Rooting out the root causes of order fulfilment errors: a multiple case study

Figures & data

Figure 1. Overview of order fulfilment and warehouse operations.

A series of images displaying the flow of goods into a warehouse, after which a person picks an order into a box and sends it off to the customer via a truck.

Figure 2. Illustration of how transactional data from information systems is matched with video of physical goods flow to enable the video analyst to search for and analyse recorded sequences.

A series of images that show how an order picker is being recorded via multiple video cameras. Another person is sitting by the computer analysing the recorded video material.

Table 1. Overview of case company characteristics.

Characteristic	Company A	Company B	Company C	Company D	Company E	Company F
WH location	Sweden	Sweden	Sweden	Sweden	Sweden	Belgium
WH type	Distribution	Distribution	Contracted distribution	Contracted distribution	Distribution	Distribution
Customers	Other WHs and direct customers	Direct customers	Stores and direct customers	Stores	Stores and direct customers	Other WHs and direct customers
Goods ownership	Self	Self	Clients	Client	Self	Self
Type of goods	Spare part	Pesticides & feed for agriculture	Electronics and accessories	Hardware and tools	Car parts and accessories	Spare part
Size of goods	Small to very large	Small to large	Small	Small to large	Small to very large	Small to very large
Value of goods	High	Low to High	High	Low to High	Low to High	High
Operations with IVA	Picking	Picking	Packing, Consolidation	Picking	Picking, Consolidation	Picking, Consolidation
Experience with IVA	6 months	1 year	2 year 6 months	2 years	1 year 3 months	7 months
IVA configuration	Stationary	Mobile	Stationary	Mobile	Stationary	Stationary, Mobile
Technologies used	Barcode, AS/RS, goods-to-picker station, WMS	Barcode, WMS	Barcode, goods-to-picker station, WMS, TMS	Barcode, forklift screen, WMS	Barcode, goods-to-picker station, WMS	Barcode, RFID, pick-by-voice, WMS

Table 2. Summary of data collected from the case companies.

Company	Observation	Archival Data	Interview	Interview detail
				Date	Duration	Role (years of experience)
A	No	No	Yes	3/3/22	61 min	WH operations manager (20), Outbound team manager (8)
B	No	Yes	Yes	3/9/22	55 min	Transport manager (22)
				3/28/22	25 min	Customer service team (N/A)
C	Yes	Yes	Yes	3/14/22	56 min	Security and business development manager (15)
D	Yes	Yes	Yes	3/15/22	42 min	Operating manager (16)
				3/15/22	63 min	Production manager (10)
				4/19/22	30 min	Customer service representative (N/A)
E	Yes	Yes	Yes	3/16/22	87 min	Production manager (12)
F	Yes	No	Yes	3/16/22	52 min	Production System Specialist (17)
				3/18/22	29 min	Production System Specialist (17)

Figure 3. Example of causal map linking error types, causes, preventative actions, and observed effects of using intelligent video analysis.

A total of 12 text boxes arranged in four levels, three boxes per level. Between the different levels, the boxes are connected to each other via multiple many-to-one and one-to-many relationship lines. The connections show how error types are connected to error causes, which require certain actions to be resolved.

Figure 4. Observed relationship between error sources, how they are measured, and resulting error metrics.

On the top, a big circle is included reading ‘error metrics’. Below, multiple text boxes are connected via solid or dotted lines. The text boxes represent captured error data and the sources of errors. The solid lines show that the error was caused inside the warehouse; the dotted lines indicate that the error was caused outside of the warehouse.

Table 3. Overview of benefits with intelligent video analysis for dealing with fulfilment errors made outside of the warehouse.

Benefits with IVA	A	B	C	D	E	F
Able to conclude errors were made outside the warehouse by viewing video sequences of correctly picked, packed, and shipped orders	✓	✓	✓	✓	✓	✓
Able to deny customer claims that originated from errors made outside the warehouse		✓		✓	✓
Able to act toward transporter for errors made outside the warehouse (missing or damaged item)			✓	✓		✓
Received reimbursement from transporter			✓	✓
Avoided costly RFID investment to resolve errors in consolidation, when in fact the issue was rooted elsewhere						✓
Able to identify errors made outside the warehouse at supplier site, leading to planned joint improvement project with suppliers			✓	✓

Figure 5. Accepted claims in relation to total order output at company E.

A graph covering the time period 2020 to 2022 with a plotted line indicating the percentage of accepted claims of total orders. The line is gradually falling until halfway where it levels out.

Figure 6. Links between errors made in outbound warehouse operations and the underlying causes.

Multiple text boxes arranged in three levels, three boxes in the first and third level, and seven boxes in the second level. Between the different levels, the boxes are connected to each other via multiple many-to-one and one-to-many relationship lines. The connections show how error types are connected to perceived error causes based on analysis of video recordings. The error causes are grouped based on the origin of error.

Table 4. Summary of different levels of using intelligent video analysis in feedback to employees based on our empirical findings.

	Case company
Level of action	B	F	C	E	A and D
1. Feedback to the group of operators	✓	✓	✓	✓	✓
2. Discussion on improvements with the group		✓	✓	✓	✓
3. Feedback to individual operators			✓	✓	✓
4. Video presentation to individual operators				✓	✓
5. Input for performance evaluation					✓

Figure 7. Conceptual illustration of how intelligent video analysis can impact a warehouse performance metric for fulfilment errors.

A graph showing a solid line that is gradually declining, after which it becomes dotted and levels out. The line represents number of errors in three stages. First, without using video recording. Second, when video analysis is used. The final stage, which is dotted, indicates a potential future using artificial intelligence, questioning if errors can be completely eliminated.

Data availability

The data that support the findings of this study are available from the corresponding author [[email protected]] upon reasonable request.