Navigating the Future: 9 Ways Tech and AI are Impacting Supply Chains
Supply chains are the backbone of the global economy, orchestrating the seamless flow from manufacturers to consumers. They are intricate networks covering vendor selection, raw materials, manufacturing, warehousing, and end-user delivery. Beyond meeting consumer demands, they drive economic growth, create jobs, drive profitability and brand share, and fuel innovation.
In crises like the Covid-19 Pandemic, resilient supply chains shine. Today’s dynamic business landscape demands agile responses to tech shifts, changing preferences, and disruptions. Global supply chains, complex and swift, navigate regulatory changes. Rapid evolution enhances efficiency and cost-effectiveness, enabling organizations to meet demands and conquer challenges. Innovation and data-driven insights are paramount for staying competitive.
In this series of blog articles we will explore the leading trends shaping future Supply Chains. This first article will discuss The Impact of Technology on Supply Chains, with subsequent posts covering the following topics:
– Supply Chain Operations: trends impacting functions such as manufacturing, warehousing and logistics as well as trends in automation.
– Supply Chain Optimization: optimizations such as supply collaboration, e-commerce, data & analytics and supply chain flexibility & agility. The post will also discuss sustainable supply chains.
– People in Supply Chain: the “people” impact resulting from wide-scale changes and how an organization can prepare people for this change.
– Supply Chain Business Environment, Supply Chain Resilience and Change Management: what organizations can expect going forward and how this will impact their supply chains.
Trends in Supply Chain: The Impact of Technology and AI on Supply Chain
In the landscape of global commerce, the importance technology will play in the future of supply chains is paramount. Artificial Intelligence (AI), Cybersecurity, and advanced tools are emerging as pivotal technological components all working together to develop Digital Supply Chains:
- AI brings unprecedented efficiency to supply chain management by optimizing processes, predicting demand patterns, and enhancing decision-making through data-driven insights. Its ability to analyze vast datasets enables organizations to streamline operations, minimize costs, and respond swiftly to market fluctuations.
- Advanced tools, such as Digital Supply Chain Twins, Supply Chain Control Towers, Blockchain and the Internet of Things (IoT) devices foster transparency, traceability, and real-time monitoring, promoting a resilient and agile supply chain.
- Robust cybersecurity measures are indispensable in safeguarding sensitive information, assuring the integrity of transactions and mitigating the risks associated with increasingly complex digital ecosystems.
As technology continues to evolve, its integration into the supply chain not only enhances operational efficiency but also fortifies the sector against emerging challenges, ensuring a future-ready and adaptive foundation for global commerce.
AI is providing exciting advancements in supply chains, as exhibited from the following examples:
- Supply Chain Mapping: AI-driven supply chain mapping involves utilizing artificial intelligence algorithms to analyze and visualize the end-to-end flow of goods, information, and processes within a supply chain, offering comprehensive insights and optimization opportunities. Wal-Mart, Tyson Foods, Maersk and Siemens are real world examples of companies using Supply Chain Mapping to find and engage with alternate suppliers as well as pre-qualify alternate suppliers.
- Machine Learning and Predictive Analytics: Machine learning in the supply chain applies algorithms to learn from data, predict trends, and optimize decision-making. This boosts forecasting accuracy, enhances inventory management, and improves overall operational efficiency. Continuous adaptation to new information ensures a responsive and agile supply chain. Companies like DHL, Maersk, UPS, and Vibronyx Inc. leverage AI-driven predictive analytics to analyze historical data, identify patterns, and forecast future trends, enabling proactive decision-making, risk mitigation, and operational optimization.
- Operational Performance: Specific examples of how AI can optimize operational performance include AI-powered forecasting that can equip operations with improved intelligence to reduce demand-supply mismatches, AI-based algorithms that automate goods retrieval from warehouses for smooth order fulfillment and chatbots to improve customer service. Additional examples include solutions supporting fleet management platforms that optimize routes for a faster and more economical movement of goods and AI-powered autonomous vehicles that can reduce driver costs.
