| Employee | Dir | Time | Location |
|---|---|---|---|
| Alice Smith Engineering | IN | 08:15 | Main Boom A |
| Bob Jones Operations | IN | 09:05 | Main Boom B |
| Charlie Davis Logistics | IN | 08:55 | Main Boom A |
| Diana Prince Management | IN | 08:30 | Executive Boom |
| Alice Smith Engineering | OUT | 17:05 | Main Boom B |
| Bob Jones Operations | OUT | 16:45 | Main Boom A |
| Employee | In | Out | Status |
|---|---|---|---|
| Alice Smith Engineering | 08:15 | 17:05 | On time |
| Bob Jones Operations | 09:05 | 16:45 | Late |
| Charlie Davis Logistics | 08:55 | — | No clock-out |
| Diana Prince Management | 08:30 | — | No clock-out |
| Evan Wright Facilities | — | — | No-show |
Managers act on exceptions, not raw logs. Items 5 and 6 implement the Top 9 KPI set and the nine alert rules from the access control brief.
One-glance count of what needs action today. Each tile filters the log below on tap.
Turns individual events into a pattern that can be raised in one-on-ones and department reviews.
Boom data already carries this. Flag anyone trending past safe weekly hours — fatigue and labour compliance.
Single site-health number tracked weekly. Feeds the manager digest and the Wages reconciliation.
The full management KPI set from the brief, rendered as a single scannable band above the event table.
Every rule from the brief, each configurable and each showing today's trigger count. Routing matters as much as the rule: three are security-critical and should bypass the digest entirely.
| Manager digest | Today |
|---|---|
| Arrives more than 1 hour early | 2 |
| Employee arrives late | 2 |
| Employee leaves early | 1 |
| Outside more than 60 min at lunch | 3 |
| More than 5 exit / re-entries in a day | 1 |
| Missing entry or exit record | 2 |
| Immediate — security & HSE | Today |
|---|---|
| Unauthorised after-hours or weekend access | 0 |
| Multiple failed access attempts | 1 |
| Occupancy exceeds safe limit | 0 |
| Staff member | Start | End | Days |
|---|---|---|---|
| Pieter van Wyk | 6 Jul | 10 Jul | 5 |
| Andre Botha | 3 Jul | 3 Jul | 1 |
| Jerome Adams | 1 Jul | 2 Jul | 2 |
| Naledi Khumalo | 29 Jun | 1 Jul | 3 |
| Sipho Ndlovu | 22 Jun | 23 Jun | 2 |
| Jerome Adams | 15 Jun | 19 Jun | 5 |
| Grace Nkosi | 11 Jun | 12 Jun | 2 |
Total days hide the real signal. Frequency, clustering and note compliance are what turn this log into something a manager can act on.
Frequency-weighted score. Five separate Mondays scores far higher than one five-day flu. Thresholds trigger HR review automatically.
| Staff | Spells | Days | Score |
|---|---|---|---|
| Jerome Adams | 3 | 12 | 108 |
| Lisbon Dlamini | 3 | 7 | 63 |
| Pieter van Wyk | 2 | 10 | 40 |
Flags any spell over two days logged without an attached note, so records close cleanly for audit.
Exposes Monday and Friday clustering instantly — a pattern worth a conversation, filterable per department.
Each spell closes with a confirmed return date and optional RTW check, feeding the wages report automatically.
| Staff | Expected | Status |
|---|---|---|
| Pieter van Wyk | 13 Jul | Returned |
| Thandi Zulu | 8 Apr | Unconfirmed |
Four fields the modal is missing. Number of days should also count working days, excluding weekends and public holidays — 01–03 Jul gives 3 either way, but a Friday-to-Monday spell would not.
| Absence type | Sick / injury / family responsibility |
| Paid or unpaid | Drives the wages run |
| Reported by & when | Was it called in on time? |
| Working days only | Auto-exclude weekends |
The form saves blind. On save it should flag anything crossing a policy line — before the record reaches the wages run rather than after.
| Account & contact | Status | Plan of action | Follow-up | Est. close | Prob. | Rating |
|---|---|---|---|---|---|---|
| Acme Corp Jane Doe | In progress | Schedule demo | 01/07/26 | 15/08/26 | 70% | Amber |
| Globex Homer S. | Open | Send initial proposal | 28/06/26 | 30/09/26 | 30% | Red |
| Growthpoint Kobus Blom | In progress | Technical submission | 04/07/26 | 22/08/26 | 65% | Amber |
Win probability alone hides stuck deals and never explains losses. These four make the pipeline diagnostic rather than descriptive.
