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Live Product Innovation, Part 1: The Role Of In-Memory Computing

John McNiff

In Part 1 of this series, we look at how in-memory computing affects live product innovation. In Part 2, we’ll explore the impact of the Internet of Things (IoT) and Big Data.

In our previous series, we examined key drivers changing the way we use product data throughout the enterprise. Digitalization of customer experience, distributed manufacturing and engineering, and IoT and networked processes are transforming business processes and models. The proliferation of data – and the commoditization of technology to capture, store, share, analyze, predict, and learn from data – are enabling “live” product lifecycle management (PLM).

As a result, product data can no longer reside only in engineering systems. Instead, it must synchronize with and facilitate physical and digital twins throughout the product lifecycle. Ultimately, it must extend beyond PLM to manufacturing, cost management, order fulfillment, and service delivery. Likewise, the R&D process must leverage input from manufacturing, customer service, market trends, and more.

What makes this live product innovation possible? The answer is in-memory computing. Or I should say, the right in-memory computing, because not all in-memory platforms are created equal.

Greater volumes, greater speed

Described simply, in-memory computing processes data while the data resides in memory. This allows for orders-of-magnitude faster analysis of vast data volumes. But to extend PLM beyond R&D and engineering, in-memory platforms also must allow:

  • Real-time data access across enterprise domains, with no locking
  • Analytics directly on line-item-level data, without the need to extract to data warehouses
  • A massive reduction of data footprint – typically up to a factor of 10
  • Simplified coding through the removal of database aggregates

With these capabilities, data sets previously segmented because of volume and performance concerns can be left intact. That means data from manufacturing, maintenance, warranty, service, finance, CRM, and marketing can potentially be deployed in one architecture and analyzed in real time across functions.

It also means R&D and engineering can access previously untapped information sources in real time to support product decisions. They can achieve this without the need to pre-aggregate and assemble huge data warehouses, at least for relevant business data.

Other capabilities of an effective in-memory platform include:

  • Tight connection of manufacturing, supply chain, maintenance, and service data and processes
  • An open integration framework for cross-discipline authoring tools
  • The ability of 3D models to be part of the user interface (UI) across enterprise processes
  • The ability to manage complex integrations of CAD and authoring tools

Thanks for the in-memory

What are some outcomes of these capabilities? Product cost is increasingly important to understand early, because it dictates the profitability of a service-based contract. By leveraging the simulation and predictive capabilities of an effective in-memory platform – and by integrating business data from an EP environment – engineering can understand, simulate, and predict variations in design costs based on materials, routes, suppliers, and production techniques. It can achieve this in real time, either before the product is launched or during product redesign.

For project and program management, simulating the effect of a design initiative on an existing fleet of assets allows much better understanding of the impact on new and existing projects and programs. Will a new project delay a critical stage gate? Will a seemingly minor change in resource allocation require more labor at higher cost? These factors can significantly alter cost projections across a portfolio.

Finally, understanding system reliability in the field, as well as product quality throughout the manufacturing process, allows designers to correlate data from materials, suppliers, producers, and processes. It was extremely cumbersome to do this in the past.

All this might sound like powered-up analytics, and to some extent it is. But if you add integration of PLM, ERP, CRM, supplier relationship management (SRM), and enterprise asset management (EAM), you gain an incredible ability to rapidly move from insight to action. A common platform allows far more agile management of interactions across silos. And simplification of user interactions and the underlying data model allows massively accelerated process modifications, as well as solution development and extension.

Want to learn more about live product innovation? Join us for “Logistics & SCM, PLM, Manufacturing, and Procurement 2017,” held March 6-8 in Orlando, Fla. I’ll be presenting on “The Impact of IoT on Product Design.” Hope to see you there.

How else is SAP innovating in supply chain? Learn more at SAP.com or follow us on @SCMatSAP for the latest news.

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John McNiff

About John McNiff

John McNiff is the Vice President of Solution Management for the R&D/Engineering line-of-business business unit at SAP. John has held a number of sales and business development roles at SAP, focused on the manufacturing and engineering topics.

Consumer-Driven Digital Enterprise: The Digital Future Of Consumer Products

Jim Cook

Across industries, there’s a lot of talk about how digital is rewriting the rules of engagement.

We are shown examples of how digital disruption is impacting almost every aspect of businesses – from reinventing business models to transforming business processes. Re-imagining a business platform is almost a requirement in today’s consumer-led and data-driven economy. (You know the businesses that are quoted in every article on digital.)

A key question here, though, is: whether the consumer products industry is indeed facing digital disruption, or does it really need deeper digital innovation? Disruption turns an industry on its head by offering consumers something that previously did not exist, while innovation enhances an existing value proposition – making it better, faster, or cheaper.

