How?

Think.

Design Thinking and Value Engineering combined.

Ian H Smith

Think is the first element of digital innovation we consider at Being Guided: it is a combination of Design Thinking1 and Value Engineering3, as expanded upon below.

Design Thinking.

Our Design Thinking based on the Stanford d.school1 method. This is five iterative stages to create a solid foundation for digital innovation:

Empathize.
The first and most important stage in Design Thinking is Empathize. This where you are creating receptivity and rapport among a broad set of decision-makers and influencers as stakeholders. In turn, this generates the trust required to move to the Define stage.

Define.
Clearly articulating the problem to be solved. After gathering insights, define the core problem in a human-centered manner. This stage is about synthesising observations and articulating the problem in a way that guides the creation of a compelling argument for a solution.

Ideate.
Generating a range of creative ideas to solve the defined problem. This phase involves brainstorming and exploring potential solutions, encouraging out-of-the-box thinking. It's essential for innovation, as it embraces creativity in the discovery of effective outcomes.

Prototype.
Turning ideas into tangible products. Prototyping means a hands-on approach to the rapid transformation of Current State, generating a simpler, more effective Future State with the right solution. Prototyping is crucial for visualising how the solution will work.

Test.
Gathering feedback and refining the Prototype. Testing includes feedback collection on reactions to the solution offered. This helps in understanding the prospect's experience, identifying issues, and validating the effectiveness of what has been proposed.

Section image

Value Engineering.

When we apply Design Thinking to maximise human receptivity and rapport, we also include the rigour of Value Engineering3.

Value Engineering was originally conceived by Lawrence D. Miles3, a General Electric engineer. Miles' techniques have saved design engineers, manufacturing engineers, purchasing agents and service providers millions of dollars.

To quote Miles, it was necessary to show "why so much unnecessary costs exist in everything we do and how to identify, clarify, and separate costs which bear no relationship to customers' needs or desires."

Value Engineering eliminates waste and determines value over price. This is calculating the cost of purchasing (or crucially, not purchasing) any high-value product or service in a timely manner. It is quantifying time-based value versus the cost of delay or doing nothing.

As Value Engineers, we set the scene mapping your ideal customer's needs with your offering. This is where we apply Design Thinking to enable you to build receptivity, rapport, trust and truth with buyers - early and often.

From a financial perspective, we start with a simple question for the buyer:

What is the cost of NOT buying the product or service?

Firstly, let's look at the Return On Investment (ROI) Model - a general formula:

ROI = (Cost of Investment / Net Profit)×100%

To adapt this formula for an As-Is vs. To-Be comparison, consider:

Net Profit: This will be the difference in profits between the Future State (To-Be) and the Current State (As-Is).

Cost of Investment: This is the cost incurred to move from the Current State (As-Is) to the Future State (To-Be).

Given the above considerations, the formula becomes:

ROI = (ProfitTo−Be − ProfitAs−Is / Cost of Transition) × 100%

Where:

Profit To-Be = Profit or (benefit) in Future StateProfit As-Is = Profit (or benefit) in Current StateCost of Transition = Cost to move from As-Is to To-Be

Note: If you're measuring benefits other than strict monetary profits, such as time saved or other intangible benefits, ensure you can convert these benefits into a monetary value for this to be valid.

To calculate the Return On Investment (ROI) with the specified inputs, we can formulate several equations. Let's define the variables first:

BVAs-Is = Current State (As-Is) Business Value generated per annum without Solution.BVTo-Be = Future State (To-Be) Business Value generated per annum after investing in Solution.

COS = Cost of Solution.ROI = Return on Investment as a ratio relative to the Cost of Solution.

CoD = Cost of Delay per day when not investing in Solution.CoDN = Cost of Doing Nothing per day when not investing in Solution.

CoDday = Cost of Delay per day when not investing in Solution.

CoDNday = Cost of Doing Nothing per day when not investing in Solution.

Calculating ROI from Solution: Net_Gain - BVTo-Be - BVAs-Is

Calculating ROI: ROI - Net_Gain - CDI / CDIThe ROI is expressed as a ratio. Multiply by 100 to get a percentage.

Cost of Delay (CoD): This represents the loss per day by delaying the Solution purchase. Assume the delay starts from the beginning of the year and goes on for d days:CoD = BVTo-Be - BV As-Is (d x CoDday) - CDI / CDI

Cost of Doing Nothing (CoDN): This is the loss per day for not implementing the Solution. Similarly, for d days:CoDN = BVAs-Is - (d x CoDNday) - CDI / CDI

Summary.

Creativity is the craft of problem-solving2. At Being Guided we embrace the Stanford d.school approach to Design Thinking1, where moving through five stages of digital innovation is underpinned by a continuous flow (Ideaflow2) of experimentation, followed by validation.

Value Engineering reinforces the Ideaflow from Design Thinking and enables the generation of a solid financial case for both investing in digital innovation and, from a sales perspective, can be a powerful part of Guided Selling with any high-value product or service.

References.

  1. The Hasso Plattner Institute of Design. (2004) Stanford d.school. https://dschool.stanford.edu/about
  2. Utley, J., & Klebahn, P. (2025). Ideaflow: Why creative businesses win. Portfolio/Penguin Random House.
    https://www.penguin.co.uk/books/452064/ideaflow-by-klebahn-jeremy-utley-and-perry/9781529146233
  3. Miles, L.D. (1947). The Lawrence D. Miles Value Engineering Reference Center Collection.
    https://minds.wiscon.edu/handle/1793/301