Let's say I was in an ops review last month where every team including Warehousing, Dispatch, and Customer Support shared their "updates."

But even with all the updates, no one could clearly answer the most important question: Are we improving or declining in delivery this quarter?

That’s the pain KPI examples for operations teams are meant to solve. Not a dashboard for decoration. A way to make performance visible, so you can manage it.

Operations KPI

An operations KPI is a number that tells you whether your operations system is delivering what the business promises, consistently, at an acceptable cost.

A KPI is not:

• a task count with no quality bar
• a report you cannot explain
• a metric you track because another company tracks it

A good ops KPI has four properties:

• Operational: tied to a real workflow (receiving, picking, dispatch, production, service)
• Actionable: someone can change it with specific actions
• Comparable: you can compare week to week
• Trusted: the team accepts the data is “close enough” to act on

Why operations KPIs matter in African SMEs

When ops metrics are unclear, you end up managing by vibes.

That’s how you get:

• stockouts you notice only when customers are already angry
• late deliveries you discover after churn
• overtime that “just happens”
• procurement leaks that hide inside petty cash
• a team that works hard but cannot show contribution

If you want one practical frame, think like a customer.

Your customer experiences operations as:

• speed (lead time)
• reliability (on-time performance)
• quality (right item, right condition)
• communication (visibility when things go wrong)

Ops KPIs turn those into measurable commitments. The classic “reliability” metric in supply chains is OTIF (On Time In Full). If you’re not familiar with it, here’s a clean explainer of OTIF and how it’s calculated from Shopify.

Constraint acknowledgements (the real world you’re operating in)

If you lead ops in Africa, at least three constraints show up fast:

1. Data is messy.
   Orders live in WhatsApp, inventory lives in someone’s head, and your “system” is a spreadsheet last updated two Fridays ago.
2. Time is limited.
   You are doing procurement in the morning, dispatch in the afternoon, and firefighting all day.
3. Measurement culture is fragile.
   People hear KPIs and assume punishment. So they hide issues instead of fixing them.
4. Documentation is thin.
   No standard work, no definitions, no owners. You can’t improve what you haven’t named.

This article assumes that reality and still gives you a workable KPI setup.

Common mistakes teams make with ops KPIs

Here are the patterns I see over and over, especially in growing SMEs.

1. Tracking too many KPIs.
   If you track 30 metrics, you manage none. Cap it at 6–10.
2. Using vanity KPIs.
   Example: “number of deliveries” without an on-time bar. You want outcomes, not activity.
3. No operational definitions.
   If two people calculate “delivery time” differently, you will fight the metric instead of fixing the process.
4. No owner, no meeting, no loop.
   A KPI without a review rhythm becomes wallpaper.
5. Targets with no baseline.
   People set “95% OTIF” because it sounds nice. If your baseline is 52%, you just created theater.
6. Punishing the messenger.
   If late delivery is always blamed on dispatch, dispatch will learn how to hide late delivery.
7. Ignoring cost and capacity.
   You can improve speed by spending more. You need at least one cost metric to keep you honest.

Step-by-step process to build your KPI system (without drowning in spreadsheets)

Step 0: Pick the operational promise you make to customers

Write one sentence:

“We deliver ____ to ____ within ____ days, with ____% accuracy, and we communicate delays within ____ hours.”

You need this because KPI selection is not a menu. It’s a consequence of what you promise.

Step 1: Map the ops value chain (in 20 minutes)

Pick one core flow:

• Order-to-delivery (most commerce and distribution)
• Procure-to-stock (inventory-heavy businesses)
• Make-to-ship (manufacturing)
• Ticket-to-resolution (service operations)

List 5–8 steps. Example order-to-delivery:

1. Order received
2. Order confirmed
3. Inventory allocated
4. Pick and pack
5. Dispatch
6. Delivery
7. Proof of delivery + issue resolution

Now every KPI you choose should attach to a step.

Step 2: Choose 6–10 KPIs using a balanced scoreboard

A simple scoreboard for ops teams:

• Reliability (did we do what we said?)
• Speed (how long did it take?)
• Quality (how many defects?)
• Cost (what did it cost?)
• Capacity / productivity (how much can we handle?)

If you choose one KPI per category, you already have a usable dashboard.

Step 3: Define formulas, owners, and data sources

For each KPI, define:

• formula in one line
• data source (system, spreadsheet, manual count)
• owner (one person)
• update frequency (daily, weekly, monthly)

If you do nothing else, do this. It prevents endless debates.

Step 4: Set baselines and targets

Baseline first. Two to four weeks of data is enough to start.

Targets should be:

• realistic for your constraint level
• adjustable as your processes stabilize
• paired with initiatives (otherwise it’s just pressure)

Step 5: Run the weekly ops review loop

Keep it boring. Boring is good.

A solid weekly rhythm:

• 10 minutes: KPI scorecard (green, yellow, red)
• 15 minutes: top 2 KPI misses and root causes
• 10 minutes: commitments (who does what by when)
• 5 minutes: risks for the week (fuel, staffing, supplier delays, outages)

If you want to scale this beyond spreadsheets, this is where tools help. Some teams I’ve worked with moved KPI tracking into a structured goals workflow so ops targets stop living in random files. Talstack’s Goals and Analytics modules are useful in that phase because they keep metrics tied to owners and review cycles, instead of “someone please update the sheet.”

KPI examples by operations area (with definitions)

Below are KPI examples for operations teams you can actually use. I’ll keep them operational and definable.

