Supply chain
Predictive demand models for a company with €80M in revenue: stockouts anticipated by 4 weeks and stock levels sized on data.
Read the case study (in Italian): Ottimizzazione magazzino
Data analytics and reporting
We turn data from ERP, MES and spreadsheets into operational dashboards: production KPIs, order progress, margins. Efficiency becomes measurable and visible every day.
Request a proposalSeika Innovation's data analytics and reporting service turns data scattered across ERP, MES, CRM and spreadsheets into operational Power BI dashboards: production KPIs, order progress, margins by customer and by job, demand forecasts. The goal is not the report: it is a faster, better-informed decision.
The engagement always starts from the people who use the numbers: which decisions need to be made, how often, with which data. Then we build backwards (data model, source integration, dashboards) so that every indicator has an owner and a real use. Reports rebuilt by hand every week are one of the most widespread and least visible inefficiencies: automating them frees up time and reduces errors.
Where the data allows it, the analysis becomes predictive: statistical and machine-learning models to anticipate demand, stock levels and bottlenecks. In a project for a company with €80 million in revenue, predictive models anticipated stockouts by 4 weeks.
The engagement follows four phases, from the decisions to support to the daily adoption of the dashboards.
01
Mapping of sources: where the data is, in what format, with what quality. Missing data, duplicates and hand-built reports that can be automated emerge immediately.
02
Integration of sources into a coherent, documented data model: ERP, MES, CRM, spreadsheets. A single version of the numbers, shared between departments and management.
03
Power BI dashboards built with the people who will use them: KPIs defined together, with explicit formulas and clear responsibilities. Production, orders, margins, sales, stock.
04
Training teams to use and maintain the dashboards, so the know-how stays in the company. Follow-up to check that the numbers really drive decisions.
The first question is never which dashboard to build, but whether the data can be extracted at all. In most Italian manufacturing companies the sources are these, and they all coexist:
Supply chain
Predictive demand models for a company with €80M in revenue: stockouts anticipated by 4 weeks and stock levels sized on data.
Read the case study (in Italian): Ottimizzazione magazzinoLogistics
Automatic matching of ERP, spreadsheet and email data for load planning: -30% on loading times.
Read the case study (in Italian): Ottimizzazione carichiProduction
Data-driven production scheduling: -25% late deliveries with the same production capacity.
Read the case study (in Italian): Scheduling produzioneFAQ
01
Not just dashboards: it defines with the company the decisions to support, integrates data sources into a coherent model, builds automated reports with shared KPIs and trains people to use and maintain them. The report is the last step, not the first.
02
It depends on the production model, but the typical core includes: OEE for equipment efficiency, on-time delivery (OTD), cycle time and lead time, scrap and rework, resource saturation and order progress. Seika Innovation's guide to production KPIs (in Italian) describes them one by one, with formulas.
03
Yes, and it is the most common situation. Power BI integrates heterogeneous sources (ERP, MES, CRM, spreadsheets) into a single data model. The initial audit establishes what to use right away and what to structure better over time.
04
ERP reports show that system's data; operational decisions almost always require crossing several sources: orders, production, warehouse, costs. Power BI joins the sources, refreshes automatically and puts the same numbers in front of management and departments.
05
The first operational dashboards typically arrive within a few weeks, starting from the data already available. The complete data model and the extension to other departments follow in phases, with priorities set by the value of the decisions supported.
06
Yes. Every project includes training teams to use and maintain the dashboards, and Seika Innovation delivers dedicated training programmes on Power BI, Copilot 365 and Power Automate as part of its AI training service.
Seika Innovation
A data audit shows what you already have in house and what is missing to decide better. The first dashboards arrive within a few weeks.
Request a proposal