Higher margins through efficient supply chains McKinsey’s cost-driver model shows where and how Germany’s grocery retailers can save up to EUR 3 billion a year in the supply chain.

by Christoph Glatzel, Jochen Großpietsch, and Alexander Hübner

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December 2012

This article, translated from the German, was originally published in Akzente, a regular McKinsey publication on consumer goods (in German). If interested, contact the authors for more information.

Well-stocked shelves and low costs for transport, inventories, and personnel—this is the picture of success in supply chain management. German grocery retailers still have work to do to realize this ideal, however, as a recent McKinsey study shows.

In 2011, Germany’s grocery retail sector spent around €18 billion or 14.8 percent of net revenues on supply chain activities—from the industry’s warehouse to the supermarket shelf. The single-largest cost block of nearly €5 billion was in-store logistics, followed by warehouse logistics at €3.6 billion and distribution to the stores at €3.3 billion (Figure 1). In addition, there are the costs for inbound goods transport, direct store delivery, and inventory as well as the opportunity costs for sales losses due to stockouts.

Exhibit 1: Supply chain costs for German retailers add up to €18 billion

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Simulation model reveals potential savings

But these expenses need not be so high. A cost model developed by McKinsey makes the costs in the supply chain transparent and highlights opportunities for improvement. For example, the German grocery retail sector can save nearly €3 billion a year if it succeeds in optimizing the structures underpinning logistics and sales and in increasing efficiency. Those savings are equivalent to more than 2.5 percent of the industry’s net revenues.

The McKinsey model maps the main cost structures in grocery retail with a reverse engineering approach. It simulates the supply chains and extrapolates the costs incurred on the basis of retailer-specific indicators as well as structural data on sales and logistics. This furnishes the basis for deriving concrete improvement potential (see text box at end).

Profit improvements are possible along the entire grocery retail supply chain. For example, more efficient in-store logistics can generate savings of 0.8 percent of net revenue. Further savings are attainable by shrinking the share of direct store delivery, bundling inbound deliveries more tightly, and optimizing the warehouses (Figure 2).

Retailer-specific approaches identified

The model reveals savings opportunities of more than €3 billion for the German grocery retail in the aggregate. It also offers the individual retailer the opportunity to compare performance with competitors based on operational indicators and thereby identify optimization potential.

McKinsey used an outside-in analysis to quantify the supply chain costs of a full-range supermarket at 16 percent of sales. Then, along the supply chain, targets were calculated for each cost block. The basis was provided by performance benchmark figures from international best-in-class competitors, taking into account retailer-specific assortment and format strategies. The result: if the supermarket succeeds in matching the level reached by the best performer in each cost block, it can increase its margin by around 3.5 percentage points. The most promising opportunities lie in the following three approaches:

Optimizing the logistics structure. By reducing the number of warehouse locations, a retailer achieves regression effects that allow warehouse automation: with higher inventory volumes stored in fewer warehouses, the high cost of investing in new picking technologies pays for itself sooner. With only a few warehouses, retailers also benefit from higher bundling effects in inbound transport, for which discounts can be negotiated on the basis of the new or improved sourcing locations and logistics.

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Exhibit 2: Grocery retailers can save along the entire logistics chain

The British supermarket chain Morrisons has been optimizing its warehouse network for some years now. Initially, Morrisons reduced the number of regional distribution centers in southern England to achieve economies of scale. The remaining DCs were located in such a way as to reduce the transport distances to the stores. Finally, Morrisons also reduced the number of national distribution centers for slow-moving items to avoid unnecessary costs.

In Spain, Mercadona also profits from better warehouse structures. Following a period of strong growth, it has invested in automating its distribution centers. In Germany, Edeka has opened Germany’s first fully automated grocery retail distribution center in Hamm and plans to add more. The discount chain Lidl is currently ramping up an automated picking system. In all such cases, the prerequisite for efficient automated picking is having sufficiently large-scale inventory volumes to work with, and these have to be built up through growth and specific bundling efforts.

A further attractive option for German grocery retailers is to expand cross-docking. In France, Carrefour successfully operates one of Europe’s largest cross-docking platforms for fast-moving goods. Following chilled products, Carrefour is now converting its packaged goods assortments to cross-docking in “consolidation and collaboration centers.”

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By optimizing its logistic structure, the full-range supermarket we mentioned before can increase its margin by a total of 2 percentage points. While it is true that having fewer locations does raise distribution costs to some extent as a result of the greater distances between the warehouse/distribution center and the stores, these cost increases are generally largely outweighed by the cost advantages of creating more efficient transport units.

