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Look inside: Product category
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What would you like to know?

Tip: to look at one year, region or product, open the Filters panel with the arrow at the left edge. Every page follows your choice (it opens on 2025 to 2026). Not sure what a chart means? Move the mouse over it.
Good to know: revenue and gross margin in this app are sales figures (from sales invoices and item costs), not the financial-statement revenue and margin.

Business Summary

One clear view of sales, profitability, customers and operations. Figures are sales figures, not financial statements.

Alerts & watch-list

What needs attention right now.

Daily Summary

Sales deliveries, production and warehouse deliveries in one view.


One page for the day: sales, production and warehouse deliveries against target, with the things that need attention.
Daily Sales Report (PowerPoint)
Downloads your 5-slide Daily Sales Report for the day chosen above.
iLitres delivered per day over the last 30 days, by category.
iLitres blended and packaged per day over the last 30 days.
iThis month so far: litres delivered on the sales lines against the warehouse report.

Sales Tracker

Litres delivered each day and this month, with total volume and open volume, from the sales line file.



By product category
iFrom the sales line file. Total volume = Pack Size x Quantity (what was invoiced/ordered). Delivered = Pack Size x Qty. Shipped. Open = Pack Size x Outstanding Qty. Lines count on their Shipment Date. MTD = month to date, YTD = year to date. Attainment compares litres delivered with the budget for the days worked so far (green 100%+, amber 90-100%, red below 90%).
iLitres delivered each day over the last 30 days, split by category. The dashed line is the daily target from the budget.
iThis month by category: total volume (grey), delivered (green) and open (red).

iThese are the order lines read from the sales line file (read-only here). To change something, correct it in the source system and save a new export.
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Warehouse Deliveries

What the warehouses delivered this month and this week, and how it differs from the sales lines.


Sales deliveries against warehouse deliveries
iSales deliveries = litres delivered on the order lines (Pack Size x Qty. Shipped). Warehouse deliveries = orders fulfilled in the warehouse report. Difference = sales minus warehouse: a negative number means the warehouse reports more litres than the sales lines show. MTD = month to date, WTD = week to date (Monday to the chosen day).
iSales deliveries (blue) against the warehouse report (purple), month to date and week to date.
Warehouse report
iThe warehouse report as received: litres fulfilled by location and brand, month to date and week to date.

Sales Operations

Production blend by week, urgent orders to close out, deliveries pending and regional performance, in one place.


Type the urgent orders, pending IBT deliveries and regional volumes in the operations file (saved in the tracker_data folder); this page updates when you save it. Download a blank operations file

Production blend by week

iLitres blended in each week of the month by brand, from the Production Blend sheet of the operations file.
iLitres blended in each week of this month, by brand.

Products urgently needed to close out orders

Eterna and Castrol
iEterna and Castrol products urgently needed to close out orders, from the Urgent Orders sheet of the operations file. An order stays here until its 'Date closed' is filled.
Third Party
iThird Party products needed to close out orders. Rows with the same Group (for example Simba or Texaco) get a sub-total.

IBT / deliveries pending

iIBT / deliveries pending by warehouse or customer, from the IBT Pending sheet of the operations file. Tonnage is the truck load needed; volume is in litres.

Regional performance

iLitres achieved against budget by region and brand, from the Regional Performance sheet. Achievement = achieved / budget (green 100%+, amber 90-100%, red below 90%).
iAchievement against budget for each region and brand, month to date. The dotted line is 100% of budget.

Tracker Data

The files behind the trackers, when they were last saved, and any rows that need fixing.


iThe trackers read three Excel files from the tracker_data folder: the sales line export, the warehouse report and the operations file (production by week, urgent orders, IBT pending, regional performance). This page shows when each was last saved and lists anything that needs fixing.

Sales Dashboard

How much we sold, to whom, and where.

