Is There a Competitive Advantage for Companies Using Blockchain?

Published on May 9, 2018

Updated on Oct 16, 2019

Do you mostly buy from your ‘favourite’ local store? What if all the outlets of your favourite store knew your shopping habits and preferences? Provided your data is kept secure along the transaction chain, are the promise of hyper-personalization and ultra-high convenience worth the trade-off?

Thankfully, this isn’t hypothetical. Companies using blockchain or distributed ledger technology are able to track records easily on a global scale. Not only in retail, but almost every industry is applying blockchain to simplify its processes and offer personalized solutions to its customers.

In this article, we’ll discuss what makes blockchain a compelling technology and its continuing adoption across industries.

Why are Companies Using Blockchain?

Blockchains are encrypted, growing lists of records. It records every single transaction with a time-stamp. No one, including the owner, can modify the ledgers (or records) in a blockchain.

The following features make the blockchain technology a perfect fit for transactional record-keeping in different industries.

Distributed: Blockchain is a decentralized technology, i.e. there’s no authority looking after the framework and operations. The data is accessible to all participants in the network.
Immutable: One of the key advantages of blockchain over any other technology is unchangeability. Post-transaction, no one including the creator can modify the records.
Robust: Traditional communication channels involve many indirections. For example, a bank executes transactions in its centralized database. Then the bank sends the corresponding email/SMS to the user about the transaction. Blockchain is a decentralized technology i.e. users have direct access to the transaction settlements. Companies using blockchain are more robust towards internal and client services.
Encrypted: Encryption is core to the security in blockchain technology. It means only the authorized users and participants can access the information. It also secures the identity of the participants. Ciphertexts (encrypted data, which is meaningless to external users) protects the information from intruders.
Consensus: The consensus algorithms are core to the blockchain architecture. The consensus is a decision-making process for a group of active nodes (participants). Participants agree to the decision made by the algorithm.
Tracking: It is easier to track transactions in a blockchain. The technology records every transaction with a time-stamp thus preventing corruption.

The finance industry was an early adopter of blockchain technology. In fact, the credit for the popularity of this technology goes to ‘bitcoins’, which are completely digital financial transactions.

Blockchain Adoption Across Industries

Here’s an overview of how industries using blockchain are enhancing the operations.

Use of Blockchain in Supply Chain and Logistics

E-commerce is certainly giving a boost to the supply chain and logistics industry. But, are traditional record-keeping compatible with the growing demands? Because, today, to deliver a product from point A to point B might include multiple geographies and involve multiple entities, invoices, payments, and extend over months. However, tracking shipments and business transparency is one of the key challenges that the supply chain industry struggles with. Companies using blockchain in the supply chain domain can benefit in the following ways-

Payments and fund transfers are fast and simple for stakeholders at the international level.
It’s possible to keep a track record for the product from its source of origin to end-users. For example, Walmart uses blockchain to track pork it sources from China. It records where each piece of meat came from, processed, stored, its sell-by-date, and the buyer.
Since every participant can collaborate and share records, blockchain ensures transparency in information sharing.

Blockchain in Financial Services

Statista expects that the global blockchain technology market will reach $23.3 bn by 2023. It also suggests that the financial sector will cover more than 60% of investments in this technology.

Financial services can harness blockchain for robust cross-border payments and processing, P2P payments, micropayments, and currency exchange. Investors, day traders, and market makers can also deploy blockchain for clearing and settlement in almost real-time.

Blockchain in Travel

Travel is one of the fastest-growing aspects of the global economy. Both customers and travel & tourism service providers can harness blockchain applications. Customers need not hassle with forex and can access in-depth travel-related information of the destination.

Travel businesses can bring transparency in flight and hotel bookings. For instance, for flight and hotel for a customer, a travel agency needs to share information to the customer and different firms. Blockchain can reduce manual dependencies by sharing relevant information to different stakeholders instantly.

In the list of travel companies using blockchain, Winding Tree is a leading name. It is a decentralized travel ecosystem startup that connects travellers to service providers like airlines, hotels, and tour guides directly. By eliminating the third-party fees associated, it reduces travel overheads. Blockchain’s LIF tokens, Smart Contracts, and ERC827 protocol are at the core of Winding Tree’s travel technology.

Blockchain in Insurance

The insurance industry often struggles with double-booking, counterfeiting, and premium diversions through unlicensed brokers. Distributed ledger technology in insurance can help to minimize the instances of fraudulent activities.

Smart contracts, insurance claims automation, UAVs (unmanned aerial vehicles) for underwriting, and shared databases to simplify insurance can bring transparency in the insurance industry.

Blockchain Benefits in Healthcare

The traditional healthcare record-keeping is cumbersome and the surgeon might lose important remarks, allergies, etc. while going through manual files and folders. Blockchain can track one’s medical history since birth. Also, every minute detail of diagnosis would be available to the medical professionals, even if the patient loses the prescriptions and reports.

WHO reports that developing nations produce about 10%-30% of the counterfeit drugs. Moreover, the counterfeit drug market hit $200 billion worth in 2018. Blockchain can track the drug right from sourcing the raw materials to manufacturing and distribution, reducing the instances of this critical challenge of counterfeiting.

Concluding Remarks

The International Data Corporation (IDC) predicts- investment in blockchain solutions will reach $11.7 billion in 2022 from $552 million during 2018. The blockchain trends that different industries will witness include-

Blockchain as a Service (BaaS)
Favourable regulations around the world towards blockchain
Consumer-centric digital assets
Additional security layers
Use of blockchain technology for better user experiences (UX).

Building blockchain systems are transforming the transaction value chain across industries. Talk to our experts to learn how blockchain is shaping the future of digital enterprises. Drop us a word at hello@mantralabsglobal.com

Contributing Authors: Nidhi Agrawal (Content Writer @Mantra Labs)

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?

How blockchain is disrupting businesses?

Why are Companies Using Blockchain?

Blockchain Adoption Across Industries