Feature-Based Cost Analytics:A Next-Generation Solution For Reducingthe Direct Materials Spend

With cost savings a high priority, industrial manufacturers are

looking for new and innovative ways to manage direct materials

cost without sacrificing product quality. Feature-Based Cost

Analytics o�er a viable solution.

One of the most di�cult challenges faced by large industrial manufacturing

companies is accurately pinpointing and managing the costs associated

with the direct materials and components they need to manufacture their

products and equipment. According to studies from the Center for Advanced

Purchasing, direct materials costs account for more than 35 percent of sales

for many manufacturers. The top 100 manufactures spend more than $400

billion per year on these direct expenses.

In today’s world of global competition, �uctuating market conditions and

high labor and operating expenses, the challenge has become even more

daunting. To maintain margins and pro�tability, corporate leaders are

seeking new ways to reduce costs across the board by as much as �ve to

ten percent annually. One of the targets of these aggressive cost-cutting

measures – especially by manufacturers of complex industrial equipment – is

the procurement of direct materials, which typically falls under a company’s

design engineering and materials purchasing functions.

What you do not measure, you cannot improve. It follows then that engineers

and purchasers need to achieve greater visibility into their direct materials

spend so they can begin to identify the true cost drivers and capture cost

reduction opportunities. In this article, we will analyze the current situation,

identify the shor tcomings of traditional materials cost management methods,

and discuss how an alternative approach – Feature-Based Cost Analytics –

can help companies gain a clearer and more accurate picture of the true

costs of materials.

1 Feature-Based Cost Analytics: A Next-Generation Solution For Reducing the Direct Materials Spend

True Cost Knowledge is Elusive for Most

Traditionally, in industrial manufacturing environments, product design engineering and

direct materials procurement have been discrete and linear functions. First, engineers design

the product and specify the parts and materials needed to produce it. Then, purchasing

professionals research to identify the best network of materials suppliers and negotiate to

secure the best available cost.

This is not as simple as it sounds because a company may need to procure thousands of

materials and parts to manufacture complex products and equipment. To streamline the task,

a manufacturer will typically group commodity materials or families of parts or materials

into packages for bidding and analysis. Many employ manual methods to approximately

“cost” these commodities or family groupings, using as a basis some function of weight,

size, type of material used, or fabrication method (e.g., casting, machining, drilling).

In today’s environment of constrained resources and increased customer demands, one-o�

analyses and manual number-crunching modeling are far too labor intensive and time-consuming

to adequately address the scope of the problem. When dealing with a large number of items to

analyze, it becomes extremely di�cult to identify and prioritize exact sources of cost reduction

opportunities. These opportunities are not easily accessible when the data resides in a stack

of spreadsheets and the expertise is held by individual purchasing experts. As a result, most

manufactur ers ar e able to apply a more basic should-cost analysis to only a small percentage

of the materials and parts they buy – and these items may not even represent the most critical

areas for cost reduction analysis.

In general, engineers are not as familiar with the commercial side of products, parts and

materials, while purchasers and �nancial personnel are less knowledgeable about the product

design and engineering side. The result is that a large gap exists between the design, procurement,

production and �nancial functions of the business. If a cooperative product modeling e�ort were

to take place early in the process, accompanied by knowledgeable input on material and supply

costs, ther e could be proactive collaboration to create the same products, but at a lower cost.

Procurement professionals typically do not have the data sets required to perform true cost

analysis and identify the related core cost drivers. To perform this level of analysis would

require access not only to purchasing and �nancial data, but also to related engineering

information. In addition, they would need to have a technology platform through which

they could mine these data sources and “crunch the numbers” automatically to perform

in-depth comparisons and analyses on a vast volume of materials and parts.

Most large organizations today have installed business information systems and realize the

intrinsic value of relational databases and business intelligence to improve sales, marketing

and �nancial performance. Yet, in most industrial manufacturing companies, procurement

automation remains largely tactical and transaction-based, rather than strategic and results-

oriented. In the various spend analytics tools on the market today for procurement professionals,

the automation provided typically relates to streamlining procurement processes and costs

by analyzing conditions such as supplier reliability, redundant supply sources, and the like.

A tool that focuses more on part features and attributes would allow companies to better

understand the true cost structure of their products so they could more accurately identify

cost reduction opportunities.

2Feature-Based Cost Analytics: A Next-Generation Solution For Reducing the Direct Materials Spend

A data warehouse by itself cannot deliver value. It is valuable only when users know which

data to extract and what they want to do with it – and possess the analytical tools with

which to interrogate, manipulate and analyze the data for decision support. Lacking these

capabilities, most manufacturers rely on the purchasing department’s knowledge and skills

in negotiating agreed-upon prices with suppliers – without a speci�c frame of reference as to

what the materials should truly cost.

