Category Strategies for Digital

strategyEarly adopters of Digital forces – social, mobile, cloud, big data & analytics, and AI & robotics have a clear competitive edge in their line of business. It is becoming the new basis of competition, helping organizations build new business and operating models. Sourcing arena is transforming rapidly as Digital services become increasingly important to the business environment. Digital demands companies manage an ever larger pool of smaller deals that are, in many ways, different from their traditional category scenarios. Hence Digital technologies are complicating an already intricate procurement value chain.

Digital poses a tremendous change to the way of working for procurement that require a fundamental “rethink” regarding organization and capabilities, both of which will need to be reshaped over time. Successful Digital category strategy encompass the niche players along with traditional providers to optimize the costs and right balance the skill gaps with capability mix. It’s bringing in a new set of players who do things differently instead of just bringing more suppliers into the mix. So managing an eco-system of multiple suppliers throws a different challenge to client procurement organizations. Few of the challenges/opportunities of integrating Digital into procurement models and what are the ways to handle them is provided below.

Changing category value proposition: Procurement organizations can create new business models for itself and move from being a cost center to a profit center. This is possible because procurement possesses strategic know-how about suppliers and their markets and a deep expertise about the goods and services that are procured, as well as the alternatives on offer, including emerging innovations

New ways to contracting: Clients have to manage broader ecosystem of suppliers, disparate processes, service levels and pricing units. Procurement, legal and vendor management teams who are accustomed to dictating their standards to suppliers need a different approach to manage the complexity driving the standardization wherever possible.

Confidentiality and IP Rights: Confidenti­ality and intellectual property provisions and other restrictions on use of data abound in signed Digital contracts. Companies should review their Digital sourcing deals closely to ensure that they don’t restrict their data use or analysis rights.

Realize the full potential of Digital: Procurement organizations should have the knack of leveraging the Digital as a new frontier to change the world of customer needs. From big data analytics to 3D printing — is revolutionizing organization’s operational and administrative processes and creating innovative digital products and services.Reflecting the effects of Digital cutting-edge technologies and data management on strategic and operational procurement , category strategies for Digital demanding a constant change.

In conclusion, procurement organizations need innovative category approach encompassing Digital services to accelerate adoption of new technology formats for operational efficiency, cost optimization and for business growth.



Pragmatic solution for data-age


300 hours of video are uploaded to YouTube every minute! Almost 5 billion videos are watched on YouTube every single day. This is one example of the pace of data growth and overall size of data is piling into zetta-bytes faster. Where am I going with this is relevance of rapid analysis of data measured in hours or days rather than the stereotypical months of traditional data mining. The result is an opportunity to derive meaningful insights to business with more emphasis on predictive analytics.

Predictive analytics is nothing new. Predictive analytics is a way to identify the probability of future outcomes based upon historical data. For example, from customer perspective, companies can predict a likely lifetime customer value or the probability of either loyalty or churn. Let us look at few use cases to examine the relevance and importance of predictive analytics in the world of enormous data.

A fashion retailer story: Predictive analytics helped in analyzing the campaign spends and predicting incremental campaign impact of spend. This helped the retailer in understanding where not to spend: Having a close look predictive analytics helps in understanding the relationship between customer segments and the marketing campaigns being interacted with. This retailer predicted the probability of a particular channel influencing online or offline purchases within specific customer segments, and while this was enormously useful in understanding how to spend budgets targeting more personalized digital campaigns, it was equally insightful into identifying spend that wasn’t contributing to incremental value. The power of predictive analytics came in determining, should the retailer pay for this ad, or will a sale happen organically through another channel or communication that might cost nothing or next to nothing? This allowed the marketing team to choose the right channels to most effectively and efficiently reach different groups of prospects and customers, and second, it provided the information required to personalize by sending the right content and message to the right segments at a very granular level.

Preventing hospital readmissions: Hospitals are turning to predictive analytics as they began to feel the financial pinch of high 30-day readmission rates. Real-time EHR data analytics helps hospitals cut readmissions by five to seven percent. This demonstrates how predictive analytics in real-time can analyze EMRs data to automatically identify and target patients at the highest risk of readmission early in their initial hospitalization when there is a lot that can be done to improve and coordinate their care, so they will do well when they leave the hospital. Notably, Kaiser Permanente has been working to refine its readmissions algorithms in order to better understand which returns to the hospital are preventable and which are not, a crucial distinction for value-based reimbursements.