Sales revenue (NGN)
Volume (litres)
Sales gross margin (NGN)
Orders
Active customers
Avg order value
Avg price per unit
Top-10 customer share
Growth vs same period last year
Sales gross margin % of sales
iEach bar is one month of the measure you selected; the orange line smooths it (average of the last 3 months). Click a bar to see that month in detail.
iRegions ranked by revenue. Click a bar for the region's detail.
iHow revenue splits between product categories (Castrol, Eterna, Base Oil ...). Click a slice for detail.

Key takeaways

The main points from the figures you are looking at.

iRevenue for each year, with the change against the previous year underneath. The latest year is only part-way through (YTD), so it looks smaller until the year ends.
iThe sales reps with the most revenue in the data you are viewing. Darker bars are the top three. Click a name for detail.

Trends & Seasonality

Which months and quarters are strong or weak, and how this year compares with earlier years.

iOne line per year from January to December. A higher line is a stronger year; months where all lines rise show your seasonal peaks. Click a year in the legend to hide or show it.
iEach square is one month of one year. The darker the blue, the more revenue. A quick way to spot your best and weakest months and years.
iRevenue for each quarter, grouped by year, so you can compare the same quarter across years.
iLitres sold each month. Compare it with revenue: if revenue grows faster than litres, price or mix is helping.

Moving averages and annual totals

iDaily figures (light bars) and the 30-day moving average (line), which smooths out day-to-day swings so the real direction is easier to see.
iMoving annual total = the last 12 months added up, compared with the same figure a year earlier. It removes seasonality, so a rising line means real growth.

Growth month by month

iEach month against the previous month and the same month last year, with the moving annual total and its growth. The newest month may not be finished.

Regional Performance

Which regions are growing, which are slipping, and what each one sells.

iRevenue by region. Click a bar for the region's detail.
iHow much each large region grew (green) or shrank (red) compared with the same period last year. 'Same period' = 1 January to the latest sale date, against the same dates a year earlier.
iEach bubble is a region. Further right = bigger share of revenue; higher = faster growth; bigger bubble = more customers. Above the dashed line the region is growing faster than the company as a whole.
iWhat each region sells. Every row is a region and every column a category; the number is that category's share of the region's revenue (each row adds up to 100%). Darker = bigger share.
iRevenue of each region by year. Darker orange = more revenue, so you can see which regions grew or slipped over time.

Region scorecard: this year vs last year

iOne row per region. This year to date is compared with the same dates last year; growth is green when up and red when down. Index 100 = the average region. Use the dropdown to look at one product category.

Sales by Warehouse

Where the sales are loaded from: the Business Central location code on each invoice.


iThe measure you chose, by warehouse (the Business Central location code on the invoice). Click does nothing here; use the table below for detail.
iHow each warehouse's sales split between the brands.
iMonthly trend for the six largest warehouses.

Warehouse table

iOne row per warehouse. Share is the warehouse's part of the sales in view; Customers and Invoices are counted within your filters.

Sales Breakdown

Start with the whole, then drill in: pick how to split it, then pick a value to look inside.


iFirst level: the biggest values of the first breakdown. Pick one in the box below to look inside it.
iSecond level: what is inside the value you picked at the first level.
iThird level: what is inside the value you picked at the second level.

What Drives Sales

Which brands, regions, reps, warehouses, pack sizes and seasons make an invoice bigger or smaller.


iWorks on invoices (one row per invoice, described by its biggest line) in the data you are viewing, so your filters apply. It shows association, not proof of cause.
iThings that make an invoice bigger than the rest. Bar = how much bigger the average invoice is than the average of all other invoices.
iThings that make an invoice smaller than the rest.

All comparisons

iHow to read this table: each row is one group, for example the region West or the month March. Typical invoice is the average invoice for that group, next to the average for everyone else. Compared with the rest shows how much bigger (+) or smaller (-) the group's invoices are. What it means says whether the difference is large enough to trust. Very small groups (under 30 invoices) are left out.
iEvery group compared with all other invoices. Reliable = the difference is unlikely to be chance (statistical test at 95%). Groups with fewer than 30 invoices are left out.

