Guess, a leading apparel brand sources material from several hundred suppliers worldwide, though it does not own factories itself. When manufacturing units shut down during the pandemic, Guess was unable to figure out if their contractors could source alternatives for raw material and send finished products. And this is just one example of the importance of supply chain visibility. Even in evolved organizations, vital supplier information is locked away in spreadsheets and presentations, making it super hard to make sense of the data even if supply chain managers can access it.
Moreover, manual ways of managing supply chain data are no longer viable. Technology solutions for different parts of the supply chain often work in silos without interacting with one another.Traditionaldata processing methods are slow and find it hard to manage semi-structured formats. Even if businesses invest the time required to create data lakes, the data silos persist because the supply chain is generating data faster than the ability to aggregate. The solution thus is to break down data silos and combine the data for easier analyses.
Successful supply chains rely on visibility and predictability, and a troubled environment can aggravate challenges for supply chain managers. Managing supply chain networks is complicated by several factors that interact with each other in a way that defies attempts to achieve desired outcomes. Moreover, in the traditional approach, supply chain professionals rely more on their gut reaction.
Network modeling is an approach that deliberately steps away from the ad-hoc approach that supply chain managers usually employ. It helps alleviate concerns by building more flexible and resilient supply chains that are less prone to disruptions. Supply chain managers can run scenarios, evaluate, and implement changes in response to dynamic situations such as new products, demand changes, new suppliers, changes in laws and so on. This can help businesses reduce supply chain costs, improve service levels, and make proactive decisions by stabilizing the key pillars of a robust supply chain: technology, strategy and operations.
The importance of network modeling
Data Extraction, Loading & Transformation Helps
Extraction, Loading and Transforming, or ELT, of data, makes it easier for businesses to extract massive data sets from multiple sources, load it into a single data warehouse and then transform the data at the target system. It uses cloud technology to modernize the tasks of data warehousing and managing big data so that businesses can focus on mining their data for actionable insights.
Let’s look at the ELTprocess in detail:
Data is being generated in structured, semi-structured, and unstructured formats, and must be consolidated to be useful. So you need to ensure that all your supply chain data can be seamlessly ingested and consolidated, and can be stored in a centralized repository.
But accessing and extracting data can be complex and painstaking, especially when you need to deal with different APIs and user interfaces of multiple tools installed for managingthe supply chain.Modern data extraction techniques help you manage your data more efficiently and consolidate it as a single source of truth. The data extraction process enables organizations to bring the data from multiple data sources through data pipelines for further analysis.
Next, you need to load the transformed data from all of your sources into a single
place that offers you a unified view. Cloud-based data warehouses offer almost infinite storage capabilities and scalable processing power, making ELT ideal for handling large amounts of supply chain data. Additionally, a key advantage of ELT is the flexibility and ease of storing new, unstructured data.
Unfortunately, almost every tool will present its data in a different format, and in order to consolidate the data for use you will need to change its format into a common one. This could be as simple as a common nomenclature like referring to California as “CA” instead of “CALIFORNIA”, or could involvechanging the data format from JSON to SQL. Specific data driven rules are combined with custom business driven rules and applied to the data to transform it into a format that can link with other data formats. This process also involves data cleansing, deduplication, restructuring, filtering, integration etc.
Supply chains have millions of data points to manage making supply chain management a mammoth task. Whether your objective is to improve operations, monitor customer service levels or improve logistics, supply chain data quality is critical. Data must be frequently checked and synchronized to even start planning. But with so many data silos and multiple versions of data and errors from manual entries, relying on data quality and finding a single source of truth is a hassle. If all that supply chain data is coordinated and integrated, you will always have an accurate overview of required resources for production, inventory, supply and demand.
But for a unified view of the supply chainthat includes ecommerce data sources and touchpoints ( CRM, PIM, ERP, CPQ etc.), scores of exported spreadsheets and other data repositories have to be collected, reconciled and merged each time adding to time and effort. It could also result on data no longer being up to date. With ELT, you can process data faster and scale, without having to struggle with integration and interoperability issues that slow you down. It is also less time and cost intensive, and gets you easy access to fresh data.
Why Supply Chains Need ELT
However, while ELTthe technique seems easy, managing all of the details involved can be complicated. It can take a while to refine your ELT process such that you can rely on the data you’ve transformed and loaded. Moreover, those pesky data silos will continue to prevent you from important analysis by breaking your data up into multiple different places. If your organization has an ingrained culture of data separation, changing how employees work, employees can be difficult. You also need to consider the range of permissions and hierarchies that need to be untangled.
But the most reliable data comes from crossing between data silos, so mastering the ELT technique will offer big benefits. And it doesn’t have to beintimidating. Once you eliminate bottlenecks and silos in your supply chain, you can ensure that customer demands are met, the right decisions are made, your operations are more efficient and you have a strong bottom line. You can automate the data management process, consolidate data from multiple production and distribution lines onto a centralized system, and builda 360-degree view of the supply chain for data-driven insights.
Axidio offers supply chain managerscloud-based ELT data management capable of integrating their breadth of data into a single repository, ensuring always updated supply chain data for analytics with comprehensive insights. Speak to our experts to understand how we can help.