Days each opp has sat in its current stage. Past 45 days it flags stale and enters the manager digest.
Mandatory dropdown on close-lost. After one quarter this becomes the most useful slide in the sales review.
Value × probability gives a realistic forecast alongside the raw total, per rep and per region.
Links route map visit actuals to stage movement — proves whether meetings actually advance deals.
| Salesperson | W1 actual | W1 target | % | W2 actual | W2 target | % |
|---|---|---|---|---|---|---|
| Jonathan Coetser | 7 | 6 | 117% | 5 | 6 | 83% |
| Brett Jooste | 15 | 12 | 125% | 14 | 12 | 117% |
| Gerhard Janse Van Vuuren | 29 | 15 | 193% | 18 | 15 | 120% |
| Karl Stadler | 16 | 15 | 107% | 15 | 15 | 100% |
| Bradley Saffy | 18 | 15 | 120% | 12 | 15 | 80% |
Coverage and meeting counts are inputs. These link them to outcomes, cost and forward risk.
The 14 unworked accounts ranked by value and days since last touch, rather than hidden behind a filter.
| Account | Value | Last touch |
|---|---|---|
| Aperture Science | 2.1M | 190d |
| Hooli | 1.4M | 92d |
Pulls tour cost from the travel portal and divides by meetings held and opps advanced.
A static 3.0 says little. The trend shows whether the campaign is actually deepening relationships.
Reps whose next-week target is at risk based on meetings booked today — coach before the week, not after it.
The Gantt shows that things slip. These show why, and what each slip costs downstream.
When a process turns red the planner picks a cause. One quarter later you know what actually causes delays.
Answers "if we accept this project, what breaks?" — per process, next 30 days.
Target vs actual quantity hides quality loss. Shown beside output so "on target" cannot mask rework.
When a step slips, auto-list which projects and delivery dates move — so sales hears it from the system, not the client.
| Primary | Secondary | Tertiary | Responsible | Start | Arrived | Resolved | Duration | Status |
|---|---|---|---|---|---|---|---|---|
| Maintenance | 4kw laser | Limit switch problem | Lisbon, Tshepo | 08:07 | 08:08 | 09:16 | 1h 09m | Resolved |
| Maintenance | Slitting line | Brake not working | Jerome | 09:00 | 09:01 | 10:10 | 1h 10m | Resolved |
| Consumables | Compressor | Compressor — no oil | Tshepo, Lisbon | 09:16 | 09:17 | 10:31 | 1h 15m | Resolved |
| Maintenance | Tube mill | Broken cylinder | Jerome | 12:27 | 12:28 | 12:45 | 18m | Resolved |
| Electrical | Slitting line | Motor overload trip | Andre | 14:30 | 14:52 | 16:05 | 1h 35m | Resolved |
| Mechanical | Tube mill | Bearing failure | Pieter | 18:40 | 19:10 | — | — | Open |
| Consumables | Welding station | Out of wire | Sipho | 20:15 | 20:20 | 06:30+1 | 10h 15m | Resolved |
| Maintenance | Press brake | Hydraulic leak | Jerome | 22:10 | 22:45 | — | — | Open |
Today's log is reactive. These make it predictive, and link downtime to money and production.
Breakdown count and mean time between failures per machine over 90 days. The machine that fails weekly justifies its own plan — or replacement capex.
Downtime hours × line rate gives lost output and cost, linked to the production planner's process load.
Weekly averages against a target line. The 22-minute arrival on the overload trip becomes a visible trend rather than an anecdote.
