Salesforce Ventures, New View Capital, GV, Menlo Ventures, and Scale Ventures. Read more on the Scout Blog or follow @scoutrfp on Twitter or LinkedIn.

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CONTACT: BLASTmedia for Scout RFP

Emily Cress

317-806-1900 x 137




Copyright Business Wire 2019.

PUB: 04/10/2019 09:00 AM/DISC: 04/10/2019 09:00 AM

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Digital thread on blockchain technology

Blockchain technology can be leveraged in building genealogy and digital threads for products across several industry verticals.


Manufacturers constantly strive to serve their customers better and, at the same time, use customer insights to improve product performance and reduce costs. Thanks to falling costs of sensor technology and the cloud’s ability to store and analyze huge amounts of data, manufacturers have found new ways to gather insights into both the product and the customer. They have also started extending this to their own engineering and manufacturing operations, making it possible to create a digital thread of the products they conceive, produce, sell, and service. Blockchain allows us to capture, maintain, and share the huge amount of data as well as use it for any advanced analysis or simulation of past events or future scenarios.

This paper explains various components of the system and how organizations can use them effectively to become leaders in their field of operations.

Why digital thread?

Digital thread is a framework that integrates design, engineering, suppliers, manufacturing, maintenance, warehouse, transportation, warranty and service operations, and systems to provide a seamless flow of information. It leverages the captured data/digital thread of the product to perform key analytics across the complete product lifecycle in order to optimize processes capability, efficiency and effectiveness, product design, asset performance/OEE, manufacturing operations, service operations, customer satisfaction, and to improve and maintain brand reputation. Digital thread includes key traceability/genealogy attributes/records of the product and its parts and features the manufacturer’s name, part number, country of origin, manufacturing plant, production line information, date of manufacturing, actual process parameters applied during manufacturing and assembly of the individual product, process history, lot code/date code, and other relevant records. This helps OEMs, service providers, and stake holders to:

  • Isolate products in the field with potentially faulty components or certain traceability attributes (such as serial number, manufacturer’s name, date code/batch number)
  • Proactively recall/repair products assembled with faulty components
  • Track the complete history of the product (cradle to grave) during its lifecycle to better predict operational behavior and improve product reliability through better design and process capabilities
  • Effectively manage warranty and service to improve customer satisfaction and brand loyalty
  • Facilitate better supplier management process
  • Reduces liability costs.

Having a digital thread of any product improves product reliability, customer service, product design, and brand reputation. It also reduces the number of fraudulent warranty claims and leads to a better competitive position.

Key requirements for implementing digital thread projects

The implementation of a digital thread depends on the type of product and industry. It may be necessary in order to comply with regulatory requirements, bolster customer satisfaction, or serve as a competitive differentiator. Usually, the channel master/OEM leads the digital thread project implementation across the chain. Selecting a digital thread solution requires identifying the right use/business case. It is equally important that business KPIs be clearly mapped with the business problems/case or goals to be resolved or achieved. This approach will help business and IT teams to:

  • Determine data elements and records that need to be captured along the supply chain
  • Identify the source of data records
  • Determine how and how often (sampling plan, frequency) the data will have to be collected
  • Estabilsh which parts (along with their respective supplier/manufacturers) will be impacted since the genealogy records of identified parts will have to be associated with the serial number of the product during the manufacturing and assembly process of the product
  • Collect key stages of data record processing
  • Existing system capabilities (including desired system capabilities)
  • Need of AIDC (automatic identification and data capture) technologies such as RFID, bar code, IT system (e.g., MES, WMC, transport management system, IIoT, etc.) to capture the desired data along the supply chain.

Because multiple parties are involved in a supply chain to move the final product from raw material suppliers to the end consumer and because the product passes through various process stages, locations, and entities during its journey, it creates many transaction records in various systems. In order to build a digital thread, each system must capture, validate, and store the necessary data elements in real time and store the captured data for future reference and key operational analytics. Each party in the supply chain may have their own set of systems for storing data, or each party can send data to a centralized repository to maintain it securely. Below are the key requirements for implementing digital thread projects:

Supplier alignment and compliance: alignment of parts suppliers is essential in implementing the digital thread. The digital thread of any product includes the genealogy records of the parts (manufacture/supplier name, manufacturer/supplier part number, OEM part number, date of manufacturing/date code, batch/lot code, serial number of parts, etc.) that have been used to manufacture and assemble it. All the data elements should be standardized across suppliers. Moreover, OEM/manufacturing partners should continuously evaluate suppliers to ensure completeness and accuracy of the records as per the contractual requirements between the OEMs and the suppliers (smart contracts).

