3 Step Guide For Manufacturing To Adopt Data-Driven Mindset
Step 1- Identifying the right Assets
Traditional Vs Digital Manufacturing
Let me start by defining the difference between traditional vs digital manufacturing assets
Having defined data as a primary asset, a majority of the manufacturing industry today are not able to place data as an asset but more as an afterthought. Data is collected at various touch points through secondary digital assets, as represented in the figure, but turning this data into tangible & intelligent insights is a big challenge in the sector.
All manufacturing data is valuable in increasing productivity & profitability across verticals – if the gap between collecting data sets & drawing insightful information from it is filled. By considering BigData/Data as an asset, companies have advanced substantially in responding to customer needs, controlling operational costs & cross-functional silos in both planning & communication. Data allows companies to make key modifications to planning & production processes essentially in real-time to reduce the likelihood of bottlenecks or breakdowns which can be costly in terms of manpower, materials, and resources.
Read more on how to monetize your organizational data
Step 2: Determining where you stand in the ‘Digital Journey’
The Digital Journey of a business is determined by when and how a business moves from Digitization to Digitalization to Digital Optimization to Digital Transformation. You may think how does ‘data’ play a role in determining where you stand in the Digital Journey?
Let’s take an example- When manufacturing industries were transitioning from Industry 2.0 to Industry 3.0 i.e the age of automation & mass production, many software systems like ERP, SAP, MES, SCADA, CRM etc; were implemented. Today if your organization is leveraging one or more of these systems you are standing on the cusp of ‘Digitization’ where you have got your processes automated & resting on a digital platform. Now your organization is generating data from each of these automated processes, still, I am not terming your organization as ‘Digitalized’. Wondering why? Let’s take a look at the diagram below;
The position of your business in the digital journey is dictated by how you look at data (digital information) and if you implement ways to capitalize that to make your enterprise a data-driven business. To put it in layman’s terms gathering as much data as possible & turning it into insights & finally intelligence that will prove beneficial for your organization. Gain a deeper understanding of the stages in digital journey here
Step 3: An ‘organization-driven’ ideology
Manufacturing organizations may be on different levels of data-driven ideology (advanced, literate or ignorant); they are at the initial stages of achieving Digital Transformation in its true sense.
Manufacturing requires dynamic & quality leadership & an enterprise-wide transformation driving towards a single goal. Some key factors of the leadership team should be
- Empathy towards customers & partners & a combined understanding of creating & delivering innovative products & solutions
- Making R&D, product development & manufacturing engineering processes highly productive & performance oriented
- Leadership needs to develop interest in data security, business intelligence, data management & use of BigData
Manufacturing has seen a more radical change in the workplace in the last two decades than it has in the last 50 years. With the advent of Industry 4.0, technology leaders in manufacturing need to become data-driven decision makers. Strategic roles like CIO & CDO can direct manufacturing organizations on a company-wide digital transformation strategy.
Organizations will be able to achieve digital & business transformation in a true sense if it is driven by people with the right skillsets, encouraging a growth mindset and a responsible & knowledge driven leader.
Stay tuned to GS Lab as we launch a detailed analysis of all the above touch points to help your manufacturing organization achieve excellence through data-driven capitalization of resources!