What is needed is a new process and information platform that can help product design and

development engineers and purchasing executives and managers uncover the root causes of

direct spend materials costs. One new and different approach for industrial manufacturers

to consider is called Feature-Based Cost Analytics.

Feature-Based Cost Analytics: A Next-Generation Solution

Feature-Based Cost Analytics represents an innovative, intelligent solution with a top-down,

feature-based approach to cost management of individual direct material parts. The concept

was developed by top experts in purchasing, data mining and statistical analysis, with practical

input provided by manufacturing and procurement best practices leaders, and product

design engineers.

Feature-Based Cost Analytics combine new, feature-rich data marts and sophisticated data

mining algorithms to generate predictive cost models. These models enable users to analyze

part featur es, constr uct “should-cost” curves across individual families of parts, and determine

the key drivers that a�ect the cost of the parts. Using this automated approach, an analysis

that previously took purchasing agents months to complete, could instead be done in weeks

or even days.

Unlike traditional linear product costing and material purchasing methods, Feature-Based Cost

Analytics calls for concurrent interaction and collaboration across engineering, procurement

and �nancial functions, even as early as the design phase. The objective of Feature-Based

Cost Analytics is to transform these front-end processes and make them faster, more accurate

and more strategic, with the end goal of complete cost alignment of the direct materials spend.

3 Feature-Based Cost Analytics: A Next-Generation Solution For Reducing the Direct Materials Spend

Evolution of Procur ement Best Practices

InventoryControl

TOO

LS MRP/ERP eProcure SpendAnalytics

Supplier Relationship Management

Fea tur e B ased Cost An aly tic s

• WIP costs• Tr ansaction costs

LEVE

RS • Tr ansaction cost

• Price ( vol)• Price ( mkt) • Price ( vol)

• Complexity cost• Quality and risk cost drivers• compliance

• Design cost drivers

Tra nsactionManagement

VALU

E Leverage

RationalizeStandardize

Supplier Management

Pro duct C ostMana gem ent

Successfully discerning a true “should-cost” view of their products’ materials and components

can help manufactures negotiate more reality-based prices with their suppliers. In addition,

the information integration and concurrent collaboration among these front-end processes

will help companies bring their new products to market with tighter cost control and within

shorter timeframes.

In the design phase especially, Feature-Based Cost Analytics can help companies design

products from the ground up with cost control in mind. Using �nished 3-D drawings, a

collaborative team can review the original design and possibly suggest design modi�cations

or alternative manufacturing processes that will result in lower production costs without

sacri�cing product quality or functionality.

Feature-Based Cost Analytics tools can be deployed in a functionally rich analysis

platform. Such a platform can turn a company’s expert knowledge and experience, into

sets of standardized rules, and make process steps clearer and more predictable. It is like

having a “brain-in-a-box.” By using this kind of intelligent, dynamic toolset to perform

statistical analysis over time, a company can learn how to make more strategic product

design and materials costing decisions.

Feature-Based Cost Analytics in Action

Feature-Based Cost Analytics software enables data to be extracted from a company’s existing

design, purchasing and �nancial systems at the parts feature level. A powerful analytical engine

uses sophisticated algorithms to pinpoint potential “wins” – or cost savings opportunities –

across several different scenarios (including product design, sourcing and pricing) and identify

the true driving factors of each product’s cost. This helps create a truer should-cost picture

than either the commodity or family grouping approach. By taking a top-down approach and

incorporating a wealth of domain knowledge and standardized rules, Feature-Based Cost

Analytics improves both the e�ciency of the costing process and the accuracy of results.

Take, for example, a manufacturer of wiring harnesses for the automotive and industrial

vehicle industry. Design engineers design the product and prepare to put it out for bid through

purchasing using a Request-for-Quotation (RFQ) form. In this document, they provide the

requisite CAD drawings and specify the size, materials to be used, types of connectors, and

similar information. Lacking an intelligent data system to estimate the costs involved, setting

the costs from that point on is typically left up to competitive bids from suppliers.

With an intelligent Feature-Based Cost Analytics system, however, the front-end team

would know the real cost of the materials, connectors and other components. Procurement

professionals would then be empower ed to work with preferred suppliers on a feature-

based cost basis, where both purchaser and supplier have a clear understanding of the cost

drivers. They can potentially work together to achieve a lower cost by modifying the

design, materials and/or production processes. Many progressive manufacturers already

encourage close cooperation between procurement and the supply network – but the right

analytical tools make the process faster, easier and more e�ective.