Fulfillment and logistics KPIs

• OTIF (On Time In Full)
  Percent of orders delivered on time and complete. Strong reliability KPI. See OTIF definition and calculation details. 
• On-time delivery rate
  Percent of deliveries delivered by promised date/time.
• Order accuracy / perfect order rate
  Percent of orders delivered with correct items, quantities, and condition. (In SCOR language this is close to “Perfect Order Fulfillment,” a standard reliability metric in supply chain frameworks.) 
• Delivery lead time
  Median time from order confirmation to delivery.
• Failed delivery rate
  Percent of deliveries that require re-attempts.

Inventory and procurement KPIs

• Inventory accuracy
  Match between system stock and physical count.
• Stockout rate
  Percent of SKUs with zero available stock when demanded.
• Inventory turnover
  How often inventory cycles through the business over a period. Clear definition and formula overview here.
• Supplier on-time delivery
  Percent of POs received on time.
• Purchase price variance (PPV)
  Actual unit price minus standard/expected unit price.

Manufacturing and production KPIs

• OEE (Overall Equipment Effectiveness)
  A composite metric: Availability × Performance × Quality. It’s a standard way to quantify how effectively equipment is used, not just whether machines are “running.” 
• First pass yield (FPY)
  Percent of units produced right the first time (no rework).
• Scrap rate
  Scrap as a percent of total input.
• Changeover time
  Time to switch from product A to product B.

Service delivery and field operations KPIs

• SLA compliance
  Percent of tickets/jobs completed within promised time.
• Mean time to resolution (MTTR)
  Average time to resolve issues.
• Repeat visit rate
  Percent of jobs that require a second visit due to incomplete work.
• Customer wait time
  Especially useful in clinics, banks, telcos, and service centers.

Cost, quality, and reliability KPIs (cross-cutting)

• Cost per order / cost per delivery
  Total ops cost divided by orders delivered.
• Returns rate
  Returns as a percent of shipped orders.
• Damage rate
  Damaged items per 1,000 shipped.
• Takt time (when you operate a flow line)
  Takt time translates customer demand into the pace production should meet. It’s a practical concept from Lean that helps align capacity with demand. 

Tables you can reuse

Table 1: KPI library for ops teams (what to track, how to calculate, why it matters)

Table 2: KPI selection guide (what to track based on maturity)

Quick Checklist (keep this near your desk)

• Pick one operational promise (speed, reliability, quality) and write it down.
• Map one flow (order-to-delivery, procure-to-stock, ticket-to-resolution).
• Choose 6–10 KPIs across reliability, speed, quality, cost, capacity.
• Define every KPI with a formula, owner, data source, and update frequency.
• Baseline for 2–4 weeks before setting aggressive targets.
• Run a weekly ops review with actions, not storytelling.
• Protect honesty. No punishment for red metrics. Fix the process.

Copy-paste scripts

Script 1: KPI rollout message to your ops team (Slack or WhatsApp)

Team, we’re introducing a small set of ops KPIs so we stop guessing and start improving.
This is not for punishment. It’s to surface issues early and fix processes.
We’re tracking 8 KPIs weekly. Each KPI has an owner and a simple definition.
Our first goal is to establish a baseline for 3 weeks, then set targets together.
If a KPI turns red, we focus on root cause and one corrective action. No blame.

Script 2: Weekly ops review meeting opener (2 minutes)

Quick start. Here’s what we’re solving: customer reliability, speed, and cost control.
We’ll review the scorecard, then spend time only on the top two misses.
For each miss: cause, fix, owner, due date.
If we don’t leave with commitments, the meeting did not happen.

FAQ (integrated, practical)

1) How many KPIs should an operations team track?

Start with 6–10. More than that usually becomes reporting work, not operational improvement.

2) What are the best KPI examples for operations teams running logistics and delivery?

A strong starter set:

• OTIF
• on-time delivery rate
• delivery lead time (median)
• cost per delivery
• failed delivery rate
• order accuracy

3) What if my data is unreliable?

Assume it will be unreliable at first.

Pick KPIs that can be measured with “good enough” counts:

• delivery timestamps
• proof of delivery
• weekly stock counts for top SKUs
• returns and re-deliveries

Then gradually improve inventory accuracy and system capture.

4) How do I set targets without benchmarks?

Don’t start with benchmarks. Start with a baseline.

After 2–4 weeks, set targets as:

• a small improvement (5–15%)
• tied to one concrete initiative (route planning, pick accuracy, supplier cadence)

5) Which KPI should I track for inventory health?

Inventory turnover is a common one, and it helps you see whether cash is stuck in stock. A clear definition and formula overview is here.
Pair it with stockout rate so you don’t “improve turnover” by under-stocking.

6) What KPIs matter most for manufacturing operations?

If you have equipment-based production, OEE is a strong summary metric because it blends downtime, speed loss, and defects.
Pair it with FPY and scrap rate to keep quality visible.

7) How often should we review operations KPIs?

Weekly for most SMEs.

Monthly is too slow. Issues compound fast when supply is unstable, demand is spiky, or logistics is unpredictable.

8) Do KPIs need to tie to employee performance reviews?

Not always, especially early.

If your measurement culture is fragile, use KPIs for process improvement first. Once data stabilizes, you can connect KPIs to role scorecards and performance conversations. Teams that want this structure often move from spreadsheets to a system where goals and reviews live together. Talstack’s Performance Reviews, Goals, and Analytics can support that transition when you’re ready.

One next step

Pick one flow (order-to-delivery, procure-to-stock, or ticket-to-resolution) and choose eight KPIs from the tables above.

Then run your first weekly ops review with the scorecard, even if the data is imperfect. The loop is where the improvement starts.