Data analytics drives excellent organisations: how to do we move from good to great? Accelerate in process improvement, use your data company-wide and get outstanding results.
The speed of innovation in digitisation is increasing day by day. We see many new technologies that allow companies to accelerate. In this way they can raise the bar towards increased productivity & flexibility. A requirement for survival in today's highly dynamic market environment.
Regardless of whether you, as a company, use the latest technologies or rely mainly on legacy systems such as an ERP (Enterprise Resource Planning) system, data analysis is relevant for both. Common is that all technologies generate a large amount of data. Data that is available but is in the blind spot of the organisation. Do you recognize this situation? Then there is a good chance that you can get more out of this than you do today. Potentially insights are hidden in the data that can help you tackle your current & future challenges.
After all, 20% to 30% of the potential company turnover is lost because of ignorance of weaknesses in the processes.
Data Analytics & Insights
Since there are many new data technologies on the market (process mining, dashboarding, advanced data analysis, modelling, robotic process automation (RPA), internet of things (IoT), artificial intelligence (AI), real-time optimisations & simulations, …), organisations need simplicity & clarity. As an organisation, we want to be able to master these new technologies step by step. Use the right technology where it is relevant. And especially making sure the new way of working is anchored in the entire organisation.
An overarching phased approach is therefore imperative to get started step by step with your data and use it as leverage towards the future.
- We start from a good understanding of the current situation of the organisation (what is a problem that we want to solve or what do we want to prepare our organisation for?). To obtain a profound insight, we use:
- Voice of the customer: an essential tool to identify the current & future needs of the customer. You cannot achieve customer satisfaction without knowing what is really needed. Which products & services do we want to offer in the future? Where do we want to apply process improvement & why?
- Key Performance Indicator (KPI): what are the current performance indicators and are they the correct ones? What is their target, in the short and long term? Do we make a distinction between leading & lagging indicators? Are we already visualizing the KPI in a digital dashboard today?
- Data collection: we request the relevant data from the various systems (Enterprise Resource Planning ERP, Warehouse Management system WMS, Manufacturing Execution System MES, Lab Information Management System LIMS, Maintenance Management Systems, Process control systems, energy performance system, …). You have more data than you think.
- Data cleaning: a crucial step in the data analysis roadmap. It makes datasets usable. Converting data into information that we can interpret. Only in this way you can as an organisation obtain the correct insights from your company data.
- Contextualisation: without having context information you cannot make any statements about correlation or causal relationship.
- Once we have the data in a usable format, we start the advanced data analysis. In this way we create transparency in the core processes of the organisation. We "show" the performance of processes. We always do this together with all stakeholders involved. In this way we strive for maximum involvement from the start. The data analysis uses the following data technologies:
- Process mining: With this technology we can tackle the following problems:
- “I do not have any overview on the process efficiency”
- “Where is the bottleneck in my process”
- “I want to simulate the total impact when improving 1 step”
- “I prefer to monitor my office processes to see if the process is failing”
- “I want to increase speed in solving problems”
- Advanced statistics: With the most advanced methods at the level of Six Sigma Black Belt we dive into the data. We use Design of Experiments (D.o.E.), multivariate analysis, advanced regression analysis, principal component analysis, etc.
- Augmented Intelligence (A.I.): a set of tools that uses data science & machine learning to change the way organisations explore, analyse & act on data based on the insights. This toolbox enables us to separate essential from minor issues extremely quickly (data funnelling) without prior knowledge of the process & with less need for data science & machine learning skills.
- Process mining: With this technology we can tackle the following problems:
- We identify the most important improvement enablers from the analysed data. We always take the company's strategy into account. We select short- and long-term enablers, for this we rely on advanced data simulations:
- Modelling: we model the current organisation, we make a digital twin of the organisation. This enables us to identify improvement enablers as well as to simulate specific improvement actions (what-if scenario analysis) before applying them in practice.
- Based on our many years of experience in guiding improvement projects, we convert the improvement enablers into a detailed action plan with a clear project management approach.
- Implementation of the action plan is supported by experience in process improvement, problem solving, teamwork, leadership & organisational development.
Finally, the result of a data analysis project is what counts. Data processing has added value if we acquire useful insights by analyzing them. Also how we as an organisation want to continuate this new standard.
For this we rely on dashboarding. Dashboarding must enable an organisation to see, highly visually and for every level of the organisation, the performance of the processes (in real-time where necessary). “You cannot improve what you cannot see".
