Optimal case pack quantity

Optimal Case-pack Quantity

The case-pack quantity of fast moving consumer products is a battlefield between retailers and suppliers because of conflicting interests. Retailers want small case-pack quantities because they prefer low shelf stocks. Suppliers want large case-pack quantities for lower packaging costs and more shelf space.

This is a practical analysis of the effects of case-pack quantity on costs, shelf inventory and profitability.

Retail buying managers and supplier product managers will get an actionable understanding of optimal case-pack quantities for fast-movers and slow-movers, large products and small products. They can use this information for product development, price calculation and negotiations.

 

Process analysis and root causes

Case-pack quantity of a product has many effects on the supply chain and the retail marketing mix:

  • Box prices suppliers
  • Handling costs suppliers and retailers
  • Store order quantity and delivery quantity
  • Store shelf inventory costs
  • Store shelf profitability
  • Store assortment and opportunity costs
  • Weight (kg) per case

A larger case-pack quantity will improve cost efficiency of suppliers, because box-prices and handling costs are lower per consumer unit. This cost efficiency is an important reason why suppliers prefer large case-pack quantities.

Diagram 1:market prices of single layer boxes and prices per liter content. Case price per box

Retail Economics © 2011, diagram 1 (red line: price/liter; blue line: price/box)

Diagram 1 shows that small boxes are expensive per liter content: up to 25 cent per liter. Large boxes tend to a low price of 2.5 cent per liter.

Handling costs of retailers are lower with increasing case-pack quantities: lower order-picking costs and shelf-filling costs per consumer item. Result is that retailers prefer high case-pack quantities for fast-moving articles, but they require smaller case-pack quantities for slow moving articles.

Retailers with large stores generally accept larger case-pack quantities, because large retailers generate sufficient sales per product per store. Retailers with small stores generally require smaller case-pack quantities, but small retailers represent less weight in decision making by suppliers as a result of smaller assortments and lower sales.

The case-pack quantity is also the minimum order quantity and delivery quantity of central warehouse to stores. Order costs of retailers are low and are becoming lower by introducing Automatic Store Replenishment or Computer Assisted Ordering. Order costs of retailers are therefore not an important factor in determining optimal case-pack quantity.

Large case-pack quantities require larger shelf stocks and shelf-keeping costs in retail stores. This effect is especially significant for slow-moving articles in combination with a high order-frequency. A-brand suppliers make strategic use of this effect to acquire more shelf space and higher visibility in retail stores. This is another important reason for suppliers to promote large case-pack quantities. But retailers generally do not wish to overstock A-brands; they prefer assigning more shelf-space to margin-rich private labels.

Low stock-turns due to large stocks of slow-movers decrease the shelf profitability of products, which can be a reason for deleting a product. This effect is not part of the model calculation, to prevent double counting of shelf effects and opportunity costs. In another article we will introduce case-pack quantity as one of the many drivers of shelf profitability.

Large case-pack quantities for slow moving articles will also limit the store assortment capacity. If a product uses more shelf space then necessary to avoid out-of-stocks, opportunity costs are involved of missed sales and net profit of product additions to the assortment. In other words: better 2 products than 1 product on 2 facings. Our model will take account of these opportunity costs of case-pack quantity related shelf-space above the logistic minimum.

These cost effects determine the optimal case-pack quantity of a product. The key questions are if an article is slow moving or fast moving and if the products are small or large. Diagram 2 shows total chain costs as a function of case-pack quantity of a product of average volume. In this example the optimal case-pack quantity for slow movers is 6; optimal case-pack quantity range of fast movers is 12-16, but costs of a wide range from 8-24 consumer units are marginally higher. See appendix 1 for breakdown of cost figures.

Costs per consumer unit

Retail Economics © 2011, diagram 2.

In diagram 2 the product volume is average (800 ml). Appendix 2 contains an overview of the effects on costs and optimal case-pack quantities of small products (200 ml) and large products (4000 ml).

Weight per case is a limiting factor: a case should not weigh more than approximately 15 kg for ergonomic purposes. This weight factor is important for most soft drinks, wines, fruit juices and canned foods in consumer units of 1 liter or more. Most beers and canned foods are packaged in smaller weight units, and even a large case-pack quantity will not reach ergonomic weight limits. Weight is not a cost factor and is not part of our model calculations.