Rep Scorecards

How each sales rep is performing against the average rep.

iThe 15 reps with the most revenue. Darker bars are the top three. Click a rep for detail.

Individual scorecard

iBars = the chosen rep's revenue each month. Orange line = what an average rep sells that month, so you can see when the rep is above or below the team.
iWhich categories the chosen rep sells, compared with the company as a whole. A big gap shows where the rep sells much more or much less than the team.
iThe chosen rep's biggest customers by revenue.

Sales rep scorecard

iOne row per sales rep, ranked by revenue. Growth compares this year to date with the same dates last year (green = up, red = down). Index 100 = the average rep. Use the dropdowns to narrow by region or product category.

Budget vs Actual

Are we on track? Litres and gross margin against budget, by month, product and region.


Actuals come from all sales data for the budget year; the sidebar filters do not apply here. Volume is in litres. Budget regions are North (North 1 + North 2), South, West and East; the National budget lines (Third Party, Base Oil, Chemical) sit in West. Margin uses the cost recorded in the sales files; the 2019 and 2026 lines use the nearest recorded cost of the same item, then the inventory valuation.

iBudget (orange) against actual (blue) litres for each month of the year.
iBudget against actual gross margin for each month.
iActual as a percentage of budget for each product category. 100% = target met. Click a bar for detail.
iRunning total of litres through the year: budget, actual, and a projection of where the year will end at the current pace.
iBudget and actual litres for each region. Foreign has no budget, so only its actual litres are shown. Click a bar for detail.
iActual as a percentage of budget for every region and product category. Green = target met, yellow = near, red = well short.

Budget vs actual by product category and region

iBudget against actual for every product category and region. Attainment = actual / budget: green = target met (100%+), amber = 90-100%, red = under 90%. Use the dropdowns to focus on one product or region.

Sales Gross Margin

The profit left on our sales after the cost of goods, by product, region and customer. These are sales figures, not the financial-statement margin.


Gross margin = revenue excluding VAT minus cost of goods. Cost is the average cost in the inventory valuation, applied to all periods, so historical margins are indicative rather than exact. The sidebar filters apply.

iGross margin % for each brand: the share of revenue (excluding VAT) left after the cost of goods.
iBars = gross margin each month; line = margin as a % of revenue.
iThe items with the highest gross margin, for the breakdown chosen above. Click a bar for detail.
iEach bubble is a product. Further right = more revenue (log scale); higher = better margin %. Dashed lines show the averages.

Gross margin table

iGross margin for the breakdown you chose above. Margin % = gross margin / net revenue. Cost known is the share of revenue for which a cost was found.

Market Share

Our litres against the whole market, by region and zone.

Market size is the Mordor Intelligence estimate for Nigeria (litres). Share = our litres / market litres. Only complete years are compared, exports (Region = Foreign) are left out, and the sidebar filters do not apply here. Zones come from the state of each sale; your industries and categories are matched to the market's industries and product types by the tables in src/01_settings.R (market_industry_map, market_product_map). Segments with no sales (for example Metalworking Fluids) show 0% so they stay visible as room to grow.

iOur share of the Nigerian lubricant market for each complete year (our litres divided by the market's litres).
iMarket share for each item in the year you choose above.
iOur litres and the market's litres, both set to 100 in the first year. If our line is above the market line, we are growing faster than the market.
iLight bars: the litres we would need each coming year just to keep our latest market share as the market grows. Blue line: the litres we actually sold.

Year-by-year table

Product Analytics

Which products and categories sell most and how each one is changing.


iThe ten products with the highest revenue.
iRevenue split by category for each year, to see how the product mix is changing.
iBars are products from highest to lowest revenue; the orange line adds them up (right-hand scale). The dark-blue products together make the first 80% of revenue: these are the ones to protect.
iEach bubble is a product. Further right = more litres sold; higher = more revenue. Bubbles high but to the left earn a lot per litre; bubbles to the right but low sell a lot at a low price.
iShare of revenue (across) against growth (up) for the top 40 products. Top right = stars (big and growing); bottom right = cash cows (big, slow); top left = question marks (small, growing fast); bottom left = dogs (small, slow). Dashed lines = company growth and median share.

