Before reading further, I ask you to stop here and think: what do the title’s three words mean to you? How do you interpret “Data-driven efficiency”? Is it part of your day-to-day work? Is it something that requires or enables actions and decisions in your work? What are the enablers and requirements for you to achieve data-driven efficiency? If you ask the same questions from your colleagues, can you expect similar answers to yours?
We can start answering these questions by checking the English dictionary for their definitions;
Data - information, especially facts or numbers, collected to be examined and considered and used to help with making decisions
Data-driven - happening or done according to information that has been collected
Efficiency - the quality of achieving the largest amount of useful work using as little energy, fuel, effort, time, money etc. as possible.
This gives us quite a clear definition: “Use data to define actions which provide the best outcome.”
Data requires a variety of actions throughout its lifecycle. It has to be created, collected, verified, owned, maintained, cleansed, represented and integrated between systems. So, one may say that handling data takes a lot of effort and gives you pieces of information that do not improve your efficiency on their own.
In many organisations, efficiency - the best outcome - is a blurry target, as there are a lot of different views of what it means. If you happen to have the same situation with your data and have your facts and numbers inaccurate or not available, the decision-making to define actions that improve efficiency gets very difficult.
Business targets are an essential requirement for reaching data-driven efficiency, while digitalisation and emerging technologies are its enablers. The ability to collect, share and process data increases day by day and these technologies provide more and more real-time data from all levels of your operations and supply chain. Therefore, it is a common challenge to have plenty of different technologies providing huge amounts of data but not know exactly how to make the best out of it.
At Midagon, we help our clients to proceed in their pursuit of improved operations. To succeed in data-driven efficiency, data and technology need to be aligned and seen as enablers for the targeted business benefits.
Our maturity assessment identifies organisational capabilities and development needs, providing a foundation for successful decision-making and target-setting before development initiatives are launched.
For me, data-driven efficiency means continuous actions to implement strategic decisions to improve the business from carefully selected perspectives so that data and technology is used optimally. Examples of the results what I have seen achieved with data-driven efficiency include:
Business targets are truly visible and guide daily decisions and actions in operations
As the data flows in integrated systems it engages teams and stakeholders to drive shared targets and increases cooperation
Utilising data enables new services to customers and provides business advantage compared to competitors
De-centralised decision making - users are able to make timely decisions quickly based on real time data
Quick feedback of the actions taken in form of achieved results (KPI’s) enhances continuous improvement and learning
Job satisfaction increases as only the relevant information and targets are easily available. This ensures smooth operations.
With the help of our methods and expertise, together we can discover and achieve the production transformation opportunities that data-driven efficiency offers in your business landscape. We can start the discussions for example from these questions aiming to define your current state;
Do you collect, share and process data to support your business targets?
Does your processed and represented data steer your actions and guide your decisions?
Is your data, technology and business maturity aligned?
Is your data architecture enabling integration, data flow and transparent reporting?
Do you have clear responsibilities to maintain master data and quality of master data in good shape?
How to move from historical data to data that enables proactive or predictive operations?
I’m happy to discuss more in detail with you of your needs in this area! This blog resumes our industrial production transformation blog series - read also: “Manufacturing Efficiency- Point of View”.
About the author:
Antti Ruohola has 15+ years of experience in supply chain roles: leading factory operations, Material Management and Purchasing. His expertise includes:
An innovative and result-oriented mindset. Readiness to challenge and create new ways to find a solution.
Broad understanding of supply chain E2E processes from inbound to outbound.
Project business experience; cooperation with engineering, project management and supply chain management.
Practical knowledge of leading productivity improvement-, cost cutting-, subcontractor ramp up/down- & layout change projects.
Experience in LEAN implementations.
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