What do you do if your problem-solving approach isn't working for complex business scenarios?

It depends on when such problems arise. After going live with the ERP, then it will be very easy to identify the root cause since we do have proper documentation for both functional and technical designs based on the gathered requirements from the businesses users. The engagement for the required team will be based on the problem analysis against the requirements and the designs documents in place. Systems analysts, designers, developers with the business users will be involved to identify the root cause of such problems.

Assuming a firm's business goal and objectives are to operate as a click-and-mortar business by adding a Webstore to increase its annual revenue by 60% and to double its operating profit margin over the next three years. This must be accompanied by a logistics strategy like having a network of collection points where parcels are delivered by a logistics partner instead of delivering directly to every Webstore customer. Its operating profit margin objective can thus be achieved by effective logistics cost control. The ERP strategy is aligned with the business strategy to achieve the firm's goal and objectives.

In the whirlwind of digital transformation, it's not uncommon for businesses to outgrow their initial ERP goals. A reassessment might reveal a misalignment between your ERP's capabilities and your evolving business model. Consider, for instance, the rise of remote work: has your ERP adapted to facilitate seamless, decentralized access? Reflecting on such aspects can lead to a strategic pivot, ensuring your system supports not just current operations but also future growth. Personally, I've seen how flexibility in goal-setting can turn apparent setbacks into opportunities for innovation.

Tapping into the expertise of ERP specialists is akin to finding a navigation system in the maze of digital challenges. These professionals not only bring a helicopter view of your problems but also deliver bespoke solutions that are often out of reach for in-house teams. For example, they might suggest integrating advanced analytics for better decision-making or adopting cloud-based ERP solutions for enhanced flexibility. This approach not only accelerates problem resolution but also empowers your team with new knowledge and skills. Reflecting on this, it's clear that external expertise is not just a lifeline—it's a catalyst for transformation and growth.

Fostering a culture of collaboration within your organization can unlock hidden solutions to ERP challenges. By bringing together diverse perspectives from various departments, you create a rich tapestry of insights that can address problems more holistically. Consider, for example, how insights from the sales team can dramatically improve inventory management features, or how feedback from finance can enhance reporting accuracy. Such collaboration not only solves immediate issues but also builds a foundation for a more resilient and adaptable ERP system. Reflecting on my own experiences, I've found that the most innovative solutions often emerge from the collective brainstorming of a diverse team.

Embracing flexibility is about agility to respond swiftly to changes in the firm and its environment. That is why an ERP project cannot be considered as successful upon its go-live. It is the continuous business process improvement throughout the ERP lifecycle which is the main priority. The ERP vendor selection is critical in identifying the ERP system which enables this long-term flexibility with its upgrades providing the capabilities to embrace the evolutionary changes in best practices, technologies and legal and regulatory compliances. Composable and collaborative ERP, enterprise applications platform and low-code/no-code development platform are some of these flexible ERP capabilities.

ERP real-time and historical data combined with other data sources like competitor, economic or weather data is analyzed to provide analytical insights for solving problems by making better operational or strategic decisions. Advanced data analytics is classified as: Descriptive: association rules (market basket analysis), clustering (customer segmentation) Diagnostic: root cause analysis of past events (performance reasons for marketing campaign, product demand or medical treatment plan) Predictive: classification (credit risk), regression (impact of advertising on sales), forecasting (demand prediction) Prescriptive: a combination of descriptive and predictive analytics for actionable insights (fraud identification, sales lead priority).