For industrial manufacturers of any complex products or equipment, having access to such

a fast, automated and intelligent parts analysis and procurement platform would deliver the

promise of more e�ective cost management of direct materials right to the desktop.

4Feature-Based Cost Analytics: A Next-Generation Solution For Reducing the Direct Materials Spend

5 Feature-Based Cost Analytics: A Next-Generation Solution For Reducing the Direct Materials Spend

Bottom-Line Bene�ts and ROI

So what is the primary bottom-line bene�t of Feature-Based Cost Analytics? Direct spend

cost reduction. Manufacturing companies today have to work harder than ever to hold

the line on product development, materials and production costs if they hope to remain

competitive and pro�table.

With the powerful analytics of a Feature-Based Cost Analytics approach, organizations

would be able to factor in both internal and external elements in balancing their direct

materials spend and their pricing structures. They would also be able to leverage the combined

domain knowledge of their design, engineering and procurement experts to identify innovative

ways to lower costs.

If this new approach enabled a large organization to realize cost savings across commodities

of even three to four percent, the �nancial return would be signi�cant. However, it is more

likely that for many companies, the cost reductions may rise as high as 15 to 20 percent for

particular parts, depending on the number and types of parts identi�ed for analysis.

A Powerful Way to Improve Business Performance

The new cost management paradigm represented by Feature-Based Cost Analytics o�ers

advantages for all functions involved in product cost management - design, engineering

and pr ocurement – as well as for bottom-line business performance. Data mining has

brought a new level of discipline, speed and accuracy to managing and mining huge

volumes of data. And, with the addition of today’s sophisticated data mining and statistical

analysis tools, the possibilities for leveraging the data for direct spend cost reductions have

never been more promising.

Next-Generation Cost Management Delivers True Cost Savings

Feature-based cost management based on data mining and analysis is not a new concept.Variations of feature-based costing have been used successfully in service industries – by insurance providers, home mortgagelenders, and even on-line retail operations such as Amazon.com.Traditionally, industrial manufacturers haveconsidered their products and systems to be far too complex and customized to allow them to standardizecosts based on rules-based analysis of product features or attributes.This is no longer the case.

Some progressive companies already using Feature-Based Cost Analytics for product development and materialscosting report operational and business bene�ts, as well as a rapid return-on-investment. Manufacturers havesigni�cantly reduced the process time for identifying parts cost drivers and have produced cost savings in themillions of dollars.

Key Elements to Look For

Some of the important elements to look for in selecting a Feature-Based Cost Analytics solution include:

• In-Depth Visibility into parts data, down to the speci�c feature level.

• High Accuracy of calculations and scenarios, with critical insights into root causes of cost misalignments and identi�cation of cost reduction opportunities.

• Fast Execution of cost modeling and analysis of large volumes of parts to streamline front-end processes,shorten product development time, and speed up time-to-market.

• Common Portal to provide a common language and uni�ed platform through which design, engineering and purchasing professionals can communicate and collaborate in real-time.

• Ease of Use through intuitive navigation, convenient access to information sources and analysis functions,and a �exible user interface.

• Fully Scalable to accommodate growth and change as business needs require.

• Evolving Value Over Time through the incorporation of tribal knowledge and experience, accurate historic records, standardization of many product features and attributes, and statistical analysis for dynamic supplier contract management

Selecting and Implementing a Feature-Based Cost Analytics Solution

To be e�ective, a Feature-Based Cost Analytics Solution must o�er the deep functionality and �exibility toadapt to an organization’s unique business process – although making a transition from manual processes tomore automated work�ow modes will always require some process adjustments in the way people interactand perform their work.

For those involved in the front-end processes discussed here – product design, engineering and procurement –the beauty of a technology-enabled automated solution is that it not only takes the guesswork and unreliabilityout of cost estimating,but also eliminates the tedious “heavy lifting” of intensive number crunching.

When evaluating and selecting a solution, it is important to thoroughly test and vet the system to be sure that itmeets the needs of the business. Once selected, implementation and rollout should begin on a small scale to allowperformance evaluation and working through any challenges or obstacles prior to a full-scale implementation.This step will help to minimize risk and ensure that the needs of all stakeholders are met.Also important is toallow su�cient time and resources for education and training to ensure that end users “buy-in” to the systemand fully understand the value it can deliver to them personally, and to the enterprise.

6Feature-Based Cost Analytics: A Next-Generation Solution For Reducing the Direct Materials Spend

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