ERP Disasters: 5 Cautionary Tales of Insufficient Data Quality

According to Chatuverti et al., Nike’s $400 million loss was a result of over-ambitious goals and insufficient testing of the new ERP system. The critical failure to match product inventories with customer demand led to excess inventory and delayed orders. This misstep highlights the significance of data integrity in forecasting and inventory management, where accurate data is vital for the system to produce reliable output. Data quality issues here were exacerbated by the decision to forgo the standard software template in favour of custom solutions, which were not thoroughly vetted for their complex inventory​​.

MillerCoors’ $100 million legal dispute over its ERP system centred on inadequate planning and expertise in data handling. Beardwood & Miller thematised the decision to implement an ambitious SAP warehouse management software without addressing the data architecture and quality concerns led to defects and inefficiencies. The failure highlights the dangers of rushing through data-intensive processes without ensuring data accuracy and consistency. The substantial number of defects identified post-rollout demonstrates the lack of robust data validation processes that could have prevented the ERP system's failure​​.

In the late 1990s, Hershey's rush to modernize its IT systems resulted in a significant ERP failure, primarily due to insufficient attention to data quality. According to Gross, the condensed implementation schedule led to the bypassing of essential systems testing, which would have been critical in identifying data integrity issues. Consequently, Hershey's was left unable to process a $110 million of orders, despite having the necessary inventory, because the untested ERP system could not manage the data properly. This incident serves as a stark reminder that data quality is not merely a technicality but the backbone of any ERP system's effectiveness​​.

Haribo’s switch to SAP S/4HANA that resulted in a 25% sales decline underscores the critical role of data quality in supply chain management. Eisner et al. thematised the ERP system's inability to manage the data complexity of Haribo’s operations caused disruptions and missed deliveries. The case points to the necessity of ensuring data quality across the supply chain, highlighting that even with modern ERP systems, the data they rely on must be managed and assessed extensively to prevent operational breakdowns​​.

In what became an archetypal tale of ERP woes, Lidl's seven-year journey with SAP's ERP system culminated in losses amounting to a staggering five hundred million Euros. The project, known as "eLWIS," aimed to modernize Lidl's inventory management system but was fundamentally flawed from the outset due to a rigid adherence to existing business processes.

According to Kroker, Lidl's approach to inventory management, which prioritized purchase prices, clashed with SAP's design that typically relies on retail prices. This fundamental discrepancy led to a substantial misalignment between the system's capabilities and the company's needs. The extensive customizations attempted to bridge this gap resulted in the incorporation of poor-quality data, which was one of the contributors to the challenges faced.