final version - ijbir040403 malinin

Int. J. Business Innovation and Research, Vol. 4, No. 4, 2010 321

Copyright © 2010 Inderscience Enterprises Ltd.

The method for transforming a business goal into a set of engineering problems

Len Malinin Gen3 Partners, 10 Post Office Sq., Boston, MA 02109, USA E-mail: [email protected]

Abstract: Presented is a high level approach to engineering project management and, more particularly, a method for transforming business goals of a client into a set of engineering problems which, when resolved, make the business goals achievable. A guideline for selecting main parameters of value (MPVs) of an industrial product is discussed, and then a transition from MPVs to a set of physical and technical variables (parameters) responsible for the selected MPVs is shown. Improvement of the identified variables (parameters) can be achieved using inventive problem solving combined with traditional engineering methods.

Keywords: business innovation; business goals; engineering problems; main parameters of value; stakeholder; decision making; coefficients of sensitivity.

Reference to this paper should be made as follows: Malinin, L. (2010) ‘The method for transforming a business goal into a set of engineering problems’, Int. J. Business Innovation and Research, Vol. 4, No. 4, pp.321–337.

Biographical notes: Len Malinin has been at the forefront of innovation, leading professional teams of innovators in technology development, technology transfer and innovation consulting. His work has led to funded products and newly formed companies. Prior to GEN3, he developed advanced data processing algorithms, worked for an MIT spin-off, and published a monograph on rotor vibration control. He holds several patents and has presented at international conferences.

1 Introduction

Success of a business is determined by achieving its business goals. Achieving business goals often depends on introduction of an innovative product with a novel set of features or modified technical or physical properties. However, translating business goals into technical requirements for a new product is far from obvious.

Customers buy products for a variety of reasons, however, a limited number of parameters really influence the customer’s buying behaviour. These parameters, which are responsible for the purchase decision, are called the main parameters of value (MPVs).

In many cases, the purchasing decision depends on multiple factors which need to be carefully analysed by the vendor. For example, decision of a fleet which new truck to buy

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is determined by projected annual operating cost (including fuel, maintenance, repair, insurance), revenues (which depend on payload, loading/unloading time), drivers’ preferences (including appearance of the truck), and other factors.

MPVs reflect buyers’ attitudes towards products. MPVs can, therefore, vary between different stakeholders and market segments. For example, MPVs of a truck dealership, such as margin and factory support, differ from MPVs of an owner-operator looking to buy a truck. Aging truck drivers form a segment that places a greater importance on cab comfort, while the fleet owners may place a greater importance on fuel economy.

Multiple stakeholders contributing to a single purchasing decision may influence this decision in opposite directions. For example, truck drivers’ preference for a massive square protruded engine compartment leads to specifying a truck with poor aerodynamics and high fuel consumption, whereas the fleet’s manager would rather specify an aerodynamically shaped truck with low drag.

Once MPVs are defined as the parameters, based on which purchasing decisions are made, then an innovative approach is required to achieve significant movement along these MPVs. However, MPVs (such as fuel consumption) often represent only a general direction, which makes it difficult to apply problem solving tools directly to improve upon the MPVs. In many cases, it is first required to derive specific physical or technological variables responsible for the given MPV. At this level, the problems can be solved to improve these variables. The changes in these variables can then be back propagated to the improved MPVs.

The need to relate business goals with technical parameters of a new product was stated in several recent publications. A prioritisation methodology for product features, where preferences are measured by trading stocks whose prices are based upon share of choice of new products and features in a focus group was discussed by Dahan et al. (2007). While promising for faster new product development decision, this approach is limited to consumer products. A quantitative research tool discussed by Whipple et al. (2007) is free from this limitation and presented in the form which does not require training in statistics or special software. This approach, however, is based solely on the customer responses. Marine and McAllister (2007) present a new product development process which integrates business objectives and user observations, is applicable to both industrial and consumer products, and has been applied to development of over 250 products. While efforts are made to not just observe users but analyse their intentions and tasks they perform, it is not applicable when a user is not available for observation (which is the case with many industrial products).