PM scheduled per machine by hours-run or calendar. Overdue items surface here and in the technician's mobile app.
"Out of wire" and "no oil" are stock failures, not machine failures. Splitting them shows how much downtime procurement could remove — and feeds the Bryan consumables dashboard.
Jobs, average resolve time and share fixed without a repeat within 7 days. For load-balancing and coaching, not blame.
| Technician | Jobs | Avg fix | First-fix |
|---|---|---|---|
| Jerome | 3 | 44m | 100% |
| Lisbon | 2 | 1h 12m | 100% |
| Andre | 1 | 1h 35m | 50% |
| Vehicle | Plate | Linked user | Status | Odometer | Last trip | Fuel MTD |
|---|---|---|---|---|---|---|
| Toyota Hilux | H 44821 | Jonathan Coetser | On road · Gauteng | 84,210 | Today 10:42 | 1,840 |
| Ford Ranger | R 30917 | Brett Jooste | On road · GP HQ | 61,077 | Today 09:15 | 1,565 |
| Isuzu D-Max | D 88342 | Pool vehicle | Parked · Bryan yard | 112,940 | Yesterday | 990 |
| Toyota Hiace | H 20455 | Karl Stadler | Idling 40+ min | 97,301 | Today 11:02 | 2,120 |
| Nissan Navara | N 61208 | Bradley Saffy | In workshop | 129,884 | 3 Jul | 610 |
Worth agreeing scope now, while it sits inside the 80-hour estimate. Adding these later costs materially more.
Fills plotted against distance since last fill. Points off the line indicate leaks, theft or card misuse. The fuel data already exists — this is the highest-value 5% of the build.
Odometer-based service intervals plus registration, insurance and permit expiry — same pattern as maintenance PM.
Fuel plus maintenance plus insurance divided by distance. The number that decides replace-versus-keep, and it feeds travel cost-per-meeting.
Share of working days each vehicle actually moved. Idle vehicles are capital to reallocate; overworked ones explain breakdowns.
Since user and vehicle are linked, generate trip logs with a one-tap business/private toggle. Feeds expense claims and tax logbooks with no admin.
| Trip | Km | Type |
|---|---|---|
| GP HQ → HB Realty | 34 | Business |
| HB Realty → Home | 19 | Private |
Harsh braking, speeding and idling per 100km, shown to the driver in their own app first. Frame as insurance-premium reduction, not surveillance.
This screen is the front door to everything. Right now it navigates; it should triage.
Each Titan card shows its exceptions before you click in, so the overview becomes triage rather than navigation.
One row of cross-Titan numbers at the top of the overview — the executive's ten-second morning check.
One line from every module per direct report — the one-on-one prep sheet. The highest-value cross-Titan build in this document.
| Jonathan Coetser | |
|---|---|
| Attendance | On time · 96% |
| Pipeline | 2 stale opps |
| Tasks | 1 overdue |
| W2 meetings | 2 of 6 booked |
Weekly active users per module. Shows which modules being paid for are actually used, and where training is needed.
Type a name or project code once and get results from every module. Removes the "which Titan is that in?" problem for new staff.
Auto-email each manager their top five exceptions across all modules every Monday at 07:00. This will do more for adoption than any screen people must remember to open.
| Sales | 4.0 | |
| Presentation | 3.5 | |
| Campaign | 2.8 | |
| Route Map | 4.2 | |
| Portfolio | 3.6 | |
| Intelligence | 2.5 | |
| Evaluation | 3.8 | |
| Development | 2.0 |
| Contact summary | No. | Relationship | Rating | Update |
|---|---|---|---|---|
| Active contacts | 48 | 3.1 | B | 82% |
| Opp summary | No. | Prob. | Rating | Update | Vitals |
|---|---|---|---|---|---|
| Active opps | 520 | 68.5% | Amber | 91% | OK |
| Advanced negotiation | Done |
| Drone barrier tech spec | In progress |
| Presentation level 4 | Not started |
The structure is strong. These make the scores trusted, comparable and consequential.
