GM504 Unit 4 Wrong-Item Delivery Root Cause Analysis Example

The Bin Record Nobody Owns: A Unit 4 Root Cause Analysis of Wrong-Item Deliveries

[Student Name]

Purdue University Global

GM504: Organizational Excellence and Change

Unit 4 Root Cause Analysis

[Instructor Name]

[Date]

Ridgeway Building Supply, its staff and every count are composite and illustrative; the methods and sources cited are real.

Starting From the Data

The analysis began with every delivery order that generated a correction in the baseline month: a redelivery or a credit memo issued within ten days of shipment. Billing clerks coded each correction by reason using the definitions agreed for the project. Of about 2,400 delivery orders shipped that month, 139 needed a correction.

Figure 1: The Pareto Chart

Sorted by reason, the corrections fall into five categories. Wrong item accounts for 52, short quantity for 41, damage in transit for 23, missing phoned-in add-ons for 15 and other reasons for 8. The first two bars together hold 93 corrections, about two thirds of the total. Joseph Juran's Pareto principle, which holds that the vital few categories carry most of the trouble, tells a team where to start (Juran & Godfrey, 1999). Wrong item is the largest single category and the one customers complain about most, because a contractor who receives the wrong roofing underlayment may lose a day's work. The rest of this analysis focuses on that bar. Short quantity, the second bar, will be analyzed separately, since its causes appear to differ.

Figure 2: The Fishbone Diagram

Kaoru Ishikawa's cause-and-effect diagram lays candidate causes out on labeled branches, so that a team considers the whole system rather than the first explanation that occurs to it (Ishikawa, 1986). The diagram for wrong-item deliveries was filled from interviews with six pickers, the yard lead, two loaders and the inventory clerk, rather than from a brainstorming session, so each cause on it was named by someone who does the work.

Methods. Pickers pull from whatever bin the ticket names. Substitutions are agreed verbally. Stock moved during receiving is not recorded.

System. The inventory system tracks a location for each item but has no move transaction that yard staff use. Location changes can only be made by the inventory clerk at a desk terminal.

Materials. Several products look alike, such as two brands of synthetic underlayment in similar wrapping, and fasteners sold in boxes of different counts.

People. About a third of the yard crew is replaced every year, so many pickers are new and rely entirely on the ticket.

Measurement. Nobody checks whether bin locations match the system.

Environment. During spring deliveries, overflow stock is placed wherever space allows.

The Five-Whys Chain

In a five-whys chain each answer becomes the next question, and the chain stops only at a cause the company can change. Applied to a typical wrong-item order:

The contractor received the wrong underlayment. Why? The picker pulled a different product from the bin the ticket listed. Why? The bin held a different product from the one the system said was there. Why? During a large spring delivery, the correct product was moved to an overflow rack to make room, and the similar product was placed in its bin. Why? Receiving crews move stock whenever a bin is full and have no way to record the move from the yard. Why? The location record can only be changed at the inventory clerk's terminal, the clerk is not told about yard moves and no one is assigned to keep locations current.

The chain ends not at a careless picker but at a record nobody is responsible for, which every picker trusts because the ticket gives them no other choice.

Verifying the Cause

A root cause analysis should test its conclusion, not only reason its way to it. Two checks were made.

First, a location audit compared 200 randomly selected bins against the system's records. Thirty-one bins, 15.5 percent, held something other than what the system listed. A random sample of this size gives a rough estimate with a margin of about five percentage points either way, enough to show the problem is widespread rather than confined to a few aisles (Montgomery, 2020). In most of them the listed product had been moved to overflow, and in several a similar product had taken its place.

Second, the 52 wrong-item corrections were traced back to the bins they were picked from. Of these, 38 came from bins whose contents did not match the system at the time of picking, according to receiving records and the yard lead's notes. The remaining 14 were split among look-alike products picked from correct bins, verbal substitutions and data-entry errors at order entry.

What the Analysis Rules Out

The analysis does not support the common explanation that pickers are careless. Pickers followed the tickets; the tickets pointed to bins whose records were wrong. Nor does it support adding more inspection, which would catch errors after picking without stopping them. The staging-lane check described in Unit 1 reduced corrections for a time by exactly that route and faded when the checker was reassigned.

Why the Record Fell Behind

It is worth asking how a location record came to be so far from reality without anyone noticing. Three conditions combined. The inventory system was installed seven years ago, when the yard was smaller and the inventory clerk could walk it weekly. Since then the main yard has doubled in size, receiving volume has grown and the clerk's other duties have expanded, so the weekly walk stopped. And because pickers usually find the right product in overflow after a short search, the mismatches cost a few minutes at a time rather than a visible failure, until the wrong product is close enough to the right one to be loaded by mistake. A record that degrades slowly, one move at a time, rarely triggers alarm; it simply becomes less true each season.

Contributing Causes

Two contributing causes make the root cause worse. Look-alike products mean that a picker reaching into the wrong bin may not notice, since the packaging is similar. And yard turnover means that few pickers have the experience to recognize a misplaced product. Neither would cause wrong-item deliveries on its own if bin records were accurate.

Conclusion

Wrong-item deliveries are the largest category of corrections at Ridgeway, 52 of 139 in the baseline month. The fishbone diagram and five-whys chain trace them to a bin-location record that cannot be updated from the yard and that no one is assigned to maintain. A location audit found 15.5 percent of bins mismatched, and 38 of the 52 wrong-item orders came from such bins. The next units will measure the baseline precisely and propose a change that addresses the record itself rather than catching its effects.

References

Ishikawa, K. (1986). Guide to quality control (2nd rev. ed.). Asian Productivity Organization.

Juran, J. M., & Godfrey, A. B. (Eds.). (1999). Juran's quality handbook (5th ed.). McGraw-Hill.

Montgomery, D. C. (2020). Introduction to statistical quality control (8th ed.). Wiley.