Lifetime value analysis of a subscribers of communication service providers: CSPs are lately realizing that not all customers are the same. It is important for CSPs to assign a quantifiable dollar value to each customer, in order to prioritize various sets of customers. The Lifetime Value of a subscriber provides the predicted yield from each customer over the customer life. This helps CSP in offering high priority customers loyalty bonuses, preferential treatment through personalized service, better credit norms etc.
We are living in a world of data overloading. Predictive analytics can be a true human partner from elections to sporting events to the stock market on what the future will bring. Predictive analytics elevate human kind from an educated guess to data backed decision.

Can enterprises jump directly to AI bandwagon?


What is an enterprise’s path to Artificial Intelligence? Can companies jump on to AI hype bandwagon? Sharing a perspective to trigger a dialogue and seek your inputs.

The following journey seems to be a logical path to AI.

Step 1: Enterprise has to start with basic levels of automation

Step2: Define the path for robotics play. With basic levels of automation, leverage RPA with still human dominance.

Step3: Next pave path to robotics dominance with human assistance making it autonomics

Step4: Elevate to cognitive automation with pure robotics play having human oversight. Ingrain cognitive intelligence as a logic step to AI.

Step5: Land on Artificial Intelligence arena. Can companies jump here directly, we need to think creative.

IoT enabling Manufacturing Platforms progressively deliver better Connected Cars


In the Digital age customers prefer experience over features. Aided by plethora of IoT technologies, the evolution of connected cars with a balance of features vis-à-vis experience is phenomenal from vehicle-to-infrastructure (V2I) to vehicle-to-vehicle (V2V) and now vehicle-to-everything (V2X) leveraging vehicle-to-cloud (V2C). In my view IoT technologies are enabling connected cars to deliver value from integrated ecosystem of industries including automotive, telecom, health, insurance and industrial automation. Manufacturing platforms are designing and delivering connected vehicles that are truly leveraging the aplenty Internet of Things technologies as described below.

  • V2I, V2V and V2X end point connectivity with mobile wi-fi offload (wi-fi hotspots, 802.11u, 3G/4G LTE/latest 5G), DSRC roadside infrastructure (802.11p), consumer network (femtocells, dealer hotspots) and energy service providers (charging stations etc.)
  • In-Vehicle-Infotainment enabled with intuitive human-machine-interface (HMI), advance driver assist solutions (ADAS), voice communications, around view monitor, and rear seat entertainment with 4G LTE speeds available via built-in mobile hotspot enables services such as Internet radio, video streaming, Web browsing and personalized music etc.
  • Public/Private/Enterprise cloud connectivity offering real-time navigation, weather forecast, traffic information and online route planning, audio & video streaming, health data updates and remote car monitoring
  • Telematics promoting passenger safety with smart SOS (e-Call), a wide range of security features that keep drivers connected and safe in the event of an emergency, including automatic crash notification, stolen vehicle tracking, and roadside assistance.
  • Leapfrog towards Autonomous Vehicles with advanced state of the art embedded sensors and actuators, contextual voice recognition, interconnectivity V2I/V2V, improved decision making algorithms beyond ADAS and efficient navigation technologies enabling hands-free driving with right balance of driver-in and driver-out of the loop scenarios
  • Vehicle advanced diagnostics and analytics enabling cost reduction across industrial value chain tying insurance telematics, remote diagnostics, and condition-based maintenance
  • Enabling modern day payments with electronic toll collection, parking reservation and payment

Auto OEMs Growth Strategies


Robust growth projections in auto sales coupled with unevenness in global markets is forcing Auto/OEMs to react strategically to shifting demands. In this post, highlighting 3 evolving trends in auto industry and the challenges confronted by OEMs to sustain growth and profitability in next 3 to 5 years.