Product category scorecard

iOne row per product category (or category), compared with the same period last year. Choose the grouping in the dropdown.

Product scorecard (with ABC class)

iEvery product with its revenue, litres, price per litre and share of revenue. ABC class: A = products that together make the first 80% of revenue, B = the next 15%, C = the rest.

ABC / XYZ

Which products matter most by value (A, B, C) and how steady their demand is (X, Y, Z).


ABC ranks items by revenue: A = the first 80% of revenue, B = the next 15%, C = the last 5%. XYZ measures how stable monthly demand is (coefficient of variation): X = stable, Y = variable, Z = erratic. Items with fewer than 6 months of history are not given an XYZ class (shown as n/a). Cut-offs can be changed in src/01_settings.R.

iEach box counts the items in one class. Rows: A = items that make the first 80% of revenue, B = the next 15%, C = the last 5%. Columns: X = steady monthly demand, Y = variable, Z = erratic. AX items are your core; CZ items are the long tail to review.
iThe further the curve bows to the top-left, the more revenue depends on a few items. Dashed lines mark the 80% and 95% cut-offs between A, B and C.
iHow many items sit in each ABC class, split by how stable their demand is.

Suggested stock and planning policy by class

Item classification (ABC / XYZ)

iEvery item with its ABC class (value) and XYZ class (how steady demand is). CV is the demand variability: the lower it is, the steadier the demand. Use the dropdowns to filter by class.

Inventory Analytics

What is in stock, how many days it will last, and what to reorder or clear.


Scope: Castrol, Eterna and Base Oil. Stock is a snapshot from the inventory valuation file; demand is measured from sales of the same item code. The sidebar filters do not apply here (stock is not split by region).

Reorder list: out of stock or low cover

iItems that are out of stock or about to run out. Suggested order is the number of packs needed to cover lead time plus safety stock at the current demand. Sorted by order value.

Excess and dead stock

iItems holding more stock than demand needs (overstock) or with no sales in 12 months (dead stock). Excess value is the stock value above the level that would cover the overstock limit.

All stocked items

iEvery stocked item in scope with its stock, cost, demand and status. Use the dropdowns to filter by brand or status, or download the full list.

Customer Analytics

Who our customers are, how much they buy and how dependent we are on the biggest ones.


iThe 15 customers with the most revenue. Click a bar for the customer's detail.
iBars are customers from highest to lowest revenue; the orange line adds them up (right-hand scale). If the line climbs steeply, a few customers carry most of your revenue.
iNumber of customers by how recently they bought: Active (within 180 days), At risk (181-365 days), Lapsed (over a year).
iRevenue earned from customers in each status. A large 'At risk' bar means a lot of revenue could be lost if they do not return.
iHow many customers made their first purchase in each year (within the filters you have chosen).

Customer list: value, class and status

iEvery customer with revenue, share, ABC class and how long since they last bought. Status: Active (bought in the last 180 days), At risk (181-365), Lapsed (over a year).

Customer Retention

How many customers are new, loyal, won back or lost, month by month.


iThis page always looks at the full history (the year and date filters do not apply), because who is new or lost depends on earlier purchases. Sales rep and region limits of your login still apply.
iCustomers who bought in each month: new (first ever purchase), returning (also bought last month) and recovered (came back after at least one month without buying).
iRevenue (excluding VAT) from new, returning and recovered customers in each month.
iNewly lost = customers whose last purchase was exactly the chosen number of months ago. Recovered = customers who came back that month. The line is the running number of customers with no purchase in the chosen period.
iRepeat rate = share of last month's buyers who bought again this month.