Manufacturing partners: If the manufacturing process is outsourced, the manufacturing partners also need to align their data collection (building a digital thread) strategy as per the OEM’s requirement. This would require that the digital thread of each unit of the product be sent from the manufacturing partner’s system to the OEM system.Too build a digital thread in real time, manufacturing partners must implement integrated information systems so they can collect the right data along the process (ERP, MES, DCS/OPC, test equipment, quality, WMS, etc.).

Genealogy records for parts supplied by suppliers can be captured in ERP at the receiving area. However, linking the genealogy records of parts with their upper-level assembly (serial number) will normally take place at the respective stages of the production process (as per the process flow/routing) through the MES/shop floor system followed by BOM validation. Similarly, process parameter records can be captured during the manufacturing phase (through OPC/DCS – MES) followed by validation with machine programs/process specification. Validation rules must be built at each stage of the production flow to ensure that the data/digital thread are correct and complete. In essence, a complete process history/digital thread of each unit has to be built as the product moves along the supply chain, touching different systems where each system will capture, associate, and store parameter/data elements (along with the time stamp) validated with specifications.

OEM: While receiving and updating digital thread records from the manufacturing partner’s location, the records have to be validated as per the SLAs/contractual requirements at the receiving server of the OEM. If the digital thread for any unit is incorrect, the system should raise an alert to the manufacturing partner so that they know which records need to be updated. These records are crucial to performing key analytics.

Sales organization: should associate the product serial number with the sales order to capture customer details (name, location, product installation site, etc.).

Transport: should capture the complete history of the product while it is in transit. Food, pharmaceuticals, and other perishable products that require specific temperature, humidity, or other packaging parameters should be monitored in real time and the data recorded in respective databases for future references.

Warranty and service: service teams should capture the date of installation, product performance parameters, and other details, such as actions taken to resolve issues when installing and repairing the product.

IT system: the IT system requirement depends on the supply chain party and on the role that party plays in the end-to-end supply chain. For example, tier-one suppliers have to provide genealogy records of the part they are supplying to the OEM/manufacturing partner. To capture genealogy records at batch level, suppliers at the very least need an ERP system to capture batch records of parts. Similarly, OEM/manufacturing partners must implement both ERP and MES systems to receive material in ERP and associate parts with upper level or top level assembly and capture process records from each production stage during production process. This is possible with MES or shop floor system. Therefore, the system required will depend on the supply chain party and their role.

Figure 1 – Digital thread implementation architecture with centralized database

Use of AIDC technologies in the digital thread – Automatic identification and data capture technologies provide the basis for implementing the digital thread and help identify objects for data needs be collected. This technology includes barcodes, scanners, and RFID tags. Technology helps to ensure that data elements are captured in an accurate and timely way into the system. Manual entries are mostly offline, sometimes biased, and easy to manipulate and this will always mislead and may have severe impact on business. Also, it is practically impossible to capture, validate, and send operational and tracking records manually in high-volume and complex manufacturing processes.

Infrastructure: Implementation of digital thread also requires the right infrastructure; the bandwidth of the network should be identified based on the operation’s complexity, product complexity, real-time vs batch processing of data, cost, etc. Database capacity is another critical need and data retention period needs, performance, analytics requirements, on-premises vs cloud, customer requirements, regulatory compliance, etc. should also be considered by each party in the supply chain when selecting the right size.

Audits: Digital thread systems should support quick audits to ensure supplier conformance, data quality , validation rules, etc.

Figure 1 indicates the high-level, digital thread solution architecture for a simple product from tier-one suppliers to tier-one customers. The solution depicts key data elements can be captured to build digital thread with single centralized data base and with basic reporting through a simple analytics engine (just for reference).

How can blockchain support the digital thread?

A blockchain is a distributed database technology that builds on a tamper-proof list of time-stamped transaction records. Its innovative power stems from allowing supply chain parties to transact with others they do not trust over a global network in which nobody is trusted. It enables by a combination of peer-to-peer networks, consensus-making, cryptography, and market mechanisms. Each constituent of this database represents a “block.”