An interesting design method, proposed by Peoples and Willcox (2004) for aircraft program design, is based on the real options theory and takes into account not only aircraft performance but also factors such as aircraft demand, market uncertainty, and development and manufacturing cost. This method, which does not consider individual stakeholders and operates with generalised technical parameters, can be combined with the one described in the current article.

This article is intended to help a practitioner looking for a concrete step-by-step approach to develop a procedure for his application. The consideration in this article is limited to industrial (B2B) products. A technique for consumer (B2C) products is currently in progress.

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2 Review of known approaches

While the need for the transition stated in the title of this article seems fairly noticeable, a literature review reveals scarcity of publications that would offer actionable procedures capable to accomplish the stated goal.

Traditionally, directions for product upgrade resources have been selected within the framework of the Six Sigma method. Lately in several publications the relationship between business goals and product parameters has been discussed without using the Six Sigma paradigm. A recent book (Mital et al., 2007) discusses a ‘holistic approach to product development’ based on the premise that ‘new products are expected to contribute to the overall business goals’. According to a publication of Collaborative Product Development Associates (2007), linking customer needs or market input with product specifications is a challenge in the face of rising complexity. Critical Parameters Management is discussed as a tool for tracking the critical design parameters. Conka et al. (2008) proposed a combined decision model for selecting and prioritising in research and development projects. This technique, however, is focused on project portfolio selection, rather than on product characteristics.

The majority of studies for product upgrade resources have been done within the framework of the Six Sigma method. While the method was originally developed by Motorola primarily as a statistical approach aimed at product quality, its define stage accounts for the value a project will provide to the customers. Whereas the importance of focusing projects on the right goals is reiterated in multiple Six Sigma and Quality Function Deployment (QFD) publications (e.g., Vinodh et al., 2006; Rahman and Qureshi, 2008; Kaldate et al., 2006), the determination is based primarily on Voice of the Customer (Ishikawa, 1990). Further emphasis is made on focus groups and surveys and subsequent statistical processing of gathered data. It is stated that ‘the greater the level of detail, the easier the translation of the customer’s demands into internal specifications’ (Pyzdek, 2003). Specifically, stakeholder benefits for the External Customer are broken down into two categories, Customer Satisfaction and Quality Improvement (CTQ, ‘critical to quality’). Subjectivity in interpreting Voice of the Customer, selecting the scores and data processing often leads to a conclusion that assessing Six Sigma projects is an art as well as science.

Within the framework of the QFD approach, more specifics are provided in a few recent patents. Relationship between quality parameters of the system (CTQs, ‘critical to quality’) and technical parameters of its subsystems (KCPs, ‘key control parameters’) is described in two US patents, granted to General Electric, 6,351,680 (Ali et al., 2002) and 6,725,183 (Cawse, 2004). The first patent aims to facilitate application of the QFD method for the systems ‘having numerous levels and components’, while at the same ‘preserving the system architecture’. The set of CTQs is determined based on Voice of the Customer.

Upon close analysis, the method described in US 6,351,680 has the following limitations:

• It is based solely on Voice of the Customer (the set of CTQs and their importance are based on customer expectations).

• The CTQs of the method, which are based on customer requirements, include subjective factors and subjective weight coefficients. Therefore, the transfer

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functions which relate KCP to CTQ remain subjective, even though they are made quantitative by using ranks and weight coefficients.

• The KCPs are strictly limited to the existing architecture (structure, schematics) of the product under consideration.

The approach below presents a specific way to determine MPVs of an industrial product. When MPVs of a stakeholder are determined in the proposed approach, they can depend both on the MPVs of the stakeholders downstream and upstream. By way of contrast, in the QFD method the CTQs of an upper level system only depend on the CTQs of the lower or the same level systems, but not on the upper level systems.

