In this article we will focus on High Technology enterprises that are dependent upon Manufacturing and Manufacturing Enterprises and how to leverage Machine Learning for High Technology and Manufacturing. In the High Technology Industry especially in the Telecommunications and Networking ecosystem as well as companies that provide services which leverage these products, the convergence of cloud computing and software driven functionality. This has resulted in a complexity of monetisation models that these organisations have to deal with that are straining the current processes that these enterprises have in place. This has required a shift in the way companies approach marketing, sales, business operations (quote to cash process) and supply chain management (forecast to delivery process). Some examples of these are given below. Traditional approaches of relying on active sales with little focus on marketing has to be replaced with highly focused marketing to drive customer acquisition, adoption and retention in a Cloud, SaaS or Hybrid offering world. Ability to have a unified customer view with more accurate install base is becoming a necessity  instead of an option as enterprises realize that without this they tend to leave more revenue on the table in terms of renewals of service, contracts, or fail to meet customer expectations be it for marketing purposes or customer service. This is compounded when there is a multi-tier model from OEM, to Service Provider, to End Customer that is usually in play in this space. With the complexity of offerings and the pace of business change that is occurring, many companies ability to forecast demand and have it drive the supply chain efficiently is not able to keep up.  The result is lost opportunities for optimizing costs and or maintaining customer delivery levels as per expectations or both. The disruptive change brought about by cloud computing,  software and resulting hybrid offerings on the High Technology industry has caused a significant impact. Startups exploiting the ability to leverage Cloud, SaaS and Software solutions to take on established enterprises is leveling the playing field (example Palo Alto Networks/Arista v/s Cisco). The enterprises are now forced to look at productivity drivers in all of their processes (engineering and operations.) Artificial Intelligence and Machine Learning provide the options that will help make this transition much easier. With globalization there was a major shift to provide low cost manufacturing. Recently there has been increased focus on local manufacturing as individual countries start taking up initiatives on this theme. Enterprises are looking to make their operations efficient wherever they are. Artificial Intelligence and Machine Learning provide options that will help these companies achieve their goals. Artificial Intelligence and Machine Learning (AI and ML) –  Artificial Intelligence and Machine Learning has existed 35-40 years ago but they were limited to those organizations (government, scientific, universities) that had access to the huge computing power that was required to successfully build and leverage these solutions. Today with the advances in computing technology and the availability of both private and public cloud computing the ability to leverage AI and ML solutions is getting more and more accessible. This is because especially with the availability of public cloud platforms such as Google, AWS and Microsoft it is becoming possible to focus on the development of the AI and ML solution that solve the business use case and focus on creation of the foundational infrastructure to build such solutions on (the spread of effort between infrastructure and actual solution building typically was 90 to 10 percentage points). Google has pioneered the usage of AI and ML to drive their own revenue generating solutions and now they have opened up the best in class public cloud platform for machine learning to the general public. This is called the Google Cloud Platform. While a number of standard AI and ML solutions are available (example Vision API, Speech API, Translate API, Natural language processing API and so on) it also enables the ability to have custom Ai and ML solution that solves a specific business use case by leveraging TensorFlow (which is also open source and can run on any platform). AWS also has some form of AI and ML components on its enterprise leading the public cloud platform but these cannot be leveraged with the ease of the GCP, AI and ML components. Similarly there are proprietary solutions from IBM (eg Watson) and Salesforce (Einstein) but these are either very proprietary (IBM) or solve a very narrow problem (Einstein) High Technology landscape Impact of Cloud Computing The move to cloud computing, has given traditional hardware and software providers heartburn for years (Gartner). Not everything has to be on public cloud, some companies have chosen to have their own private clouds. With cloud computing and the focus on everything as a service, some of the high technology companies see the demand for their hardware based product portfolio decrease as more and more software driven offerings that are cloud based have provided better and cheaper alternatives. These companies have been forced to align with the market and focus on Cloud and SaaS based and hybrid offerings in addition to their traditional offerings. This has led to huge stress on their engineering and operational processes and here is a sampling of some