The pandemic appears to be behind us, but new supply chain challenges are constantly emerging. Persistent global trade disruptions, evolving consumer preferences, and extreme weather conditions impacting production and transit continue to throw supply chain operations into flux.

To navigate this ever-changing landscape—and make both agile and profit-optimizing decisions—businesses need a robust SIOP (sales, inventory, and operations planning) process. At the heart of an effective SIOP process is a clear and trustworthy picture of your business.

Sadly, this clear picture remains elusive for many companies due to siloed data. The last few years of upheaval have exposed businesses’ lack of ability to properly transform their vast amounts of operational data into information that can solve their complex issues. The challenge often manifests when an organization cannot agree on what the issue even is, because there is no alignment on a “single source of truth.”

Current off-the-shelf options that aim to develop a clear picture through comprehensive planning, modeling, and forecasting tools require clean, reliable data to do so. Such options often take months to implement and can fail to meet expectations—especially when the underlying data is poorly understood. 

Many companies still rely on tools like Excel to perform analysis, which can also lead to issues. Static user files are invariably disconnected, leading to version control and calculation inconsistencies. Firms waste time comparing disparate versions and drawing erroneous conclusions rather than focusing on the root cause of problems and potential solutions.

How do you build a single source of demand truth? 

Developing a respected single source of truth is as much about the process design as it is the systems architecture. We see three key components to managing data effectively, which we help clients implement with our middleware:

  1. Streamlined data acquisition, ensuring easy access and understanding for users.

    The first step is to assess any gaps between available data and the needs of the organization. As supply chain and operations experts, we understand what businesses need to be successful and have the capability to conduct rapid data validation of existing information. We pull data from various disparate systems to create a full picture of the business.

  2. Robust data validation, along with levers to quickly correct errors.

    This critical step of data cleansing—whereby we model existing data, explore outliers, and test scenarios—builds trust within an organization and creates a baseline for future analysis. The process can highlight gaps in current operations and identify data integrity issues such as duplicate demand. It also enables creation of a consensus source of truth and comprehensive dashboards showing how demand impacts operations.

  3. Clear dashboards, designed in collaboration.

    These dashboards convert raw data into easily understandable insights and guidance for informed decision-making. By focusing on the most essential data, the dashboards are more user-friendly and simplify decisions. Moreover, constructing dashboards that serve multiple departments fosters trust in the process, laying the foundation for ongoing success.

Once you establish a single source of demand truth, existing forecasting software can more effectively model future demand and supply. The modeled forecast data is displayed in interactive, hierarchical dashboards used to collaborate with sales and product management teams to obtain a single true consensus demand plan.

Our approach in action

We recently worked with an industrial manufacturing client that struggled with both excess inventory and component shortages, leading to on-time delivery issues. These two separate problems were driven by the same root cause: an ineffective demand-planning process. 

The demand forecasting process was conducted without an understanding of the underlying order demand. This resulted in a failure to plan for enough high-volume parts (resulting in component shortages) as well as overly optimistic volume forecasting of lower-volume parts (creating excess). Adding to the issue, forecasting did not model seasonality accurately, leading to manufacturing constraints in peak seasons.  

By implementing our SIOP and data management strategies, the client came away with enhanced data accuracy, comprehensive dashboards, and a structured meeting protocol. This led to improvements in forecasting accuracy and component and capacity planning, which resulted in better part availability. In the first month of deployment, the client witnessed a $10M decrease in inventory, which improved to a $35M reduction within one quarter. Additionally, fill rate jumped from the low-80% range to the low-90% range within five months, boosting customer satisfaction.

Boost your data management processes today

Our approach delivers tangible results within the first weeks of implementation. By enabling clients to link data across their organization and align on data-driven decisions, we help clients close the physical and digital gap and optimize their operations. 

Following our robust SIOP process and approach to data operations will equip your organization with the necessary insights to address whatever supply chain disruptions come next.