The proposed approach can be projected on the familiar Define-Measure-Analyse-Design-Verify categories. Quality parameters of the system (CTQs) are similar to MPVs, and KCPs are similar to the physical or technological variables. However, while the QFD method calls for documenting and breaking down customer requirements into the KCPs within the framework of a given product design or manufacturing process, the propose approach is free from this limitation. In what follows below an attempt is made to reduce the art component and develop problem statements for the projects to be undertaken via a formal procedure. The P&L (profit and loss) based approach for an industrial product, described below, is based solely on the objective data, with no mapping scores involved (it can be therefore called ‘Voice of Reality’, rather than ‘Voice of the Customer’).

3 The proposed method: step-by-step

The proposed method has been applied to multiple products across several industries. Industry-specific variations can be introduced by a practitioner, and in a given new product development cycle certain steps of the method can play greater or lesser role. For instance, depending on the application and selected MPVs, obtaining some of the required numerical data may require considerable efforts. In this case, using ranking and pair comparisons instead of partial derivatives should still enable the user to determine which product parameters need to be improved first.

The basic version of the proposed method includes the following steps:

1 Identify the stakeholders in the value chain and the primary stakeholder.

2 Identify MPVs of a product for the primary and other stakeholders. For a commercial product, identify MPVs based on the annual P&L statement.

3 Select a set of MPVs for improvement.

4 Determine the underlying technological and physical variables of the product responsible for the selected set of MPVs. Apply Functional Modelling or/and Cause-Effect Chain approach (Ladewig, 2007; Wixson, 1999).

5 Select the underlying variables that yield maximum improvement of MPVs. To that end, determine the coefficients of sensitivity and physical/technological limits of the MPVs for the underlying variables.

6 Formulate the set of problems: How to change a given physical variable in a way that improves the selected MPV.


Prioritise the problems, based on the coefficients of sensitivity, development potential, match to the core competencies, match with the strategic directions, development time, problem scale, and restrictions.

7 Solve the selected problems.

8 Back propagate the technical solution to estimate business impact and achieving the success criteria.

The steps are described below in more detail.

Step 1 Identify the stakeholders in the value chain and the primary stakeholder

It is critical for the success of the project to identify the stakeholders that make the purchasing decisions, and MPVs of these stakeholders. There could be more than one stakeholder that makes these decisions.

The product characteristics of a prospective product must improve these MPVs. For instance, referring to Figure 1, in the automotive or commercial vehicle industry a component maker supplies components to an OEM. The OEM sells cars or trucks to a fleet. The fleet in its turn hires end users (drivers). Each of the three stakeholders downstream from the component maker (the OEM, the fleet, the end user) has its own set of MPVs of the truck, which are not entirely independent. For example, the MPVs of the decision makers in the fleet could be low operating cost, and legal compliance, while the MPVs of the truck drivers include the shape of the truck and noise level in the cab. Usually, the ‘upstream MPVs’ reflect in some form the ‘downstream MPVs’, for instance, the OEM’s set of MPVs reflects MPVs of the fleets and of the end-users.

Likewise, a medical device company making catheters sells them to a hospital purchasing organisation (HPO), which distributes them to the hospitals. In the hospital the catheters are operated by a nurse or a doctor (radiologist, urologist), and hosted by a patient. Each of these stakeholders (HPO, a nurse and a patient) has its set of MPVs.

From the point of view of a company striving to achieve its business goals, the major factor for determining features (characteristics) of a product is the set of MPVs of the stakeholder which is the nearest in the value chain (the primary stakeholder). For example, for the medical device vendor, the characteristics of the product should be determined primarily based on the MPVs of the HPO.

When considering which additional features need to be introduced in a product to achieve sales growth, it is also important to understand the MPVs of the greater system (the super-system), of which the product under consideration is a component. For instance, for the medical catheter the MPVs of the end users (the nurse, the patient) are ease of introduction (measured as time, or in dollars at given hourly rate of the nurse). For the greater system where the nurse operates, the hospital, the MPVs include cost of infection treatment and cost associated with the related mortality (both depend on probability of infection as a function of time the catheter is hosted by the patient), and cost of monitoring the vital signs of the patient. The latter cost is not related to the main function of the catheter and cannot be addressed if the product is analysed by the QFD process which preserves system architecture. However, if the product is analysed by the proposed method, then the MPVs of the super-system are to be systematically studied with the aim to generate problem statement of the type ‘is it possible to address the given MPV of the super-system by additional features of the product under consideration’.