Month by month

iMonth-by-month customer counts. The latest month may not be finished, so its numbers are lower than they will end up.

Receivables

Money customers still owe on lubricant invoices, and how late it is.


iMoney still owed by customers on lubricant invoices, grouped by how late it is, measured against the invoice due date.
iThe customers who owe the most, split into amounts not yet due and amounts overdue.
iOutstanding amount by warehouse (location code on the invoice).

Open invoices

Download Excel (with sales lines)

iEvery lubricant invoice with money still outstanding, oldest first. Remaining includes VAT, as in Business Central. Days overdue is negative when the invoice is not yet due.

RFM Analysis

Groups customers by how recently, how often and how much they buy.


Each customer is scored 1-5 on Recency (days since last purchase), Frequency (number of orders) and Monetary value (revenue) within the data currently in view, then placed in a segment.

iHow many customers fall in each RFM segment (see the segment guide below for what each one means).
iRevenue earned from each RFM segment. Compare with the previous chart: a small segment earning a lot is worth protecting.
iEach dot is a customer. Further left = bought more recently; higher = more revenue (log scale); bigger = more orders. Colour shows the RFM segment.
iRecency score (across) against frequency score (up); 5 is best. Each box shows how many customers and how much revenue sit there. Top-right customers buy often and recently.

Segment guide

Customer list with RFM scores

iEach customer scored 1-5 on Recency, Frequency and Monetary value, then placed in a segment. Use the dropdown to see one segment, for example Champions or At Risk.

Lifetime Value

How much each customer is worth over time, and who to protect.


Estimated future value = customer's average annual revenue x r / (1 + d - r), where r is the observed year-to-year customer retention rate and d is the discount rate. It is a planning estimate, not a guarantee. Complete years only are used to measure retention.

iOf the customers who bought in a year, the share who bought again the next year. The dashed line is the average used in the lifetime-value estimate.
iEstimated future value of customers grouped into tiers (top 10%, next 20%, the rest).
iThe 15 customers with the highest estimated future value. Click a bar for the customer's detail.
iHow customer future values are spread: most customers cluster at low values and a few are very large (log scale).

Customer lifetime value

iEstimated future value of each customer. Annual value is their average yearly revenue; Future value is what we expect from them from now on given retention and the discount rate; Tier groups them by future value.

Industry

Which industries buy from us and how each is changing.


iRevenue by customer industry. Click a bar for detail.
iRevenue split by industry for each year, to see which industries are gaining or losing weight.

Industry scorecard: this year vs last year

iOne row per industry, compared with the same period last year. Use the dropdown to look at one product category.

Forecasting

What we expect to sell in the coming months, how reliable that is, and a download for Excel.


Forecasts use the data in view (the sidebar filters apply) and need at least 12 months of history; two or more years give seasonal forecasts. Items are ranked by revenue.

Forecast by item

Download forecast to Excel
iFor each item: what was actually sold in the last N months, what is forecast for the next N months, and the change. Volume is in litres; revenue and gross margin are in naira. The forecast continues each item's own trend and seasonality, so treat it as a guide, not a promise.

Look closer

iLast N months (actual) against the next N months (forecast) for the chosen measure. A taller forecast bar means growth is expected.
iThe selected item's history (solid line) and forecast (dashed line). The shaded band is the likely range (Holt-Winters method only).
Month-by-month forecast for the selected item
iThe month-by-month forecast for the selected item: litres, revenue and gross margin.

What if? Scenario planning

Try a change on the selected item and see what it does to litres, revenue and gross margin over the forecast months. Nothing is saved: set all three sliders to 0 to go back to the forecast.

iThe selected item and measure: the normal forecast (dashed grey) against your what-if scenario (solid). The gap between the lines is the effect of your change.
iMonth by month for the selected item: the normal forecast and the scenario, with the difference. Price and cost changes only affect revenue and gross margin; volume changes affect everything.

