From Aalsmeer, at the heart of the international flower trade, we help growers, traders, processors, and exporters get more from their data.
Demand, availability, and prices change quickly, while data on purchasing, sales, inventory, production, logistics, and finance is often spread across different systems. We bring this data together so you can manage sales, availability, and profitability more effectively.
In floriculture, trade, availability, and logistics are closely connected. An order may appear commercially attractive but yield less once purchasing, waste, processing, packaging, transportation, and claims are taken into account.
Growers, traders, processors, and exporters have different priorities, but their questions overlap. What is actually available? Which orders can be delivered in full and on time? Where is inventory sitting idle? And which products, customers, and sales models deliver the best bottom-line results?
The necessary data is often spread across trading software, inventory and production systems, Floriday, logistics solutions, and financial records. As a result, there is no shared view of what happens from cultivation or purchasing through to final delivery.
Data from trade, inventory, production, logistics, and finance does not align automatically. As a result, it often remains unclear where profitability is generated, where it is lost, and which deviations require immediate attention.
An inventory position is only meaningful when it is clear what is freely available, reserved, in transit, in production, or already sold. Without that distinction, an item may appear available even though it can no longer be delivered.
Labor, outsourced work, materials, waste, and overhead are often tracked in different systems or manual records. This means the total amount spent is known afterward, but not always the actual cost of one bouquet, production line, or order.
A product, customer, or sales channel may perform well in terms of revenue but disappoint once purchasing, waste, processing, packaging, transportation, and claims are taken into account.
Seasonality, contract terms, spot trading, and changing customer demand make it difficult to align purchasing, production, and inventory. As a result, shortages and surpluses often become visible only at a late stage.
Delays, rejections, corrections, wait times, and manual work are spread across different processes and systems. Without a shared view, it is difficult to see where capacity is being lost.
When data from multiple systems is combined manually, reporting takes a great deal of time, and the figures may already be outdated by the time they are delivered. As a result, decisions are based on hindsight rather than on what is happening now.
Good analysis does not start with a long list of KPIs, but with the questions that different teams use to guide their daily decisions.
In many organizations, systems are technically connected, but their data is not yet aligned in terms of meaning. Integrations, exports, and manual files provide separate pieces of the story, while a reliable overall view of production, costs, revenue, and margin is missing.
Industry-specific solutions such as KBT-Pro, Match Online, FreshPortal, Florisoft, and Floriday contain data on products, lots, supply, orders, customers, and logistics. To support effective management, this data must be connected with inventory, production, processing, transport, and finance.
It is also important to align the meaning of key concepts. Available inventory is not always freely available for sale, an order may be confirmed but not yet delivered, and revenue may be recognized at a different time from the commercial sale. The same products, customers, and lots may also be recorded differently across systems.
Quotes, orders, order lines, sales types, customers, countries, and sales channels form the basis for commercial analysis. Connecting this data with prices, volumes, discounts, cancellations, and delivery statuses provides insight into sales volume, customer behavior, and performance by product, customer, and market.
Purchase orders, suppliers, growers, lots, quality grades, and purchase prices show where products come from and the terms under which they were purchased. Linking this data to sales, losses, and claims makes it easier to assess performance by supplier, lot, or product.
Product groups, varieties, colors, sizes, packaging, and other product attributes form the basis for assortment analysis. This makes it possible to examine price trends, availability, volume, and margin down to the product or lot level.
Unallocated inventory, reservations, expected receipts, products in transit, work inventory, production orders, processing steps, labor hours, outsourced work, material consumption, losses, and lead times together provide insight into what is actually available and what happens between procurement and delivery. This helps identify shortages, surpluses, and operational bottlenecks earlier.
Pick and pack statuses, shipments, transport planning, routes, delivery times, and returnable packaging flows determine whether orders are delivered in full and on time. When linked to the customer, product, lot, and carrier, this data helps identify recurring delays and corrections.
General ledger data, procurement costs, labor costs, outsourced work costs, packaging costs, transport costs, facility costs, energy, claims, budgets, and forecasts clarify the financial returns generated by trade and production.
By bringing direct and indirect costs together, it becomes possible to assess not only revenue and gross margin, but also the contribution to the final result.
When data from trade, inventory, production, logistics, and finance is brought together, it provides a foundation that enables different teams to work from the same definitions. Not only to review past performance, but also to adjust course more quickly.