It is important to distinguish between the two, because hype often causes businesses to overlook the true value of digital transformation. Companies may presume such radical changes have nothing to do with them, especially if they are already in a dominant market position. So while digital is dramatically changing industries such as retail and healthcare, the disruption in the consumer product industry may not be as severe – not yet anyway. Instead, what consumer products businesses should focus on is how they can transform digitally to gain the capacity to build and grow “live brands.” This is preparation and not protectionism.

Create direct customer experiences: Secure the dominant market position

The digital age has fundamentally shifted customer and consumer expectations. Consumers increasingly value outcomes over products. To build ongoing engagement and loyalty, consumer products companies need to sense and engage consumers and customers in the moment, i.e. build “live brands” by seamlessly delivering highly personalized experiences – anytime, anywhere.

This ability to create direct customer experiences helps consumer products companies create a sharper competitive edge to secure dominant market positions. Leading consumer products companies know this well.

Red Bull sets a fine example in creating direct customer experiences to protect and strengthen its brand. Today, it has moved beyond a beverage company into a content media company spanning web, social, film, print, music, and TV – creating brand experiences of exhilaration and adventure. Red Bull collects data from every touch point that it has with the consumer, building an enhanced profile of every individual so that it can respond with products that consumers desire – whenever and how they want them.

Procter & Gamble recently launched an online, direct-to-consumer subscription business for its Tide Pods (its highest-priced laundry detergent). The service (currently only available in Atlanta), branded Tide On Demand, offers free shipping of Tide Pods at regular intervals. P&G has also been testing its delivery laundry service – Tide Spin – in Chicago. While the direct-to-consumer services may not form a bulk of its revenue, they allow P&G to quickly build a live understanding of its customers, their preferences and habits, and then hone in on these insights to create new offerings that customers want.

Build a real-time supply chain: Support lasting customer loyalty

As consumer products companies move towards sensing and engaging customers in the moment, they also need to ensure a fast and profitable response to dynamic demand.

This necessitates connecting customer insights that brand owners have collated and analyzed with supply chain insights to accelerate time to market. Ultimately, it is about transforming previously linear supply chains into customer-centric demand networks – where demand information is captured through new signals from various sources (such as retailers, wholesalers, sites like Amazon, directly from customers, or the Internet of Things) and fulfilled through the orchestration of a network of internal and external partners.

With that, consumer product companies can start getting answers to questions such as:

  • What are my short-, mid-, and long-term views of expected demand across channels?
  • How can I combine supply chain planning with strategic, financial, sales, and operational goals?
  • How can I extend planning by collaborating with customers, partners, and suppliers?
  • How can the company translate the plan into actionable targets for fulfillment systems?

All these should go full circle to help make manufacturing more responsive, optimizing capacity to help ensure availability of finished goods produced just-in-time to meet demand, thereby also lowering inventory costs.

Consumer products companies need to consider how they can create the digital future today. We invite you to learn more about digital transformation for the consumer products industry, where you will get access to valuable resources including whitepapers and customer case studies.

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Jim Cook

About Jim Cook

Jim Cook is the Industry Advisor for consumer industries in South East Asia, with over 20 years’ experience of IT and business consulting. He has held various roles from solution architect, project and program management, business development as well as managing an SAP partner organisation. Jim is passionate about transformation within consumer driven organisations. Jim is particular interested in customer engagement solutions and the value that can be achieved from end to end SAP deployments.

Live Product Innovation, Part 2: IoT, Big Data, and Smart Connected Products

John McNiff

In Part 1 of this series, we looked at how in-memory computing affects live product innovation. In Part 2, we explore the impact of the Internet of Things (IoT) and Big Data on smart connected products. In Part 3, we’ll approach the topic from the perspective of process industries.

Live engineering? Live product innovation? Live R&D?

To some people, these concepts sound implausible. When you talk about individualized product launches with lifecycles of days or weeks, people in industries like aerospace and defense (A&D) look askance.

But today, most industries—not just consumer-driven ones—need timely insights and the ability to respond quickly. Even A&D manufacturers want to understand the impact of changes before they continue with designs that could be difficult to make at the right quantity or prone to problems in the field.

The Internet of Everything?

Internet of Things (IoT) technologies promise to give manufacturers these insights. But there’s still a lot of confusion around IoT. Some people think it is about connected appliances; others think it’s just a rebranded “shop floor to top floor.”

The better way to think about IoT is from the perspective of data: We want to get data from the connected “thing.” If you’re the manufacturer, that thing is a product. If you buy the product and sell it to an end customer, that thing is an asset. If you’re the end customer, that thing is a fleet. Each stakeholder wants different data in different volumes for different reasons.

It’s also important to remember that the Internet has existed for far longer than IoT.