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Step 2 Identify MPVs of a product for the primary stakeholder and other stakeholders

Every stakeholder in the value chain has its own set of the MPVs. For instance, MPVs of an OEM typically include investment costs, variable costs, assembly hours, and other parameters. MPVs of a fleet include fuel cost, maintenance cost, repair cost, driver retention cost, legal compliance cost, and others. Developing a prospective product without clear understanding what stakeholder will benefit from what features will undermine its acceptance on the market. To that end, it is crucial to identify the MPVs of each stakeholder.

Only those parameters that can be quantitatively expressed are identified as MPVs. If possible, the MPVs should be brought to the common denominator, e.g., expressed as cost components, because purchase decision-making is driven by cost structure. Alternatively, significant items in the P&L statement can be identified and expressed via characteristics of the product. Then measurable parameters, which depend on the characteristics of the product and on which depend the P&L items are selected as MPVs.

From that standpoint, it is more productive to determine MPVs of an industrial product based on the articles of the P&L or income statement of the primary stakeholder, which depend on the characteristics of the product. For instance, listed above MPVs of a fleet, namely fuel cost, maintenance cost, repair cost, driver retention cost, legal compliance cost, can be determined from the P&L statement. All these cost components depend on characteristics of the truck, some more and some less directly.

Table 1 Discerning MPVs from a P&L statement

P&L Item, $ Product characteristics taken as MPVs Factors not related to the

product

Lost revenues = downtime, % x annual revenues

Lost Wages = downtime, hours x (# of staff x hourly wages)

Scrap and start-up costs =

# of shut downs x (shut down cost + start up cost)

Equipment amortisation =

# of shut downs / (lifetime # of cycles) x equipment cost)

Product cost = cost of CB x # of CBs

Catastrophic cost = (1 – reliability of CB) x equipment cost

Maintenance of CB = service hours x hourly wages

Cost of turning on = # of shut downs x connection time

x x hourly wages

In our experience, focusing on the MPVs of the nearest stakeholder (the direct customer of which the new product is offered) is more productive than combining MPVs of multiple stakeholders. MPVs of other stakeholders downstream are reflected in the direct customer’s MPVs, but it would be a mistake to fine tune the product based on these downstream MPVs. The advantage of using P&L articles as MPVs is that such approach is based on objective data which are generally available. Using P&L articles as MPVs also differentiates this method from QFD approach.


On the other hand, MPVs of a prospective product are better identified based on P&L articles of the greater system (the super-system), of which the system under consideration is a component.

Depending on the available data, if the significant cost items in the P&L can be expressed through top level characteristics of the product, these characteristics can be taken as MPVs. For instance, for an industrial circuit breaker (CB), the expense items from the P&L statement can be expressed through the characteristics of the product as shown in Table 1. In this case, the P&L items are expressed via product characteristics, as well as factors, not related to the product. The parameters that are related to the product (e.g., reliability of the CB) are selected as MPVs.

Step 3 Select a set of MPVs for improvement

For the given stakeholder, MPVs for improvement can be selected based on the competitive landscape (whether next generation of the product can dramatically change a certain MPV), match with core competencies, impact of the expected improvements on position of the product on the market with respect to the competition, and other factors.

