Optimising Supply Chain Data Management: Webinar
In recent years, the significance of resilient operations and data management within the supply chain has been starkly highlighted due to substantial disruptions. Nevertheless, many organisations are still clinging to manual processes, even though data is a valuable asset that drives the modern supply chain. This reluctance is costly not only in financial terms but also in terms of competitiveness and agility. Failing to implement proper supply chain data management is akin to navigating a modern cargo ship by the stars- you might get there in the end, but at unnecessary risk to the cargo and definitely long-after ships using GPS.
There are no valid excuses for not adopting proper supply chain data management in the data-driven age. Supply chain professionals are turning to automated data quality initiatives to simplify operations and reap the same benefits, without the added workload. Enhanced data quality and accuracy empower supply chain managers to maximise production despite market instabilities, redefining the modern supply chain.
The benefits of automation extend beyond the product side and encompass various supply chain facets such as vendor, customer, contract, and logistics. Cutting-edge technologies like SAP BTP, coupled with supply chain data management tools like Maextro, provide a no-code, innovative solution to automate data processes without disrupting existing systems or processes.
Watch the webinar event below to discover how Maextro & Handshake- a tool for better business relationships and onboarding using the power of SAP BTP is helping customers manage their product catalogues and data maintenance in SAP.
What is supply chain data management?
Supply chain data management involves the strategic management, analysis, and optimisation of master data related to the supply chain. It encompasses the collection, storage, and processing of data to enhance decision-making, increase efficiency, and ensure transparency in supply chain operations. This practice enables businesses to track inventory, monitor performance, and respond to market demands effectively. It also aids in risk mitigation and cost reduction, contributing to a more resilient and competitive supply chain ecosystem.
Why do supply chains need better data management?
Bottlenecks, duplicate data, payment issues, and reporting anomalies all stem from dirty data taking its toll on a supply chain. These issues unquestionably result in an organisation losing both time and money, as well as the ability to adapt to market changes with agility and flexibility. Inadequate visibility and data quality also restrict a business from recognising supply trends, which are another avenue to enhance business agility.
Data-driven businesses need supply chain data management to consistently generate clean, accurate data, crucial for informed decision-making. Businesses are increasingly realising that more effective data management leads to a more agile, adaptable, and efficient supply chain. Moreover, by leveraging business process automation, these improvements can be implemented sustainably with reduced workload for colleagues- a win-win all around.
The benefits of data quality on the supply chain
Supply chains intricately weave their way through business operations, connecting with touchpoints right throuh. Incorporating data quality processes within a supply chain unlocks transparency and visibility. This empowers supply chain professionals to analyse, optimise, and innovate processes, resources, and business relationships, resulting in a more efficient supply chain.
Failure to manage data quality can have various adverse consequences for businesses:
- Delays in product time-to-market.
- Increased forecasting errors.
- Procurement of incorrect orders or stock.
- Disruptions in cross-selling or upselling opportunities.
- Elevated data management and administrative costs.
However, by prioritising data quality, businesses can directly contribute to their success in several ways:
- Cost reduction through streamlined procurement, production, and logistics, as well as decreased inventory carrying costs and waste.
- Improved customer service by ensuring timely product and service deliveries.
- Increased agility, enabling quick responses to changes in customer demand or market conditions and facilitating adaptation to supply chain disruptions and the exploration of new goods flows and network configurations.
- Competitive advantage achieved by faster and more cost-effective delivery of products and services.
- Improved business relationships with suppliers and vendors, great for flexibility and leniency- should it be needed.
Crucial data sources for the supply chain
For those not directly involved in supply chain operations, it’s easy to underestimate the multitude of data sources crucial for maintaining maximum efficiency. Each of these sources necessitates its unique set of rules, input types, and reports to uphold quality and generate meaningful information. These sources include:
- Demand data: Employed for forecasting product or service demand, encompassing historical sales data, market trends, customer behaviour, and other relevant information that aids in predicting future demand.
