A hardware importer in Birgunj had rū 1.2 crore tied up in inventory at the end of Ashadh. The owner knew sales were good - revenue had grown 18% that year. What the owner did not know was that rū 38 lakh of that rū 1.2 crore had not moved in over 90 days. A full shelf of PVC connectors ordered during the construction boom had slowed to a trickle as the peak project season ended. The capital was sitting in a warehouse generating zero return while the business was taking overdraft facility at 12% interest to fund working capital for the next import order.
This is not unusual. Most trading businesses in Nepal carry a significant portion of slow-moving stock at any given time - not through poor planning, but because the analysis that would reveal the problem has never been done. Inventory analytics turns the transaction data that already exists in the system into answers: which items are moving fast, which are stagnating, which are generating the best return per rupee of capital tied up, and which should be discounted, returned to suppliers, or discontinued.
The analysis does not require new data - every business that records sales and purchases has the inputs. What it requires is a system that can run the calculations and present the results in a form the purchase manager and CFO can act on.
The Core Analytics Every Inventory Manager Should Run
Stock turnover ratio measures how many times the average inventory is sold and replaced over a period. Higher turnover means capital is cycling faster - goods arrive, get sold, and are replenished without sitting idle. A business with rū 50 lakh average inventory and rū 2 crore annual COGS has a turnover ratio of 4 - meaning inventory cycles approximately every 90 days. A ratio of 8 means every 45 days. Whether 4 is good or bad depends entirely on the industry and product type: a fresh food distributor should target 50-100x annually; an industrial equipment importer might reasonably run at 2-3x.
Days Inventory Outstanding (DIO) converts turnover ratio into a number that is more intuitive for operational decisions. DIO = 365 / Stock Turnover Ratio. A turnover of 4 means DIO = 91 days - stock sits for an average of 91 days before being sold. For a business paying 12% interest on working capital, every rupee of inventory costs rū 0.03 per month in financing cost. 91 days of a rū 1.2 crore inventory = rū 36,000 per month in financing cost for capital that is sitting idle. When the purchase manager understands DIO in these terms, over-buying decisions look different.
Stock turnover and DIO are the two numbers that connect inventory management to financial performance. A business that tracks these monthly is managing working capital. One that does not is managing warehouse space.
ABC Analysis - Not All Stock Deserves Equal Attention
ABC analysis categorizes inventory into three tiers based on their contribution to revenue or COGS. Category A items are the top 20% of SKUs by value that contribute approximately 70-80% of total revenue. Category B items are the next 30% of SKUs contributing roughly 15-20% of revenue. Category C items are the remaining 50% of SKUs that contribute only 5-10% of revenue - but often represent the majority of inventory headcount and management effort.
For a hardware importer with 400 SKUs, the ABC split might look like: 80 A-items (steel rods, cement, GI pipes) driving 75% of revenue; 120 B-items (fittings, fasteners, tools) driving 20%; 200 C-items (specialty connectors, adhesives, accessories) driving 5%. The 200 C-items require the same purchasing, receiving, storing, and reporting effort as the 80 A-items - but deliver a fraction of the value. ABC analysis does not mean eliminating C-items - it means managing them differently: lower safety stock, less frequent ordering, higher focus on days-outstanding before they become dead stock.
Import-dependent Nepali trading businesses often accumulate C-category stock during aggressive pre-festival buying. When a supplier offers a discount on a slow-moving item as part of a larger order, the purchase manager accepts it because the A-items in that order are genuinely needed. The C-item fills a shelf and ties up capital. Running ABC analysis before placing each large import order - and comparing the proposed order against each item's ABC category and current DIO - prevents the accumulation of slow-moving stock that looks like a bargain at the time of purchase and a problem six months later. This discipline is particularly important for businesses importing from India and China where minimum order quantities often force bundled purchases.
ABC analysis run in a pivot table inside the ERP takes minutes when the transaction data is already there. Rank all items by COGS contribution over the last 12 months. The top 20% by value are A. The next 30% are B. The bottom 50% are C. Set different minimum stock policies, reorder frequencies, and attention levels per category. Review the C-list quarterly for items that have moved from C to dead stock.
ABC analysis does not reduce the number of SKUs - it changes how you manage each tier. A-items get tight reorder controls and frequent review. C-items get lean stock policies and quarterly dead-stock checks. The same effort applied more intelligently produces better working capital outcomes.
Identifying Slow-Moving and Dead Stock Before It Becomes a Write-Off
Slow-moving stock is inventory that has not moved within a defined period - typically 60 or 90 days for fast-moving consumer goods, 120-180 days for capital goods or spare parts. Dead stock is inventory that has not moved in over 180 days and has low probability of moving without intervention. The earlier a business identifies an item moving from slow to dead, the more options it has for recovery.
Recovery options in order of descending value: first, bundle the item with a fast-moving A-item at a discount to clear it with customers who are already buying; second, return to supplier if the purchase terms allow it - many Indian and Chinese suppliers accept returns of unused import goods within a period, especially if the business relationship is ongoing; third, mark-down sale specifically targeting the slow-moving category - often more effective as a targeted promotion than general discounting; fourth, write off with the associated tax implications.
Margin analysis per SKU is a dimension most Nepali businesses have never calculated. High-volume items often have lower margins than they appear because the COGS figure includes all the purchase cost layers - including older batches bought at higher prices. A pivot report showing margin by SKU across the last 12 months frequently reveals that the top-volume items are not always the top-margin items. Some C-category items by volume are actually A-category items by gross margin contribution. This finding changes which items get priority attention in pricing, promotion, and restocking decisions.