Reducing the share of direct store deliveries (DSDs). When the share of DSD decreases, it reduces the transport costs in the entire manufacturer-to-retailer supply chain and also lowers order planning in the store. At the same time, warehouse operations benefit from economies of scale, as higher volumes improve the utilization of storage space and picking capacities.

The catching-up that German grocery retailers need to do here is apparent from comparisons with the UK. While UK manufacturers deliver less than 5 percent of their total sales volume directly to stores, this figure in Germany can still be as high as 30 percent. And because British retailers control a much larger chunk of the sourcing logistics, they also are more successful at bundling the otherwise fragmented flow of goods from small manufacturers. If the full-range supermarket in our example pulls all of the levers to reduce the share of direct store-door deliveries, it can increase its margin by 1 percentage point.

Segmenting and aligning inventories to demand. Experience shows that most inventory reach levels and shelf layouts don’t really take into account the speed at which goods are sold. Stores typically have overstocks of slow-moving goods and stockouts of the most popular products as the latter are under-presented, i.e., with too low facings. For superior results in shelf stocking, the best approach is to use holistic forecasting processes, revised planograms, and active sales management. Here, too, the full-range supermarket analyzed has several attractive levers to pull. It can increase its margin by 0.2 to 0.4 percentage points if it systematically differentiates demand planning in line with the sales rates for different merchandise.

Our retailer example shows that end-to-end supply chain optimization along the most important levers pays off. If the full-range supermarket reaches the target level identified in each area, its logistics costs will fall by more than €0.50 per case. Not only will the supermarket save a lot of money, but its optimized supply chain will also contribute to higher on-shelf availability and thus create greater customer satisfaction.

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In 2011, grocery retailer supply chain costs in Germany amounted to 15 percent of the sector’s net revenues. With help from a proprietary model that enables performance comparisons with competitors, retailers can significantly improve their margins by identifying key improvement levers and structural improvements along the supply chain■

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About the authors: Christoph Glatzel is a principal in McKinsey’s Cologne office, Jochen Großpietsch is a principal in McKinsey’s Barcelona office, and Alexander Hübner is a consultant in the Munich office.

Copyright © 2012 McKinsey & Company, Inc. All rights reserved

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McKinsey’s cost-driver analysis: Method and approach

The cost-driver model developed by McKinsey makes performance differences in the supply chain of grocery retailers transparent. Through reverse engineering, the retail supply chains are mapped and performance differences measured using operating performance indicators – taking into account retailer-specific logistics and sales structures.

On the one hand, the model captures structural logistics data such as the number of warehouses and distribution centers, the shares of slow- and fast-moving goods, and the number of stock keeping units. On the other hand, it also compiles structural sales parameters such as the number of stores; number of A, B, and C suppliers; items per format; and delivery frequency per store type. In addition, it covers efficiency indicators such as shipment sizes, share of direct store delivery, degree of warehouse automation, truck utilization, number of stops per delivery route, processing times for order planning, and shelf replenishment in the store, and the number of cases per standardized transport unit.

Because the model includes the retailers’ various logistics and sales structures, it enables fair comparisons. For example, it takes into account that a company with higher merchandise variety or many different store types naturally has higher supply chain costs. The result of the cost-driver analysis is thus much more meaningful than a simplistic comparison of individual figures, such as supply chain costs as a percentage of sales revenue. Consistent with this, performance differences can only be measured by mapping the individual supply chains and calculating their results for selected indicators.

The cost-driver model distinguishes between the following cost blocks:

• Inbound costs for transport from the supplier’s loading dock to the retailer’s distribution center or warehouse

• Warehousing costs for entering and issuing stock, storage, and picking

• Distribution costs for transport, loading at the warehouse or distribution center, and unloading at the stores

• Instore costs for order planning, inbound goods receiving at stores, and shelf replenishment

• Direct store delivery costs for order planning, transport, and replenishing shelves with merchandise that suppliers deliver to stores directly

• Inventory costs for capital tied up in stock in distribution centers, warehouses, and stores

• Lost gross margins due to shelf stock-outs.

The calculation of the distribution costs aggregates data for the number of stores, warehouses, and distribution centers; the weekly delivery frequencies, the average number of stops per delivery route, the number of pallets and roll cages per truck, the average loading and unloading times, and the annual revenue and total sales volume per category. Using route planning software, the lengths of the delivery routes can be calculated on the basis of average distances between warehouses and stores. Most of the data can be compiled from simulations, annual reports, store visits, and database searches. With this information, it is possible to determine and compare the cost structures of individual competitors. This reveals supply chain potential where the gap to best practice is the largest.