Step 4 Determine the underlying technological and physical variables of the system responsible for the selected set of MPVs

Figure 1 Different stakeholders and transition from one MPV (fuel cost) to the underlying variables (see online version for colours)

Several tools, based on TRIZ (theory of inventive problem solving), Six Sigma or other methods, can be used to determine the underlying technological and physical variables of the product, such as function analysis to build a functional model of the product under consideration (Moehrle and Wenzke, 2006; Kaufman and Woodhead, 2006) or Cause

Driver retention cost

Maintenance cost

Fuel cost

Repair cost

Legal compliance cost

Fleet MPVs

Engine losses

Aerodynamic losses

Rolling friction losses

Sub MPVs

Speed

Air viscosity

Surface energy

Yaw angle

Air temperature

Air density

Drag coefficient (Cd)

Physical parameters Fleets

OEMs

Component maker

End users

Stakeholders

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Effect Chain Analysis (Forrest, 2003). In this process, a lower level MPVs (sub-MPVs) can be introduced, as illustrated in Figure 1, which shows this transition for one MPV (fuel cost) to a set of sub-MPVs (engine losses, rolling friction losses, aerodynamic losses), and then from one sub-MPV (aerodynamic losses) to physical variables (drag coefficient, air density, etc).

This transition to physical variables, rather than components of the existing system, can lead to such problem statements as ‘How to reduce speed of the truck surface relative to the ambient air’, which in its turn leads to such solutions as pneumatic blowing. This differentiates this method from the QFD approach, which can only target improvement of the existing components of the product.

Step 5 Select the underlying variables that yield maximum improvement of MPVs

To that end, determine:

5.1 coefficients of sensitivity of the MPVs with respect to the underlying variables

5.2 physical/technological limits of the underlying variables.

5.1 Coefficients of sensitivity of the MPVs with respect to the underlying variables

The first tool to evaluate the impact of physical variables on the MPVs is the Coefficients of Sensitivity. The coefficients show the sensitivity of MPVs to changes in the physical variable. The greater the Coefficient of Sensitivity, the greater a change in the MPV with a change in the physical variable. A Coefficient of Sensitivity Zij can be calculated as:

iij

j

MPVZ

VΔ

=Δ

(1)

where Δ(MPV)i is the change in (MPV)i due to a change ΔVj in variable Vj. In the above formula, the changes Δ(MPV)i and ΔVj are calculated as percentage figures. If analytical expressions are available, then Zij can be calculated as a partial derivative:

iij

j

MPVZ

V∂

=∂

(2)

Physical/technological variables V1, V2, V3, etc., can in their turn depend on other variables W1, W2, W3, …. For instance, aerodynamic drag (V1) depends on the tractor-trailer gap (W1), the projected area (cross-section; W2), yaw angle (W3), and other parameters.

In this case, the dependence of (MPV)i on a root cause variable Wj is determined as:

i kij

k j

MPV VZ

V w∂ ∂

=∂ ∂

(3)

As a result of Step 5.1, an N x M matrix of coefficients Zij will be built (N – number of MPVs, M – number of physical variables).


For instance, in the truck example, for the MPV = fuel cost, V = drag coefficient is one of the physical variables that impacts fuel cost. It is known that for 1% of drag reduction, approximately 0.5% of fuel economy can be achieved at the cruising speed of 60 mph, so in this case Zij ≈ 0.5. (For the given drag, fuel economy also depends on several other factors).

The Coefficients of Sensitivity can be calculated by using either well tested mathematical models, or by evaluating the statistical data available through known sources, or through interviews in the field and ‘fuzzy’ (not quantitative) references. If Zij are difficult to calculate, they can be ranked instead (e.g., via pair comparisons), and by doing so one can determine what set of variables Vj stronger affect the given (MPV)i.

The following examples illustrate determining coefficients of sensitivity of one MPV (fuel consumption of a truck) with respect to several physical variables. (Though MPV is expressed in gallons per mile, it can be easily converted into dollars.). It is recommended to change an underlying physical variable by Δ = 10%, then to calculate the respective percentage change in MPV, and Zij as a ratio. Instead of the 10% increment, another value of Δ can be taken if it is practical for a different application, or a partial derivative can be used instead. However, for further comparison it is important to make sure that Δ(MPV)i and ΔVj are expressed as percentage figures.

