| Course | MT438 Analytics in the Digital Supply Chain |
|---|---|
| Unit | Unit 3 |
| Paper type | Performance metrics exercise |
| Length | About 940 words, 6 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | BS in Business Administration |
| Updated | October 2026 |
Free sample paper for MT438 Unit 3
Fill Rate Three Ways: An MT438 Unit 3 Performance Metrics Exercise for One Quarter of Lawn Products Orders
[Student Name]
Purdue University Global
MT438: Analytics in the Digital Supply Chain
Unit 3 Performance Metrics Exercise
[Instructor Name]
[Date]
Oakhurst Lawn and Garden, its customers and every figure are composite; the sources cited are real.
Purpose and Data
This exercise defines and computes six supply chain performance measures for Oakhurst Lawn and Garden for April through June 2026. The quarter's order records supply 3,406 orders, 17,930 order lines and 2,104,000 bags ordered, of which 1,987,000 shipped. Each measure below is presented as a card: its name, the formula in words and symbols, the inputs and the result. Definitions come first because, as Caplice and Sheffi (1994) observed in their review of logistics metrics, two departments can use one metric name for quite different arithmetic, and a number without its formula cannot be checked.
Card 1: Unit Fill Rate
Formula: bags shipped divided by bags ordered, or U = S / O. Inputs: 1,987,000 shipped and 2,104,000 ordered. Result: 94.4 percent. Unit fill suits a planner watching volume. One missing bag on an order of 400 lowers it by a fraction of a point and no more.
Card 2: Line Fill Rate
Formula: order lines shipped complete divided by order lines placed, or L = Lc / Lt. Inputs: 16,316 complete lines out of 17,930. Result: 91.0 percent. Line fill suits a warehouse watching picking, because each line is one pick. A line one bag short counts as a failed line, so line fill falls further than unit fill for the same shortage.
Card 3: Order Fill Rate
Formula: orders shipped complete in every line divided by orders placed, or F = Oc / Ot. Inputs: 2,759 complete orders out of 3,406. Result: 81.0 percent. Order fill matches how a store receiving manager sees a delivery: one short line spoils the entire order.
One missing bag lowers unit fill by a sliver, line fill by one line and order fill by a whole order.
Reading the Three Together
The three fill rates are presented side by side because the unit of count changes the answer by more than thirteen points. None of the three is wrong. A report that says "fill rate was 94 percent" while a retailer's scorecard says 81 may be describing the same quarter honestly. The exercise therefore recommends that every fill rate Oakhurst publishes name its unit in the label itself: unit fill, line fill or order fill.
Card 4: Order Accuracy
Formula: orders with the right items in the right quantities, as picked, divided by orders shipped. Inputs: 3,375 accurate orders out of 3,406. Result: 99.1 percent. Accuracy is kept separate from completeness. A picking error, such as a sun-and-shade bag where a dense-shade bag was ordered, is a different failure from a stock shortage, and blending them would hide which department needs to act.
Card 5: On-Time Delivery
Formula: orders delivered on or before the reference date divided by orders delivered. The reference date has to be named, because it alone moves the result. Against the date each customer requested, 3,065 orders were on time, 90.0 percent. Against the date Oakhurst promised in its confirmation, 3,181 were on time, 93.4 percent. The difference comes partly from the system's plus-seven default found in the Unit 2 audit: some "requested" dates were never requested by anyone.
Card 6: Perfect Order
Formula: orders that were on time, complete, undamaged and correctly documented, all at once, divided by orders delivered. Counted directly, 2,411 of 3,406 orders met all four conditions, 70.8 percent. A common shortcut multiplies the four component rates instead: 90.0 percent on time, 81.0 complete, 97.9 undamaged and 97.7 correctly documented give 69.7 percent. The shortcut assumes failures strike independently. They do not. Failures cluster on the same troubled orders: a late truck was often late because the order waited for a short product, so the same order fails twice. Counting directly is the honest figure; the product is only an approximation, and here it understates performance by 1.1 points.
Card 7: Inventory Turns
Formula: turns equal the year's cost of goods over the average inventory it carried, valued at cost, and days of supply equal 365 over turns. Cost of goods for the trailing year was $148.6 million. Averaging thirteen month-end balances, December 2024 through December 2025, gives $31.01 million, so turns are 4.79 and days of supply 76.2. Averaging only the opening and closing December balances, $24.31 million and $25.47 million, gives $24.89 million and 5.97 turns. Both Decembers fall in the off-season, when Oakhurst's warehouses are nearly empty; spring stock peaks near $39.8 million in March. The two-point average flatters turns by about a quarter. Chopra and Meindl (2019) treat average inventory as the stock carried across the whole period, which in a seasonal business means every month, so the exercise reports the thirteen-point figure.
Comparison With a Retailer Scorecard
Oakhurst's largest home-improvement customer reports Oakhurst's performance for the quarter as 84.7 percent. That figure is neither Oakhurst's 93.4 percent on-time rate nor its 81.0 percent order fill. The retailer counts a delivery as good only when it arrives inside its appointment window at the retailer's own dock and complete, which is closer to a two-condition perfect order measured by the customer. Gunasekaran et al. (2004) argue that supply chain measures should reflect what the customer experiences, and the scorecard is the clearest view of that. The exercise recommends that Oakhurst reproduce the retailer's definition internally so that the two sides can argue about performance rather than about arithmetic.
