Create one live operational view across customer orders, ERP, warehouse picking, stock, substitutions, dispatch and delivery. ORBN connects the underlying events and builds role-specific workflows so teams can resolve exceptions while there is still time to protect service and margin.
Operational clarity begins with the state of a real order, product, pick, load or exception—not a presentation layer. We identify the events each team needs, connect them to stable business records and surface the smallest useful action at the right point in the day.
Follow an order from acceptance and credit release through allocation, picking, substitution, packing, dispatch and delivery. Customer service sees the same current state as warehouse and operations, with source evidence behind every status.
Expose waves, work queues, shortages, catch-weight differences, holds, damaged stock, late picks and unconfirmed dispatches. Prioritise material exceptions instead of asking staff to scan every order for trouble.
Route a late, conflicting or high-value exception to the team able to resolve it, with age, impact, reason and supporting records. Acknowledgement, escalation and closure become measurable workflow rather than another notification stream.
Relate fill rate, substitutions, order changes, waste, delivery outcomes and customer contacts to the operational events that caused them. Leaders can investigate a measure without losing the line-level detail needed to act.
Every view should help a named role decide, intervene or confirm. We remove metrics that are interesting but unactionable and make the age, impact and ownership of each exception obvious.
A status such as available, released or delivered means different things across ERP, WMS and transport systems. Definitions, authority, timestamps and lineage stay visible so a clean dashboard does not hide conflicting facts.
When a source is late or an integration fails, the view must show uncertainty rather than present stale data as truth. Fallback work, alert ownership, recovery and reconciliation are part of the operating design.
Score one representative flow—for example, a next-day customer order from acceptance to delivery. The weakest definition, event or ownership assumption usually matters more than the dashboard technology selected.
Every slider starts at 1 for an unproven assumption. Use current evidence, not a roadmap, product brochure or best-case demonstration.
The largest risks are currently in definitions, events or ownership. Trace one real order and its exceptions, name who acts, and identify what evidence each source can provide before committing to dashboard scope.
This scorecard supports initial scoping. It is not a food-safety, accessibility, security, financial or regulatory assurance review.
Each platform may be working as designed while the overall operation remains hard to see. The cost appears in calls, spreadsheet checks, defensive stock, late customer conversations and senior people repeatedly reconstructing what happened.
Sales, warehouse and transport each hold part of the story. Customer service asks several people for an update, then gives the customer information that may already be stale.
ERP on-hand, WMS location stock, allocated quantity, held batches and saleable availability disagree because each answers a different question without saying so.
A wave can look active while a high-value order is stalled on one shortage, label or catch-weight exception. The issue becomes visible only when the load is due to leave.
Warehouse changes remain on paper or in a specialist screen while customer service and finance continue using the original order until reconciliation catches up.
Broad email and chat notifications make everyone aware but nobody accountable. Important exceptions age beside harmless technical messages with no escalation path.
Month-end measures reveal poor fill rate, waste or delivery performance after the customer conversation and operational decision window have passed.
A control tower is a chain of definitions, events, correlation, prioritisation and response. The user interface is the final layer. Building the chain in this order keeps the view explainable and prevents another reporting product from becoming a hidden source of truth.
Choose an order, purchase order, item, wave, shipment or delivery as the thing being observed. Define its valid states, expected timestamps, relationships and responsible systems before combining feeds.
Collect acceptance, hold, allocation, pick, short, substitute, pack, dispatch, arrival and proof events with stable identifiers, event time, source time, actor and reason—not only a mutable latest-status field.
Relate the customer order, ERP order, warehouse work, shipment, route and invoice without assuming their IDs or line structures match. Preserve the mapping and source evidence needed to explain a discrepancy.
Apply service cut-offs, warehouse milestones, stock rules and delivery windows to identify what is genuinely at risk. Include confidence and freshness so missing data is not mistaken for normal operation.
Present impact, age, reason, related records and a permitted next action. Capture acknowledgement, hand-off, resolution and any customer communication so the exception does not disappear when a person changes shift.
Track detection and resolution time alongside fill, service, waste, credit and delivery outcomes. Remove noisy rules, fix recurring causes at source and retain an audit trail when definitions or thresholds change.
Different roles need different slices of the same event chain. These examples illustrate the design questions; final measures, thresholds and responsibilities must be agreed against the real operation and service promise.
| Operational question | Evidence required | Useful action | Guardrail |
|---|---|---|---|
| Which accepted orders are not safely released? | Order version, credit or compliance hold, warehouse acceptance, cut-off and promised date | Resolve the hold, correct the record or contact the customer before warehouse capacity is lost | Do not treat a missing acceptance event as a rejected order without checking source freshness |
| Which picks threaten today’s dispatch? | Wave, priority, work state, remaining lines, shortage, exception age, dock and load deadline | Reassign work, resolve stock, approve a substitution or change the load plan | Prioritise business impact rather than colouring every late scan red |
| Can sales promise the available quantity? | On-hand, held, allocated, inbound, unit, location, batch and freshness by owning system | Confirm, constrain or qualify availability using the agreed commercial rule | Expose the meaning and timestamp; never present an unexplained blended stock total |
| Which substitutions need customer approval? | Original and proposed item, allergen or specification flags, price, quantity, customer rule and cut-off | Approve, reject or contact the customer with a traceable decision | Do not automate a safety, specification or contractual decision without explicit policy |
| Which deliveries are likely to miss service? | Dispatch time, route plan, stop sequence, vehicle progress, delivery window and live exception | Replan, notify customer service or issue a proactive customer update | Keep estimated arrival separate from confirmed proof of delivery |
A wide dashboard programme can spend months standardising everything without changing a single response. We begin with a bounded decision and extend only after users act faster and the underlying data survives production conditions.