We realized that blockchain has several capabilities that can be leveraged in building genealogy and digital threads for products across industry verticals (aerospace, defense, food, automotive, pharma, consumer electronics, communications, etc.) that will benefit OEMs, suppliers, service providers, consumers, and governments. In this paper, we discuss the feasibility of implementing the digital thread on blockchain technology for manufacturing industries. The objective of this section is to discuss how blockchain technology can support the implementation of digital threads

In essence, blockchain is a peer-to-peer database that offers access to the history of all previous states. Furthermore, Some of the blockchain’s characteristics that directly support digital thread solution implementation are:

  • Distributed data bases and peer-to-peer network: Blockchain can capture and link specific data blocks across all the stages, from suppliers, manufacturing, assembly, testing, etc. up until te final unit of the product reaches the customer. Also, from customer site product can send its operational data to the blockchain, so it eliminates the need for investment in data storage capacities to handle the millions of records generated during the manufacturing and assembly process.
  • Support smart contracts between supply chain stages (inter organization and within organization): Blockchain’s smart contract functionality enables validation rules that can be written between the supply chain stages (external and internal) to ensure that data is not missed and that data is accurate and complete. For example, in order to build the digital thread, association of genealogy records of key parts to upper level assembly/product, the respective suppliers must provide certain mandatory genealogy records of that part, which requires validation at the OEM end while receiving that part at the OEM end to ensure supplier compliances. Also, during the manufacturing and assembly process, genealogy records of parts have to be associated with product serial numbers. This requires validation from BOM and routing files to ensure that the correct part is being assembled at the correct stage and requires validation of actual process parameters from recipe or machine programs. Smart contracts can be specified along the manufacturing and assembly process. At each stage, validation will be performed and if anything does not meet the smart contract’s terms and conditions, the system will raise an alarm on a real-time basis to avoid any potential issues that may impact the business.
  • Build trust by ensuring data accuracies and authenticity: Through the use of smart contracts and validation scripts, blockchain provides excellent capabilities to ensure (right from the source of the data) that the right data is being captured from the right source in the right sampling plan at the right time across all the stages of the supply chain. Data blocks that consist of records become immutable once on the blockchain. This is key to ensuring trust between parties located thousands of miles apasrt in global and complex supply chains. Moreover, a copy of the entire blockchain is held on every node on the network and consensus is achieved either by proof-of-work or proof-of-stake algorithms or by forming smart contracts.
  • Easy to audit: A blockchain by design is perfectly auditable. Each individual operation’s records (for example part genealogy records from a supplier/manufacturer) can be easily recorded, validated, and archived. These can be easily audited for supplier non-compliance, therefore auditing is as simple as joining the blockchain network, as this allows one to replay past operations. It is combined with cost-effective guarantees of authenticity for every record.
  • Access to the right stakeholders/users – security: An interesting aspect is that blockchain can be used as an authentication provider. Using their key pair, users register their identity on the blockchain. Another way to enable security is to provide membership services to manage user identities on a permission blockchain network through the Certificate Authority peer. Membership services provide a distinction of roles by combining elements of public key infrastructure (PKI) and decentralization (consensus). By contrast, non-permission networks do not provide member-specific authority or a distinction of roles. With blockchain technology, monitoring transactions will be more secure and transparent. Digital thread is actually a series of transaction nodes that link to capture data while products move from one point to other. By utilizing this technology to capture and store data across the supply chain, companies can reduce time delays, cost, and human error.
  • Cost of implementation: Blockchain significantly lowers the cost of a transaction while ensuring data accuracy, completeness, transparency, and security. It eliminates the need for a centralized trusted party and the need for investments in data storage capacity (for example, in some of industries, the OEM is required to retain product data for more than 15 years (regulatory compliance)).

Architecture diagram – digital thread on blockchain

Challenges to implementing the digital thread on blockchain

Below are the challenges we should consider when considering blockchain for digital thread solutions:

  1. New technology: Resolving challenges such as transaction speed, the verification process, and data limits will be crucial in making blockchain widely applicable.
  2. Frequent configuration: Each product requires configuration and the development of validation rules/smart contacts to capture product-specific and accurate data along the supply chain – a time consuming but one-time activity.
  3. Integration concerns: Blockchain applications offer solutions that require changes/modifications and the customization of existing IT systems. To implement digital thread projects on time, smoothly, and within budget, companies must strategize the system integration and customization. Also, it is not yet clear how integration between blockchain and other systems, such as ERP, MES, PLM, and WMS, will occur.
  4. Higher energy consumption: Use substantial amounts of computer power to validate millions of records generated across the supply chain
  5. Control, security, and privacy : While solutions exist, including private or permissioned blockchain and strong encryption, there are still cybersecurity concerns to be addressed before the general public will entrusts their personal data to a blockchain solution.
  6. Cultural adoption

    Blockchain represents a complete shift to a decentralized network which requires the buy-in of its users and operators. Conversely, the digital thread can easily be implemented on a centralized solution (on premises or cloud based).
  7. Cost

    Blockchain offers tremendous savings in transaction costs and time but the high initial capital costs could be a deterrent.


