Data quality-based process enabling: Application to logistics supply processes in low-volume ramp-up context

Burggräf, Peter; Dannapfel, Matthias; Förstmann, Ruben; Adlon, Tobias; Fölling, Carsten

doi:10.1109/icimp1.2018.8325838

Cited by 4 publications

(3 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simplification and optimisation of logistics activities through technological development and the diversity of information systems [8,16,17,27] show their weaknesses, while considerable resources are spent on correcting errors in product data when receiving or shipping products, returning products, and the like [2,6,28]. Confirmation of the above statements can be found sporadically in various studies on the realisation of logistics processes.…”

Section: Introductionmentioning

confidence: 95%

“…Database architecture [29,30], automatic identification of products (coding structure and content) [31], data quality [10,32,33], data carriers [6,[28][29][30][31][32][33][34], supply chain digitisation [2,35], and data synchronisation [2,36,37] are the most researched topics around PI collection and management. The abovementioned studies focus on technological solutions and communication platforms and do not target the quality of the exchanged data, which is indispensable for executing logistics processes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In recent product data studies, two perspectives can be recognised: that of the manufacturer [6,28] and that of the retailer [2,10,21,38]. Retailers are usually the initiators of automatic product data exchange, as they invest a considerable amount of their resources in manually entering product data into their information systems.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Impact of the Product Master Data Quality on the Logistics Process Performance

Božić,

Živičnjak,

Stanković

et al. 2024

Logistics

View full text Add to dashboard Cite

Background: The importance of up-to-date product master data in the digital age should not be underestimated. However, companies still struggle to ensure high-quality product data, especially in the field of logistics. Hence, the focus of our research lies in the disregard of the importance of product data quality to the performance of logistics processes. Methods: The analysis of the influence of product data on the performance of logistics processes was carried out using data from two fast-moving consumer goods (FMCG) distribution and retail companies. Data were gathered via interviews, while process activities were timed using a stopwatch, and interruptions were documented. The significance of the impact was determined using inferential statistical procedures based on the variable and the measurement scale type employed. Results: The quality of product master data has a significant impact on the performance of logistics processes; while managers are aware of the complications, they lack the motivation to detect and analyse such inaccuracies. Conclusion: The findings enhance comprehension of the obstacles generated by inadequate product data in logistics, which obstruct optimisation, and offer numerical proof of the impact of product data quality on logistics performance, thus expanding the current body of research.

show abstract