Over the last year, manufacturing and supply chain organizations have been under increased pressure due to sudden changes in demand brought about by the Covid-19 pandemic. However, for many, the lack of an overarching and accurate view of their business has left them unable to adequately respond to these disruptions, make informed business decisions, and plan for growth, highlighting a number of long-standing issues in their data management strategies.
While these organizations tend to have a wealth of data, their current processes and supporting technologies have not been designed to work in unison. This has left them with a variety of different systems that generate data and produce reports independently, resulting in decisions being made separately and efficiency being compromised. These silos are only being added to by a lack of technology and expertise that is needed to integrate data from a growing range of internal and external data sources.
Therefore, implementing technologies to link these silos in which data and processes sit, to obtain a single, accurate view across their entire business must be a priority for supply chain and manufacturing organizations. It is only by addressing these data management issues that lie at the heart of their business that they will be able to gain the resilience, agility, and faster more accurate decision-making capabilities needed to futureproof their operations, and to successfully and intelligently respond to disruptions.
Stepping away from data lakes
Historically, the data lake approach was seen as the solution to organizations’ data management problems, however, as demand for real-time insights have increased, data lakes with their mixture of taxonomies, metadata and structures, have proved murky. In fact, they make it difficult for firms to integrate, normalize, and harmonize all their data so that they can gain a consistent and comprehensive overview and are able to use it. Extra complexity is added due to the growing availability of real-time data and the subsequent requirement to harmonize this alongside batch data.
Ultimately, data lakes have themselves proven to be, in effect, just another silo, and many organizations are now looking for a way to combine both real-time data and batch data in a way that allows them to gain actionable insights.
Harmonizing data with a smart data fabric
To fulfil their data integration needs, what organizations need now is new models of harmonization that bring together disconnected processes, applications, and data. This requires the use of advances in data management technology delivered by ‘smart’ data fabrics, complemented by AI and machine learning (ML), along with new API-driven development approaches. Smart data fabrics allow existing applications and data to remain in place, thereby enabling organizations to get the most from previous investments and extracting business value from data stored in lakes and external sources quickly and flexibly to power business initiatives. This includes everything from scenario planning to risk modelling.
The fabric intelligently connects and automates processes that cross existing system boundaries in a non-disruptive manner without having to ‘rip and replace’ existing or legacy systems. It interweaves disparate data, including real-time event data and data from supply chain partners, and allows for exposing, connecting, and orchestrating services and microservices. The result is a comprehensive and overarching perspective that enables frictionless interactions between functional areas and delivers greater flexibility and efficiency, and better insights led by AI.
With businesses demanding more from the increasing levels of batch and real-time data they have available to them to gain increased efficiency and deliver value to customers; smart data fabrics are clearly the best route forward. This ability to leverage more data in the moment enables organizations to gain the vital capabilities they need to make better business decisions based on data, and in turn respond faster and better to crises while increasing revenue and reducing risk. It is also much cleaner architecturally, and simpler from an implementation, maintenance, and application development standpoint.
Embracing advanced analytics
Advanced analytics technologies like AI and ML fuel a variety of use cases within the supply chain – one of the most significant of which is demand management. In this scenario, AI enables manufacturers to predict and model demand to manage situations proactively, rather than just reacting to them. While some organizations focus on aggregated demand forecasts based on historic data, those excelling in the field of AI have started to break down planning to a regional basis or to go down to individual customer requirements or products, using (near) real-time data. By conducting more detailed and accurate forecasting processes, manufacturers make considerable improvements in performance and profitability.
Sales and operations planning (S&OP) processes could also be transformed by AI and analytics bringing together stakeholders and data from across sales, production, procurement, and other departments. This cross-departmental infusion of data can make a big difference to manufacturers and help them make more informed decisions, including a quicker response to fluctuations in demand, setting up promotions when production surpluses are projected.
A path to increased supply chain resilience
For manufacturing and supply chain organizations, the smart data fabric marks a turning point in data management. It provides higher performance as latency is reduced due to the elimination of connections between the different layers of the architecture, allowing organizations to incorporate transaction and event data into analyses and processes in near-real-time.
With a smart data fabric at the heart of their infrastructure, businesses will be able to overcome the difficulties they have had previously with unifying different types of data from many sources. In turn, they will be able to gain the kind of intelligent insights that drive up the quality of critical decision-making and provide the firmest possible basis for future resilience and greater overall efficiency.
Joe Lichtenberg works in Product and Industry Marketing at InterSystems.
Established in 1978, InterSystems provides innovative data solutions for organizations with critical information needs in the healthcare, finance, and logistics sectors and beyond. InterSystems’ cloud-first data platforms solve interoperability, speed, and scalability problems for organisations around the globe. InterSystems also develops and supports data management in hospitals through the world’s most proven electronic medical record, as well as unified care records for health systems and governments through a powerful suite of healthcare data integration solutions.