  • First, macro price and cost pressures. Consumers are demanding lower auto prices and expecting high-end features (e.g. sophisticated infotainment) to be standard. OEMs are challenged with managing steeply increasing material costs and provide alternative low cost & efficient inputs.
  • Second, expanding regulatory controls demanding tighter fuel economy, safety features and green vehicles which is getting more expensive. This push OEMs to source light weight composites, invest in safety engineering and as well share the burden of increased green costs.
  • Third, shifting focus for vehicle content with increasing electronic and software content posing challenge to OEMs to differentiate their products building new technology (hardware, software and mobile) capabilities.

OEMs are experiencing 5 primary drivers in the following order of impact and likely scenarios that will emerge out of them for OEMs.

  1. Lighter vehicles with clearly feasible low cost Carbon Fibers as an alternative to other metals and composites that offers fuel economy. Dialogue is on with OEMs to exploit ORNL and Weyerhaeuser efforts on lowering carbon fiber raw material cost to a level of US $5 to $7 per lbs.
  2. e-Mobility, meaning electrical/hybrid powertrains, including batteries, as well as in lightweight & aerodynamic drag-reducing technologies resulting in lower costs and enabling safety & green vehicle. As the storage costs are attainable close to $200/KWh, the total cost of ownership for battery electric vehicles (BEVs) would compare favorably to HEVs and PHEVs even if gas prices stay in the range of $3 to $4. This is leading to a scenario of consumer purchase decisions based on TCO basis to trade-off content and other features against petroleum expenses which increases revenues for automotive suppliers and OEMs.
  3. Increasing infotainment & telematics content as a result of consumer demand for customization, entertainment and communication (V2V, V2I). The cost of electronics and software content in autos was less than 20 percent of the total cost a decade ago and today it is as much as 35 percent. One scenario is of integrating in-vehicle-infotainment (IVI) leveraging connected car solutions with driver assist features that helps commuters experience connected world on-the-go and as well designing an early warning systems that enabled auto manufacturer predict vehicle part failure, take early correction actions and estimate future warranty claims. Telematics features, including semiautonomous driving aids such as automatic parallel parking and lane-keeping assistance as well as sensor-based reporting on car maintenance and usage, also present the scenario to forge a closer relationship with customers and increase margins. OEMs and dealers are piloting offerings on more convenient proactive service, alerting a car owner to upcoming maintenance or repairs. In addition, telematics features affording opportunities for tie-ins with insurers, such as offering discounts for customers who drive safely.
  4. Platforms and part consolidations. Emerging use of 3D printing with composites for making automotive parts in less time is opening doors to mass production of automotive parts. It is projected that share of components as % of vehicle costs that will have sustainable basis for creating value will fall about 55% today to just over 40% by 2020. Part consolidation with one-piece design that can potentially reduce vehicle weight by 35 to 40%. Also test rig optimization in virtual product development for vehicle technology is another lever for OEMs.
  5. Composite Recycling advancements. Few real life examples include, closed loop CFRP recycling technology by BWM for its i3, collaboration between BMW and Boeing for recycling etc.

OEMs are closely watching these trends and drivers to come with a path forward and define a roadmap what companies, teams and the OEM industry as a whole should follow to be future ready.

Is big data creating 3₵ to 5₵ business value per 1GB of data?

“Data is a capital asset in digital economy”

Big Data Value

Rough-cut estimate on the absolute $ value being created by 1 GB of data is 3₵ to 5₵ without factoring qualitative value creation of data. The value created by big data is constantly increasing with further advancements. This put in a context the overall potential of Big Data that is approximately growing at 40% annually taking the size of data to 44 Zettabytes by 2020. It is also projected that about 37% of that data will be useful if tagged and analyzed properly.

I was trying to understand and unveil how Big Data creates value to businesses? In true sense, the value is co-created by virtue of big data interconnecting the businesses with consumers. Getting further deep into the big data resources and platforms co-creating value helps the businesses carve out competitive strategies in the data-age. Businesses creates and offers “Platforms” as a means for consumers to create and share data back. Summarizing below on platforms being deployed by businesses to engage with consumers to capture big data and there by the co-creation of value.

