MT438 Unit 4 Operations Dashboard Critique Example

Emphasize, Hide, Distort: An MT438 Unit 4 Critique of a Lawn Products Operations Dashboard

[Student Name]

Purdue University Global

MT438: Analytics in the Digital Supply Chain

Unit 4 Dashboard Critique

[Instructor Name]

[Date]

Oakhurst Lawn and Garden, its dashboard, its accounts and every figure are composite; the sources cited are real.

The Dashboard and the Method

The dashboard under review is the screen Oakhurst Lawn and Garden's operations managers open every Monday during the spring season. It reports May 2026 in six tiles: an on-time-in-full gauge, a fill rate line chart, a forecast accuracy figure, a freight spend pie, an inventory value bar chart by plant and a traffic-light table of account status. An annotated screenshot accompanies this paper with numbered callouts matching the sections below.

Each tile is put to three questions: what the display emphasizes, what it hides and whether anything distorts. That frame keeps the critique on what the screen leads a manager to believe and do, and away from color preferences. Few (2006) defines a dashboard as a display of the information needed to achieve objectives, laid out so a manager can take it in at one look; by that standard, a tile that leads a manager to the wrong conclusion at a glance is worse than no tile at all. The tiles are discussed in order of consequence, not in their position on the screen.

Tile 1: The On-Time-in-Full Gauge

The gauge emphasizes a single number, 94.2 percent, in green. It is computed as the simple average of five accounts' on-time-in-full percentages: 86.1, 97.0, 96.8, 95.9 and 95.1. That average treats an account placing 285 orders the same as one placing 1,209. Weighted by orders, 3,431 in May with 247 failures, the true figure is 92.8 percent.

What the gauge hides matters more than the 1.4 points. The home-improvement account, at 86.1 percent, carries 168 of the 247 failed orders, about two-thirds of them, and it is the only account that charges fines. The other four accounts are all above 95 percent. An averaged gauge in green tells the Monday meeting that service is fine when the one customer whose dissatisfaction costs money is receiving markedly worse service than everyone else.

The one account that fines Oakhurst holds two-thirds of the failures, and the green gauge averages it away.

The distortion is the averaging of averages. The fix is a weighted figure beside a per-account view, and the paper recommends both.

Tile 2: Forecast Accuracy

The tile shows 81 percent forecast accuracy. It is computed at the total level: total forecast volume for May came within 19 percent of total shipments. Measured product by product, weighted absolute percentage error is 42 percent, so accuracy in the same sense is closer to 58 percent.

The tile emphasizes the aggregate and hides the cancellation underneath it. Over-forecasts on some bags offset shortfalls on others, so a planner reading 81 percent sees a forecast that is roughly right, while the plants are building too much of one product and too little of another. Hyndman and Koehler (2006) note that an accuracy measure has to be chosen for the decision it supports. Production is scheduled by product, so the tile should report error by product, weighted by volume.

Tile 3: The Fill Rate Line

The fill rate line runs on a vertical axis from 90 to 100 percent and shows May's dip from 95.8 to 94.4. On that axis a 1.4-point fall fills 14 percent of the chart's height, so it looks dramatic.

It would be easy to call the zoomed axis a distortion, and many critiques do. That would be unfair. Rates that move within a narrow band need a narrow axis to show any movement at all; a zero-based axis would flatten the line and hide a real decline. Cleveland and McGill (1984) found that readers compare values most accurately when they sit as positions on a shared axis, and less accurately when lengths or areas must be compared. A line chart encodes its values as position, so a narrowed axis on a line does not mislead the way a truncated bar chart does.

The real faults are what the tile omits. There is no target line, so a reader cannot tell whether 94.4 percent is acceptable, and the axis is unlabeled, so a reader who glances at the shape may not notice that the whole chart spans ten points. The paper recommends keeping the axis and adding both a labeled range and the 96 percent target Oakhurst agreed with its customers.

Tile 4: Freight Spend by Carrier

Freight spend appears as a tilted three-dimensional pie with seven slices. Perspective enlarges the slice at the front, which happens to be the second-largest carrier, so it reads as the largest. Seven slices of similar size are hard to rank in any pie, and harder when tilted, because the eye is comparing angles and areas distorted by depth.

The tile also hides what a freight manager needs. Spend by itself does not say whether a carrier is expensive; a carrier with the most loads will have the most spend. A sorted bar chart of spend by carrier, with cost per load beside each bar, answers the question the tile implies.

Tile 5: Inventory Value by Plant

The bar chart shows inventory value at Decatur and Macon in dollars. It emphasizes size, and Decatur's larger bar reads as a problem. It hides the context that would make the figure meaningful: Decatur ships about twice Macon's volume, so more stock there is expected. Without days of supply beside each bar, the tile cannot say whether either plant holds too much or too little. The paper recommends replacing value with days of supply by plant, with value available on hover.

Tile 6: The Traffic-Light Table

The account status table colors each account red, amber or green, but nowhere states the thresholds. A manager cannot tell whether amber means 94 percent or 90, and two managers reading the same table may act on different assumptions. Color alone also fails for the roughly one in twelve men with red-green color vision deficiency. Each color should carry its numeric value and the threshold should be printed on the tile.

A Mixed Calendar

Two tiles, forecast accuracy and freight spend, report fiscal period five, which ran April 26 through May 30, while the other four report calendar May. The difference is small but invisible, and it means that freight spend and on-time performance on the same screen describe slightly different weeks. A single period, stated in the dashboard header, removes the problem.

Four Tiles in Place of Six

The paper proposes four tiles, each tied to a decision. On-time in full by account, weighted, with fines shown for the accounts that charge them, supports the weekly decision on which customer's loads get priority at the dock. Fill rate against its 96 percent target, on a labeled narrow axis, supports the production planner's decision to add a shift. The spread of product-level forecast errors, showing the ten largest misses, supports the planner's decision about which products to rebalance. Days of supply by plant supports the decision on whether to move stock between Decatur and Macon. Freight spend moves to a monthly carrier review, where cost per load can be discussed with the carriers themselves.

Fewer tiles, each answering a question someone actually acts on, will make the Monday screen slower to admire and faster to use.

References

Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American Statistical Association, 79(387), 531-554. https://doi.org/10.1080/01621459.1984.10478080

Few, S. (2006). Information dashboard design: The effective visual communication of data. O'Reilly Media.

Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting, 22(4), 679-688. https://doi.org/10.1016/j.ijforecast.2006.03.001