Compare price, volume, and margin by product, customer, country, sales type, and period. This reveals where revenue is growing, which customer-product combinations make a real contribution, and where returns are under pressure.
Combine open orders, unallocated and reserved inventory, expected receipts, production, and historical sales volumes. This helps identify emerging shortages, surpluses, and slow-moving inventory earlier.
Analyze purchase prices, delivery reliability, quality, product loss and claims by supplier, grower or lot. This allows you to assess commercial terms and operational performance together.
Track lead times, capacity, labor hours, material consumption, outsourced work, waste and rejected output by product, order or operation. This shows how much time and cost are actually required to produce, for example, a mono line, mixed bouquet or flower cart.
Connecting this data with procurement, sales and finance provides better insight into the actual cost and profitability of each product or order.
Track order statuses, picking and packing processes, loading, transport and returnable packaging. This reveals where delays or corrections occur and which orders are delivered in full and on time.
When the focus is on transport planning, flows of goods, fulfillment and delivery reliability, our approach to supply chain & logistics is also a good fit.
Connect actual revenue and margin with budgets, forecasts and expected demand. This allows sales, operations, procurement and management to anticipate seasonal patterns and deviations sooner.
These insights can be translated into daily trade and inventory dashboards, weekly sales reports, production and logistics overviews, budget monitoring and forecasts. This enables procurement, sales, production, logistics, finance and management to work from the same definitions and figures.
We’ll discuss the challenges you’re currently facing, what data is available and where the greatest opportunities lie to improve control over trade, availability, production, logistics and profitability.
For Multi Color Flowers in De Kwakel, we developed a Data & Analytics environment for procurement and sales. Multi Color Flowers specializes in custom coloring and finishing of floriculture flowers for the professional market.
Using Qlik Cloud Analytics, we bring together FreshPortal data on procurement, sales, inventory levels and margins in near real time. Users can compare trends across different periods, investigate deviations and make adjustments sooner. Procurement and sales also automatically receive periodic reports with results by week, month, quarter and year.
Procurement and sales data are analyzed in the same environment. Users can compare prices, quantities, revenue and margin by period, product or other relevant breakdown.
This shows how commercial performance is developing and where changes in purchase or sales prices affect results.
Current and historical inventory levels are connected with procurement and sales data. This provides better insight into the relationship between available inventory and actual sales.
Comparing periods reveals deviations and changes in inventory and sales sooner.
Analyzing procurement, sales and inventory together provides insight into the margin behind the revenue. Users can see where results are improving and where price, volume or inventory trends are putting pressure on margins.
This allows procurement and sales to work from the same data and identify more quickly where further analysis or action is needed.
Within Topgeschenken Nederland, we support Topbloemen.nl, among others, with Data & Analytics for daily performance management, planning and forecasting. Sales, order, inventory, marketing, service and financial data are brought together, enabling operations and management to better anticipate seasonal peaks, availability and expected demand.
For Pentanova, we developed an analytics solution for internal logistics systems at the Aalsmeer Flower Auction. Data from planning, maintenance and technical installations is combined to provide operations, engineering and management with insight into logistics flows and system performance.
Floriculture involves multiple disciplines at once. Procurement, sales, operations, logistics and finance each view the same trade, inventory and production from a different perspective. We bring this information together and ensure that teams can manage performance using the same definitions.
We start with the questions that need better answers. We then map the processes, definitions and data sources and determine what data is needed to connect products, lots, customers, orders, inventory, production, logistics and costs.
If the question extends beyond a single application, we can help with strategic advice, data strategy, AI Readiness or an independent BI tool selection.
For example, we connect KBT-Pro, Match Online, FreshPortal, Florisoft and Floriday with ERP, inventory and production systems, logistics applications and financial accounting systems.
In doing so, we account for differences in product and lot registration, order statuses, availability, delivery times, and revenue and margin definitions.
On that basis, we develop dashboards, analyses, and reports for areas including:
Where needed, we add planning, periodic reporting, and user input.
Depending on the existing environment, we work with Qlik, Power BI, Microsoft Fabric, TimeXtender, or a combination of these.
If you already have an analytics environment, we build on it where that makes sense. We can improve models and dashboards, add data sources, harmonize definitions, or support a migration. If you are unsure about the current approach, a Second Opinion Data & Analytics can be a good first step.
