Supply chain · Lot-size optimization

Lot-Size Optimization Beyond the Formula

How much should a company produce or buy at a time? PROVADIT designed an enhanced economic order quantity model that connects operating costs, forward demand and inventory. Data quality and availability set clear limits on how precisely it can answer that question.

Explore the case

Industry: Supply chain · Lot-size optimization
Our role: Solution design and specification; warehouse implementation by the client’s data teams
Focus & technology: EOQ / Andler · Cost Modeling · Inventory Policy
Scope: Cost logic, data sourcing, fallback rules and inventory integration

Client anonymized.

The starting point

Which lot size makes economic sense, and can the data support it?

An international company used calculated inventory norms to review excess stock and planning parameters. Lot sizes were a key input: larger lots mean fewer production runs or purchase orders, but more stock between replenishments. Smaller lots reduce this cycle stock while increasing the frequency of supply events.

The classic EOQ, or Andler, formula balances fixed ordering or production costs against inventory holding costs. The difficult work lies in identifying the relevant costs, connecting the data and understanding where assumptions stand in for missing facts.

Our contribution

PROVADIT’s contribution

PROVADIT designed the enhanced cost model, defined its data inputs and fallback rules, and specified how the result would feed the cycle-stock component of the inventory calculation. The client’s own data teams handled the technical implementation on its central business-warehouse platform.

Reflect the cost of making and buying
The fixed cost of a supply event extends beyond a single setup fee. The design includes setup and teardown, quality inspection, transportation and purchasing administration. It selects the relevant cost components for internal production, external purchasing and make-or-buy scenarios.
Use forward demand and differentiated holding costs
PROVADIT specified planned primary and dependent demand over the next 12 months as the demand basis. Unit valuation and material-group-specific holding-cost rates complete the economic inputs, with a defined standard rate where a specific rate is unavailable.
Define fallbacks without hiding their limits
Where material-and-plant-level costs were unavailable, the design used defined hierarchies of broader product or site groupings and standard allowances. This extends calculation coverage. It also reduces specificity: a usable fallback is an assumption, not evidence of the actual cost for that material.
Make the result useful for inventory decisions
The enhanced EOQ feeds the cycle-stock component of the inventory norm. This links lot-size discussions to excess-stock analysis, planning-parameter review and working capital. It provides a reference to assess, rather than an automatic instruction to change orders.

Inside the work

Where the calculation reaches its limits

A mathematically correct calculation can still be a poor planning reference. Data availability, data quality and operational feasibility need to be assessed alongside the result.

Data availability limits precision
Setup, inspection and transportation costs are not recorded at the same level of detail for every material and site. Aggregated values and defaults fill gaps, but cannot establish the missing material-specific facts.
Data quality determines the meaning of the result
Demand, costing, valuation, procurement types and material hierarchies come from different sources. Inconsistent or outdated inputs can distort the economic comparison even when the formula itself is correct.
Forward demand remains uncertain
Forecast revisions, product changes and shifts in dependent demand change the calculated lot size. A forward-looking basis makes the model relevant to planning, but does not remove demand uncertainty.
An economic quantity must also be feasible
Minimum batch sizes, supplier minimum orders, equipment capacity, cleaning, packaging, shelf life and quality-release requirements may prevent the calculated lot size from being used directly. These constraints need a separate operational review.
A local optimum can shift costs elsewhere
Reducing cycle stock at one site may increase changeovers, transport events or inventory elsewhere. The reference needs to be interpreted across the supply network, not just for one material at one plant.

Deliverables

The work delivered. The decisions it supports.

The design gives planners a more complete basis for discussing lot sizes and their inventory effects. It also makes clear where the calculation relies on grouped costs or standard assumptions and where better source data is needed before a decision can be trusted.

A cost model that distinguishes production, purchasing and hybrid supply scenarios.
Specified demand, valuation and holding-cost inputs with documented sourcing rules.
Cost fallback hierarchies that make the trade-off between coverage and precision explicit.
An explainable lot-size reference connected to cycle stock and inventory-policy analysis.

Engagement context

The engagement covered the calculation logic and its integration into the inventory model. Production scheduling, purchasing decisions and the operational assessment of a proposed lot size remain with the client’s teams.

What this illustrates

Lot-size optimization requires a discussion across planning, operations, procurement and finance. A smaller lot may lower inventory while increasing changeovers or transportation costs. The useful result is a transparent trade-off, with clear assumptions and an honest view of where better data is needed.