of these. Rise of Digital Marketing as Traditional Marketing does not scale up with needs for Cloud, SaaS and Hybrid offerings The traditional approach where Marketing was based on traditional methods of collecting contact information based on marketing events, contact acquisition from sales is highly limited when it comes to selling Cloud, SaaS and hybrid solutions. This needs a more scalable digital marketing focus that can leverage multiple marketing channels (social media, email, events, advertisements, webinars) in a coordinated way. The contact management needs to be current and accuracy of key attributes in terms of contact’s decision making, influencing capabilities and their interests becomes key in ensuring that marketing campaigns are efficient and effective. Artificial Intelligence and Machine learning provides key capabilities in this area with sentiment analysis, propensity analysis, customer segmentation, contact management, product recommendations and so on. Virtual Sales is key for enterprises to keep up with Demand Generation needs for Cloud, SaaS and Hybrid offerings Similar to marketing the advent of complex offerings that enterprises have to offer has resulted in the need for a scalable virtual sales capability that is driven via automation and aided by Digital Marketing. Traditional sales and account management cannot scale up as the sales are lower in average deal value and higher in transaction count. Similar to Digital Marketing, Artificial Intelligence and Machine Learning can provide the needed automation capabilities needed for effective virtual sales by leveraging the customer segmentation, propensity analysis, product recommendations for new sales and renewals. Install base management is becoming very complex in a multitier distribution world The install base management drives many critical processes within an organization. High Tech enterprises that have complex offerings that have a combination of hardware, software, cloud and SaaS offerings are finding more and more impacts of disconnected install bases on their core operational processes (example renewal of service contracts and so on). Artificial Intelligence and Machine Learning can help reduce the gaps and improve the accuracy of install base management. Managing Value Chain Planning processes has become very complex The key driver of Value Chain Planning is Demand Forecasting since it controls the entire demand and supply balancing across the Value Chain. The complexity of the offerings has resulted in a decrease of accuracy levels provided by the current demand forecasting solutions in place causing more and more dependency on human interventions through overrides. However, this results in lost opportunity for increased revenue and also reducing costs. Artificial intelligence and Machine Learning solutions provides opportunities to improve accuracy of Demand Forecasting and help reduce dependency on manual intervention and enable exception based demand forecast management. In addition, Artificial Intelligence and Machine Learning provide opportunities to follow-up the streamlined forecasting with a more responsive inventory optimization (avoiding excess and obsolete inventory), Ai and ML driven distribution management to optimize costs while maintaining or exceeding service levels by having dynamic intelligent route selections that can move in consistency with changes in supply chain which current solutions are unable to do. Fully automated Manufacturing Operations For the Manufacturing Industry in general, Artificial Intelligence and Machine Learning provide capabilities for achieving higher automation in the plants. Examples are preventive maintenance (predicting failures ahead of time), leveraging sensor data and ML bots to prevent or minimize defects, manage energy consumption, fully automated inventory management on the shop floor and so on. Recommendations For the High Technology Industry the following are the high level use cases where it is recommended Artificial Intelligence and Machine Learning be looked at to drive innovative and significant business transformation that can improve the profitability by increasing revenue, reducing costs or both.
  • Effective Digital Marketing with Ai and ML driven Sentiment Analysis, Customer Segmentation, Contact Management, Propensity Analysis, Product Recommendations and so on.
  • Enhancing and augmenting existing Sales capabilities with Virtual Sales capabilities
  • Improving Install Base management (impacts renewals and related processes)
  • Improving Demand Forecasting
  • Optimizing inventory in the entire supply chain (Reducing Excess and Obsolete Inventory)
  • Optimizing distribution management (especially for micro delivery, last mile optimization, perishable goods supply chains and so on)
  • Automation of Manufacturing Processes
  • Improved Preventive Maintenance and Quality Management
Finally, due to the ease of designing, developing and deploying the Ai and ML solutions it is recommended that enterprise look at leveraging Google Cloud Platform with its best in class Ai and ML capability for achieving desired business results with agility and speed. Google has several partners to recommend that enterprises use when they are interested in deploying Ai and ML solutions on Google Cloud Platform. One such partner is Pluto7 which specializes in AI and ML solutions for Retail, High Technology and Manufacturing Industries. Visit to know more.

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