How to improve your on time in full delivery?

Case study

How to improve your on time in full delivery?

Making your OTIF healthier by using advanced analytics

Organizations often struggle to guarantee an agile, flexible and transparent supply chain that is capable of coping with a continuously shifting playing field. An increase in complexity comes along with an increased amount of available data. Using this data to maximize supply chain efficiency is not straightforward – however necessary to make informed decisions. It requires selecting the relevant data, assuring a steady flow of accurate data, and translating the data into useful interpretations. In this article, we explain a case in which Deloitte supported a world famous designer of seasonal consumer goods to improve on time, in full delivery of their products by using advanced analytics.


The client’s products are produced by third parties, mainly in Asia. To allow for on time delivery at the various Distribution Centers (DCs), the products need to be ordered at least 15 weeks before the required delivery date. The supply chain is described on the left hand side of Figure 1. Because production orders are based on customer orders, (e.g. retailers order in advance) it is of utmost importance for our client to have the products delivered OTIF (On Time, In Full) at the DC in order to be able to fulfill the needs of the end customer. One of the indicators of a healthy OTIF is the so called Coverage Rate. This is an indicator that measures the OTIF week by week, from the moment that the orders are placed at the factories (15 weeks before the arrival at DC) until the arrival of the products at the DC.

Use data analytics to maximize supply chain efficiency
Figure 1: Simplified example of the supply chain


The complication in this particular case was driven by the number of (distinct) products and different nodes in the network. The order-portfolio for one particular season’s worth of products contains 56 million items with the same “in store date”. The 56 million items are divided into over 26.000 orders that are sourced from geographically diverse locations, thus requiring different routings to their final destination. The network that the orders traverse becomes a complicated grid of linked production facilities, consolidation points and logistics hubs that needs to be navigated and monitored (see Figure 2).

Use data analytics to maximize supply chain efficiency
Figure 2: High number of nodes in the supply chain lead to a complicated grid

The lack of overview on this complex structure impacted the OTIF, leading to a situation where products were not delivered on time, or deliveries did not match the ordered quantity. Not meeting the OTIF led to negative financial consequences (expediting costs, lost sales) that were often signaled on the requested delivery date, which left virtually no time to react.



In order to enhance the OTIF across all items and to facilitate informed decision making when the OTIF could not be met, the client asked the following questions:

  1. How can we track/ monitor OTIF performance at different nodes across the network?
  2. How should we organize supply chain control and work effectively with sales and operations to make the right decisions (e.g. expediting orders, postpone deliveries)?
  3. How can we identify weak links in the supply chain to focus our continuous improvement activities?



Deloitte built a supply chain visibility model that tracked the 26.000 orders through each node in the supply chain and provided a notification whether shipments were on track to meet the OTIF. In order to show the impact on OTIF, a dashboard was developed that clustered orders based on destination, source, related SKUs, and customers (see insert on the clustering algorithm). The data analysis resulted in a comprehensive view on delivery performance and allowed for better insight into the impact of late deliveries. The data was used during the Sales and Operations Planning meetings to make fact-based decisions on where and how to intervene regarding expediting, split deliveries, and re-allocation to get OTIF back on track.

Figure 3 shows the dashboard where the OTIF per item per client (or group of clients) is tracked. It shows, per node in the network and per order, whether the orders are on schedule to meet the required delivery date. Having visibility on where delays might arise in an early stage in the delivery process and the capability to anticipate the impact on client groups will support our client to make informed decisions regarding required corrective actions (e.g. expediting shipments, reallocating, split delivery).

Use data analytics to maximize supply chain efficiency
Figure 3: Order status tracking dashboard


By using advanced analytics to build a model that tracks inbound orders along the supply chain, our client was able to implement a control tower function within the supply chain organization that actively monitors shipments and can proactively intervene where necessary. This control tower used the insights gained to make fact based recommendations on short-term corrective actions required and identify sustainable improvement opportunities to enhance the supply chain and supplier performance.



The example presented in this article demonstrates the power of applying advanced analytics in a supply chain. In this situation, the already available data contained the information needed to improve the different processes and achieve the supply chain goals, but the sheer volume of data and number of variables were simply too large to analyze. The complexity of the supply chain resulted in a direct need for solutions that could support fact-based decision making. Deloitte supported the client with both the initial supply chain analysis as well as the development of several decision support tools. This led to significant cost savings as targeted interventions could be planned in time, making this a very successful example of how the application of advanced analytics can be leveraged as a key differentiator in supply chain improvement initiatives.


This article was written with the help of Guido Diepen, Sjors Broersen and Juri van de Gevel.


More information about Supply Chain Analytics?

Would you like to know more about Supply Chain Analytics? Please contact Naser Bakhshi: or +31 (0)88 288 3874 or Robert Jan Huizing: or +31(0)88 288 3154.

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