Along with the importance of AI to the future of supply chains, there are various advanced tools, some of which will work hand-in-hand with AI capabilities that will play an important role in supply chains:
- Digital Supply Chain Twin: A Digital Supply Chain Twin is a virtual simulation of a physical supply chain that uses real-time data and artificial intelligence to analyze and predict its behavior. It helps organizations test scenarios, model different options, and understand the impact of decisions and disruptions on network operations. Real world examples of companies utilizing Digital Supply Chains include Google, FedEx, DHL, GE, Rolls-Royce and Pratt & Whitney.
- Supply Chain Control Tower: A Supply Chain Control Tower is a centralized platform that provides end-to-end visibility and real-time monitoring of supply chain activities spanning areas such as transportation, warehousing, inventory management and manufacturing. This enables proactive decision-making, issue resolution, and optimization of logistics processes. Real world examples of companies utilizing Supply Chain Control Towers include Coca-Cola, IBM, Nestle, Procter & Gamble and Unilever.
- Blockchain: Blockchain in the context of the supply chain acts as an immutable and transparent digital ledger, enabling secure and traceable recording of transactions, shipments, and processes across a decentralized network, thereby enhancing trust, reducing fraud, and optimizing transparency throughout the supply chain ecosystem. This technology ensures that each participant in the supply chain has access to a consistent and incorruptible record of transactions, fostering efficiency and accountability. Real world examples of companies utilizing Blockchain include Walmart to track products back to their origin, British Airways and Maersk to manage cargo, and Nestle for their Supply Chain management.
- Internet of Things: The Internet of Things (IoT) in the supply chain embeds sensors and connected devices for real-time tracking and data collection, fostering increased visibility and efficiency. This enables proactive decision-making, optimizing overall supply chain performance. Facilitating seamless communication between devices, IoT enhances management, provides actionable insights, reduces delays, and offers a comprehensive understanding of the entire supply chain ecosystem. Companies like Amazon, Volvo, and Maersk Line use IoT for warehouse management, monitoring car part shipments, and tracking containers globally.
- Smart Logistics: Smart Logistics integrates cutting-edge technologies such as IoT sensors, data analytics, and automation to optimize the entire logistics process, enabling real-time monitoring, predictive analytics, and efficient decision-making for improved supply chain performance. Real world examples of companies utilizing Smart Logistics include Amazon using Kiva robots to move goods efficiently across its fulfillment centers, DHL for better inventory management and forecasting, and Chronicled to automate traceability and instantaneously approve financial transactions in the shipping industry.
- Cybersecurity: Cybersecurity is set to shape the future of the supply chain industry with key trends. The integration of AI and machine learning enhances adaptability to evolving threats. Blockchain technology fosters transparency and traceability. The rise of quantum computing prompts the development of quantum-resistant encryption. Convergence with IoT devices demands robust security protocols. Emphasizing collaboration and adopting a proactive, risk-based approach are crucial for staying ahead of cyber threats as supply chains digitize.
To reiterate the message at the beginning of this post, supply chains must be agile and adaptable to thrive. Enhancements in technology, such as in the tools listed above will play a significant role in allowing your supply chain to meet future challenges. In addition, as organizations begin utilizing these tools, they will also need to ask themselves the following questions to assess their readiness for implementation:
- What is the intention, or future state design of the supply chain, and will the implementation of these tools get the organization to where they need to go?
- Is the organization ready to implement, and if not what is needed to be ready?
- What is the pre-work needed by the organization to use various AI tools across their supply chain. Pre-work will span areas such as data preparation, machine learning pre-work, capital investment planning and training.
Today’s supply chains are far from the simplicity of a few years ago, as captured in this post showcasing key technological trends. At The Gunter Group, our team of consultants has extensive experience spanning numerous industries and organizations. We’re poised to offer valuable guidance, address your questions, and help develop supply chains that tackle the diverse challenges of the future.
Stay tuned for our next post, delving into the intricacies of Supply Chain Operations.
ARTIFICIAL INTELLIGENCE & YOUR BUSINESS: 3 THINGS TO KNOW
For Starters: This is Not Skynet
Artificial intelligence is all around you. You have been using it for a while, probably without even knowing it. Gmail finishes your sentences. Your phone corrects your spelling and grammar. Instagram decides what to show you next. Spotify creates perfect playlists of new music. Advertisements know exactly what you’re thinking. You use AI hundreds of times a day.