Unfortunately the turnover of products is not a uniform or static figure: small stores tend to sell less and large stores tend to sell more of each product. Also buying patterns will differ from store to store and certainly between regions and countries. This means that optimal case-pack quantities for one store can be different from optimal case-pack quantities for another store.

 

Retailers prefer smaller case-pack quantities

A break-down of the total chain costs reveals a different pattern for retailers and for suppliers. Optimal case-pack quantities for retailers are always lower than for suppliers.

Suppliers prefer larger case-pack quantities in the range of 12-48 consumer units. Suppliers are mainly concerned with the packaging costs per consumer unit, which decrease with case-pack quantity. Suppliers experience high total packaging costs for small case-pack quantities, irrespective of the turnover of the product in retail stores. Larger case-pack quantities generate lower costs per consumer unit.

For retailers each product has an optimal case-pack quantity, depending on size, weight and turnover of the product. These optimal case-pack quantities are mainly in the range of 6-12 consumer units. Large case-pack quantities of slow-moving food products cause over-facing on store shelves, especially in smaller stores. This over-facing reduces possibilities of retailers to expand the overall store assortment. Over-facing is also one of the root causes of low shelf productivity and reason to delete a product.

Fast-moving products cause low costs for retailers, irrespective of case-pack quantity; in the example the optimal case-pack quantity is between 8 and 12. Slow-moving products however cause rapidly increasing costs, when case-pack quantities are higher than 6, up to extremely high levels due to high shelf costs and opportunity costs. See diagrams 3 and 4.

Costs of fastmovers Costs slowmovers

© Retail Economics 2011, diagrams 3 and 4.

 

Implications for retailers

Slow-moving products in large case-pack quantities are a problem for retailers. There are 5 solutions (besides higher sales):

  • Demand a smaller case-pack quantity from the supplier
  • Delete or refuse slow-movers with large case-pack quantities
  • Break down the case into smaller units in the retailer warehouse
  • Store redundant articles in the backroom of the store
  • Increase the number of facings of slow-movers

Each of these solutions is realistic, but each solution has different consequences for different products.

1. Demanding a smaller case-pack quantity from the supplier is the first and most efficient action. Reducing the case-pack quantity by 50% will cost a supplier 2-3 euro cents extra per customer unit. The cost savings for the retailer (including opportunity costs) of reducing the case-pack quantity of a slow-mover (sales of 1 CU/store/wk) from 12 to 6 consumer units is 20 euro cents in the above example; the cost savings of a reduction of the case-pack quantity from 24 to 6 consumer units is even 60 euro cents per consumer unit. Even de reduction of the case-pack quantity from 16 to 8 consumer units of a medium-mover (sales of 6 CU’s/store/wk) is profitable from the point of view of the total chain. The retailer should accept a small price increase of the supplier, because resulting net profit is high.

Suppliers will argue that changing the case-pack quantity for one retailer is very expensive. Repackaging or producing extra case-pack quantities is much more expensive than uniformly changing the case-pack quantity. If this solution is selected, several retailers should work together or calculate the maximum acceptable cost increase.

Suppliers might also argue that smaller case-pack quantities will decrease the facings and visibility of the supplier brands, and that additional space will be filled with competing brands. This will give retailers some leverage in negotiations: by accepting to fill additional space by products of the same supplier, the supplier will have great encouragement to accept a retailer request.

2. Deletion or refusing slow-movers with large case-pack quantities is a next option for retailers. Retailers might choose a different brand that matches retailer requirements. Such possible action will give retailers even greater leverage. Suppliers should always estimate the relationship between case-pack quantity and potential retail distribution of its brands.

3. Breaking down cases into smaller units in the retailer warehouse is an expensive option, but in practice this is done for expensive cosmetics and fresh deserts with limited conservation time. Retailers sometimes choose to change order quantities to 1 consumer unit, and then deliver single items in crates to stores.

4. Storing redundant articles in the backroom of stores is not the best alternative for slow-moving food products. It will make it possible to present slow-movers on 1 facing, but it confuses inventory control and introduces large extra handling costs.

5. Increasing number of facings of slow-movers is very expensive because of high opportunity costs. Each extra facing for a product reduces space for other products on the shelf. More than 1 facing for slow-movers will greatly decrease stock-turns and increases chances that a product becomes obsolete. This option is only feasible when products generate high absolute gross margins and if products also show high space elasticity (if an extra facing of the product generates more sales than introduction of a new product).