Figure 2 Sensitivity of fuel consumption to speed of a truck (see online version for colours)

Example 1 V1 = Truck speed

Changing V1 by 10%, MPV changes can be calculated using known statistical or numerical data. Using a plot shown in Figure 2 (Lawrence Livermore National Laboratory et al., 1998), showing the dependence of fuel consumption on the truck speed for class 8 truck for different drag coefficients Cd, we select as a baseline the point corresponding to 60 mph speed and Cd = 0.6, as shown on the plot. For the Cd = 0.6 curve, 10% change in speed corresponds to 12% change in fuel consumption (Figure 2), therefore,

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∆V1 = 10%; ∆MPV = 12.1%.

∆MPV / ∆V1 = 1.21.

Example 2 V2 = Aerodynamic drag.

Using the same plot shown in Figure 2, for the same baseline point, 10% change in aerodynamic drag (at the same 60 mph speed) leads to 6% change in fuel consumption (Figure 3). Therefore,

∆V2 = 10%; ∆MPV = 6%.

∆MPV / ∆V2 = 0.6.

Figure 3 Sensitivity of fuel consumption of a truck to drag coefficient (see online version for colours)

Example 3 V3 = Tractor-trailer gap

In this case, fuel consumption depends on aerodynamic drag, which in its turn depends on the gap. Using formula (3),

∆MPV / ∆V3 = ∆MPV / ∆V2 * ∆V2/∆V3.

We already know ∆MPV / ∆V2 = 0.6 (Example 2). Then ∆V2 / ∆V3 can be found from Figure 4 (Truck Aerodynamic Styling, 2001). Assuming the baseline dimensionless gap = 0.2, for ∆V3 = 20% increase in the gap, we get ∆V2 = (0.825 – 0.813)/0.813 = 14.8%, so ∆V2 / ∆V3 = 0.74, and:

∆MPV / ∆V3 = 0.6 * 0.74 = 0.42.


Example 4 V4 = Tire pressure

In this case, instead of taking the values from the plot, we use a known reference. According to the Goodyear tests (www.roadstaronline.com/2005/07/036a0507.asp), 15% under inflation of the tires results in a 2.5% drop in miles per gallon. Then:

∆V4 = 15%; ∆MPV = 2.5%.

∆MPV / ∆V4 = 0.167.

Figure 4 Variation of drag coefficient with gap length between tractor and trailer (wind tunnel model) (see online version for colours)

5.2 Physical/technological limits of the underlying variables

The second ranking instrument is the distance of the considered physical variable to its physical /technological limit. The greater this distance, the greater the improvement of the variable that can be achieved.

The physical variables are also evaluated in terms of how much they can be changed based on their technological or physical limits. This is necessary to ensure that effort is not expended on variables that are not likely to change much. The more the gap between the variable’s current value and its limit(s), the more the room there is for changing it. If the variable is close to its limits, further improvement is limited, even after significant effort. Only those variables are selected that are not too close to their technological or physical limits.

For example, considering aerodynamic drag coefficient as a variable, its current value for a typical truck is 0.6 (Figure 5). One can assume the physical limit of the drag coefficient to be that of a droplet-shaped body, Cd=0.05. While this shape is impractical for a truck, Cd=0.19 was reported by Vauxhall for the EV1 Electric Vehicle (The Electric Vehicle, 2001), NASA reported test results of a properly modified truck where Cd=0.24 was achieved (Saltzman and Meyer, 1999), and a 1998 report states that ‘it is conceivable that present day truck drag-coefficients can be reduced from Cd = 0.5 – .7 to maybe as

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low as Cd = 0.3’ (Lawrence Livermore National Laboratory et al., 1998). Therefore, there is considerable scope for further improvement and hence the variable ‘drag coefficient’ can be selected.

Some publications recommend determining room for improvement of a given variable based on the S-curves of evolution. According to the commonly accepted paradigm, as an engineering system evolves, it consecutively passes through three stages:

1 slow development

2 rapid growth

3 maturity, sometimes followed by the fourth stage

4 decline (Utterback, 1994).

