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Guide · Inventory and replenishment

Reorder point or periodic review? Set the policy item by item, and fix the records first

No single policy fits every item. Segment items by value, variability and criticality, check that inventory records are accurate, and then set each item's policy and safety stock from your own forecast error and lead-time variability. Tuning a replenishment rule on inaccurate records optimises the wrong number.

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Why one policy is the wrong starting point

Inventory policy is a trade-off between ordering, holding and shortage costs, and the right safety stock depends on forecast error and lead-time variability rather than average demand alone. Segmenting items by value and uncertainty works better than applying one policy to everything, which is why the playbook starts by classifying items and then sets policy item by item.

Fix the records before the rule

In nearly 370,000 inventory records from 37 stores of one retailer, 65% were inaccurate, and error rates varied systematically with the item, the store, how products were distributed and how auditing was done. The problem was structural, not random. The implication is direct: automated replenishment on inaccurate records optimises the wrong number, so measure record accuracy and repair receiving, returns and audit processes before changing the rule. The 65% figure is one retailer's, not a benchmark.

Measure what you are trading off

Because the policy is a trade-off, a pilot has to measure both sides, for example shortages and holding cost, and not just one. If you have not measured forecast error and lead-time variability, that is the first task, not a software purchase.

The path, step by step

These steps come from the Inventory and replenishment playbook. Each has a checkpoint you can observe.

  1. Segment items by value, variability and criticality

    Classify items by annual value, demand variability and operational criticality. Assign a policy tier to each segment instead of managing every SKU with the same rule.

    CheckpointSegments covering most of the value and all critical items, with a named policy per segment.

    Common failureApplying one safety-stock rule to every item because the ERP defaults to it.

  2. Fix record accuracy before tuning policy

    Audit system quantities against physical counts by category, trace errors to receiving, returns and cycle counts, and fix those processes before changing reorder points.

    CheckpointRecord accuracy measured by segment, with the largest error sources named and owned.

    Common failureRaising safety stock to compensate for records that are wrong.

  3. Set the policy item by item

    For each segment, set review period, reorder point or order-up-to level, lead time and target service level. Document why the parameters differ.

    CheckpointA policy table with parameters, rationale and the service level each item is meant to achieve.

    Common failureCopying a reorder point from a spreadsheet whose assumptions nobody remembers.

  4. Pilot and measure both sides of the trade-off

    Run the new policy on a controlled group and track fill rate, inventory days, expedites and write-offs together. Do not judge it on stockouts alone.

    CheckpointFill rate and inventory days move in the intended direction together, with expedites counted.

    Common failureDeclaring success because stockouts fell while inventory and write-offs quietly rose.

What this guide does not settle

The models are established but assume reasonably stable demand and reliable data, which many operations lack, and the record-accuracy result is one retailer. Treat both as prompts to measure your own operation.

What the research found

Each finding below is quoted from Portolan's research library, with the limits the library records for it. Applying a result from another setting, such as a hospital or a factory, to yours is an analogy, not a proof.

Silver et al. · 2016Established

Inventory and Production Management in Supply Chains

Inventory policy is a trade-off between ordering, holding and shortage costs, and the right safety stock depends on forecast error and lead-time variability, not just average demand. Segmentation by value and uncertainty is more effective than a single policy applied to every item.

Where it stops. This is a textbook synthesis of established models; it assumes reasonably stable demand and reliable data, which many operations lack.

DeHoratius & Raman · 2008Established

Inventory Record Inaccuracy: An Empirical Analysis

In nearly 370,000 inventory records from 37 stores of one retailer, 65% were inaccurate. Error rates varied systematically with SKU, store, product distribution and auditing practice, so the problem was structural rather than random.

Where it stops. One retailer's records. The 65% rate is not a universal benchmark, and the analysis is associational rather than a controlled improvement trial.

Syntetos et al. · 2005Established

On the categorization of demand patterns

Demand series can be classified using the average inter-demand interval and the squared coefficient of variation of demand sizes, which predicts which forecasting method will perform better. The method was tested on 3,000 real intermittent-demand series from the automotive industry.

Where it stops. The cut-offs are often misapplied, and the evidence comes from one industry's intermittent-demand series.

Cachon et al. · 2007Established

In Search of the Bullwhip Effect

Using U.S. industry-level data, the authors found the bullwhip effect in wholesale industries but generally not in retail or most manufacturing industries. The pattern is more selective than the standard supply-chain story suggests.

Where it stops. The analysis uses aggregated industry data, which can hide firm-level amplification; the authors could not verify detailed industry percentages here and the finding should not be generalised to every supply chain.