- Inventory data: Tracing product and raw material inventory levels, covering details such as stock levels, lead times, and reorder points.
- Transportation data: Monitoring the movement of goods and services within the supply chain, including information on shipment schedules, carrier performance, and delivery times.
- Production data: Ensuring efficient production processes by managing details like production schedules, machine performance, and labour productivity.
- Supplier data: Managing supplier relationships and their compliance with obligations, involving information about supplier performance, contract terms, and pricing.
- Financial data: Tracking the financial performance of the supply chain, encompassing data such as costs, margins, and revenue.
5 steps of supply chain data management
- Collection of Data: At this stage, companies collect data from various sources, including suppliers, customers, logistics providers, and internal systems. This data can encompass information on sales, inventory levels, production schedules, shipping status, and supplier performance and may arrive in any format (structured, unstructured, semi-structured).
- Analysis of Data: In this step, companies utilise various tools and techniques to analyse the data gathered in the first step. This may involve data mining, predictive analytics, and machine learning algorithms. The goal is to identify patterns, trends, and insights that can inform decision-making and enhance supply chain performance.
- Sharing of Data: At this stage, companies share data with supply chain partners, including suppliers, logistics providers, and customers. This can help improve coordination, reduce uncertainty, and enhance visibility across the supply chain.
- Visualisation and Reporting of Data: In this step, companies use data visualization tools to present the insights and analysis generated in the second step. This can include charts, graphs, and dashboards that help managers quickly identify areas of concern and take action to address them.
- Actioning Improvements: In this step, companies use the insights gained from the previous step to continually improve their supply chain performance. This can involve making changes to processes, policies, and systems to optimise supply chain efficiency, reduce costs, and enhance customer satisfaction.
Supply chain data management automation
The automation of supply chain data management offers businesses a transformative advantage. By streamlining data collection, analysis, and sharing, automation enhances efficiency and accuracy, reducing the potential for errors visa rule-based input and information extraction. Moreover, it accelerates decision-making through real-time data insights, making it easier to respond to market changes swiftly. Automation minimizes administrative burdens and allows for data-driven improvements, which optimise supply chain operations, cut costs, and enhance customer satisfaction. Ultimately, automating data management empowers businesses to stay competitive, agile, and sustainable, positioning them for success in an increasingly complex and fast-paced business landscape. Discover how Maextro is a tool designed automate supply chain data management here.
Maextro supply chain data management FAQs
My organisation is migrating from SAP ECC to SAP S/4HANA. Do I need to reimplement Maextro?
How is Handshake and Maextro Licensed?
Licensing for Handshake and Maextro is straightforward, designed to offer cost-effectiveness and flexibility for your specific needs. It operates on a simple pricing structure that is based on data objects rather than the number of users or entities. This approach ensures efficiency and keeps costs low, aligning with your individual requirements. For detailed information and tailored licensing options, please don’t hesitate to reach out to us today. We’re here to provide you with the necessary details and guidance.
Can Handshake talk to multiple SAP systems?
Yes, Handshake can communicate with multiple SAP systems. To facilitate this, Maextro must be installed on those individual SAP systems, and Handshake operates seamlessly with Maextro as its backbone for data management and integration with the various SAP systems.
How secure is my data on Handshake?
Your data’s security on Handshake is of paramount importance. Handshake is an SAP-certified product, offering a high level of data protection. It ensures that all data is meticulously managed and governed in strict adherence to your SAP security policies, all through automated governance processes within Maextro.
For more detailed information about the specific security features and protocols in place, we encourage you to reach out to us. We are readily available to provide you with comprehensive insights and address any specific security concerns you may have.
Does Maextro replace SAP MDG?
No, Maextro does not replace SAP MDG. Instead, Maextro complements and collaborates with SAP MDG, working alongside it to enhance and extend data governance and management capabilities.