A 4.0 means little without direction. Trending each section quarter-on-quarter makes the review about movement, not a snapshot.
Score against team median and rank per section — anonymised for the rep, named in the GM view.
Tag each metric as system-calculated or human-judged, and auto-feed everything possible. Scores people cannot argue with drive behaviour; scores that feel arbitrary get ignored.
Eight equal sections means appearance moves the total as much as sales. Let the GM set weights per role and show both totals.
Any section below 3.0 generates an action with an owner and date, carried into next quarter's card. The card stops being a grade and becomes a coaching loop.
DM scores, GM approves, rep acknowledges in their own app with an optional comment. Creates the audit trail the GM/DM/Command audit line already implies.
| DM scored | 02 Jul |
| GM approved | 04 Jul |
| Rep acknowledged | Pending |
| Marcus Vance Sales | Medical + bloods |
| Elena Rostova HR | Ophthalmologist |
| James Carter Finance | Medical + bloods + ophth. |
This module lives or dies on trust: aggregate for management, personal for the employee. Items 7 and 8 implement the iPhone and Apple Watch metric sets from the health brief.
Managers see department averages with a minimum group size of five — no individual medical ratings, BMI or named compliance status. This single choice decides whether staff opt in at all, and answers POPIA and UAE health-data obligations.
Overlays program participation with access control data: sick days and lateness per department, before and after program start. This is the chart that proves return to the board.
Sick days avoided × average day cost against program spend — the health module priced in the same language as maintenance and fleet.
Eligible, registered, active this week, consented to share. With six registered workers, growth of the program is itself the KPI to manage.
Department step challenges, personal streaks and milestones in the employee's own app — voluntary, and team-level leaderboards only. This is what sustains engagement past month one.
The overdue list is good; close the loop. One tap books the clinic slot, syncs to the person's calendar and marks the access control window as medical — no HR chasing.
| Marcus Vance | Slot offered · 28 Jul 09:00 |
| James Carter | Booked · 25 Jul |
Auto-synced rather than manually entered. Removing the "log daily metrics" burden visible in the current mockup is the single biggest driver of sustained participation.
Clinical-grade trends that populate the medical rating and the targets-vs-outcomes radar without manual entry. HRV and resting heart rate can flag illness early — surfaced to the employee only, never to management.
| Status | Task & link | Assignee | Due |
|---|---|---|---|
| Due today | Review Q3 proposal for Acme Corp Acme Corp Q3 Expansion · Opportunity | Sarah Jenkins | Jul 14 |
| Overdue | Follow up on MSA negotiation GlobalTech Industries · Account | Michael Chang | Jul 10 |
| Due today | Prep for discovery call TechFlow Solutions · Meeting | Emma Watson | Jul 14 |
| Upcoming | Send onboarding welcome kit Alice Robertson · Contact | David Smith | Jul 15 |
| Upcoming | Quarterly account review Stark Industries · Account | Sarah Jenkins | Jul 16 |
| Overdue | Update CRM contact details | Michael Chang | Jul 08 |
| Due today | Finalize statement of work Wayne Ent. Implementation · Opportunity | Emma Watson | Jul 14 |
| Sarah Jenkins | 92% | |
| Michael Chang | 78% | |
| Emma Watson | 85% | |
| David Smith | 64% |
The list shows what is open. These show what is at risk, what is stuck, and where the work originates.
"Outstanding 13 days" only appears once a task is opened. Charting the whole queue by age makes genuinely stuck work visible without clicking through.
Completion rate alone does not show who is buried. Open tasks per person by status distinguishes poor performance from an unfair queue — David Smith's 64% reads differently either way.
Every task links to an opportunity, account, contact or meeting. Grouping by source shows where workload originates and which accounts consume the most effort.
Tasks closed against tasks created each week. If creation outruns completion the backlog grows regardless of how good the daily figures look.
The panel already states that overdue tasks require escalation in CRM — but a human has to notice first. Make the ladder automatic and time-based.
The per-person app version should show today plus overdue only, with one-tap complete and snooze. A full CRM table on a phone will not be used in the field.