The supply chain of 2025 will re-imagine the way the world does business with digital factory and retail technologies. Within the next decade, we will move away from mass-produced products to more meaningful, personal items created in small quantities, and Panasonic IoT solutions and robotics will make this all possible.
Sharing point of view from Greg Brady is the CEO and founder of One Network Enterprises, a global provider of a secure and scalable multi-party business network. For more information, contact the author at email@example.com or visit http://www.onenetwork.com. Enjoy!
There’s a lot of buzz and hype about artificial intelligence (AI) in supply chain management (SCM). That’s understandable given its potential. AI can offer a huge benefit to supply chain managers, but only if it is based on solid fundamentals that take into account the diverse and dynamic nature of today’s modern supply chains. More importantly, it needs to consider the availability of the timely and accurate data needed to make smart decisions.
Before addressing what AI can do, it is critical to first understand what it is. In the simplest terms, AI is intelligence exhibited by machines, or when machines mimic or can replace intelligent human behavior, such as problem solving or learning. In essence, AI is machines making decisions whether that is deciding which chess piece to move where, or how to adjust an order forecast based on changing demand.
Despite its benefits, when looked at through the lens of a supply chain executive, AI is relatively useless unless it’s able to add value to support better decision-making.
Why AI Hasn’t Delivered in SCM
In the race to use AI, many companies have made attempts to implement it, but the results have been disappointing. This is because typical SCM systems today:
- Require armies of expensive planners
- Run complex engines at each step in the process and at each node in the supply network
- Are usually in conflict with other functions and/or partners
- Miss huge opportunities hidden in the network because they are locally sub optimized
- Work on stale data and thus promote bad decisions
- Use dumbed-down, over-simplified problem models that do not relate to the real world
These SCM limitations have severely suppressed return on AI investments. For example, typical Retail/CPG supply chains still carry 60-75 days of inventory. The average service level in the store is about 96 percent, with promoted item service levels much lower at the 80 percent range. The Casual Dining segment on the other hand, carries around 12 – 15 days of inventory with relatively high waste and high cost-of-goods-sold. So, unless AI can make a significant impact on these metrics, it’s simply not delivering.
Key Requirements for AI in Supply Chain Management
Having worked with hundreds of supply chain executives, on dozens of software implementations, I’ve studied the AI issue a lot. What I have found is there are eight criteria that are required for a successful AI implementation. Miss one of these and you’ll be lucky to achieve mediocre outcomes, but when you meet them all, you can indeed achieve world class results. For the AI solution to offer optimal value in supply chain, it important to ensure the following:
1. Access to Real-Time Data
To improve on traditional enterprise systems with older batch planning systems, new AI systems must eliminate the stale data problem. Most supply chains today attempt to execute plans using data that is days old, but this results in poor decision-making that sub-optimizes the supply chain, or requires manual user intervention to address. Without real-time information, an AI tool is just making bad decisions faster.
2. Access to Community (Multi-Party) Data
The ability to access data outside of the enterprise or, more importantly, receive permission to see the data that is relevant to your trading community, must be made available to any type of AI, Deep Learning or Machine Learning algorithms.
Unless the AI tool can see the forward-most demand and downstream supply, and all relevant constraints and capacities in the supply chain, the results will be no better than that of a traditional planning system. Unfortunately, this lack of visibility and access to real-time, community data is the norm in over 99 percent of all supply chains. Needless to say, this must change for an AI tool to be successful.
3. Support for Network-Wide Objective Functions
The objective function, or primary goal, of the AI engine must be consumer service level at lowest possible cost. This is because the end-consumer is the only consumer of true finished goods products. If we ignore this fact, trading partners will not get the full value that comes from optimizing service levels and cost to serve, which is obviously important as increased consumer sell-through drives value for everyone.
A further enrichment of the decision algorithm should support enterprise level cross-customer allocation to address product scarcity issues and individual enterprise business policies. Thus, AI solutions must support global consumer-driven objectives even when faced with constraints within the supply chain.
4. Decision Process Must Be Incremental and Consider the Cost of Change
Re-planning and changing execution plans across a networked community in real time can create nervousness in the community. Constant change without weighing the cost of the change creates more costs than savings and reduces the ability to effectively execute. An AI tool must consider trade-offs in terms of cost of change against incremental benefits when making decisions.