References
Caplice, C., & Sheffi, Y. (1994). A review and evaluation of logistics metrics. The International Journal of Logistics Management, 5(2), 11-28. https://doi.org/10.1108/09574099410805171
Chopra, S., & Meindl, P. (2019). Supply chain management: Strategy, planning, and operation (7th ed.). Pearson.
Gunasekaran, A., Patel, C., & McGaughey, R. E. (2004). A framework for supply chain performance measurement. International Journal of Production Economics, 87(3), 333-347. https://doi.org/10.1016/j.ijpe.2003.08.003
The Purdue Global MT438 Unit 3 instructions
For MT438 Unit 3, students compute a set of standard supply chain measures from supplied or gathered records and show the working for each. Every measure needs its definition stated before the number, in words and in a formula, because the same name can hide different arithmetic. Inputs should be listed so a reader can repeat the calculation. Where a measure can be computed more than one way, such as fill rate by units, lines or orders, the exercise rewards showing the variants and saying which suits which decision. Composite measures like perfect order invite a comparison between counting directly and multiplying components. Inventory turns depend on how average inventory is taken, so state the balances used. Many prompts close by asking how the figures compare with a customer's own scorecard. Keep interpretation brief and attached to each card rather than collected in a long essay at the end.
How the MT 438 Unit 3 example is put together
The Oakhurst sample sets out its purpose and data in one paragraph, citing Caplice and Sheffi on metric names that hide different arithmetic. Seven cards follow. Unit, line and order fill each get their own card, and a highlighted sentence shows how one missing bag affects each differently. A short section reads the three together and recommends naming the unit in every label. Order accuracy is kept apart from completeness. On-time delivery is computed against requested and promised dates, linking the gap to the Unit 2 default. The perfect order card counts 2,411 good orders directly, then shows the multiplied 69.7 and explains clustering. Turns use $148.6 million of cost of goods with both averaging methods and Chopra and Meindl on average stock. The last section reconciles the retailer's 84.7 percent, with Gunasekaran and colleagues on measuring what customers experience.
MT438 Unit 3 rubric: what earns full points
Precision before arithmetic is the thread instructors follow through a Unit 3 metrics set. A number without its formula cannot be checked, so cards that state the definition first collect the accuracy points even when a figure is slightly off. Naming the unit of count for fill rate and the reference date for on-time delivery is credited because each choice moves the result by several points. Graders reward students who notice that multiplying perfect order components assumes independence, and who say what that assumption costs. The averaging method behind inventory turns is a frequent marking point in seasonal businesses. Comparing internal figures with a customer's scorecard shows the student understands that measures exist for someone. Neat, consistent presentation, such as one card format repeated, helps the grader find each answer. Sources should support definitions and be cited in APA style, as Purdue Global expects throughout the BS program.
MT438 Unit 3 help: mistakes that cost points
The commonest slip in Unit 3 is reporting a single fill rate with no unit, which leaves the grader unable to judge whether 94 percent is good or merely counted generously. Students also confuse order accuracy with order completeness and blend picking errors with shortages. On-time measures often omit the reference date entirely. Perfect order is frequently computed only by multiplying its components, with no note that failures cluster. Turns built on opening and closing balances alone can flatter a seasonal business by a wide margin, so check whether your two balances both fall in a quiet month. Some exercises bury the formulas in a paragraph instead of placing them where the reader looks. Before submitting, check that each card could be recomputed by a classmate from what you wrote. If any card needs a number you did not show, add the input.
Get an MT438 Unit 3 example written to your instructions
Order records, summary counts or an inventory table: whichever the Unit 3 case contains, send it together with your instructions and grading rubric. Built from that material, the composite exercise gives each formula before using it, computes the metrics your instructions list, and explains what each one leaves out. No charge applies to the opening custom sample, which takes 24-48h. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
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MT438 Unit 3 questions, answered
Which fill rate formula should I use?
Follow whatever formula the course or case names; where none is named, pick one on purpose and explain the choice. Unit fill suits a planner watching volume, line fill a warehouse watching picking, and order fill a customer who wants complete deliveries. Reporting more than one, with each formula stated, is often the strongest answer because the gaps between them tell a story.
Is perfect order the product of its components?
Only approximately. Multiplying on-time, complete, damage-free and documentation rates assumes failures happen independently. In practice they cluster, since a rushed or troubled order is likely to fail more than one test, so the directly counted rate usually differs. Count it directly when order-level data exists, and use the product only as an estimate.
Why do my inventory turns look too high?
Check the average inventory in the denominator. In seasonal businesses, averaging only the beginning and ending balances can pick two low points and inflate turns. An average of monthly balances across the year is more representative. Also confirm that cost of goods and inventory are both valued at cost, not one of them at selling price.
Where can I find a free MT438 Unit 3 sample paper?
The complete MT438 Unit 3 exercise is on this page, with seven metric cards computed from one quarter of composite lawn products orders and a reconciliation with a retailer scorecard.
How many orders did the MT438 Unit 3 sample count as perfect?
2,411 of 3,406 orders, 70.8 percent, were on time, complete, undamaged and correctly documented at once, against 69.7 percent if the four component rates are multiplied.