What is it and why is it interesting for your business
On-Time In-Full (OTIF) reporting is a key performance indicator (KPI) used to measure the efficiency and reliability of a supply chain. It summarizes the completeness of delivered orders and the timeliness of their delivery. This metric is crucial for businesses as it directly impacts customer satisfaction, inventory management, and overall operational efficiency.
OTIF reporting helps businesses identify bottlenecks, improve delivery performance, and enhance customer relationships. By ensuring that products are delivered as promised, companies can reduce costs associated with delays and shortages, and maintain a competitive edge in the market. What gets measured, gets done…
Note that OTIF reporting can be done from various perspectives: you vs. your customer perspectives but also your vendors vs. you.
How does it work
Our approach to OTIF reporting involves blending a best-practice BI approach with a method proven to work in various supply chain contexts. We always see 4 steps:
- Preparation: as OTIF highly depends on definitions, we always should start by aligning & agreeing on what we want to achieve, who to involve & what is success. It is essential to decide on what perspective you are taking (vendor or customer) and, as no two supply chain processes are the same, we do this by truly understanding the business process and from there sharpening the definitions of the OTIF KPIs. Before we touch any technics, the definitions on how they should be calculated should be defined and signed off.
- Data Integration & Validation: Next, we’ll need to the data to feed this calculation. Very often, OTIF will require integration of data from various sources, including at least your ERPs, but possibly also broader your TMS (Transport Management System) or WMS (Warehouse Management system). To calculate OTIF we’ll need order & sales data, shipment details and inventory levels. Data coming from these sources needs to be checked on accuracy and completeness before starting to build a data model and reporting. This step is called validation.
- Reporting & Analysis: With the calculations done of OTIF, we now need to make them actionable through clear & actionable OTIF reports and dashboards. As, once more, no two business processes are the same we believe every OTIF report should be at least somewhat tailored to the specific needs of every customer. Our generic OTIF report gives a general overview but also allows to identify patterns and root causes of delivery failures. These include examining factors such as lead times and transportation issues for plants or products.
- Continuous Improvement: As any dashboard, the final phase is iterating & we establish feedback loops to continuously monitor and improve report relevance & performance. This involves regular reviews and updates to the reporting process based on the latest data and customer feedback.
What do we need to pay attention to
When implementing OTIF reporting, next to the this, also data accuracy and completeness is important.
- Data Accuracy: Ensure that all data sources are accurate and up-to-date to provide reliable insights. Low data accuracy might lead to low-quality of insights.
- Data Completeness: Ensure that all relevant that is available. Use data from multiple systems if needed to get a complete view of the process.
- Stakeholder Engagement: Involve all relevant stakeholders in the process to ensure effective implementation and acceptance of the KPIs and their definitions. It is important to include all relevant stakeholders from early on to buy them in on the project.
By focusing on these areas, businesses can effectively utilize OTIF reporting to enhance their supply chain performance and achieve greater operational efficiency.
The next steps in unlocking insights
Effective data analysis encompasses a variety of approaches, one of which is conducting a root cause analysis. This method allows us to identify critical factors, such as the best and worst-performing plants, uncover bottleneck routes, and address inefficiencies. With a well-designed data model and thorough data cleaning, these analyses can be performed with ease and reliability.
Building on these insights, the next step is to develop predictive models using historical OTIF data. These models enable us to forecast future performance and take proactive measures to prevent potential issues, ensuring better outcomes.