Blockchain provides an excellent platform for digital thread implementation. Its capability to validate each record captured along the supply chain, including production processes (i.e. in end-to end journey of product), helps OEMs ensure accurate and tamper-proof data that builds trust between the supply chain and external parties. The digital thread can meet the objectives of various stakeholders in the chain (design team, suppliers, OEM, warranty and service providers, etc.) and helps to improve product reliability, process capability, and plant capacity by eliminating unplanned downtime. It also enables effective warranty management and significantly improves brand reputation while keeping cost at optimum levels.

Implementing the digital thread on blockchain might be challenging initially since blockchain is a new technology. Therefore, it is highly recommended to go with a low-risk approach, i.e. a POC that uses blockchain internally (within the organization) with a couple of critical parts suppliers/manufacturers as a database for recording part and product genealogy data, supplier/manufacturer data, internal data, use of smart contracts at selected stages, i.e. from part supplier (supplier to OEM), inspection and test records of parts (based on smart contracts that may include verification of mandatory records and SLA,s), date of manufacturing, as-built records (where part is associated with product to create genealogy as per BOM validations), capture process parameter that influence product performance, etc. along with the validation scripts. Implementing and testing a smaller portion of digital thread use cases will help organizations develop the skills they need for full-scale implementation.

For successful implementation, the key is to select the right blockchain service provider. Doing so will help organizations achieve their targeted objectives. There are a few cloud-based blockchain services from both start-ups and large platforms (such as Amazon, IBM and Microsoft), that make implementation of business solutions much easier. It is highly recommended to have a collaborative model between blockchain service providers, parts suppliers, manufacturing partners, and other supply chain entities to achieve business objectives in a timely manner.

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Altoros Introduces Blockchain Development Services by Implementing a Modern DLT Technology

Distributed ledger technology allows for reducing supply chain deviations, making speculative ordering manageable, and cracking a bullwhip effect in …

Press Release Summary:

  • Provides flexible improvements in business operations
  • Reducing deviations in supply chain processes
  • Minimizing the number of failed fulfillments

Original Press Release:

Altoros offers Blockchain Development Services for Logistics

Sunnyvale, Calif, Nov. 20, 2018 – April 2, 2019 – A full-cycle software development company Altoros has officially announced the launch of blockchain development services for logistics and supply chain. The main aim of the service launched is to blare the problems with inventory management, stock handling, product tracking, and transportation.

As an early adopter of Cloud Foundry, Blockchain, and TensorFlow technologies, Altoros has vast expertise in building enterprise-grade software solutions that can improve business processes and boost operational efficiency.

Altoros team has comprehensive experience in creating bespoke logistics and supply chain solutions by implementing a modern DLT technology. Distributed ledger technology allows for reducing supply chain deviations, making speculative ordering manageable, and cracking a bullwhip effect in your supply chain management.

The core benefits of implementing a blockchain-based supply chain solution are:

  • flexibility improvements in business operations;
  • reducing deviations in supply chain processes;
  • intuitive planning process;
  • better synergy between stakeholders;
  • minimizing the number of failed fulfillments;
  • minimizing the bullwhip effect.

The primary goal of Altoros’s team is to evaluate clients’ needs and create a right blockchain solution to enhance their internal and external processes, achieve their competitive advantages through refining planning accuracy and speed of business operations, reducing manual errors, and de-risk their business operations.

To learn more about the solution, please visit

To get more information about the company, its services, and own products, refer to

About the company

Altoros is a 300+ people strong consultancy that helps Global 2000 organizations with a methodology, training, technology building blocks, and end-to-end solution development. The company turns cloud-native app development, customer analytics, blockchain, and artificial intelligence into products with a sustainable competitive advantage. Assisting enterprises on their way to digital transformation, Altoros stands behind some of the world’s largest Cloud Foundry and NoSQL deployments.


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