  1. At a basic level businesses deploys “transactional platforms” to process buy and sell tractions e.g. kiosks, POS systems, payment gateways etc.. The buyer role of consumers generates “transactional big data”. Purchasing behavior, price, product category, color, numbers, buying cycle, location, and demographics is the source of transactional big data. This data can help business to profile consumer behavior, run tailored campaigns and create shopping guides in the provision of customized services.
  2. Use Case: PayPal processes more than 20+ terabytes of log data every day using Hadoop data platform for sentiment analysis, event analytics, customer segmentation, recommendation engine and sending out real-time location based offers.
  3. The second popular platforms are “virtual social networking platforms”. These communication platforms support consumer community communication and enable the collection and transmission of “communication big data”. The information generated from communication platforms is non-transactional in nature and offers mountains of value. Consumer’s favorite topics, trends, emotional feelings, or characteristics could be reflected by these data. Businesses leverage communication platforms to attract customers with different themes e.g. enable customers to stay, review, give thumbs-up, or forward their favorite threads. Opinion leaders may emerge after frequent interactions. Some businesses grant opinion leaders certain authority, such as casting a ballot for controversial topics etc.
  4. Use Case: Delta Airlines Airline uses communication platform for sentiment analysis to analyze flyer tracking experience. Delta monitors tweets to find out how their customers feel about delays, upgrades, in-flight entertainment, and more. For example, when a customer tweets negatively about his lost baggage with the airline prior to boarding his connecting flight. The airline identifies such negative tweets and forwards to their support team. The support team sends a representative to the passengers destination presenting him a free first class upgrade ticket on his return along with the information about the tracked baggage promising to deliver it as soon as he or she steps out of the plane. The customer tweets like a happy camper rest of his trip helping the airlines build positive brand recognition.
  5. The third type of platforms that support businesses’ effect to attract consumers to participate actively in product improvement and to re/configure new services or new business decisions. Businesses use collaborative features these participative platforms to accurately reflect personalized demand and generate substantial amounts of participative big data from consumers. The data collected on these participative platform is shared among R&D, designers, engineers, managers, and procurement for potential actions.
  6. Use Case: P&G leveraged participative platform and resulting big data successfully into its new product development process, by aggregating consumer data from multiple brand touchpoints and using it to both launch and promote new products. For example, P&G used them to determine how the molecules in certain household products like dishwashing liquids will react over time to refine the product.
  7. The forth type of platforms that support businesses in acquiring new knowledge shared by consumers who build connections across diverse ecosystems. Establishing or joining a multi-brand and multi-industrial virtual community is an efficient approach for businesses to establishing a transboundary platform. The intermediary consumers generates transboundary big data. Transboundary big data refers to data generated by consumers who share different service ecosystems and facilitate the export and import of knowledge across different ecosystem boundaries. Consumers act as intermediaries because the Internet significantly reduces switching cost and searching cost for consumers and enables them to try different brands, products, or purchases.
  8. Use Case: A largest automaker use transboundary big data to support predictive marketing that helps automaker build brand loyalty by boosting its aftermarket service revenues. Automaker analyzed customer data from multiple sources, vehicle data and the qualitative notes written by technicians to entice auto owners to come to its service centers.

Big data and Internet-based technologies have empowered consumers and forced businesses to be more consumer-centric. The advent of the big data and Internet significantly reduced information asymmetry between consumers and businesses, increased customers’ bargaining power and, consequently, changed the power structure. As a result, big data co-create the value by interconnecting business with consumers and defining win-win scenarios. Hence the necessity to progressively excavate consumer big data and analyze potential demand in advance of competitors have become a fundamental requirement for businesses in the fiercely competitive global market.

Artificial Intelligence – Techniques & Use Cases

The artificial intelligence (AI) market is estimated to grow from $0.42bn in 2014 to $5bn by 2020, at a CAGR of 54%. In another report, BofA Merrill reckons the market will blossom to $153bn over the next five years – $70bn for artificial intelligence-based systems, and $83bn for robots. That compares to roughly $58bn in 2014. This growth can be attributed to the factors such as diversified application areas, improved productivity, and increased customer satisfaction.

With recent advances, AI is gaining confidence to drive business growth. In this blog, I have taken a close look at inventory of AI Techniques / Technologies and applicable use cases. The following chart provide “Artificial Intelligence – Techniques & Use Cases” snapshot.