5. Decision Process Must Be Continuous, Self-Learning and Self-Monitoring
Data in a multi-party, real-time network is always changing. Variability and latency is a recurring problem, and execution efficiency varies constantly. The AI system must be looking at the problem continuously, not just periodically, and should learn as it goes on how to best set its own policies to fine tune its abilities. Part of the learning process is to measure the effectiveness “analytics,” then apply what it has learned.
6. AI Engines Must Be Autonomous Decision-Making Engines
Significant value can only be achieved if the algorithm can not only make intelligent decisions but can also execute them. Furthermore, they need to execute not just within the enterprise but where appropriate, across trading partners. This requires your AI system and the underlying execution system to support multi-party execution workflows.
7. AI Engines Must Be Highly Scalable
For the supply chain to be optimized across an entire networked community of consumers to suppliers, the system must be able to process huge volumes of data very quickly. Large community supply chains can have millions if not hundreds of millions of stocking locations. AI solutions must be able to make smart decisions, fast, and on a massive scale.
8. Must Have a Way for Users to Engage with the System
AI should not operate in a “black box.” The UI must give users visibility to decision criteria, propagation impact, and enable them to understand issues that the AI system cannot solve. The users, regardless of type, must to be able to monitor and provide additional input to override AI decisions when necessary. However, the AI system must drive the system itself and only engage the user on an exception basis, or allow the user to add new information the AI may not know at the request of the user.
AI in the Real World Today
Sounds good in theory, but how does it work out in practice? Now that we have addressed the key fundamentals, let’s look at how some actual companies have achieved applying these criteria.
For instance, one of the major problems in Casual Dining is anticipating and meeting demand for the restaurants, corporate owned or franchised. This is especially important during Limited Time Offers (LTOs). Using the eight criteria outlined above, a global, casual dining company connected to a real-time, multi-party network, and was able to rapidly achieve their objective function – excellent customer service at the lowest cost.
The company constantly monitors Point-of-Sale (POS) data, and is using AI agents to recognize and predict consumption patterns of consumers. In addition, intelligent AI agents create the demand forecast and then compare it to the actual demand in real-time. When there is significant deviation, the agents make the decision to adjust the forecast, and additional agents adjust replenishments. They then propagate those adjustments across the supply chain to trading partners in real time at all times considering the cost of change and the propagation impact.
This drove a remarkable improvement in forecast accuracy. During promotions, the company achieved over 85 percent forecast accuracy at the store level and even higher at the DC level. This represents at least a 25 percent improvement over traditional approaches.
Intelligent agents also optimize restaurant orders autonomously by recognizing the impact of projected restaurant traffic trends and impact on LTOs and therefore the orders. The system runs on an exception basis but allows the managers to review the decision criteria and override orders where the managers may have local information such as inventory issues or local store traffic issues. This has resulted in much faster order placement and order accuracy of over 82 percent, which reduces both inventory and waste dramatically while increasing service levels to the consumer. This is a significant improvement to all other known implementations in the marketplace.
Because the algorithms are highly scalable, they are processing over 15 million stocking locations continuously throughout the day.
Prior to the AI-based, multi-party execution system, restaurant managers had to interact with nine different ordering systems and manually create their own orders based on general guidelines, rules of thumb, and spreadsheet-based or manual calculations.
With AI implemented on a sound foundation, this company can now anticipate, manage, and serve demand at the lowest possible cost. During LTO’s, when demand fluctuations would overwhelm a restaurant manager, intelligent agents monitor demand in real time, and autonomously orchestrate the supply chain to align supply with demand. Thus, the company can meet its goal and maintain high service levels while reducing cost to serve.
These are not isolated results. Also in the food marketplace, another CPG-Retail implementation achieved 99 percent in-stock, in-store, with 25 days of supply (DOS) across the supply chain. The inventory results are less than half the standard DOS in this marketplace and 3 percent points higher in in-store in-stocks
AI-based solutions are being deployed at two large automotive tier one suppliers with results ranging from 16 – 40 percent reductions in inventory as well as significant reductions in expedited freight costs.
AI Delivers Value in SCM Today
As you can see, laying the proper groundwork for AI pays huge dividends. There’s no doubt that AI offers even greater promise in the future, but, as these results show, there are significant benefits and dramatic results waiting for companies that focus on the fundamentals and put AI to use today.