For some of us, this is an uneasy truth at first glance. We imagine computers ruling our world with cold efficiency, slowly robbing us of our freedoms. But AI is not the villain from our favorite dystopian movies. As fun as it is to get lost in the world of Terminator‘s Skynet, I,Robot‘s VIKI, or Captain Marvel‘s Supreme Intelligence, AI is far less sinister in real life.
AI is a tool, helping to solve problems that require enormous computing power. It’s lines of code that process millions of haystacks worth of data to pull out a single needle in a matter of seconds.
The point: AI is everywhere, and it’s not the far-off villain of Isaac Asimov horror fiction. AI is a tool that is seamlessly integrated into hundreds of your daily experiences. It’s not just for nerds anymore.
Especially in business, there are a few things you should know about this tool if you expect to remain competitive in the coming decade.
3 Things You Need to Know:
(1) AI is Now a Commodity
Until recently, artificial intelligence was mostly the subject of science fiction writers; today it is the subject of your average software engineer. The application of AI has come a long way.
The business community has witnessed an integration of AI on a grand scale. Ubiquitous in all markets, it is written into many of the functions that we use on a daily basis. Furthermore, companies like Amazon and Google have used unimaginably large collections of data to perfect AI tools, and have commoditized them in the form of products like AWS and Google Cloud.
Some have chosen to ignore AI, not seeing value in tools they can barely understand. Meanwhile, fields that lean heavily on AI (like data analysis and business intelligence) have expanded rapidly in recent years. For example, CIO.com lists “BI Analyst” as the most in-demand tech job of 2019. AI is changing business.
A great example comes from an interview with the Harvard Business Review, MIT Sloan School professor Erik Brynjolfsson. He describes an AI program that reviewed recorded conversations of successful sales, and then listened in on active conversations between salespeople and potential customers. While they were on a sales call, the program used the data from successful pitches to make suggestions about words or phrases that the sales person could slip into their conversation to help close the sale. This small application of AI boosted sales by 50%.
Brynjolfsson strongly believes that the only thing holding businesses back is a lack of imagination by business executives on how to use these new tools in their businesses.
(2) Your Competitors Are Using AI
Even if you have a few data analysts on staff, you’re most likely not getting the most out of your software. Since AI is everywhere, it’s hard for CIO’s, tech leads, or business owners to find and use the full range of the tools that are available to them. For instance:
your CRM could be generating leads for your sales department in places they wouldn’t have thought to look
your supply chain solution could be dramatically cutting waste by ordering supplies to be delivered for the lowest shipping cost at the exact moment they are needed
your security solution could be identifying fraud and malware threats before they strike, saving you the time and money you would have spent recovering from one employee clicking one email
your ERP could be spotting spending trends and suggesting campaigns to your marketing team
You might ask yourself, Is it worth all the hassle? Do I really need to do all this? I’m getting along fine without AI, why would I change? If you’re asking yourself this question, you’re looking backwards, not forwards. Failing to make the most of AI is not just a missed opportunity; failing to utilize AI is an increasingly significant liability.
The proof is in the numbers. Netflix claims that a machine learning tool saves it $1 billion a year. Amazon used AI to influence the decision-time of online shoppers and cut it by more than a third. HBR found that companies using AI for sales were able to increase their leads by more than 50%, reduce call time by 60%, and realize cost reductions of 40%. If you don’t take advantage of AI, you will lose out to someone who is.
AI is now a necessity; it’s simply integrated into everything you do. Your CRM, ERP, website, and applications are all using AI. If you aren’t making the most of it, then this low-hanging fruit is spoiling inches from your hand. That is, if it’s not being snatched up by your competition.
(3) The Catch: It’s Not Magic
AI is certainly low-hanging fruit, and it doesn’t take an enormous investment to get more out of it. But it’s not a magic solution that will fix everything. AI is a complicated tool, and getting the most out of it requires knowing how to use it. Utilizing AI takes work. And worse, if you don’t use it correctly, then AI could actually lead you in the wrong direction. Ray Dalio put it best, “Be cautious about trusting AI without deep understanding.”
