Processes, every business has them, and the way they work have a one-on-one impact on a company’s overall efficiency. A process is not always defined or mapped, and that is unfortunate, because sometimes just looking at one can make us think, this can be better, more efficient, and less costly.
Visualizing a process isn’t that straightforward, hour long workshops, documentation, multi-system landscapes and other factors that can create quite the effort do not make it easier. Processes can have multiple directions, exceptions, and just complexities that make it difficult to differentiate it from maybe another process, or even possible to identify. Luckily, there are methods and technologies that can help organizations understand how a process works, how efficient it is, and most important, where it can be improved.
Since the introduction of management and execution systems in businesses, there has been an abundance of data. This data is used to keep information on resources or create transactions in and between systems to execute processes, which can be monitored. All of this data can be considered as valuable information that can say a lot about your business and executed processes. Now, the art is to capture this data in a way that portrays the story as it is, in enough detail to use it to create business decisions that improve your business and its processes.
Generally, this data is captured and processed through business intelligence systems that provide specific reports, summaries, dashboards,… on how certain aspects of the business is functioning. This information is is important, but it gives a limited view on what is happening, where it is happening, and why it is happening. It provides the user with predefined business metrics that assist in business decision making on a local level, based on a static view of the business process.
So how can we improve? We can begin by using the data to create a more holistic view of your end-to-end processes, with a dynamic build of the processes as the baseline. When letting your data draw out the process, we get a highly accurate view on how our process is actually functioning from different angles such as time, cost, and variants of the process. These angles allow us to look at the actual process and all its (unwanted) variants. We can then drilldown into the process and see, based on the data, where and why we have unwanted variants, high throughput times, or increased costs.
We can apply this method to many parts of the organization, one of them being the company’s supply chain. Let’s look at four ways data can be used to improve our company’s supply chain.
The warehouse is a key point in any supply chain. Acting as a buffer between demand and production output, its efficiency and effectivity have direct impact on important factors such as cost and time. In early days, the warehouse was seen as a cost item that does not bring direct value to the business, but that changed in the course of time. As warehouse operations can influence KPIs such as on time delivery or delivery accuracy, it has a direct impact on customer satisfaction which is a high-value KPI in any business. It’s therefor important for any business involved in warehousing to create an efficient operational environment that can help create the warehouse as a differentiator to the competition.
So how do we measure productivity in the warehouse and what data do we use? Internally, picking efficiency is considered as an interesting indicator for warehouse performance. Based on activity data from the warehouse management system (WMS), we can map out the picking process in all its variants, with a view on time and cost. This view enables us to dive into issues and their root causes, which allows us to find solutions and improve our warehouse processes.
For example, late picking starts can result in trucks not being filled on time, causing delays in shipping. Short picks can delay the total picking time for an order which also results in an increase in the total order to delivery process. When diving deeper into the short picking issue, we can see that in a majority of the cases, it is due to a product that has not been replenished sufficiently and on time. In this case, we can update our master data and increase replenishment quantity, or advance / prioritize the replenishment start time.
In this case, we can use our data to see where there are frictions in our picking process, when diving deeper into the data and process we can find root causes to these issues and provide adequate solutions to counter them.
When thinking about inventory, carrying costs come to mind. Inventory costs include handling costs and costs of capital tied up in inventory. Inventory has two components: cycle stocks that pass through the distribution center to satisfy demand and safety stocks that serve as insurance, a buffer against unexpected demand or interruptions in supply which can result in possible stock outs. Because of carrying costs, having too much safety stock can bring unnecessary extra costs, but too few can result in stock outs and lost revenue.
It is the goal of the inventory responsible to find the perfect balance between the cost of inventory and the risk of not being able to deliver. This balance can be dynamic, as stock consumption is influenced by for example seasonality.
As most stock is managed in a central ERP or WMS system, we can see the evolution of consumption within our organization, to even a local level. Having a view on our historical stock movements we can look for the patterns where the safety stock is not matching the historical consumption of a certain material. When finding these materials, we can then reduce safety stock and reduce inventory carrying costs.
Looking at the effectivity and efficiency of a supply chain in an order management process, on time and accurate delivery can give a good indication on performance. The right goods being delivered at the right time for the right quantity are important for a customer, and have a direct impact on customer satisfaction.
We can look at the order to delivery process, which encompasses all data from order reception to goods being delivered at the customer. Having data of all activities between order and delivery available, we can look at each individual step and time between to spot bottlenecks or unwanted activities. Looking into the cases where deliveries have been delayed, we can spot that in the majority of cases we had short picks for the requested products, which can be a master data issue. In other cases we can see between sales order and delivery creation we see many price adaptations, which cause an increase in lead time from the order reception to the order delivery. Again here our pricing master data needs to be adjusted to avoid these adaptations.
Organizations and customers are becoming increasingly aware of the environmental impact of the products they are buying. It become more important for purchasing organizations to have a greater understanding of the origin of materials, supplier production processes, recycling,… All of these environmental aspects will therefor need to be incorporated into the supplier assessment process.
Supplier assessment data is nothing new to an organization, but having it available at key-decision moments can be crucial. It’s possible that purchasers do not have the information available when in the procurement to pay process, or that for high-spend suppliers no rating is yet available or made. By looking into the data, it becomes possible to evaluate purchase orders based on sustainability rating, or identify high spending on suppliers with no assessment, which can trigger the creation of one.
We see four different aspects of the supply chain which can be improved by using the company’s data to draw out its process. Even though we have four different KPIs, the approach remains the same:
Interested in how your data can be used to improve your organizations efficiency and effectivity? Send us a message and let’s connect!