Seasonal inventory pattern analysis is the third tier of inventory analytics that most businesses skip. For Nepali traders, demand patterns are highly seasonal: Dashain and Tihar for consumer goods, pre-monsoon for construction materials, fiscal year-end for institutional purchases. An ERP that tracks sales by month across two or three years can show the seasonal index for each item - how much above or below average demand is in each month. This index is the foundation for seasonal buying plans that build the right stock at the right time rather than guessing from experience.
The window for acting on slow-moving stock narrows every week. An item at 60 days inactive has recovery options. At 180 days, the only option left is usually a markdown sale or write-off. Quarterly slow-moving analysis is the difference between managing the problem and cleaning it up after it has already cost you.
Using Inventory Analytics to Make Better Purchasing Decisions
The purpose of running inventory analytics is not reporting for its own sake - it is making better purchasing decisions before each order is placed. A purchase manager with a full inventory analytics picture walks into each supplier negotiation knowing: which items to prioritize, which to order minimally, which to skip entirely, and what the current DIO for each category tells about capital efficiency.
Margin analysis by product category changes which products get premium shelf space, which suppliers get the most attention, and which items are worth pushing through the sales team at a discount to free up capital. Without this data, these decisions are made on instinct, relationships, and volume perception - all of which can lead a business to optimize for turnover rather than margin.
"The most expensive inventory mistake in a Nepali trading company is not the bad purchase - it is keeping the bad purchase on the shelf for six months before admitting it was a mistake."
A pattern seen across import-dependent businesses in Kathmandu and Birgunj
Same reorder frequency and safety stock for fast movers and slow movers. Capital tied up inefficiently.
A-items get tight controls and attention. C-items get lean policies. Capital follows value, not habit.
Capital locked in unsellable goods for months. Write-off decision made too late for partial recovery.
60-day and 90-day slow-mover lists available instantly. Recovery options pursued while stock is still sellable.
Purchase decisions based on volume and revenue. High-volume items assumed to be most profitable.
High-volume and high-margin items identified separately. Pricing and promotion decisions grounded in actual margins.
Pre-festival stock build based on last year's memory. Over-buying and under-buying both common.
Month-by-month demand index per item drives buying plan. Dashain build-up is data-driven, not guesswork.
35% of inventory value generating zero return while overdraft funds new purchases at 12% interest.
Regular inventory analytics reduces excess holding by 15-20%. Capital recycled into faster-moving items.
Frequently Asked Questions
Stock Turnover Ratio = Cost of Goods Sold / Average Inventory Value. Average inventory is typically calculated as (Opening Stock + Closing Stock) / 2 for the period. Use 12 months of COGS and the average of your opening and closing annual inventory values for an annual figure. For a monthly calculation, use monthly COGS and the average of the start and end-of-month inventory values. An ERP that tracks COGS per transaction and maintains a running inventory value makes this calculation available in a report at any time - no spreadsheet compilation needed.
The definition of slow-moving depends on the product category and normal sales velocity. For consumer goods that typically turn every 30-45 days, an item not sold in 60 days is slow-moving. For capital goods or spare parts that may turn every 120-180 days, the threshold is longer. A practical starting point is to flag items with zero sales movement in the last 60 days as slow-moving and items with zero movement in the last 120 days as dead stock candidates. Review the flagged list and adjust the threshold based on what makes commercial sense for each product category in your business. The specific number matters less than the habit of running the analysis regularly.
Yes - ABC categorization should be re-run at least annually because product demand shifts. An item that was a solid B-category product last year might have moved to A-category after a construction project drove demand, or dropped to C-category if a competitor launched a cheaper substitute. Running ABC analysis on a fixed date each year - typically at the start of the Nepali fiscal year in Shrawan - gives the purchase manager an updated view of which items deserve the most attention and capital commitment for the coming year. Some businesses run it quarterly for A-items and semi-annually for B and C items to catch demand shifts before they become procurement mistakes.
Built-In Pivot Analytics for Inventory Intelligence
MISAC's built-in pivot table reporting allows inventory managers to analyze stock movement across any dimension without exporting to Excel. ABC analysis by revenue contribution, aging report by last-movement date, margin per SKU using FIFO cost, stock turnover ratio by category or by location - all are available as pivot queries on the transaction data that is already in the system. The analysis that used to take a day of spreadsheet work takes minutes inside MISAC.
The AI-assisted demand analysis learns from transaction patterns across the business's sales history. Seasonal demand spikes - Dashain for consumer goods, pre-monsoon for construction materials, Ashadh for year-end institutional buying - are identified automatically and factored into demand projections. The system can flag items that are historically C-category but show an unusual demand spike, prompting the purchase manager to investigate whether a temporary demand driver is at play or whether the item has genuinely moved category. This pattern recognition is the difference between reacting to demand changes and anticipating them.
For trading businesses with 200-600 SKUs across multiple locations, the combination of real-time inventory tracking and built-in analytics changes how purchasing decisions are made. The purchase manager goes into each supplier meeting with a current ABC analysis, a slow-moving stock list for potential returns, and a seasonal demand index showing exactly which items need to be built up before the next festival period. MISAC Intelligence Pvt. Ltd. builds this inventory intelligence specifically for the trading and distribution businesses that form the core of Nepal's commercial economy.
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See how MISAC's pivot analytics helps a Nepali trading company run ABC analysis and identify slow-moving stock before it becomes a working capital problem.