The beauty of AI-based solutions is that they learn and drive continuous improvement over time. They get more precise and sophisticated as they gather more data and more experience. The sooner you start, the better the results you’ll see in future, and the further ahead you will be. With the right AI solution in place, you can outpace your competitors today, and be well positioned for reaping even bigger rewards of AI’s promise tomorrow. ~Greg Brady
Even years into the deployment of the internet, many believed that it was still a fad. Of course, the internet has since become a major influence on our lives, from how we buy goods and services, to the ways we socialize with friends, to the Arab Spring, to the 2016 U.S. presidential election. Yet, in the 1990s, the mainstream press scoffed when Nicholas Negroponte predicted that most of us would soon be reading our news online rather than from a newspaper.
Fast forward two decades: Will we soon be seeing a similar impact from cryptocurrencies and blockchains? There are certainly many parallels. Like the internet, cryptocurrencies such as Bitcoin are driven by advances in core technologies along with a new, open architecture — the Bitcoin blockchain. Like the internet, this technology is designed to be decentralized, with “layers,” where each layer is defined by an interoperable open protocol on top of which companies, as well as individuals, can build products and services.
Like the internet, in the early stages of development there are many competing technologies, so it’s important to specify which blockchain you’re talking about. And, like the internet, blockchain technology is strongest when everyone is using the same network, so in the future we might all be talking about “the” blockchain.
The internet and its layers took decades to develop, with each technical layer unlocking an explosion of creative and entrepreneurial activity. Early on, Ethernet standardized the way in which computers transmitted bits over wires, and companies such as 3Com were able to build empires on their network switching products. The TCP/IP protocol was used to address and control how packets of data were routed between computers. Cisco built products like network routers, capitalizing on that protocol, and by March 2000 Cisco was the most valuable company in the world. In 1989 Tim Berners-Lee developed HTTP, another open, permissionless protocol, and the web enabled businesses such as eBay, Google, and Amazon.
The Killer App for Blockchains
But here’s one major difference: The early internet was noncommercial, developed initially through defense funding and used primarily to connect research institutions and universities. It wasn’t designed to make money, but rather to develop the most robust and effective way to build a network. This initial lack of commercial players and interests was critical — it allowed the formation of a network architecture that shared resources in a way that would not have occurred in a market-driven system.
The “killer app” for the early internet was email; it’s what drove adoption and strengthened the network. Bitcoin is the killer app for the blockchain. Bitcoin drives adoption of its underlying blockchain, and its strong technical community and robust code review process make it the most secure and reliable of the various blockchains. Like email, it’s likely that some form of Bitcoin will persist. But the blockchain will also support a variety of other applications, including smart contracts, asset registries, and many new types of transactions that will go beyond financial and legal uses.
We might best understand Bitcoin as a microcosm of how a new, decentralized, and automated financial system could work. While its current capabilities are still limited (for example, there’s a low transaction volume when compared to conventional payment systems), it offers a compelling vision of a possible future because the code describes both a regulatory and an economic system. For example, transactions must satisfy certain rules before they can be accepted into the Bitcoin blockchain. Instead of writing rules and appointing a regulator to monitor for breaches, which is how the current financial system works, Bitcoin’s code sets the rules and the network checks for compliance. If a transaction breaks the rules (for example, if the digital signatures don’t tally), it is rejected by the network. Even Bitcoin’s “monetary policy” is written into its code: New money is issued every 10 minutes, and the supply is limited so there will only ever be 21 million Bitcoins, a hard money rule similar to the gold standard (i.e., a system in which the money supply is fixed to a commodity and not determined by government).
This is not to say the choices Bitcoin currently offers are perfect. In fact, many economists disagree with Bitcoin’s hard money rule, and lawyers argue that regulation through code alone is inflexible and doesn’t permit any role for useful discretion. What cannot be disputed, however, is that Bitcoin is real, and it works. People ascribe real economic value to Bitcoins. “Miners,” who maintain the Bitcoin blockchain, and “wallet providers,” who write the software people use to transact in Bitcoin, follow the rules without exception. Its blockchain has remained resilient to attack, and it supports a robust, if basic, payment system. This opportunity to extend the use of the blockchain to remake the financial system unnerves and enthralls in equal measure.