AI is a tool, and just like any tool it can be used improperly. With AI, bad input means bad output. There’s an art to using this tool.
Here’s a simple illustration. At one time or another, most of us have used the online radio service, Pandora. The process is simple. Tell Pandora a song or an artist that you like, and it searches an enormous music library to play a song that is similar to your input. You rate the suggestion in order to help Pandora hone in on your taste. This is AI at work, learning from your preferences.
But a tool is only as good as its users. If you vote thumbs down on your favorite song, then Pandora won’t play it again. Or if Pandora hadn’t invested in a large and diverse enough music library, it wouldn’t be able to return songs similar to the ones you like. The tool needs to be used properly in order for you to get the most of it.
AI solutions in business are no different: you need to use the tool properly in order for it to work properly.
So How Do I Do it Right?
There are three main components of a good AI implementation in business: know yourself, know what you need, and use the right data. If you don’t have all three of these components then at best you’re not getting the most out of AI, and at worst the tool will lead you in the wrong direction.
First: Know Yourself
An AI solution isn’t worth the investment if it doesn’t solve the specific problems facing your business. This makes sense in theory, but is hard for executive leaders to get right in practice.
The reason for this is not hard to grasp. CIO’s or VP’s of Sales have deep knowledge of their own departments and the business verticals relevant to them, but good tech integrations require organization-wide implementation, and this always pushes beyond the knowledge of a particular individual or department. It’s hard to see beyond the boundaries of your silo.
We begin every project with a current-state assessment. This seems like a logical first step, but it’s often overlooked. It involves gathering requirements that clarify the current-state needs and processes that are affected by a solution. This gives you a clearer understanding of what you need in the future. Many executives assume they already know this, but even the best leaders have blind spots.
A current-state assessment is the best starting point for any kind of project work, but it is especially important with AI. If you don’t have a crystal clear understanding of what you need from an AI solution, then all that will change is the speed in which you receive unusable or incorrect answers to your business problems.
A worthwhile software integration must always begin with a careful look inward, with an up-to-date assessment of requirements gathering and process mapping. Failing to do this has its consequences. If AI is integrated into an organization’s workflow without this look at your current-state, the result is solutions that don’t fit your business or market.
Second: Use the Correct Inputs
What sets real-life AI apart from fictional AI is one key aspect: general intelligence. AI can solve some problems faster and better than humans, but it can’t think for itself.
For example, AI programs have bested world champions in Chess, Go, Texas Hold’Em, and Jeopardy!. But there’s an important detail: the same AI that beat champions in chess can’t even play the game of Texas Hold’Em. Another example: an AI program has to sample tens of thousands of photos before it can identify animal pictures with any reliability, whereas a 2-year-old can correctly identify cats after only seeing one example.
But it’s not just games and image recognition: there are darker examples of AI falling short in big ways:
Developers at MIT were excited about the accuracy of their AI facial recognition software, until they realized that they forgot to build inputs into the software that could identify darker skin tones.
Biases built into AI solutions in law enforcement yielded inaccurate results with huge consequences, such as falsely singling out minorities for recidivism or counseling police to target ethnic neighborhoods.
Amazon used an AI recruitment tool that spent 4 years sorting out the resumes of female applicants, even specifically flagging the word “women” as cause for downgrading a resume.
AI tools are narrow, specialized solutions: you can’t expect to solve problems without teaching it how. It takes work to shape the tool to work correctly. Well-defined and clearly-articulated problems are inseparable from successful AI integrations. The payoff comes once a computer knows how to do a task properly, and can do it at a speed and volume that humans could never achieve. The good news: this work is absolutely within your reach, and most off-the-shelf software has easy-to-use feedback loops built in to help you!
Third: Use the Right Data
Imagine searching through a deck of playing cards to find the midday market report. Or searching through a 4-pack of crayons looking for an exact match to Robin’s Egg Blue. If your data set isn’t large enough or doesn’t fit your questions, then you aren’t going to find meaningful answers. This is especially true for artificial intelligence.