Observe the people, systems, spreadsheets, calls, cut-offs and exceptions around one material order journey. Record definitions, authority, delays and the current cost of finding the truth.
Connect representative sources, correlate records and test late, missing, repeated and conflicting events. Validate that the view can explain itself before polishing the interface.
Give a named team a role-specific queue, clear response actions and support route. Run alongside the current method and compare detection, resolution and outcome evidence.
Add orders, sites, teams and measures around proven event contracts. Review alert noise, data freshness, adoption and root causes as part of normal operational improvement.
Crowbond Foodservice was coordinating more than 200 daily orders through paper, spreadsheets and manual planning. ORBN built a connected operational workflow around the real constraints, making the planning state visible, reproducible and usable by the wider team instead of leaving the answer with one person.
Daily orders coordinated through the planning workflow
Reported planning time, reduced from about eight hours
Reported reduction in overall delivery cost
Reported annual saving from the operational change
These results relate to one client engagement, not a forecast for every project. Read the scope and evidence in the Crowbond route optimisation case study.
These sources inform discovery and assurance; they do not replace advice from your food-safety, accessibility, data-protection or legal specialists.
Current UK guidance for food businesses on supplier records, traceability responsibilities and the information that must be available to enforcement authorities.
S/02The GS1 event-sharing standard for supply-chain visibility, including what happened, when, where and why across product and asset journeys.
S/03Practical guidance on data ownership, lifecycle quality, metadata, root-cause improvement and communicating limitations to users.
It is a current, explainable view of orders, stock, warehouse work, substitutions, dispatch and delivery assembled from the systems that execute those processes. Real time does not mean every field changes instantly. Each signal needs a freshness target suited to the decision, a visible timestamp and defined behaviour when its source is delayed. A useful view identifies material exceptions and the next owner; it is not simply a dashboard containing every available KPI.
Usually not. ORBN can connect the ERP, WMS, ecommerce, EDI, transport and other systems worth keeping, then create a governed operational layer around their events and records. Replacement may still be justified when a platform cannot expose essential information, its data model blocks the required process or support risk is unacceptable. We establish those constraints in discovery rather than assuming a control tower can compensate for an unsuitable source system.
Business intelligence commonly supports monitoring, analysis and periodic management decisions. An operational control tower works inside the response window: it detects an order, stock, picking or delivery exception, provides record-level context and routes an action to an accountable team. The two can share definitions and data products, but they have different freshness, resilience, user-experience and support requirements. A delayed analytical refresh may be acceptable; a stale dispatch-risk view can misdirect the operation.
Only measures connected to a decision should earn space. Common candidates include accepted orders awaiting release, fill and substitution risk, remaining pick work, exception age, dispatch readiness, delivery-window risk and reconciliation status. Leaders may also need service, waste and margin views. The definition, grain, source, freshness, exclusions and owner matter more than the label. ORBN starts with user decisions and works backwards to the minimum reliable signal set.
Yes, when the expected milestones and source events are dependable enough. Rules can compare current progress with customer cut-offs, warehouse capacity, route deadlines or delivery windows and identify records likely to breach. Predictive methods may help when sufficient quality history exists, but a transparent threshold is often the right first control. Every alert needs impact, evidence, an owner and a response path; otherwise earlier detection merely creates earlier noise.
We distinguish material business exceptions from technical telemetry, group related symptoms around the affected order or shipment, suppress known duplicates and route only actionable work to each role. Severity considers value, customer, age, cut-off and service impact rather than one universal threshold. Alert performance is reviewed after release: rules that are ignored, repeatedly reassigned or closed without action are candidates for redesign or removal.
A bounded view around one well-understood workflow can often be proved in weeks. Wider rollout depends on source access, identifiers, event quality, historical data, definitions, sites, user groups and the operational response being introduced. We plan in production slices: map one decision, prove the event chain, run beside the current method, measure response and then expand. A credible plan separates the first useful control from a longer data and integration roadmap.
Measure the operational change, not dashboard logins alone. Useful baselines include time to detect and resolve an exception, calls or spreadsheet checks per order, orders requiring manual status investigation, late substitutions, missed dispatches, fill rate, service contacts and reconciliation effort. Adoption matters where it demonstrates the new workflow is used, but a busy screen is not success if root causes, customer outcomes or staff effort remain unchanged.
Map ordering, ERP, warehouse, delivery and reporting across the complete distributor operation.
R/02Create the APIs, events, master-data contracts and recovery controls behind the operational view.
R/03Turn trusted operational history into forecasting, margin and purchasing decisions.
R/04Connect orders, stock, warehouse work, dispatch and finance with production recovery controls.
R/05Compare replacement, integration and targeted custom software against operational fit and risk.
R/06See the scope and quantified results behind a connected route-planning workflow.
Bring one order, warehouse or delivery exception your team currently reconstructs by phone and spreadsheet. We’ll map the sources, decision window, ownership and smallest production view worth proving.