Figure 5 Room for improvement for the aerodynamic drag coefficient Cd of a truck (see online version for colours)

Notes: Cd = 0.05: drag coefficient of a droplet-shaped body Cd = 0.19: reported drag coefficient of EV1 (Electric Vehicle) Cd = 0.24: reported drag coefficient of a modified truck Cd = 0.30: reported as an achievable value for a truck Cd = 0.60: current value of the drag coefficient for a typical truck

Following this logic, if the product under consideration is at an early stage of the S-curve, its key variables can be expected to be far from their limits. However, empirical analysis of the S-curve hypothesis does not present curves with easily determined stages (Sood and Tellis, 2007), and it remains to be seen whether positioning of a product on the S-curve can be used as guidance for real life decisions. Therefore, using physical or technological limits, like those shown in Figure 5, is more appropriate. From that standpoint, the S-curve on Figure 5 is only used to demonstrate the general trend; its shape may not reflect the timing of real or expected developments.

0.60

time

0.05

0.19

0.24

0.30


Step 6 Formulate the set of problems: how to change a physical variable Vi in a way that improves MPVj

As a result of Steps 3 and 5 (selection of MPVs for improvement and selection of the underlying variables to be improved), the matrix of Coefficients of Sensitivity takes the shape shown in Figure 6. Reviewing the coefficients presented in that matrix, one can select a set of the underlying physical variables that need to be improved to achieve the target values of the MPVs. Some columns or rows can be excluded upfront if the respective variables are close to their physical or technological limits, or if certain MPVs are not targeted (Figure 6). The remaining Coefficients of Sensitivity allow formulating problem statements aimed at improving specific physical / technological variables. Those problem statements can then be further filtered based on core competencies, strategic directions, development time, etc.

Figure 6 Selection of coefficients of sensitivity from the matrix (see online version for colours)

zn3

z33

z23

z13

v3

…

znmzn2zn1MPVn

z3mz32z31MPV3

z2mz22z21MPV2

z1mz12z11MPV1

vm…v2v1

zn3

z33

z23

z13

v3

…

znmzn2zn1MPVn

z3mz32z31MPV3

z2mz22z21MPV2

z1mz12z11MPV1

vm…v2v1

v3 close to its limits

MPV3 not selected for improvement

zij are low

Selected for Solutions

Step 7 Solve the selected problems

Improving the selected physical or technological variable needs to be done under the constraints imposed by the engineering system (product) under consideration. Known methods of solving inventive problems, such as TRIZ (Altshuller, 1999; Ladewig, 2007), Six Sigma (Forrest, 2003), or specific tools of these methods, such as Cause and Effect Diagrams, Function-Oriented Search, Function Analysis, Database of Physical Effects, and others, can be applied at this step. In many cases, solution of these problems leads to a contradiction that needs to be resolved. For instance, many aerodynamic devices of a truck are useful at the cruising speed, while during docking or manoeuvring they present a hurdle for the driver. Exacerbating the last statement (Altshuller, 1999; Ladewig, 2007), one can say that the devices are needed at the cruising speed and not needed at other times. Resolving this contradiction in time leads to the idea of dynamic (extendable,

334 L. Malinin

folding) devices that can be deployed at demand (Lotarev et al., 2007; Patton, 2007). Another solution of the same contradiction is to use resources of the environment (‘the super-system’) to generate the required aerodynamic features only at a high speed. This leads to design of vortex generators (Truck Aerodynamic Styling, 2001), whose operation can be interpreted as generation of a ‘virtual can extender’ which streamlines the airflow around the tractor-trailer gap at a high speed.

As another example, consider a transition from MPV, ‘Maintenance Cost’ to the problem of modifying physical variable ‘Adhesive properties of truck surface’. Database of Physical Effects (available in commercial software or on the internet, e.g., www.invention-machine.com/GoldfireInnovator.htm) offers a solution where the mirror is connected to the positive electrode. Since most dust particles have a positive electrical charge, the positive charge of the mirror repels the positively charged dust particles. The mirror surface remains clean.