Recommendations

Retailers and suppliers should work together to choose optimal case-pack quantities for average stores. Small stores will delete or will not introduces slow-moving products with large case-pack quantities, but they might accept medium case-pack quantities. For large stores additional costs of a smaller than optimal case-pack quantity are very small. The advantage of a smaller than optimal case-pack quantity is the flexibility in applying alternative ordering algorithms. See appendix 3 for the effects of alternative ordering algorithms on logistic performance and stock-keeping costs.

Retailers and suppliers should use ECQ modeling to determine the cost effects of a change in case-pack quantity, in order to renegotiate product price. This article gives proof that there is room for profit improvement for both parties, when they optimize case-pack quantities from total supply chain point of view.

 

Appendix 1: The Economic Case Quantity (ECQ) model

ECQ modeling uses case-pack quantity as optimal order-quantity and shows effects of case-pack quantity on all (but only those) costs, which are dependent of case-pack quantity:

  • Supplier packaging and handling costs of cases
  • Retailer handling costs of cases: order-picking and case opening
  • Ordering and delivery costs of cases
  • All stock-keeping costs in the store (shelf-costs and interest costs)
  • All opportunity costs related with the number of facings above the logistic minimum

Table 1: Cost overview

Case-pack cost table

Other costs, that are independent of case-pack quantity, are not included, because they do not influence the determination of optimal case-pack quantities. Examples of other not-included costs are:

  • stockholding costs at the supplier and in the retailer central warehouse (depend of product volume and product net price)
  • transportation costs of suppliers and retailers (depend of product volume) and costs of point-of-sale (fixed costs per consumer unit)

In another article we will include these costs in determining the net shelf profitability of products, as a function of many drivers including case-pack quantity. Our shelf profitability model also includes the effects of consumer price, gross margin, space elasticity and substitution effects. These additional factors are omitted in the ECQ model, because they have very limited effects on optimal case-pack quantities.

 

Appendix 2: Effects of small and large products on case-pack quantity

The product in the example is of average size, average price and average gross margin. Real figures – including the optimal case-pack quantities – may differ when products are smaller or larger. Staple possibilities also increase optimal case-pack quantities, because extra consumer units are stored at one facing.

If volume of a product is only 200 ml, more consumer units fit in one facing and optimal case-pack quantities will increase. If volume of a product is 4000 ml, less consumer units fit in one facing and the optimal case-pack quantity will sharply decrease. See the next diagrams.

Example: small product (volume = 200 ml).

Costs small fastmover Costs small slowmover

© Retail Economics 2011, diagrams 6 and 7.

 

Example: large product (volume = 4000 ml).

Costs large fastmover Costs large slowmover

© Retail Economics 2011, diagrams 8 and 9.

 

Appendix 3: Effects of ordering algorithm

In this article we use the ordering technique of “fill-to-maximum”. We exploit the full shelf capacity for safety-stock plus cycle stocks. “Fill-to-maximum” ordering techniques are simple and are used for manual ordering, but they lead to relatively high inventories.

If we use Automatic Store Replenishment (or Computer Assisted Ordering), it is possible to use “fill-to-minimum” ordering techniques, in combination with anticipation of sales during order lead-time. It means that we only stock our shelves with consumer-units which are sold between deliveries. We also take account of sales forecasts during lead-time of orders, based on historic sales data. This improved technique will deliver lower inventories and lower stock keeping costs, especially for slow-moving and medium-moving products.

Effect of “fill-to-minimum” ordering on optimal case-pack quantities of slow-moving and medium-moving products is an increase of the optimal case-pack quantity. This improvement is approximately 30 %: from 6 to 8, and from 8 to 10. This effect is the result of lower safety-stocks (better sales forecasting) and lower cycle-stocks (only sales have to be reordered between deliveries but not during lead-times).

If we maintain “old” case-pack quantities, shelf capacity for single products can be reduced, resulting in the reduction of facings for part of the assortment and a reduction of stock-keeping costs for the entire assortment.

An even more interesting result of “fill-to-minimum” ordering is the possibility to use free shelf capacity during days with low sales (Tuesday, Wednesday and Thursday) to equalize production flow in retail warehouse and store. See article on Automatic Store Replenishment.

 

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