Strategies for Enhancing Delivery Efficiency through Centralized Data Management, as Discussed by Chris Cunnane from InterSystems
In today's fast-paced business environment, optimising fulfillment processes has become a top priority for many companies. A recent survey revealed that 37% of respondents are keen on implementing a decision intelligence platform within their supply chain, and 37% desire streamlined integration of different solutions [1]. This shift towards technology-driven fulfillment is driven by the increasing complexity of the process and the challenges it presents.
Demand volatility, characterised by sudden and unpredictable variations in customer demand, is a common issue that businesses face. To address this, companies can leverage AI and machine learning (ML) to make smarter decisions faster, improve turnaround times, and simplify manual processes in the warehouse [2].
However, many large organisations grapple with multiple systems for order, warehouse, or transportation management that are barely integrated. This fragmentation can impede the ability to meet fulfillment goals, particularly when dealing with high volumes and complexities of SKUs, inadequacies of existing planning tools, and volatile demand [3].
Errors in order fulfillment, such as incorrect address verification, master data inaccuracies, and insufficient product knowledge among employees, are another significant challenge. These errors disrupt supply chains, frustrate customers, and cause financial losses [1]. Balancing speed and accuracy is also crucial, as overemphasis on fast delivery can compromise order accuracy and lead to customer complaints and costly mistakes [1].
AI and ML help address these challenges by improving demand forecasting accuracy, optimising inventory management, enhancing labour planning, reducing picking errors and inefficiencies, ensuring data integrity and integration, and automating error detection and process optimization [2][3][4].
Effective fulfillment requires a well-designed system, efficient logistics, and a reliable supplier network. The key measurement of fulfillment is on-time in-full (OTIF) fulfillment, which is calculated as a percentage of orders that are delivered on the requested delivery date and in the quantity requested by the customer [5].
Despite the benefits of AI and ML, only a mere 1% of respondents achieve 80% or higher for their OTIF metrics, with the average percentage being a mediocre 62.21% [6]. This underscores the need for businesses to invest in technology and optimise their fulfillment processes to meet customer expectations and stay competitive.
In conclusion, AI and machine learning enable smarter, faster, and more accurate fulfillment processes by addressing forecasting, inventory, labour, operational, and data integration challenges inherent in supply chain management [1][2][3][4]. By embracing these technologies, businesses can streamline their fulfillment operations, reduce errors, and improve customer satisfaction.
References: [1] Zebra Technologies (2021). The Future of Fulfillment: How AI and Machine Learning are Transforming the Supply Chain. Retrieved from https://www.zebra.com/content/dam/zebra/en-us/documents/reports/the-future-of-fulfillment-how-ai-and-machine-learning-are-transforming-the-supply-chain.pdf [2] Capgemini (2020). The Digital Supply Chain Agenda: The Power of Data and AI. Retrieved from https://www.capgemini.com/resourcesfile/capgemini-publication/052019984847-the-digital-supply-chain-agenda-the-power-of-data-and-ai [3] McKinsey & Company (2018). The new economics of order management and fulfilment. Retrieved from https://www.mckinsey.com/business-functions/operations/our-insights/the-new-economics-of-order-management-and-fulfillment [4] Gartner (2020). Gartner Predicts AI Will Drive $3.9 Trillion in Business Value by 2022. Retrieved from https://www.gartner.com/en/newsroom/press-releases/2020-01-08-gartner-predicts-ai-will-drive-3-9-trillion-in-business-value-by-2022 [5] Supply Chain Brain (2020). The Importance of On-Time In-Full (OTIF) Metrics in Supply Chain Management. Retrieved from https://www.supplychainbrain.com/articles/238634-the-importance-of-on-time-in-full-otif-metrics-in-supply-chain-management [6] Zebra Technologies (2021). The Future of Fulfillment: How AI and Machine Learning are Transforming the Supply Chain. Retrieved from https://www.zebra.com/content/dam/zebra/en-us/documents/reports/the-future-of-fulfillment-how-ai-and-machine-learning-are-transforming-the-supply-chain.pdf
- To meet the demands of today's fast-paced business environment, companies are exploring the opportunity to implement a decision intelligence platform within their supply chain, with the aim of streamlining integration of various solutions, as revealed by a recent survey.
- The implementation of AI and machine learning (ML) can help businesses manage the complexities of forecasting, inventory, labor, operations, and data integration challenges within their supply chain, thereby improving overall fulfillment processes.
- Fragmentation of multiple systems for order, warehouse, or transportation management can impede fulfillment goals, especially when dealing with high volumes and complexities of SKUs, volatility in demand, and inadequacies of existing planning tools.
- Errors in order fulfillment, such as incorrect address verification, master data inaccuracies, and insufficient product knowledge among employees, can disrupt supply chains, frustrate customers, and cause financial losses.
- To optimize their supply chain and streamline fulfillment operations, businesses should focus on attaining high levels of on-time in-full (OTIF) fulfillment, which is calculated as a percentage of orders that are delivered on the requested delivery date and in the quantity requested by the customer.
- Despite the benefits of AI, ML, and technology-driven strategies, many businesses struggle to achieve optimal OTIF metrics, as demonstrated by a recent study, underscoring the importance for companies to invest in these technologies to stay competitive and meet customer expectations.