Too Much Too Soon?
Unfortunately, the exuberance of fintech investors is way ahead of the development of the technology. We’re often seeing so-called blockchains that are not really innovative, but instead are merely databases, which have existed for decades, calling themselves blockchains to jump on the buzzword bandwagon.
There were many “pre-internet” players, for example telecom operators and cable companies trying to provide interactive multimedia over their networks, but none could generate enough traction to create names that you would remember. We may be seeing a similar trend for blockchain technology. Currently, the landscape is a combination of incumbent financial institutions making incremental improvements and new startups building on top of rapidly changing infrastructure, hoping that the quicksand will harden before they run out of runway.
In the case of cryptocurrencies, we’re seeing far more aggressive investments of venture capital than we did for the internet during similar early stages of development. This excessive interest by investors and businesses makes cryptocurrencies fundamentally different from the internet because they haven’t had several decades of relative obscurity where noncommercial researchers could fiddle, experiment, iterate on, and rethink the architecture. This is one reason why the work that we’re doing at the Digital Currency Initiative at the MIT Media Lab is so important: It is one of the few places a substantial effort is being made to work on the technology and infrastructure clear of financial interests and motivations. This is critical.
The existing financial system is very complex at the moment, and that complexity creates risk. A new decentralized financial system made possible with cryptocurrencies could be much simpler by removing layers of intermediation. It could help insure against risk, and by moving money in different ways could open up the possibility for different types of financial products. Cryptocurrencies could open up the financial system to people who are currently excluded, lower barriers to entry, and enable greater competition. Regulators could remake the financial system by rethinking the best way to achieve policy goals, without diluting standards. We could also have an opportunity to reduce systemic risk: Like users, regulators suffer from opacity. Research shows that making the system more transparent reduces intermediation chains and costs to users of the financial system.
The primary use and even the values of the people using new technologies and infrastructure tend to change drastically as these technologies mature. This will certainly be true for blockchain technology.
Bitcoin was first created as a response to the 2008 financial crisis. The originating community had a strong libertarian and antiestablishment spin that, in many ways, was similar to the free-software culture, with its strong anticommercial values. However, it is likely that, just as Linux is now embedded in almost every kind of commercial application or service, many of the ultimate use cases of the blockchain could become standard fare for established players like large companies, governments, and central banks.
Similarly, many view blockchain technology and fintech as merely a new technology for delivery — maybe something akin to CD-ROMs. In fact, it is more likely to do to the financial system and regulation what the internet has done to media companies and advertising firms. Such a fundamental restructuring of a core part of the economy is a big challenge to incumbent firms that make their living from it. Preparing for these changes means investing in research and experimentation. Those who do so will be well placed to thrive in the new, emerging financial system.
The adult beverage industry is transforming as the ‘Internet of Things’ revolutionizes everything from packaging to how we order drinks.
Smart technology is profoundly influencing the way people buy and consume things across every category, and the alcohol segment in no different. We can be sure that in the very near future it will be impossible to imagine how we functioned in a world of ‘dumb’ disconnected products. It has been reported by the World Economic Forum that the overall number of connected devices is expected to double within the next four years, from 22.9 billion in 2016 to 50.1 billion by 2020.
In this nascent era of connectivity, new devices help us buy our favorite products more efficiently and new packaging informs us about everything from terroir to tampering. Brands can utilize the data collected from smart systems to improve their products and tailor them to consumer tastes. With an eye on innovation and efficiency, smart technology developers are quickly revolutionizing the way we live – and drink.
Smart On-Premise Devices
Two new products allow imbibers to replenish their drinks on-premise without waiting at the bar. Bacardi-owned Martini recently launched a new Smart Cube that communicates with bar staff when it’s time to pour another drink. The device is added to a customer’s drink like an ice cube and then monitors the drink level in real time. It also keeps track of how many drinks have been consumed to prevent over-serving.
Malibu recently introduced their ‘Coco-nect’ cups which allow consumers to place an order for a new drink by simply twisting the base of the cup. The cup sends the order to the bar while also pinpointing the customer’s location so that the drink can be delivered to them. Once the order has been received by a bartender, the bottom of the cup changes color to let the client know that their drink is on its way.