There’s a huge amount of non-IoT data that can offer useful insights. Point-of-sale data, news feeds, and market insights from social channels are all valuable. And think about how much infrastructure is now connected in “smart” cities. So in addition to products, assets, and fleets, there are also people, markets, and infrastructure. Big Data is everywhere, and it should influence what you release and when.

New data, new processes

It has been said that data in the 21st century is like oil in the 18th century: an immense, valuable, yet untapped asset. But if data is the new oil, then do we need a new refinery? The answer is yes.

On top of business data, we now have a plethora of information sources outside our company walls. Ownership of, and access to, this data is becoming complex. Manufacturers collecting data about equipment at customer sites, for example, may want to sell that data to customers as an add-on service. But those customers are likely using equipment from multiple manufacturers, and they likely have their own unique uses for the information.

So the new information refinery needs to capture information from everywhere and turn it into something that has meaning for the end user. It needs to leverage data science and machine learning to remove the noise and add insight and intelligence. It also needs to be an open platform to gather information from all six sources (products, assets, fleets, people, markets, infrastructure).

And wouldn’t it be great if the data refinery ran on the same platform as your business processes, so that you could sense, respond, and act to achieve your business goals?

Digital product innovation platform

If you start with the concept of a smart connected product, the data refinery — the digital product innovation platform — has five requirements:

  1. Systems design — Manufacturers need to design across disciplines in a systems approach. Mechanical, mechatronic, electrical, electronics, and software all need to be supported, with modeling capabilities that cover physical, functional, and logical structures.
  1. Requirements-linked platform design — Designers need to think about where and how to embed sensors and intelligence to match functional requirements. This will need to be forward-thinking to cover unforeseen methods of machine-to-machine interactions. In a world of performance-based contracts, it will be important to minimize the impact of design changes as innovation opportunities grow.
  1. Instant impact and insight environment — The platform must support fully traceable requirements throughout the lifecycle, from design concept to asset performance.
  1. Product-based enterprise processes — The platform needs to share model-based product data visually — through electrical CAD, electronic CAD, 3D, and software functions — to the people who need it. This isn’t new, but what’s different is that the platform can’t wait for complex integrations between systems. Think about software-enabled innovation or virtual inventory made possible by on-demand 3D printing. Production is almost real time, so design will have to be as well.
  1. Product and thing network — A complex, cross-domain design process involves a growing number of partners. That calls for a product network to allow for secure collaboration across functions and outside the company walls. Instead of every partner having its own portal for product data, the product network would store digital twins and allow instant sharing of asset intelligence.

If the network is connected to the digital product innovation platform, you can control the lifecycle both internally and externally — and take the product right into the service and maintenance domain. You can then provide field information directly from the assets back to design to inform what to update, and when. Add over-the-air software compatibility checks and updates, and discrete manufacturers can achieve a true live engineering environment.

Sound like a dream? It’s coming sooner than you think.

Learn more about supply chain innovations at SAP.com or follow us on @SCMatSAP for the latest news.

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John McNiff

About John McNiff

John McNiff is the Vice President of Solution Management for the R&D/Engineering line-of-business business unit at SAP. John has held a number of sales and business development roles at SAP, focused on the manufacturing and engineering topics.

How Emotionally Aware Computing Can Bring Happiness to Your Organization

Christopher Koch


Do you feel me?

Just as once-novel voice recognition technology is now a ubiquitous part of human–machine relationships, so too could mood recognition technology (aka “affective computing”) soon pervade digital interactions.

Through the application of machine learning, Big Data inputs, image recognition, sensors, and in some cases robotics, artificially intelligent systems hunt for affective clues: widened eyes, quickened speech, and crossed arms, as well as heart rate or skin changes.




Emotions are big business

The global affective computing market is estimated to grow from just over US$9.3 billion a year in 2015 to more than $42.5 billion by 2020.

Source: “Affective Computing Market 2015 – Technology, Software, Hardware, Vertical, & Regional Forecasts to 2020 for the $42 Billion Industry” (Research and Markets, 2015)

Customer experience is the sweet spot

Forrester found that emotion was the number-one factor in determining customer loyalty in 17 out of the 18 industries it surveyed – far more important than the ease or effectiveness of customers’ interactions with a company.


Source: “You Can’t Afford to Overlook Your Customers’ Emotional Experience” (Forrester, 2015)


Humana gets an emotional clue

Source: “Artificial Intelligence Helps Humana Avoid Call Center Meltdowns” (The Wall Street Journal, October 27, 2016)

Insurer Humana uses artificial intelligence software that can detect conversational cues to guide call-center workers through difficult customer calls. The system recognizes that a steady rise in the pitch of a customer’s voice or instances of agent and customer talking over one another are causes for concern.

