Over the past weeks, we have been speaking with a large number of Fortune 500 companies to provide support and guidance on how to respond to and rebound from the disruptions caused by the global COVID-19 pandemic.
At many of these companies, CXOs have asked their teams to accelerate supply chain-related digital transformation initiatives. Why would they ask for that? The COVID-19 crisis has exposed significant weaknesses in how organizations run their value chains today.
The biggest challenge has been the lack of supply chain visibility. Many companies struggle to have one comprehensive view on all of their capacities, constraints, inventory and alternatives across the network. This has prevented most organizations from responding to sudden disruptions.
Secondly the majority of all large organizations are still very focused on what they sell to their customers, rather than focusing on what is driving end-consumer demand. For instance, large consumer product companies forecast their future demand based on historical shipments into the retail channel (sell-in). This is not giving excellent results when the underlying market changes dramatically. Instead, companies would benefit from incorporating drivers of sell-out/consumption and adopting advanced analytics/machine-learning-based techniques for a more responsive forecasting process.
One of the critical questions that come to mind is: why have all these companies not been able to prepare for surprises way earlier? The most common answer to that question is “data.” In Gartner’s “Future of Supply Chain, Reshaping the Profession” report, access to reliable and good data is one of the critical roadblocks to embarking on a digital transformation. When we interact with large organizations, we receive similar feedback: “we do not have the data to take the next step in our digitization journey.” “We would love to move to driver-based forecasting, but we do not have the data,” “we have been working on supply chain visibility for the past three years, but the master data is in different systems and often not accurate.”
While we acknowledge that master data is often inaccurate or might not exist, there is immense value in taking a fresh approach to solving the problem. First and foremost, nearly all organizations are data-rich but lack insights. This has been shown time and again in most of the deployments that the o9 team has been part of.
In this blog, we would like to provide a few practical recommendations on common data challenges and how to solve them more effectively. Most organizations have “data cleansing” projects, assigning a significant workforce to start cleansing data. But where to start? Are there alternatives to how it has been done traditionally? How do you ensure this is not a one-time but continuous exercise, as it should be?
Here are some practical approaches on how to do all this more intelligently and how the o9 platform, with the underlying Enterprise Knowledge Graph (EKG), allows for it:
- Multiple ERPs, or different systems naming the same entity differently
For example, a product is named differently in different systems (Sku12, SKU12, SKU-12, etc.). o9 can create a planning model and map the transactional data from other approaches to a single model. At a large electronics customer, we wrote intelligent algorithms to detect similar entities across systems and map them back to one thing in the EKG model.
- System of record for forward-looking master data
Master data needs to be maintained for placeholder products when planning when they become fully realized products. In the o9 EKG, we can create maps between placeholders and fully realized products. This then allows forecasts and actuals to be reconciled over planning cycles.
- Affinity graphs/relations inferred via analytics
Often demand planning products have cannibalization/halo impacts on other products. When demand for one product goes up, demand for another product could go down or up. In most planning systems today, users must maintain that relationship so planners can enter/adjust the forecasts. With o9, such links can be inferred by analytics instead of being supported by planners.
- Collaboration over master data
In some situations (for example, outsourced manufacturing), master data (capacities, routings, etc.) may come from other supply chain participants, and it is essential to review/accept these to develop meaningful joint plans. The o9 platform, with its high-fidelity representation of the value chain, makes it possible for different participants to work together to agree on master data before it influences planning.
- Connect transactional data with smart analytics to update master data
Example: supplier lead times. In the master data, the lead time from a supplier to the Factory is six weeks. However, based on transactional data, that lead time seems to be seven weeks. By applying smart analytics on transactional data, in the o9 platform, that lead-time deviation results in an alert to 1) update the planning policies, in this case, the lead-time, 2) send a message to the buyer to talk to the supplier, and 3) update master data and inventory policies for future periods.
- Connect real-time data with intelligent analytics to update master data
Organizations are increasingly investing in IoT capabilities that provide real-time information from the supply chain network. Think about a sensor at a factory line or from an inventory location. o9’s cloud-first architecture allows for robust analytics and dataflow pipelines to be built with the cloud providers’ relevant data warehousing and management services. As they become available, pipelines to ingest real-time data are crucial to ensuring master data is always kept current.
- Connect with tribal knowledge to establish a planning system of record
Sometimes, there may not be an evident system to maintain the data needed for planning. For example, a retailer may need to sustain inbound capacity & QA/QC process lead times for goods being procured, potentially wildly different based on the item. This information today is not captured in the master data but is often maintained in Excel spreadsheets or stand-alone systems (such as hand scanner system output). o9’s EKG, allowing for an accurate representation of the supply chain network, enables attributes and associated parameters to be built up over time, as necessary. This ability was actively used by one of the largest retailers in the world. They connected this data to the o9 platform and created a planning system of record close to where the planning decisions were being made.
In conclusion, master data management and maintenance is essential before embarking on a digital transformation journey. It is necessary to have a platform with a richness of representation and technical capabilities to allow this to be done effectively. The o9 platform, with the underlying Enterprise Knowledge Graph, enables a digital model of the entire enterprise. Its interconnections with customers and suppliers as a digital twin of the supply chain can support organizations looking to embark on these initiatives.
See below for an example of the digital twin of a supply chain powered by the o9 EKG, representing various data sources to provide E2E visibility.
Are you intrigued? Let’s get in touch!