On the road to digital transformation, most manufacturers are still evolving, and based on their maturity and size, are at different milestones in their journey. But what is impeding their progress, and what can they do to move faster without breaking the safe speed limit?

Cloud as a backbone for digital transformation. While digital investments have been increasing steadily, manufacturers are finding it hard to scale their efforts. They are still struggling to tap data to drive innovation. To tackle this problem, cloud must become the backbone of digital transformation. The ability to collect, ingest and analyze large amount of data in real-time is critical to this journey.

 

 

Digital Core. An important first step in this journey is to build or upgrade to a cloud supported, post-modern ERP. The ERP landscape has undergone a seismic shift in functionality and ease of use. Its architecture is almost entirely cloud enabled. As a deployment option, cloud has created a paradigm shift is the way companies see ERP today. No more multi-year deployment, no more big-IT staff to support ERP. No more expensive upgrades or falling behind.

The cloud-based architecture has also created a level playing field for smaller ERP providers like Epicor, IFS, NetSuite and others, who, in many cases, have leapfrogged their solutions to give a serious challenge to the big players like SAP and Oracle.

One important consideration in the modern ERP is to keep customization to a minimum. Cloud ERP providers are encouraging manufacturers to streamline their existing business processes, and in many instances eliminate the customization built over years before the benefits of the ERP can be realized.

As you build the digital core, your next focus should be on building an analytics platform that would be both robust and scalable to meet your current and future needs, including big data requirements.

Analytics Platform. An integrated, scalable, cloud-based ERP provides a solid foundation for building the A&BI (Analytics & Business Intelligence) capability. A manufacturing company has a wide range of use cases, from financial and sales KPIs to those related to manufacturing defects and machine sensors. In the last decade or so, A&BI tools have undergone a paradigm shift, from being IT centric to now being end-user centric, with continued focus on improving self-service capabilities. This shift is not without reason. Companies are drowning in data today, and there are not enough data scientists and IT experts who can meet all the demand for data analytics. The result is a rapid growth of citizen data scientists. If you are an SMB (Small and Medium Business), self-service is an imperative you cannot ignore. Some important considerations before you make an investment in A&BI are as follows:

  • Focus on building a platform and not just implementing tools.
  • Make self-service capability a key requirement from your A&BI platform.
  • Make Big Data an integral part of your A&BI platform, even if business is not yet asking for it. They will soon.
  • Prepare a multi-year roadmap. It’s not going to be a sprint but a journey.
  • Identify and fill the skills gap through training and hiring.

E-Commerce.  Just because you are a B2B manufacturer does not mean your website should not be top-notch and user-friendly. In fact, a former client of mine has been urgently revamping its e-commerce portal to avoid losing additional customers to its main competitor because its customers find it hard to order on its portal. An advanced web portal supported with web analytics to better understand traffic and buyer behavior on its website is imperative for manufacturers too.

 

 

Factory Automation. This is the most important phase of the digital transformation, and one that will take the longest to achieve. This should be done in at least four distinct phases:

  1. Logistics automation and shop-floor data collection. This primarily involves use of hand-held devices (e.g. RF guns) and mobile devices (phones, tablets, display monitors) for activities such as labor tracking and real-time scheduling, or shipping in the fulfillment centers.
  2. Data integration and machine interfacing. Quality-center machines such as spectrometers, hardness and tensile testing machines and hundreds of others should be integrated directly with the digital core to collect data in real-time and fed to the analytics platform for decision-making. Today, by the time the readings from such machines are analyzed and a quality issue discovered, the defective batch may have been worked on for days before it is pulled off the line.
  3. Develop and execute IoT use cases. Start with simple use cases to flush-out the entire process from setting up the IoT devices and reading data into the analytics systems, to analyzing data and automating real-time monitoring and response. Prepare a multi-year roadmap for deploying IoT.
  4. Implement robotics and machine intelligence (MI). Once you have built expertise in data ingestion and processing, you are ready to implement robotics for automation and MI, based on your use case and ROI.

As manufacturers continue to invest in digital priorities to get their legacy products to market quicker while also investing in the next-gen smart products, it is imperative that they make cloud the backbone of their future strategy while focusing on building the digital core. A consideration no less important is the investment in digital training to build a critical mass of talent to be successful on this journey.

 

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