Effective Data management in your Supply Chain
4th March 2020
70% of enterprise leaders believe they have access to most of the data from the end-to-end supply chain, while more than 40% of supply chain leaders agree that their available data is difficult to correlate because it is siloed in a number of different reporting tools and systems, or simply because it is bad data!
Source: Gartner, Jumpstarting your Digital Roadmap report
There is a revolution underway – some call it the digital revolution, others may refer this to be industry 4.0. Either way, this is redefining the way businesses collect, record, manage and dispense information across their extended value chain.
Supply chains generate more data than ever today
The last two decades have seen businesses change tremendously. What once were “linear supply chains” have evolved into complex and dynamic supply networks. Physical boundaries and borders have mostly disappeared, with information systems emerging as the only source of constant communication across the end to end supply chains.
At the same time, changing customer expectations and global variables are pushing businesses to be agile and flexible, while maintaining lowest possible costs – leading to unprecedented pressures. As a result, organisations are becoming more reliant on data and fact–based decision making.
Supply Chains today are more data rich than ever. From internal data from production, distribution, sales and procurement to external data from suppliers, customers and business partners, the function of “data management” has become the centre piece of business operations and decision making. Data is now being defined as the new oil and is proving to be the key fuel to drive competitive advantage.
Read: Nestle invests in supply chain data to win: The world’s largest food and beverage company Nestlé focuses on three key areas to win in ecommerce – brand innovation, offline-online integration, and the effective use of data to target consumers and optimise the supply chain.
Data in supply chain
Over the past two decades, businesses have invested heavily in their data collection and monitoring capabilities. This data architecture generally covers six distinct groups, including Strategy and Planning, Physical Flow, Performance Management, Order Management, Collaboration, and Sustainability. A seventh layer of reporting provides the “wrap around”, making the data usable for decision making.
This level of information management can address three key areas in a product life cycle:
– Greater sharing of information about consumer trends and market trends between trading partners can lead to greater insights into consumer behaviour, enabling both partners to better serve the consumer.
– Sharing information about real demand between two trading partners can enable the development of products that better meet consumers’ needs.
– Sharing of accurate, real-time operational information between the two trading partners can lead to better use of assets in the supply chain. This can improve product availability and consumer satisfaction at the point of purchase.
Where does this lead to?
These successes are due to data manipulation becoming faster, more flexible and more granular. At the same time, data collection is more accurate and efficient, making it easier for management to make the right decisions.
Our experience of helping consumer goods firms implement data structures lead to over 30% lower operational costs, 30% fewer lost sales, and a 60% decrease in inventories.
Source: 4C research
There are stories of effective data management all around us. Firms like Amazon (with their predictive ordering), Maersk (With blockchain driven planning) and P&G (with CPFR) have continuously been pushing the boundaries though investment in their data strategies.
The challenge: Drowning in Data
So, with more than ever data at your fingertips, the question is how to make sure you have the right data management framework to help deliver the insights you need to make truly informed decisions.
52% of organisations have little or no investment planned in their data infrastructure, with only 26% planning significant investment;
Source: 4C research
While many companies excel at collecting data, they are not managing it well enough. The big data revolution has definitely provided them with the right tools to bring together a lot of datasets, while a lack of analytical capability and infrastructure has restricted any good use.
Another issue emerges with inefficient user interaction with the systems, either due to complex systems or insufficient user training
A third and probably the most important issue is the misalignment between business strategy, supply chain strategy and data strategy, which further leads to insufficient process integration.
Getting there – the enablers
Over the last many years, we have been helping our clients in effectively developing and implementing their supply chain data strategies. From our experience there are three key enablers for an effective data transformation programme:
Effective strategy alignment: Understanding what the company wants to achieve (business strategy) and aligning with operational targets (supply chain strategy);
Capability development: Preparing the organisation for a digital era requires a full–fledged change management programme, with targeted training and recruitment;
Infrastructure overhaul: Establishing data architecture with supporting tools is important to collect and harness data in the most effective way. While one may be tempted to invest in ERPs, the answer could be in specific services like 4C – Insights, Scan Market and Supply Chain Guru.
With so many success stories around us, the benefits of effective supply chain data management are quite evident. Moreover, with the amount of data collected in supply chains increasing every day, not having a focussed strategy might just add further risks to already pressured supply chains.