Top 3 strategies for supply chain and manufacturing process efficiency using Big Data

During this economic downturn and given the likelihood of recession, many manufacturing companies need to make sense and use of Big Data, IoT Data, customer data, and supply chain data for process efficientcy to cut costs. This, ultimately, means they need to have solutions that analyze huge amounts of unstructured data using AI/ML capabilities and feed that data across their technology stack to streamline manufacturing workflows. And as business continue to demand solutions that can meet their product and process innovation requirements, the latest business transformation and Industry 4.0 initiatives for manufacturers simultaneously is presenting opportunities for them to “design anywhere, build anywhere” at a lower cost. Companies with globalized supply chains, many of which are considering near shoring, want to be able to setup industrial manufacturing operations anywhere quickly. That means IT departments, data scientists, and manufacturing teams need to be equipped with the right infrastructure and capabilities needed to roll out autonomous and robotic factories that are easy to set up and configure. But that is not an easy endeavour. So where do you start? It's all about making processes and workflows more streamlined. Here are three strategies I would consider.

Strategy 1: Unify data and processes to make innovation continuous?

Organizations often lack access to consistent data and structured processes across the product and service lifecycle, a troubling gap in their business operations. It is an underlying condition masked by symptoms like excess inventory, late to market, high procurement costs, and inaccurate demand forecasts. But companies rely on?accurate?and?timely?information to make decisions daily—from executives defining the organizational strategy to end-users driving engineering and development, manufacturing, marketing, supply chain, and operations. Because of the inherent complexity of manufacturing supply chains, it is imperative that the ideation to launch process is seamlessly aligned from engineering to manufacturing routing and quality plans.???

To make innovation a?sustainable?growth?engine,?companies?need?a single, standardized way to make sense of unstructured data and align their product records and BOMs across globalized supply chains.?This is dependent on leveraging a AI/ML capabilities to make sense of Big Data and a modern PLM software that can?provide?the product information management, product development, and quality management capabilities needed to?tie together all the processes and data across the supply chain. Because cloud-based PLM and Big Data applications can be tightly linked across IT infrastructure, companies can also?leverage?it to make innovation continuous and collaborative and capture and analyze feedback from voice of the customer, the voice?of the product, the voice of the factory, the voice of the digital twin to make faster, more informed decisions.??

Strategy 2: Ideate, innovate, design, and build anywhere and everywhere with the digital thread

Product lifecycle management systems need to be able to support a broader set of use-cases that are associated with the supply chain, manufacturing and customer experience. As supply chains become more dynamic and nearshoring manufacturing facilities more prominent, companies need to consider how they can digitize the product data that flows across the value chain. For managing, configuring, and servicing both new products and existing ones, it is imperative that your internal operations functions work hand-in-hand with your external supply chain partners (including contract manufacturers). Yet, limited collaboration, no integration between systems, and lack of common data standards are typical challenges for those companies looking to take advantage of autonomous and smart manufacturing capabilities.???

To be able to design and build products anywhere, MES (manufacturing execution systems), ERP, Supply Chain, and PLM systems need to share the same data and BOM connected by a digital thread (and APIs). The benefit of this approach is that manufacturing companies can digest all of the data (even data from connected IoT and digital twins), analyze it using machine learning and AI capabilities, and leverage it to feed the innovation funnel, make continuous product improvements, streamline change orders, and set up micro-factories and manufacturing operations from any location.??

Strategy 3: Harness advanced capabilities to enable the robotic factory?

?Robotic factory process disciplines that enable automated manufacturing and replace many manual and one-off processes requires storing and reusing most design elements in a single-solution or hybrid architecture. Manufacturers no longer require nor want to use extensive manual efforts to produce and communicate relevant documents across the product development cycle and across the extended supply chain. For robotic factories and smart manufacturing advancements to be successful, manufacturers need a common version control system for all aspects of the technical data package for a product, including CAD, BOM, quality, process instructions, and automation/robotic instructions.??

PLM and Big Data Analytics software can be used as that control system and provide companies with the capabilities needed to connect and automate manufacturing by tying together all of the data and processes across the supply chain: from cross-functional from front-end ideation, concept development, and design refinement to manufacturing execution and service. When integrated with IoT sensory data, PLM and Big Data Analytics software allows companies to extend it to a digital twin and run simulations to visualize the production line and factory floor and aggregate the visualization to the full supply chain.??

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