With each passing day, digital transformation is becoming more vital for organizations, opening the doors to new opportunities. The same applies to the ERP software as well, merging ERP and disruptive technologies to develop customized solutions paves the way to improved business functionality and a competitive edge in the long run.
Just as ERP systems have varying degrees of success, so do the digital initiatives that rally around them. Most industry experts agree that ERP systems still play a valuable role, albeit with a need to focus more on practical use-cases, developing agile solutions that scale with changes in demand and driving solutions aimed at user engagement. An efficient enterprise resource planning (ERP) solution and more productivity in mind are the two main factors that enterprises take into consideration before they adapt to digital transformation initiatives.
Besides, meeting the objectives of process efficiency or operational efficiency is the driving force behind integrating digital transformation into the workplace. Credit to advances in technology, here is how cloud, automation, AI, Machine Learning, Internet of Things (IoT), Blockchain and Robotics are some of the key ingredients for digital transformation.
Cloud ERP in Manufacturing
Cloud-based ERP is provided as a service to the business and hosted by an ERP vendor. Cloud ERP software supports similar or sometimes better functionality as on-premises systems without added cost escalations like upfront licensing fees. Cloud-based ERP software offers real-time inventory insights to sales teams, frees finance teams to respond to audit queries, keeps a close eye on cash margins by integrating and automating financial and operational business functions.
This covers multiple data sources, including inventory, order and supply chain management and helps with procurement, production, distribution and fulfilment processes.
For starters, not all clouds are equal. Multi-tenant SaaS serves numerous organizations as a single version of the ERP software infrastructure. However, data privacy concerns still loom as each organization uses the software hosted on the same server.
While single-tenant SaaS is a single version of the ERP software and its associated infrastructure serves used by a single organization. In other words, it is a private server running on a unique software instance. Although few cloud ERP vendors give their customers an option of running a private single-tenant SaaS instance or a multi-tenant SaaS instance. Besides, enterprises can also choose a hybrid ERP approach that combines the benefits of both on-premises software with a private cloud and public cloud for computing, storage and services.
The commonly available cloud ERP modules for businesses include:
- Inventory management
- Order management
- Supply chain management
- Project management
- Material requirements planning (MRP)
- Financials and accounting
- Human capital management (HCM)
- Human resource management software (HRMS)
- Customer relationship management (CRM)
Data Analytics for Finance Operations
Data forms the backbone of modern business operations it is behind the complex strategy and governance structures that help businesses leverage operational efficiencies. Data is the most crucial component in the digital transformation project that formulates practical use cases across the supply chain, employee networks, customer and partner ecosystems.
Data analytics can significantly improve the performance parameters of an organization that dwelled along with inflexible ERP systems. ERP add-ons synergize business intelligence from varied data sources for tangible improvements in decision making, performance effectiveness and organizational competitiveness.
Data insights give a sneak peek to automate and streamline finance operations from reconciliation and consolidation to compliance and reporting. Integrated end-to-end data analytics automates working capital, payments, and financial risk more efficiently. Data analytics streamline’s capital management and liquidity accounting, centralize bank account management, and real-time visibility to analyze cash positions. Viable data analytics manages liquidity positions to mitigate operational risk by optimizing straight-through workflows thereby integrating cash flows, transactions, and market data for comprehensive management of finance operations.
Automation for Inventory Management
The major challenges that growing companies face include resource planning, product and inventory management. ERP systems bring together repetitive work processes that include invoice processing, back-office processes, ERP data entry, payroll management, user termination and so on.
For effective inventory management, ERP utilizes radio frequency identification (RFID) tags, barcoding, and serial numbers to monitor any changes in inventory levels at each stage of the supply chain. This enables companies to track their warehouse inventory levels, inventory in transit and finished goods in the stores for the final user consumption.
By augmenting traditional ERP systems with robotic process automation (RPA) enterprises can gather intelligent insights into their operations and equipment capacity to maximize production levels. An automated inventory forecasting model factoring reorder points (ROP), sales velocity, and economic order quantity helps to ensure an optimal inventory control. Enterprises can thereby track any changes live and allow for scheduled equipment maintenance, thereby reducing unexpected downtime.
Artificial Intelligence Driving Customer Engagements
Superior customer experience is a vital goal that most enterprises drive for regardless of whether they are a small-scale establishment or a large-scale multinational network. AI has given rise to new information retrieval techniques. Intelligent text-enabled chatbots, can answer customer queries, solve customer service bottlenecks, and add to customer stickiness. For instance, chatbots lets sales managers enquire on stock availability across the world through intent-based questions.
Technology allows ERP solutions to connect to customized customer interactions for quick delivery of better products to the market, in-order-to let the operation team have a substantial competitive edge over those that do not. By learning from the historical data sources through machine learning models, AI and natural language processing (NLP) models enable quicker understanding of the user intent to help into swift decision making. These patterns reveal market intelligence trends and predict future outcomes that help businesses with actionable insights to proactively manage their customer relationships.