Transparency of cost and value

Part of the challenge of complexity is that it’s not always easy to see or track. Often the consequences of complexity are known only at aggregate levels, for example, when the company finds it is no longer cost competitive. Based on our client experience, we understand that it is essential to develop transparency of costs and value, so that underlying causes can be addressed and controlled.

In our experience, there are two main challenges in creating necessary transparency:

• A complete enterprise-wide view into the company’s data systems and processes
• Sufficient detail and depth of data for determining the value of product or service variety

Enterprise-wide view of data complexity
The challenge of creating true cross-functional transparency arises because data is often contained in systems residing in individual departments or business units. These systems have to be clearly linked to understand how complexity manifests itself across the value chain.

To connect the pieces, common indices that link the data in the different sources need to be identified, and in some cases created. Part of the challenge is that data environments are usually optimized towards the departments that they serve. For example, sales & marketing has different interests in defining a data field for their CRM system, than the manufacturing department has for its production scheduling or even product costing applications.

One of our clients, a manufacturer of electrical installation material, discovered through improved enterprise-wide data transparency that most of their non-quality costs in terms of invoice mistakes and queries and subsequent actions to correct them - changing payments and invoices, returning shipments, repackaging, etcetera - could be traced to their smaller customers. Their large accounts all ordered electronically, according to well defined frame contracts. The much more incidental buying behavior of their smaller clients (for which they believed they were making better margins) led to all the complexity costs along the entire delivery system.

Because these costs were not properly accounted for in the company’s pricing structure, the large accounts were effectively subsidizing the smaller ones, something they could ill afford, as the competition was most fierce for the large accounts. The solution was surprisingly simple and did not involve changing the SKU portfolio or processes in any way. Simplifying the terms and conditions for the smaller accounts was sufficient to eliminate much of the non-quality cost.

Sufficient data for determining value of variety
Not only is it necessary to connect the data across functions and business units, it is essential to have data that goes to the required depths to provide a detailed picture of complexity. Unprofitable proliferation can be caused by technologies, raw materials, SKUs, product launches, types of packages and labels, numbers and types of warehouses, manufacturing change-overs, customer segments – and that’s just a few drivers.

A clear view of detailed data is required to accurately determine profitability and the value of variety.

Another client, a business-to-business manufactured goods company, was experiencing rising levels of complexity. Every year the company ended up with more product families and more variants than the year before, and all of them had solid business cases when they were introduced. Occasional pruning helped get rid of SKUs but failed to really reduce costs - an increasingly pressing issue because their competitiveness was steadily eroding.

It took a structured, data-driven approach to gain transparency for all the complexity drivers. For example, the supply departments did a thorough analysis of all complexity costs, using detailed data, such as the costs of setting up new SKUs in the master data tables in their ERP systems. With the analysis of cost and value, they could make clear choices about variety. The company was able to reduce SKUs by 48% and divest market sub-segments for improved profitability of 5 EBIT percentage points.

Although many companies have had ERP systems for years, these systems do not guarantee cross-functional transparency, or even the level of data needed to understand complexity. To address this lack of information transparency across value chain functions, A.T. Kearney has developed an innovative Multi-Cube Data Model. This tool enables the calculation of real complexity costs and related scenario analytics.

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Oliver Scheel is a principal in A.T. Kearney's Düsseldorf office.
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