RevenueLabour Cost (overlay)Target: Labour <26% of revenue
Revenue by Category
Mains
R3,229,475 Β· 59.7%
Drinks
R1,777,840 Β· 32.9%
Desserts
R397,785 Β· 7.4%
90-Day P&L Summary
Revenue
R5,405,100
Cost of Goods
R1,628,418
Gross Profit
R3,776,682 (69.9%)
Labour Cost
R1,535,048 (28.4%)
Net Operating
R2,241,634 (41.5%)
Top 5 Revenue Items β 90 Days
Beef Fillet
R920,710
Grilled Salmon
R674,510
Prawn Linguine
R653,800
Chicken Schnitzel
R552,885
House Wine (glass)
R528,020
Executive Insight
Revenue hit R5,405,100 at 69.9% gross profit. March recovered to R1,983,400 β the best month and the first to nearly hit the 26% labour target. Feb labour at 30.1% cost R62,898 extra. Sustaining March's discipline is the single biggest near-term profit lever.
Labour vs Target
GP R3,776,682
CoGS R1,628,418
Labour 28.4%
Target <26%
Full Menu Revenue Ranking
#1
Beef Fillet
R920,710
#2
Grilled Salmon
R674,510
#3
Prawn Linguine
R653,800
#4
Chicken Schnitzel
R552,885
#5
House Wine (glass)
R528,020
#6
Veggie Burger
R427,570
#7
Craft Beer
R402,935
#8
Freshly Squeezed OJ
R342,540
#9
Cappuccino
R288,990
#10
Espresso
R215,355
#11
Chocolate Fondant
R209,685
#12
Cheesecake
R188,100
Avg Revenue by Day of Week
R35,840
Mon
R36,010
Tue
R40,160
Wed
R52,340
Thu
R78,420
Fri
R109,215
Sat
R62,180
Sun
MonβWed avg R37,337 Β· Sat is 3Γ a weekday
Labour Cost by Month
Jan 2026
29.1%
Feb 2026
30.1% β Peak
Mar 2026
26.2% β
Target <26% Β· Mar trend positive β sustain it.
Menu Insight
The top 5 items account for R3,329,925 β over 61% of all revenue. Beef Fillet alone drives R920,710. Coffee and desserts are your highest-frequency upsell opportunity β every sale adds R45βR85 at minimal cost.
Labour Alert
Feb labour hit 30.1% β R463,017 against R1,538,920 revenue. At the 26% target, Feb labour should have been R400,119. That is R62,898 excess in one month alone. Annualised: R754,776 per year if unaddressed.
90-Day Summary
Total Revenue
R5,405,100
Total Covers
6,946
Avg Spend/Cover
R778
Items Sold
53,862
Best Day
R109,215
Avg per Day
R60,056
Daily Revenue β 90-Day Trend
Jan avg: R60,735/dayFeb avg: R54,961/dayMar avg: R63,981/day
Cover Performance β 90 Days
Month
Covers
Avg/Day
Jan 2026
2,415
77.9
Feb 2026
2,094
74.8
Mar 2026
2,437
78.6
Total
6,946
77.2 avg
Avg spend per cover: R778 Β· Items per cover: 7.8
Covers per Waiter β 90 Days
Sipho
1,473 covers
Taryn
1,336 covers
Marco
1,206 covers
Nomsa
1,175 covers
Dean
1,014 covers
Priya
742 covers
Revenue per Shift β Waiter Ranking
Higher revenue/shift = more upsells and attentive service. Avg spend per cover shown.
π₯ Sipho Dlamini
R819/cover
R20,798/shift
58 shifts
π₯ Taryn Botha
R804/cover
R19,525/shift
55 shifts
π₯ Marco Ferreira
R778/cover
R18,039/shift
52 shifts
4οΈβ£ Nomsa Khumalo
R779/cover
R16,341/shift
56 shifts
5οΈβ£ Dean Swanepoel
R742/cover
R14,750/shift
51 shifts
β οΈ Priya Naidoo
R700/cover
R10,824/shift
48 shifts
Team avg revenue/shift: R16,890 Β· Team avg/cover: R778
Waiter Performance Insight
Sipho Dlamini generates R20,798/shift at 23.5 covers/shift β a realistic number for a top performer across busy and quiet services. Priya Naidoo is last at R10,824/shift with R119 less per cover. Across her 742 covers, that gap equals R88,298 in unrealised revenue this quarter.
Upsell Rate by Waiter % covers with add-on sold
Upsell = dessert, coffee, or extra drink added to cover
Sipho
42%
Taryn
38%
Marco
31%
Nomsa
29%
Dean
24%
Priya
18%
β Strong (>34%)β Average (25β34%)β Needs coaching
π― Coaching Opportunity
Dean and Priya upsell at 24% and 18%. Reaching the team average of 31% would add approximately R11,000 in revenue per 90 days β at zero extra cost.
Waiter Summary β 90 Days
Waiter
Shifts
Covers
Avg/Cover
R/Shift
Sipho
58
1,473
R819
R20,798
Taryn
55
1,336
R804
R19,525
Marco
52
1,206
R778
R18,039
Nomsa
56
1,175
R779
R16,341
Dean
51
1,014
R742
R14,750
Priya
48
742
R700
R10,824
Trend Insight
March recovered to R63,981/day avg β best of the quarter. Labour hit 26.2% for the first time. Feb dipped to R54,961/day with 321 fewer covers than Jan but similar staffing β that mismatch drove the 30.1% labour spike. Aligning rosters to forecast covers is the key operational change.