Supply chain inefficiencies are costs a business pays without any gain in service: stock nobody buys, freight paid at premium rates to cover a late order, trucks that leave half full, and planners redoing what the system already calculated. Six recur in planning operations, and each can be measured from data you already hold: inventory snapshots, order and shipment history, freight invoices and plan-versus-actual logs. A first diagnosis takes a few weeks.
Where Supply Chain Inefficiencies Hide
| Inefficiency | Symptom | Measure | Data source |
|---|---|---|---|
| Excess and obsolete stock | Full DCs, write-offs, markdowns | Value of stock above target; stock with no sales in N weeks | Inventory snapshots, sales history |
| Expediting | Premium freight, rush orders | Premium freight share of total freight spend; expedites per week by reason | Freight invoices, order history |
| Partial trucks | High cost per unit shipped | Load fill against vehicle capacity; share of loads below a set fill level | Shipment records |
| Manual overrides | Planners rework system output | Override rate and whether overrides improved the result | Plan-versus-executed logs |
| Forecast bias | Stock builds or stock-outs that persist | Bias and tracking signal by item group | Forecast and sales history |
| Network imbalance | Excess at one location, stock-out at another | Item-locations in excess while the same item is out nearby; transfer share | Inventory by location, transfer orders |
Excess and Obsolete Stock
Excess is stock above what the item needs to meet its service target: on hand minus the maximum target (safety stock plus one order cycle of demand) at each item and location. Obsolete stock is stock that will not sell at full price before it expires or goes out of range. Measure both in value, not units, and separate them, because the fixes differ. Excess calls for lower order quantities or a transfer; obsolete stock calls for a markdown, a return to vendor or a write-off.
A quick diagnostic: list every item-location by value of stock above target, then check how concentrated the list is. If a small share of items holds a large share of the excess, start there. Also flag items with no sales in the last 8 or 13 weeks. For the definitions of turns and days of cover behind these thresholds, see our guide to retail supply chain KPIs.
Expediting and Partial Trucks
Expediting means moving an order faster than planned, through premium freight or a rush order. It is a symptom of a plan that did not hold. Measure premium freight spend as a share of total freight spend, and count expedited orders each week with a reason code: late supplier, forecast miss, data error, customer change. The reason codes matter more than the total, because each points to a different fix.
Partial trucks waste freight in the other direction. Load fill is shipped volume or weight divided by vehicle capacity, whichever limit binds first. Plot the distribution of load fill across a quarter of shipments rather than the average, and count the loads below a threshold you set, such as 70%. Many partial loads on a lane that also sees expedites suggest that orders, inventory and transport are being planned separately. Our post on transportation planning optimization covers how load building and routing are optimized.
Manual Overrides and Forecast Bias
An override is a planner changing a system recommendation: a forecast, an order quantity or an allocation. Measure the override rate, the share of recommendations changed before execution, and then check whether the overrides helped. Compare the error of the overridden figure with the error the system figure would have had. If overrides do not reduce error, the time spent on them is waste, and the reason planners override, often missing data or a constraint the system does not know about, is the thing to fix.
Forecast bias is error that leans one way. Bias is the sum of forecast minus actual divided by the sum of actual, over a period: a positive value means overforecasting and stock builds, a negative one means underforecasting and stock-outs. The tracking signal, the cumulative forecast error divided by the mean absolute deviation, shows whether the bias persists. A value that stays outside a set band, commonly ±4, means the forecast model or the people adjusting it are systematically off.
What Is Network Imbalance?
Imbalance is the right amount of stock in the wrong place. A network can hit its total inventory target while one DC or store holds weeks of cover and another runs out of the same item. Measure it by counting item-locations in excess while the same item is out of stock at another location in the same region, and by the share of shipments that are lateral transfers between DCs or stores. Frequent transfers are a sign that the initial allocation or replenishment is not following demand.
A Quick Diagnostic From Data You Already Have
Two questions frame the diagnostic: do planning and execution run independently, and are inventory, orders and transportation planned separately? The data above answers both:
- Pull 12 months of inventory snapshots, sales, orders, shipments and freight invoices.
- Calculate the six measures above at item-location level, with a weekly trend.
- Rank each finding by its cost: carrying cost of excess, premium freight paid, cost of empty truck space, planner hours on overrides, lost sales from imbalance.
- Look for patterns that move together. Rising expedites alongside falling load fill point to disconnected order and transport planning. High override rates alongside persistent bias point to a forecast that planners do not trust.
When the plan and its execution are disconnected, the gap shows up as expedites and overrides; closed-loop supply chain planning covers how to feed execution results back into the plan. Use the measured values as the baseline for any improvement case, which our post on supply chain planning software ROI explains.
How Solvoyo Finds and Fixes Inefficiencies
Solvoyo’s supply chain analytics detect stock-outs, service-level breaches, excess inventory and forecast bias as they occur, trace each one to its root cause and rank it by impact, so teams fix the costliest issues first. Inventory planning labels every unit as safety stock, cycle stock, excess or dead inventory, so the excess list exists without a separate analysis. Planner changes are tracked at the transaction level with reason codes.
Because each override carries a reason, the causes behind recurring overrides, such as a missing constraint or a bad lead time, are corrected in the model once, and planners stop patching them by hand each week. Fewer recurring overrides is one reason over 95% of Solvoyo’s plans execute untouched. To run this diagnostic on your own data, request a demo.
Frequently Asked Questions
The common ones in planning are excess and obsolete stock, expediting and premium freight, trucks shipped below capacity, planners overriding system recommendations, forecast bias that persists in one direction, and network imbalance, where one location holds excess while another runs out of the same item.
Give each inefficiency its own measure: value of stock above target for excess, premium freight share for expediting, load fill against vehicle capacity for partial trucks, override rate and its effect on error for manual work, bias and tracking signal for forecasts, and the count of item-locations in excess while the item is out nearby for imbalance.
Pull 12 months of inventory, sales, order, shipment and freight data you already hold, calculate each measure at item-location level, rank the findings by cost, and look for measures that move together. Rising expedites with falling load fill, for example, point to orders and transport being planned separately.