Iowa-based startup FliteBrite has created smart beer flight paddles that help drinkers keep track of which beer they’re trying. The device also connects to an interactive app that gives detailed information about each brew. While it doesn’t currently offer the option to order more beer, one imagines that this is the next step for devices such as the FliteBrite.
Tel Aviv-based startup Glassify have developed a line of ‘smart glasses’ embedded with an NFC chip that work with a smartphone app to offer consumers incentives like free chasers, happy hour specials or food combos. The app also hooks into a bank account, allowing customers to buy drinks for their friends or go out without their wallet. While it’s fun for consumers, the glasses could also be a boon for businesses interested in tracking specifics about their sales, from what time of day certain beers sell best to which brews are more likely to be drunk in sessions.
Several companies have introduced innovations to draught systems that provide businesses with helpful analytics. TAPP is a cloud-based battery-powered smart tap handle that can track beer sales in real-time and report the timestamped data back to beermakers. The system also has options for consumer interaction, either through their smartphones or through screens in the bars.
Indiana-based start-up SteadyServ offers a similar cloud-based system that helps outlets keep track of inventory, letting them know when something needs to be reordered or if a keg will need to be changed soon. The start-up’s technology uses electronic tags to identify each beer and puts a scale under each keg. The scale monitors beer levels, giving bars essential information about what is trending or what to run on special (for example, if a keg is getting old). Nevertheless, the exciting aspect for consumers is that SteadyServ integrates with social media, letting beer fans know what’s freshly on tap and what’s about to run out at their favorite pub.
European technology company WeissBeerger has created a similar smart bar system. With the goal of “turning drinks into data,” WeissBeerger offers an integrated Beverage Analytics Hub that connects with coordinating smart bar devices via cloud technology. From monitoring keg freshness and temperature controls to consumption data, the company helps businesses serve their customers more efficiently.
Smart Home Devices
Molson Coors has taken a page from Amazon’s book and launched a connected button that allows consumers to easily order more Carling beer in the UK. Similarly to the Amazon Dash button, the Carling Beer Button syncs with an accompanying smartphone app. When pressed, it adds Carling beer to an online shopping basket at one of four retailers, Tesco, Asda, Morrisons and Sainsbury’s.
Bud Light created a smart mini fridge for the California market which holds up to 78 beers. The branded connected appliance connects with an app via wifi to let consumers know when supplies are running low. The app is also programmable with user’s favorite sports teams, allowing them to receive updates when game day is approaching. The app integrates with the beer-delivery service Saucey, allowing users to order beer for delivery in Los Angeles, San Francisco and San Diego.
In Canada, The Bud-E Fridge is part of their Goal Lab range of smart offerings which also include the Goal Lamp Glasses.
Pernod Ricard recently launched 45,000 NFC-enabled smart bottles for its Malibu coconut rum brand in the UK. Consumers can access digital content and experiences by tapping their NFC-enabled android phone on the bottle’s sunset image. Content includes instant-win competitions, user-generated content competitions, drink recipes, a bar locator service and a music playlist. The connected bottles are available exclusively through Tesco.
Several brands have utilized smart bottles that can be authenticated and tracked in order to combat the uptick of counterfeit wine and spirits. Ferngrove Wine Group, Johnnie Walker Blue Label and Barbadillo sherry have both turned to Thinfilm enhanced bottles that monitor whether a bottle has been opened and wirelessly communicates with a coordinating app. The Thinfilm carries tagged information with unique identifiers that allow brands to authenticate and track their products, even after the factory seal is broken. Thinfilm can also be used to communicate product information to consumers through their smartphones.
Medea Vodka created a fun party trick with their bluetooth enabled bottles with customizable LED message bands. The bottles can be programmed with a bespoke message that will scroll across the band. Messages are controlled through an app developed by the Medea team. The app knows which bottles are nearby and available to be registered. Once a bottle is registered to one phone it cannot be controlled by anyone else. The customizable bottles allow users to create their own messages for any social occasion.
As our belongings become more connected, we will develop the expectation that these devices will take care of our everyday chores. For instance, a refrigerator could be programmed to automatically reorder beer once supplies drop to a pre-programmed level.
Smart sensors and devices help us collect data and buy and sell more efficiently. What will we do with all of this data? The biggest boon coming from the ‘internet of things’ is the amount of intelligence we are gathering that will drive innovation and inspire new products and services.