Why do we turn to nonprofits, NGOs and governments to solve society’s biggest problems? Michael Porter admits he’s biased, as a business school professor, but he wants you to hear his case for letting business try to solve massive problems like climate change and access to water. Why? Because when business solves a problem, it makes a profit — which lets that solution grow.
When you think about accelerating impacts and long-term solutions to current supply chain challenges that impact the 3P’s (people, planet and profit), we need to adopt and develop sustainable frameworks with a holistic life-cycle perspective. There is a ton of innovation happening in the CPG space (Levi’s, Unilever, PepsiCo, etc.)
Shifting from the current ‘take-make-waste’ linear model to the circular economy is critical for businesses to continue to thrive and meet society’s needs. Waste volumes are projected to increase from 1.3 to 2.2 billion tons by 2025, and with nearly 9 billion consumers on the planet including 3 billion new middle class consumers by 2030. The challenges of addressing waste and meeting increasing demand are unprecedented. Therefore it is imperative businesses continue to re-evaluate raw materials, design, manufacturing, consumption, and end of life to keep materials and products continuously flowing through closed loop systems.
How is your company innovating in product life cycle management from design and inception to sustainable product packaging? How are you personally adopting a sustainable mindset in your home, the daily choices you make as a consumer to move toward a circular economy? The bigger question is how are YOU INFLUENCING this change?
The history of Supply Chain Management has evolved since its’ roots in the early 1900s. From improving labor processes of basic material handling and freight transportation, to more sophisticated approaches of balancing cost and efficiency trade-offs, the concept of a supply chain is no longer siloed. It requires integration of supplier-customer relationships, process synchronization, and data harmonization in a complex, dynamic network that is susceptible to vulnerabilities in a global environment. Critical processes to this relationship include real-time communication, collaboration, trust, and transparency that yield mutually beneficial outcomes and competitive advantage. In today’s world, there is a growing prevalence in leading firms advancing toward the adoption, development and implementation of Blockchain technology as a backbone of business operations. This case dives a bit deeper into Blockchain, a novel technology with the strong potential to revolutionize the Global Supply Chain. The goal of this analysis is to discuss: 1) the key technical and economic aspects of Blockchain, 2) the current Blockchain innovators, barriers, and obstacles to Marketplace acceptance, 3) the business case for Blockchain, and 4) future applications and implications of Blockchain technology.
This is a recent article written by a brilliant colleague Harry Goodnight. Great insights and perspectives on blockchain technology and why decentralization is a good thing for modern supply chains.
In business and economics, decentralization often refers to the ability to participate in a market and exchange value between peers without the interference of a third-party intermediary who most likely controls and restricts barriers of entry. As Ethereum co-founder Vitalik Buterin explains in his blog post “The Meaning of Decentralization,” blockchain is politically and architecturally decentralized, meaning no entity one controls it and there’s no central point of failure in its infrastructure. In this way, a decentralized supply chain would allow for a frictionless vehicle of business-to-business value exchange amongst even the smallest players in the industry.
Decentralization is defined as the transfer of power away from a central location or authority. As a concept, it is not new; as a business model, however, it is a powerful idea. Some sociologists claim that decentralization and centralization theories have actually been occurring in cycles for the last 4,000 years, causing the rise and subsequent fall of ruling states and empires. Throughout history, the core theory behind decentralization has remained the same: dispersing power from authorities and empowering smaller, individual entities with the ability to act in their own self-interest.
Why decentralization is necessary for modern supply chains
This is especially necessary in the supply chain industry, which has historically suffered from a number of issues that hinder its efficiency. Its main roadblock is that current supply chains are unable to become agile, which poses a significant problem in a market in which they must be able to change their configurations quickly and continually to meet the constantly-changing dynamics of supply and demand. Another major disadvantage is that methods of communication tend to vary greatly, with some companies still relying on manual paperwork. As a result, data storage becomes locked away in in proprietary systems that don’t allow for collaboration.
Supply chain companies also tend to face cultural and organizational issues, such as executing operating plans due to corporate goals, board restrictions and the competitive nature of the market. Consequently, companies have revoked social contracts, mistreated skilled laborers and underutilized their professional talent assets.
This mismanagement has serious financial consequences: for instance, $4.2 trillion is locked up in net working capital in today’s supply chains. By allowing today’s virtual supply chains to break from the company-centric, server-based environments in which they currently find themselves, they will become less brittle, more scalable and fully leverage the underutilized skills and assets available in modern-day business networks. Even a 1% improvement in Invoice-to-Cash cycle times would immediately return about $42 billion in cash to operations.
How can blockchain remedy the issues of centralization in supply chains?
When looking at its positive implications, blockchain is the most logical next step for supply chain managers and logistics providers. Blockchain was brought to the mainstream through cryptocurrencies like bitcoin and Ethereum. It creates an unchangeable digital ledger that provides a record of financial transactions in chronological order. This technology has been increasingly adapted to address gaping deficiencies in other fields, from education to voting to real estate. Through blockchain, massive networks of decentralized autonomous individuals and organizations can grow and operate seamlessly within a decentralized, distributed operating platform.
Blockchain also provides an efficient and viable solutions to the aforementioned hurdles that are restricting today’s supply chain. Specifically, it offers opportunities to synchronize processes that occur within supply networks, resulting in reduced Cost-of-Goods-Sold (COGS) and more cash freed from working capital.
The solution to many of these recurring issues in supply chain primarily involves people. By creating networks of skilled individuals and decentralized autonomous organizations, immense value can be brought to companies, supply chains, and customers. These networks align economic incentives so that everyone prospers, based on their contributions of time, skill, and intellectual property. These contributions are monitored and administered through outcome-based smart contracts on the blockchain. This new vision of decentralization has the potential to radically transform the supply chain space.
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 firstname.lastname@example.org 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
It’s no secret that Amazon is revolutionizing the retail industry. But what does that actually mean?
Which retailer is Amazon targeting now? Amazon newest target isn’t a retail chain at all — it’s your local convenience store.The company rolled out a new service today called Amazon Instant Pickup, which lets customers order basics like chips, soda and toothpaste. You can then pick them up from an Amazon locker in just two minutes.
Isn’t mimicry the sincerest form of flattery? Not if you’re a retailer that wants to stay in business. Just ask Dick’s Sporting Goods ( ). Dick’s earnings report disappointed Wall Street on Tuesday. The retailer lowered its full-year profit forecast today because of “a challenging retail environment.” Its stock fell more than 20%. Sound familiar? Last month, Amazon filed a patent to launch a competing meal-kit delivery service. Blue Apron’s shares plunged 11% following the news. And grocery stocks got clobbered after Amazon announced plans to buy Whole Foods ( ) for $13.7 billion back in June.
Is Amazon a death sentence for traditional retailers? Not necessarily. Retailers like Home Depot ( ) are surviving by selling things you can’t buy on Amazon. Today, Home Depot reported record sales last quarter and bolstered its outlook for 2017. Home owners and professional builders alike still prefer to go to stores to test out home products, especially big ticket items like flooring and appliances.
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.