In the dynamic landscape of modern Supply Chain Management, top executives face new challenges every day. From fluctuating consumer demands to global supply chain disruptions, the complexities are ever evolving. As these challenges have grown, so has the reliance on data in implementing operational decisions. But data is only valuable if we are able to convert it into actionable insights; this is where the importance of data analytics and visualization comes into play.
At Ventagium, we live and breathe data analytics and know how impactful they can be when leveraged appropriately in a business. Here, we will explore three different analytical tools related to Supply Chain Planning. From descriptive analytics to advanced visualization techniques, these tools offer a strategic advantage in navigating the complexities of today’s global supply chain. Join us as we uncover the transformative potential of analytics in reshaping the future of your operations.
The Analytical Maturity Scale
Before we dig into the specifics of the analytical tools, we must first understand when to employ them. The applicability of any data tool depends on the business need and the level of analytic maturity required. The Analytical Maturity Scale below illustrates the progression from basic data collection all the way to prescriptive analytics. As we increase our understanding and use of our collected data, we can move from the fundamental question of “What happened?” to asking “Why did it happen?” until finally questioning “What should we do next?” Understanding analytical maturity ensures that we tailor our approach to Supply Chain Planning effectively, leveraging the right tools at the right stage of our analytical journey.
Below, we share unique dashboard ideas and specific visualizations that can be created through business intelligence tools, such as Power BI. The following examples will show you how to use different types of visualizations to analyze Planning KPIs at varying stages of analytical maturity:
Descriptive Analytics for On Time in Full (OTIF) order fulfillment.
Actionable Visibility into the end-to-end Order Fulfillment process.
Predictive Analytics for Inventory Management policies.
1. Descriptive Analytics: On Time in Full (OTIF)
The "On Time in Full" (OTIF) KPI is a critical metric within Supply Chain Planning, serving as a barometer for operational efficiency and customer satisfaction. Essentially, OTIF measures the percentage of customer orders that are delivered on time and in full, meaning the right products are delivered to the right place at the right time.
Descriptive analytics evaluate historical data to gain insights into past performance and are some of the most basic in the analytical maturity hierarchy. Yet, by leveraging descriptive analytics, we can enhance the critical OTIF performance. By examining past order fulfillment processes and identifying patterns or bottlenecks that led to deviations from OTIF targets, leaders are empowered to implement focused interventions. This approach promotes a proactive planning environment where preemptive measures can be taken to optimize inventory levels, streamline production schedules, and enhance supplier collaboration, thus elevating OTIF performance and fortifying customer satisfaction.
Below is an example of a Descriptive Dashboard view that considers two main visualizations: a Process Behavior Chart (PBC) to discover important signals through time and Bar Charts that display the OTIF metric by different objects. These objects can be manufacturing plants, customers, product categories, etc. that could better help our understanding of OTIF performance.
2. Actionable Visibility: Order Fulfilment
Progressing along the Analytical Maturity Scale, we will next tackle “Actionable Visibility”, where instead of looking at the past, we focus on current, real-time performance of established processes. The purpose of these analytics is to provide timely information to act on immediately. To do so, Ventagium has developed a tool called the Action Center that enables decision-makers to have a direct understanding of what needs to be done in each specific step of a process (learn more about these concepts here).
To illustrate the effectiveness of the Action Center, let’s demonstrate it applied to Order Fulfilment. We can understand Order Fulfilment as the entire process an order passes through, from initial receipt of a customer order to the final delivery of the requested products or services, ensuring the order is fulfilled according to the agreed terms from all parties. The Planning function plays a central role in choreographing this complex dance, leveraging their expertise in demand forecasting, inventory management, production planning, and supplier coordination.
To regulate this complexity, the Action Center allows Planning to visualize and track, in real time, how orders are moving through each stage of the Fulfillment process and act immediately on any lagging orders. Below you can see an example of an Action Center displaying the number of active orders by process stage and the length of time they have been in that stage.
Notice that some cells have conditional formatting indicating urgency. Based on the established SLA, any order more than 10 days old in any status is highlighted, allowing Planning teams to take quick action to resolve roadblocks.
3. Predictive Analytics: Inventory Management
Inventory management is the overarching supervision and control of the flow of all goods into and out of a company's inventory. It requires a delicate balance between the need to maintain adequate stock levels to meet customer demand and the desire to minimize carrying costs and excess inventory. While there are many different techniques and tools used to properly manage inventory, we will focus on Predictive Analytics and Simulation, the penultimate stage of analytical maturity.
Simulations are virtual models of supply chain processes that mimic real-world scenarios and predict their outcomes. By simulating various inventory management strategies (i.e. inventory replenishment policies), Planners can evaluate different scenarios and their potential impact on operational performance and costs.
Predictive analytics, on the other hand, utilizes historical data, algorithms, and machine learning techniques to forecast future demand patterns and inventory requirements. Having this level of visibility helps minimize stockouts, reduces excess inventory, and ultimately leads to cost savings.
The following is an example of inventory simulation visualization. It uses demand forecast data to provide a reference for possible future orders and incorporates key inventory management metrics such as lead time and safety stock. Through the use of what-if slicers, the simulation provides an inventory forecast under different scenarios. Visualizing the potential impacts of various inventory management strategies supports the user in operational decision making. In this case, the simulation is only as valuable as the forecast is accurate, so any real application would require monitoring the forecast error to understand how closely the predictions match actual data.
All example scenarios have identical starting points:
Initial inventory of 18,483 units
Safety stock level of 11,310
A single order quantity of 15,000 units placed in Week 0
Demand forecast for 9 weeks from Week 0.
With these variables set, we can simulate the isolated impact of lead time on our future inventory position.
Scenario 1
In this first scenario, we consider a lead time of 20 days, consistent with our current process and agreements with vendors.
In just the first week, our inventory dropped below our established safety stock level. By the next week, inventory was replenished back to the original level. With no additional replenishments, inventory would continue to deplete until we were out of stock in mid-June.
Scenario 2
What if we were able to decrease our lead time by half? All the constraints remain the same from the previous scenario but now we reduce lead time to 10 days.
We still reach out of stock in mid-June but spend more of the previous weeks above our safety stock level. We also use more warehouse space, with inventory reaching a high of over 26,000 units. Is the higher carrying cost a worthwhile trade off to more inventory security?
Scenario 3
Finally, we want to understand how a longer lead time would affect inventory levels. This could also simulate a disruption with our vendors or transportation.
This would cause us to run out of stock much earlier, at the beginning of June. Though we see replenishment a few weeks later, we again reach zero inventory by the end of the month. Would this impact our customer relationships or disrupt future sales?
Even this simple scenario can help us understand what decisions are needed in order to maintain sufficient inventory to meet demand. This knowledge can be the catalyst to proactive negotiations with suppliers, in case current lead times do not meet the requirements to fulfil demand.
As we conclude our exploration into the transformative power of data analytics in Supply Chain Planning, it's evident that the strategic utilization of data holds the key to overcoming the obstacles of today's dynamic landscape. From descriptive analytics shedding light on historical performance to predictive analytics guiding future decisions, each step along the Analytical Maturity Scale offers invaluable insights.
At Ventagium, we understand the critical importance of harnessing these insights to drive operational excellence. Our expertise in data analytics and visualization enables us to provide tailored solutions that address the unique challenges faced by each client.
Don't let uncertainty hold your business back. Reach out to us today to schedule a demo and discover how Ventagium can help you unlock the full potential of your Supply Chain.