How accurate has this forecast been? (back-test)

The selected item and measure are forecast again using only older months, and the result is compared with what actually happened.

iThe forecast method was run on older months only and asked to predict the most recent months. Bars are what really happened; the line is what the forecast said. The closer they are, the more you can trust the forecast.
Method comparison (lower error is better)

Next year's litres by SKU and customer segment, in the layout of the Castrol forecast template. The current year is actual sales to the last sales date plus an outlook for the months left; next year is a weighted view of the last three years (60% / 30% / 10%), with one-off spikes capped and growth damped by half. Change the growth or any segment below and the tables update; the download keeps your changes as live formulas.

Download filled template (Excel)

Growth

Your adjustments

Everything is in percentage points added to the model's growth. Leave at 0 to keep the model's forecast.

Adjust one segment
Adjust one segment

By customer segment

iLitres by customer segment: last year, this year (actual to the last sales date plus an outlook for the rest of the year), and the forecast for next year with your growth and adjustments applied.

By SKU

iLitres by SKU (product and pack size). Same columns as the segment table.

How the current year was estimated

How reliable is it? (back-test)

The same method is run on older years and compared with what happened next. Read these before relying on the forecast.

iThe method was run on older years only and asked to predict the following year. Cell error = size of the miss on each SKU-segment cell; segment error = the same by segment; total error = how far off the grand total was (negative = forecast too low).
Which growth strength worked best
iEach growth strength run through the back-test. Strength 0 ignores last year's trend, 1 carries all of it forward. The one with the smallest segment error is used (unless the standard 0.5 is within 3%).
iHow well the 'rest of the year' estimate has done in past years: the estimate made at the same date, against what the year really delivered.

Customer mapping (Castrol)

Castrol segments come from the customer mapping file (customer code, market name, sector name). It is looked for in the data folder and in config; you can also upload a new copy here.

Ask Segun

Your AI analyst. Click a question below, or type your own, and get a plain-English answer from your sales figures.

Quick questions - click one

Or type your own question

Save conversation

General questions use the period and filters in the bar at the top. If you name a region, brand, rep, warehouse, customer or period in your question (for example Q3 2025 or last month), Segun works that out exactly, whatever the filters say. Press Enter to send; Shift+Enter starts a new line.

Advanced options

Reports

Create a ready-to-share report from the data in view.


One click, ready to present. Builds a branded report from whatever your filters currently show (so choose a region, product category or year first if you want a focused report). The PDF opens anywhere; the PowerPoint has one slide per page. To get a report every month without opening the app, see generate_reports.R and the README.
Download PDF Download PowerPoint

Excel Export

Download the figures you see here as an Excel workbook.


Download a multi-sheet Excel workbook of the data currently in view (your filters apply).

Every workbook also includes a 'Sales lines' sheet: the full sales lines (all columns) behind the figures in the report, for the filters you have chosen. Download Excel workbook

Transactions

Every sales line behind the numbers, searchable and downloadable.


Download all sales lines (Excel)

iEvery sales line in the data you are viewing. Use the search box to find a customer, product or invoice.

Data Check

Checks that the data loaded correctly and lists anything that needs fixing.


Upload data files

Choose the Excel files to add or replace (a file with the same name replaces the old one). Sales - Year ... files, the Posted Sales Invoices list, budget and inventory files go to the main data folder; the sales line, warehouse and operations files go to the daily tracker folder.

The app closes and reloads its data, which takes about a minute. Refresh the page afterwards and sign in again.

Files on the server

How the app matched your Excel columns to the standard fields. If a field says NOT FOUND or has a low 'Filled' %, the related charts will show 'Unknown' - send me the column names.

Cancelled invoices (excluded from sales)

Phase 2 checks: budget, inventory and cost linking

These show how well the budget and inventory files link to your sales data.

Sheets read from each sales file

Rows per file and year (after date parsing)

The 2014-2018 file should list several years here. If it shows one year or NA, the date column of that file is the problem.

All columns available after joining lookup tables


              

              

                

                  

                    
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