Tuesday, 20 Jan 2026
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Attention
Most teams treat visibility like the finish line: more dashboards, more alerts, more “control.” That mental model creates noise, not throughput.
Alerts don’t solve ops. Execution does.
If you’re drowning in exceptions, it looks like work, not failure. The team is busy. The network is moving. But the system is asking humans to be the integration layer.
A supply chain control tower is only useful when it turns signals into coordinated action across planning, transportation, warehousing, customer service, and suppliers. In practice, there are a few solution categories that matter. You can mix them, but you have to be clear about what each category is responsible for and where it breaks.
Solution category 1: Visibility and event management
This is the sensing layer: shipment milestones, carrier status updates, inventory positions, order status, dock schedules, and supplier confirmations. The output is an event stream and a prioritized exception list.
Ops-first purpose: detect risk early enough to change the outcome.
Typical outputs: late pickup risk, inventory shortfall risk, missed appointment risk, dwell risk, chargeback risk.
Where it fails: if it stops at “alert.” Without structured triage and resolution steps, visibility tools increase cognitive load and create whack-a-mole.
Solution category 2: Orchestration and workflow
This is the execution layer: who does what, in what order, with what data, and with what decision rights. It coordinates tasks across teams and systems.
Ops-first purpose: reduce handoffs and make the next best action explicit.
Typical outputs: task assignments, approvals, escalations, customer notifications, rebooking steps, and documented resolutions.
Where it fails: if workflows are built without exception paths, or if they ignore real constraints (dock capacity, carrier lead times, labor availability). Then the workflow becomes “shadow process” and people route around it.
Solution category 3: Analytics and decision support
This is the reasoning layer: root cause patterns, SLA risk scoring, network hot spots, and scenario comparisons (e.g., expedite vs. backorder vs. split ship).
Ops-first purpose: make tradeoffs explicit and consistent.
Typical outputs: recommended resolutions and the operational cost/service impact.
Where it fails: if recommendations aren’t explainable enough for operators to trust, or if inputs are stale/incorrect.
Solution category 4: Integration and data governance
This is the plumbing: mapping identifiers, normalizing milestones, aligning master data, and ensuring system-to-system updates.
Ops-first purpose: one source of truth for execution decisions.
Where it fails: brittle mappings, delayed updates, or “unknown unknowns” when a partner changes formats.
Solution category 5: Automation (rules-based and agentic)
This is the labor multiplier: repetitive steps are automated so humans focus on judgment calls.
Ops-first purpose: reduce touches per shipment/order.
Where it fails: automation that can’t handle exceptions becomes a liability during peak.
Comparison: RPA vs workflow automation vs AI agents
The quickest way to break a supply chain control tower is to treat these as interchangeable. They are not.
RPA (robotic process automation)
What it’s good at in logistics ops:
What it’s bad at (failure modes):
When it fits in a supply chain control tower: as a tactical “last-mile keystroke remover” for stable steps inside a larger orchestrated workflow.
Workflow automation
What it’s good at in logistics ops:
What it’s bad at (failure modes):
When it fits in a supply chain control tower: as the backbone. Visibility generates work; workflows ensure work gets done consistently.
AI agents
What it’s good at in logistics ops:
What it’s bad at (failure modes):
When it fits in a supply chain control tower: as an augmentation layer on top of workflows, especially for investigation and communication-heavy exceptions.
The practical takeaway
Use RPA to remove keystrokes, workflow automation to manage execution, and AI agents to reduce investigation and communication effort. A supply chain control tower should treat each exception as a work item with an owner, a deadline, and a resolution path—not as a notification.
If you want a supply chain control tower that improves speed, service, and cost-to-serve, define “good” as fewer touches, faster decisions, and fewer preventable expedites. You should be able to point to the specific mechanisms that make that true.
What good looks like checklist
To make this usable, expand the checklist into operator-grade requirements you can test during design and after go-live:
Design the supply chain control tower around execution units
Operators don’t manage “visibility.” They manage units of work: shipments, orders, loads, appointments, pallets, claims, and invoices. Your control tower should mirror that.
1) Define the top 10 exception types that drive cost-to-serve
Start with volume and pain, not theory. Consider a scenario where your team touches 2,000 shipments per week and 20% trigger an exception. That’s 400 exceptions. If half of those are “late pickup risk” and “appointment reschedule,” those two categories will dominate labor and service outcomes.
For each exception type, document:
2) Build resolution plays that reduce handoffs
A common failure mode is splitting investigation and action across teams. The control tower should collapse that where possible.
Example: late pickup risk
If the system only alerts, humans do all of the above manually. If the system provides a play with prefilled fields and defined decision rights, the operator can execute in minutes instead of juggling five systems.
3) Define automation boundaries per risk level
Not every step should be automated the same way. Use tiers:
This is where AI agents can help without becoming a liability: summarize, draft, propose, but don’t commit high-impact actions without approval.
4) Make “time to resolution” visible, not just “late/not late”
Most towers over-index on status. Operators need urgency.
Implement aging and SLA views:
Even with no fancy technology, this reframes behavior: teams stop re-reading the same alert and start pushing work to closure.
5) Close the loop into master data and partner performance conversations
A control tower should generate evidence, not anecdotes.
If address errors, missing references, or recurring appointment constraints are driving exceptions, the tower should feed:
This is how you reduce exceptions over time rather than staffing up to manage them.
Operating model: how to run the supply chain control tower daily
A control tower isn’t a dashboard; it’s a cadence.
Metrics that matter (and how not to game them)
Avoid vanity metrics like “alerts generated” or “exceptions detected.” Track measures that reflect execution quality:
Frame these as internal control signals. Consider a scenario where touches per shipment drops from 1.8 to 1.5 after standardizing late pickup resolution. That’s a meaningful labor release even if volume stays flat.
Common failure patterns to avoid
1) Building for reporting instead of action
If your first deliverable is a dashboard, you’ll optimize for visuals. Start with the top three exception plays and build the UI around those actions.
2) Treating integration as a phase you can defer
A supply chain control tower with manual updates becomes a parallel system. Prioritize write-back to at least one system of record early (TMS, WMS, OMS), even if it’s limited.
3) Over-automating before you standardize
Automating a broken process makes errors faster. Standardize the play, then automate the stable steps, then add agent assistance.
4) Ignoring partner constraints
You can’t workflow your way around a DC that has no appointment capacity. The tower must reflect constraints and offer alternative plays (reroute, split, hold, reschedule) with clear decision rights.
5) No exception taxonomy discipline
If every operator labels issues differently, you won’t fix root causes. Keep a short list, train it, and review it monthly.
Implementation path you can execute in 60–90 days
You don’t need a “big bang” tower.
A supply chain control tower is a discipline: detect, triage, execute, learn, and fix the system. If your “control” is mostly alerts, you’re paying for noise. If your control tower owns execution plays with clear decision rights and closed-loop updates, you’ll see faster recovery, fewer expedites, and lower touches per shipment.
If you want a practical control tower blueprint your team can adapt, start here: Link

Wednesday, 21 Jan 2026
Logistics vendor evaluation guide with decision criteria, vendor questions, and a 10-scenario demo script to protect margin, speed, and service quality.