This can be daunting for someone new to AI. How do I know if data is high-quality? How do I know if I have a sufficient quantity? Without the help of experienced input, executives might be making data purchases that are unhelpful, or even harmful. The consequence of using AI with insufficient or bad data is inaccurate solutions and misdirection.
One Last Consideration: Don’t Reinvent the Wheel
Your business is unique, but your problem is not. Why spend time and money custom-fitting an AI solution to your business when a tool has already been developed for just that problem? Finding the right solution might just be a matter of having someone who knows the market helping you find the solution that fits your business.
Using Artificial Intelligence Well: A Case Study
A client of ours was experiencing stagnation in their financial and customer growth for the first time in their history, and couldn’t identify the reason for the slowing growth. They turned to The Gunter Group to help them revamp their digital strategy in order to expand to new customers.
This client had years of data on their customers that they didn’t know how to leverage. They offered great service, but they didn’t understand their customers’ behavior. So we started there.
We began with collecting their data, which consisted of several different types that needed to be aggregated into one system. We helped them build a unified repository, so that any insights they sought maximized the value of their data. In addition to helping them improve the quality of their data, we also helped them refine the insights they hoped to gather. At the beginning of the process, we engaged our experienced Business Analysts to help them integrate their knowledge of their organization’s structure and business goals into the process.
With the 3 important ingredients in place, we were ready to make the most of an AI integration to explore the data. Our team helped craft complex algorithms to create customer segmentation, cohort development, churn prediction, and market share analysis. They were able to use these insights to launch highly effective marketing campaigns, and began a path to predictive analytics to enable real-time interventions in the future.
This kind of example abounds in the business community today. Artificial intelligence is quickly becoming a commodity, available to all. You can’t afford to stay behind the curve.
The Gunter Group partners with organizations in Portland, Vancouver, Bend, Salem, Reno, and Sacramento, helping them to know themselves and seize the low-hanging fruit of AI. If you are interested in learning how we can help you to do the same, reach out today!
MAKE ARTIFICIAL INTELLIGENCE WORK FOR YOU: AN EXPERT OPINION
Artificial intelligence has been in the news a lot lately. Most recently, an AI named Pluribus outplayed the world’s best Texas Hold’Em players. Perhaps this causes you a bit of concern, given that an essential element of poker is the ability to pull off a convincing bluff. Should we be worried that a computer can lie well enough to clean out the best card players in the world?
For most of us, AI hasn’t been at the forefront of our minds. Yes, we hear about recent technology advances in the news or somehow find ourselves at the one happy-hour table talking about how robots will replace us. Beyond that we probably haven’t thought too much about AI, resigning it as a topic for the distant future.
In reality, the world has changed under our feet. The future is here: AI is everywhere. It’s no longer a far off concept; AI is a commodity here and now. Companies like Amazon have started to offer AI tools that sort through unbelievable amounts of data and provide valuable insights that were previously unthinkable. AI is being used to identify sales leads, streamline supply chains, optimize logistics, instantly recognize fraud, and even create original content.
This is an opportunity. Companies of all sizes are leveraging commoditized AI tools to stay a step ahead. If you don’t take advantage of this new commodity, your competitors will.
Matt Jamison will be in Reno presenting on this topic at panel discussion on AI this month at the Nevada Center for Entrepreneurship and Technology. As a seasoned solutions architect and the Tech Services Lead at The Gunter Group, Matt’s perspective is grounded in both his tech expertise and his experience in business consulting. Click here for more information and to register for this event. If you’d like to learn more about how to effectively integrate AI into your business and can’t make it to the NCET panel, reach out today to learn more!
Interested in what we have to say about tech? Check out our blog on the future of agile in business.
Matt is an experienced solutions architect with a results-oriented understanding of the intersection between reality and architectural theory. He has the ability to plan, develop, and implement large-scale projects while maintaining impeccable attention to detail. With 18 years of functional information technology experience, Matt has end-to-end IT knowledge from layer 1 networking to application API interaction. An expert in mapping technology solutions to business needs, Matt is also able to conform to required regulations while maintaining IT best practices. Matt’s experience spans multiple industries, including healthcare, telecommunications, and security and software. He is an AWS Certified Solutions Architect. Outside of work, Matt enjoys the outdoors and all things bike-related.