Step 8 Back propagate the technical solution to estimate business impact

Once the formulated technical problems have been solved and the changes in the respective physical variables evaluated, these changes can be back propagated to the set of MPVs of the product, using the coefficients of sensitivity.

4 Managerial implications

These results have important managerial implications as many industries face a more competitive environment. Low profits, combined with increased competition, make it even more critical to bring to the market new products with features for which the customers are willing to pay premium prices. Product managers who wish to improve efficiency and profitability are challenged to implement meaningful programs and services that can positively affect the company’s financial status. This study demonstrates that as the size and complexity of a value chain increases, the importance of understanding the MPVs of the major stakeholders, especially of the nearest one who actually pays for the new product, is more evident. Coordination of multiple features of the product in such a way as to address MPVs of other stakeholders results in a more attractive product, thus meeting needed revenue targets. Therefore, product managers are encouraged to integrate the described approach in the new product development cycle to insure organisational profitability.

From the perspective of new product development, the conclusions of this study have illustrated the importance of clear understanding how the new product features affect the customer’s P&L statement and what technical parameters of the product are responsible for these features. It has been argued that understanding how the new product affects the bottom line of the immediate stakeholder (the one who actually pays for the new product) enables one to tackle better the challenges involved in specifying technical characteristics of the new product. This kind of broader view is specifically needed in relation to the kind of managerial challenges faced by a company planning to approach a major customer with a new product offering. As this study has illustrated, it is not enough for the engineering department to concentrate on developing and sharpening the new features. The company also needs to understand how the purchasing decision is made, the


nature of analysis taking place at the buyer’s side. The broadening of the scope of market analysis and business intelligence from voice of the customer (where the customer is sometimes an end user who does not actually pay for the product) towards deeper analysis of the decision making process at the nearest stakeholder is thus an important managerial challenge for a company developing a new product.

In addition to the theoretical contributions described, this study has provided new insights for practical business management. While the most important area of business management this study has contributed to is new product development, some of the insights provided by this study are also related to the whole client management process in which prospective new products are or could possibly be involved in.

5 Summary and conclusions

Unintended changes in other MPVs should be evaluated as well. For this evaluation it is recommended to use the matrix in Figure 6, which shows how the physical variable that we decided to modify in order to improve the selected MPVs affects other MPVs. For instance, improvement of the aerodynamic drag coefficient leads to solutions that result either in a sleeker profile of the truck, or in add-on aerodynamic devices, such as cab extenders, trailer skirts or boat tails (Lotarev et al., 2007; Patton, 2007). However, these solutions can potentially increase driver retention cost (Figure 1), as the drivers may be concerned with changes in visibility, access to the tractor-trailer gap and shape of the truck. This counter effect can be either assessed via drivers’ surveys, or balanced by making the aerodynamic devices also perform an ergonomic function (e.g., improved ingress/egress to the cab). This is one of the reasons we propose to express all MPVs of an industrial product as cost components, bringing all contributions of different factors to a common denominator.

The steps outlined above should lead to a clear understanding of the new value proposition for an industrial product. While the logic outlined above is fairly straight forward, in our experience it is often overlooked by major corporations developing new products or introducing new features in the existing products. Often, these new features address the needs of the end user in the value chain, whereas the purchase decision about the new product is made by an OEM having very different form the end user MPVs.

The approach described above can be used to analyse new industrial products in a wide range of industries. When adequate quantitative data cannot be obtained to fill out the matrix of coefficients of sensitivity, it is possible to draw the conclusions using pair wise comparisons and relative ranking. Such framing even at a qualitative level helps focus the project goals.

The selection of MPVs for a consumer product is quite different and should be discussed in a separate article.

Acknowledgements

The author would like to thank the anonymous referees for their helpful comments on an earlier version of this paper.

336 L. Malinin

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