Havish M Consulting
Customer Case Study · Sensory & Product Innovation
Explore the lab
Alcoholic Beverages × Havish M Consulting

Every flavour has a profile.
Make it visible.

From sensory scores to a shared flavour story. Compare profiles, see what makes a product distinctive, and bring sharper evidence into the next product conversation.

Client Alcoholic BeveragesFunction Sensory & InnovationTheme Flavour Intelligence
Original dashboard views
3views
Radar · price · 1-v-1
Attributes in the reference
19attributes
A shared sensory vocabulary
Profiles in the interactive lab
8prototypes
Synthetic, coded samples
Dimensions in the live explorer
12dimensions
Linked across every visual

Original screenshots supplied for this case study. The interactive extension uses illustrative data and does not represent measured product performance.

The Challenge

An average cannot tell the whole flavour story

A sensory review needs more than a product ranking. Teams need to see the shape of a profile, the attributes behind a difference, and how price sits alongside those observations.

Hidden signatures

Similar averages can hide very different combinations of fruit, malt, wood, spice and smoke.

Complex comparisons

Looking across many products and attributes makes small but meaningful differences easy to miss.

Price needs context

A price comparison becomes more useful when the sensory dimension is visible beside it.

One shared language

Consistent descriptors and fixed scales help sensory, innovation and commercial teams discuss the same evidence.

HMC Approach

Make the vocabulary consistent. Then make the data visual.

A practical approach for taking a sensory scorecard from the tasting table into a review-ready dashboard.

The Solution

A dashboard you can see, compare and explore

Start with the supplied Power BI screens. Then explore a new sensory lab inspired by their three core questions: what is the profile, how does price compare, and what separates two samples?

01 · Original dashboard

The three views behind the case study

The supplied screens are embedded at their original resolution. Select a screen to enlarge it.

Supplied reference images
Radar chart. Compare the shape of each product’s sensory profile across the original attribute vocabulary.
Value vs price. Explore reported sensory averages alongside the reference screen’s Gurgaon price field.
1-v-1 comparison. Inspect paired attribute intensities and the signed difference between two selected products.
02 · Interactive sensory lab

One selection. Every view responds.

Choose two coded prototypes. Explore their fingerprints, compare individual attributes, or scan the complete portfolio.

Illustrative dataset
Target fit98.1/100

Transparent similarity index

Mean intensity2.67/5

Intensity, not a quality rating

Strongest attributeAftertaste

3.6 out of 5

Scenario price₹3,800

Illustrative INR per 750 mL

Fixed scale 0–5 · 12 attributes

Sensory fingerprint

Distance from the target

Selected prototype minus target · click an attribute to highlight

Try a profile
Inspect the underlying demo scores
03 · 3D flavour space

Move through the flavour landscape

Drag to rotate. Scroll to zoom. Hover for names. Select a sphere to connect it with the sensory lab above.

3D flavour map
Drag to orbit · scroll to zoom · arrows to rotate · + / − to zoom · Home to reset
04 · Price & sensory position

Put price beside a specific sensory question

Switch the attribute and set a scenario budget. Hover or select a point to inspect its profile.

Illustrative prices
8 of 8 prototypes

Higher intensity is not higher quality. Prices are assigned demo scenarios in INR per 750 mL and are not current retail quotes.

05 · Blend scenario studio

What happens when two profiles meet?

Explore an illustrative linear mix. Move the ratio and watch the blended profile change against the target.

Interactive what-if
58% A42% B

This weighted-average model is illustrative. Sensory interactions can be nonlinear; confirm any actual formulation by tasting.

Target fit96.3/100
Weighted scenario price₹3,040
Profile RMSE0.185
Review Dashboard

See when a profile begins to drift

An additional concept for the sensory team: compare a sequence of batches with the same target, then examine the attribute behind the largest movement.

Distance from the reference profile

RMSE across 12 dimensions · select a batch to inspect it

Demo batches
0.30

Review if RMSE exceeds the chosen limit or any attribute differs by more than 0.50. Demo rule; not a production specification.

Review queue
1of 8 demo batches
flagged for review
B-106 · Review

Matured wood is the largest movement

Before vs After

A more focused sensory conversation

The intended workflow shift, without assuming a measured time saving or commercial result.

DimensionSeparate scorecardsConnected profile dashboard
Profile reviewRead averages and individual score sheetsSee the complete sensory shape on a fixed scale
Product comparisonCompare columns and mentally calculate gapsInspect paired attributes and signed differences
Price discussionReview price separately from sensory observationsPlace a chosen sensory attribute beside price
Scenario explorationRecalculate a comparison for each questionAdjust a selection, ratio or threshold in the same view
Batch reviewSearch for drift across successive scorecardsFlag a transparent rule and inspect the largest deviation
Decision handoffSend a static screenshot without the underlying contextExport scores with the dataset label, scale and comparison
Tools & Tech Used

The original business view, extended into an interactive lab

Power BI supplied reference dashboardsD3.js linked SVG chartsThree.js / WebGL 3D flavour spaceJavaScript blend and review calculationsEmbedded assets one standalone HTMLCSV export inspectable demo scores
Key Takeaways

What a sensory team can take forward

01

Compare the shape

The whole profile explains more than a single average. Keep the attribute vocabulary and scale consistent.

02

Make differences explicit

Pair every overview with an attribute-level comparison so reviewers can explain what changed.

03

Keep the judgement human

Use interactive scenarios to focus the next tasting and discussion. Validate decisions with the sensory team.

A flavour story that invites a better question.

Explore the prototype. Inspect the attributes. Discuss the next step.

Return to the sensory lab
Reference 1 of 3

Data & methodology

Know what each view represents

Original reference

The three images are the user-supplied dashboards, shown unmodified. Their underlying dataset and rating anchors were not supplied. No new interactive score is presented as a transcription of those screens.

Interactive dataset

Eight fictional coded prototypes, twelve attributes and eight fictional batches demonstrate the interaction. Ratings run from 0 to 5. A higher score means higher intensity; aftertaste represents persistence. A high average is not a quality or preference score. All scenario prices are illustrative INR per 750 mL.

Target fit

The house-style target is synthetic. All attributes have equal weight. RMSE = √[Σ(selected − target)² ÷ 12]. Target fit = 100 × (1 − RMSE ÷ 5), bounded to 0–100. This is a descriptive similarity index, not statistical confidence, a sensory validation result or a quality score.

3D map

X is the mean of Fruity and Floral; Y is the mean of Matured wood and Spices; Z is Peaty/Smoky. Each runs from 0 to 5. The map is a direct three-axis view, not PCA. Distances in this view use three derived coordinates, while target fit uses all twelve attributes. The pale reference marker is the synthetic target.

Blend scenarios

Each blended score and scenario price is the component-weighted average. “Find closest linear mix” scans every integer percentage from 0 to 100 and chooses the lowest profile RMSE. This model does not capture nonlinear sensory interactions, processing, taxes, margins or production costs. Validate an actual formulation through tasting and appropriate formulation work.

Batch review

A batch is flagged when its RMSE exceeds the adjustable limit or any attribute differs from the target by more than 0.50. These are demonstration review rules, not company specifications or validated control limits. Small floating-point differences are excluded at the boundary.

Context and dependencies

The source images define the case-study subject. Publicly available industry material provides general context for sensory analysis and liquid development. The interactive extension does not reproduce or connect to any proprietary company platform. D3 7.9.0 and Three.js r128 are embedded with their licences. The page can run locally without a server.