After delivery, we can support and manage the environment, allowing new locations, customers, suppliers, product groups, and data sources to be added in a controlled way.
Data in floriculture rarely resides in a single system. Trade data, inventory, production, logistics, and financial data are often spread across multiple platforms and applications.
We combine data from sources including industry-specific trade and ERP systems, warehouse solutions, financial software, Excel files, and custom systems to create one coherent view.
This makes it possible to connect areas such as sales, procurement, inventory, availability, production, delivery reliability, logistics costs, and margin.
We start with the decisions that need to be better informed. These may relate to trade, availability, inventory, production, logistics, margins, or forecasting.
We then determine which definitions, processes, and data sources are needed to answer those questions reliably. This prevents a large data platform from being built before it is clear which problems it needs to solve.
No. Differences between systems, missing definitions, and manually maintained files are often precisely what prompts an initiative.
We identify what is usable, where the greatest risks lie, and which improvements are necessary. Not every data issue needs to be resolved in advance, but critical discrepancies must be made visible and manageable.
No. The size of the organization matters less than the complexity of its processes and information landscape. A medium-sized company may also operate across multiple locations, trade flows, product groups, customers, and data sources.
The initiative should be proportionate to the organization. Sometimes a targeted solution for a few important questions makes more sense than immediately implementing a broad data platform.
Yes, we can work with industry-specific solutions such as KBT-Pro, Match Online, FreshPortal, and Florisoft. Data from Floriday and other systems can also be integrated.
The exact approach depends on the available APIs, databases, exports, and access rights. We first assess which data is available, how it is structured, and how often it needs to be refreshed.
Not always. It depends on the number of sources, the required historical data, the refresh frequency, and the complexity of the analyses.
For a limited use case, a lighter setup may be sufficient. When data from multiple systems needs to be combined on an ongoing basis, definitions need to be managed centrally, and different teams use the same data, a data warehouse or broader data platform usually becomes more relevant.
That depends on the scope and the quality and accessibility of the data. An initial use case can often be developed with a relatively focused approach, while an organization-wide data foundation takes more time.
We therefore prefer to work in manageable steps. We start with a clearly defined issue, specific users, and clear decisions, then expand in a controlled way to include additional sources and use cases.
By not starting with visualizations. We first establish definitions, responsibilities, and decision-making questions. We then build a data foundation that can support multiple use cases.
We also agree on who owns the KPIs, who reviews changes, and how new requirements are added. Without these agreements, an analytics environment can quickly become a collection of reports containing different versions of the same figures.
Yes. We first map the existing data sources, models, dashboards, definitions, and management processes. We then determine what can remain, what needs to be improved, and which components would be better replaced.
This can range from adding a few sources to restructuring a data model or supporting a migration. We do not start over unless there is a substantive reason to do so.
Yes, especially for a clearly defined assignment. For broader or business-critical initiatives, Bitmetric offers greater continuity: we have backup capacity, can deploy different specialists, and can scale up more quickly when needed.
We also have direct relationships with vendors such as Qlik, Microsoft, and TimeXtender. This also reduces your dependence on a single person and makes it easier to manage risks associated with engaging freelancers for extended periods, including those related to the Wet DBA.
We need input from employees who understand the processes and figures. This may include people from procurement, sales, operations, production, logistics, and finance.
They help establish definitions, verify results, and set priorities. This does not mean everyone needs to be involved throughout the initiative. A small group of subject-matter owners and decision-makers is usually more effective than a large project team.
Together, we define what terms such as availability, inventory, order, delivery, revenue, cost price, and margin mean. We also determine which system is the authoritative source for each data point and when a transaction is included.
This does not eliminate all differences in interpretation, but it does make them explainable. Each department can manage performance based on its own responsibilities, while the underlying definitions and data remain aligned.
Yes. With a Second Opinion Data & Analytics, we can assess an existing environment, architecture, migration plan, or vendor proposal.
We assess how well it meets your information needs, as well as the technical choices, manageability, costs, risks, and feasibility. Bitmetric does not have to carry out the subsequent implementation.
Would you like a better understanding of which customers, products, lots, and processes truly contribute to sales volume and margin? Barry and Eric would be happy to discuss your current environment, information needs, and a logical next step. In half an hour, you will know whether we are a good fit. Email us, call us, or schedule a call right away.