The system has led to hard results: Humana says it has seen an 28% improvement in customer satisfaction, a 63% improvement in agent engagement, and a 6% improvement in first-contact resolution.


Spread happiness across the organization

Source: “Happiness and Productivity” (University of Warwick, February 10, 2014)

Employers could monitor employee moods to make organizational adjustments that increase productivity, effectiveness, and satisfaction. Happy employees are around 12% more productive.




Walking on emotional eggshells

Whether customers and employees will be comfortable having their emotions logged and broadcast by companies is an open question. Customers may find some uses of affective computing creepy or, worse, predatory. Be sure to get their permission.


Other limiting factors

The availability of the data required to infer a person’s emotional state is still limited. Further, it can be difficult to capture all the physical cues that may be relevant to an interaction, such as facial expression, tone of voice, or posture.



Get a head start


Discover the data

Companies should determine what inferences about mental states they want the system to make and how accurately those inferences can be made using the inputs available.


Work with IT

Involve IT and engineering groups to figure out the challenges of integrating with existing systems for collecting, assimilating, and analyzing large volumes of emotional data.


Consider the complexity

Some emotions may be more difficult to discern or respond to. Context is also key. An emotionally aware machine would need to respond differently to frustration in a user in an educational setting than to frustration in a user in a vehicle.

 


 

download arrowTo learn more about how affective computing can help your organization, read the feature story Empathy: The Killer App for Artificial Intelligence.


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Christopher Koch

About Christopher Koch

Christopher Koch is the Editorial Director of the SAP Center for Business Insight. He is an experienced publishing professional, researcher, editor, and writer in business, technology, and B2B marketing. Share your thoughts with Chris on Twitter @Ckochster.

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In An Agile Environment, Revenue Models Are Flexible Too

Todd Wasserman

In 2012, Dollar Shave Club burst on the scene with a cheeky viral video that won praise for its creativity and marketing acumen. Less heralded at the time was the startup’s pricing model, which swapped traditional retail for subscriptions.

For as low as $1 a month (for five two-bladed cartridges), consumers got a package in the mail that saved them a trip to the pharmacy or grocery store. Dollar Shave Club received the ultimate vindication for the idea in 2016 when Unilever purchased the company for $1 billion.

As that example shows, new technology creates the possibility for new pricing models that can disrupt existing industries. The same phenomenon has occurred in software, in which the cloud and Web-based interfaces have ushered in Software as a Service (SaaS), which charges users on a monthly basis, like a utility, instead of the typical purchase-and-later-upgrade model.

Pricing, in other words, is a variable that can be used to disrupt industries. Other options include usage-based pricing and freemium.

Products as services, services as products

There are basically two ways that businesses can use pricing to disrupt the status quo: Turn products into services and turn services into products. Dollar Shave Club and SaaS are two examples of turning products into services.

Others include Amazon’s Dash, a bare-bones Internet of Things device that lets consumers reorder items ranging from Campbell’s Soup to Play-Doh. Another example is Rent the Runway, which rents high-end fashion items for a weekend rather than selling the items. Trunk Club offers a twist on this by sending items picked out by a stylist to users every month. Users pay for what they want and send back the rest.

The other option is productizing a service. Restaurant franchising is based on this model. While the restaurant offers food service to consumers, for entrepreneurs the franchise offers guidance and brand equity that can be condensed into a product format. For instance, a global HR firm called Littler has productized its offerings with Littler CaseSmart-Charges, which is designed for in-house attorneys and features software, project management tools, and access to flextime attorneys.

As that example shows, technology offers opportunities to try new revenue models. Another example is APIs, which have become a large source of revenue for companies. The monetization of APIs is often viewed as a side business that encompasses a wholly different pricing model that’s often engineered to create huge user bases with volume discounts.

Not a new idea

Though technology has opened up new vistas for businesses seeking alternate pricing models, Rajkumar Venkatesan, a marketing professor at University of Virginia’s Darden School of Business, points out that this isn’t necessarily a new idea. For instance, King Gillette made his fortune in the early part of the 20th Century by realizing that a cheap shaving device would pave the way for a recurring revenue stream via replacement razor blades.

“The new variation was the Keurig,” said Venkatesan, referring to the coffee machine that relies on replaceable cartridges. “It has started becoming more prevalent in the last 10 years, but the fundamental model has been there.” For businesses, this can be an attractive model not only for the recurring revenue but also for the ability to cross-sell new goods to existing customers, Venkatesan said.

Another benefit to a subscription model is that it can also supply first-party data that companies can use to better understand and market to their customers. Some believe that Dollar Shave Club’s close relationship with its young male user base was one reason for Unilever’s purchase, for instance. In such a cut-throat market, such relationships can fetch a high price.

To learn more about how you can monetize disruption, watch this video overview of the new SAP Hybris Revenue Cloud.

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