In a world that keeps getting faster and with customers that keep getting more demanding, you need to make sure your supply chain strategy stays aligned with events unfolding. Part of that strategy is making sure you can improve your demand forecast and plan accordingly.
Demand planning goes further than just looking at historical data. Historical data is a good starting point, but a proper plan includes data from external sources, like market intelligence, weather patterns, and geopolitical or economic developments. Combining information from multiple sources increases the accuracy of the planning and lengthens the horizon. Demand planning goes further than looking at general levels but provides valuable insight at the SKU level.
When you need to make strategic decisions on future demand, you need to look further. Demand modelling becomes a necessity. The more accurate you can forecast your demand, the easier it is to make sure your supply chain design is future-proof. Outcomes of your demand models give you actionable data on make-or-buy decisions. You can use them to determine the right inventory levels, keeping them as low as possible without disappointing customers.
Combining knowledge and experience with the right tools
Due to the increased digitalisation of supply, a growing amount of data is available for modelling and planning. Great news! But it also creates the need for more advanced models powered by machine learning—powerful digital tools combined with data from multiple sources and traditional demand planning. At Ahlers, we use machine learning to create demand models with Tangent Works. The Tangent Works engine enables us to predict the right demand drivers and identify the parameters that impact demand most. The tool can find correlations between the many variables that affect demand.
4 Steps to Come to a Demand Planning
Step 1: Collecting data
Historical data is always a good starting point. The whole organisation comes into play here, as input is needed from marketing, sales, operations, purchasing, and logistics. External data is added as well.
Step 2: Creating scenarios
Next up is the creation of scenarios. Together with your subject matter experts, our data analytics experts determine the drivers for demand and create the scenarios that will be run.
Step 3: Running the scenarios and evaluating the outcomes
We can use machine learning to determine the essential variables that drive your demand patterns by running different scenarios. Running worst case, “normal case”, and best-case scenarios gives an overview of possible outcomes.
Step 4: Making strategic and operational decisions based on the outcome
When all scenarios are run and the data is analysed, we use the outcomes to update operational plans, change inventory levels, or even redesign the whole supply chain.
Demand planning in an assembly-to-order environment
A large international manufacturer of radiology equipment found their lead times increasing and inventory costs rising. The manufacturer operates with a make-to-order strategy. From the moment a hospital ordered a radiology machine until it was delivered, the total lead time was 90 days. Once the order was in, the manufacturer started ordering parts and assembling the device. With a strategic target to shorten their lead-time, they began to rethink their supply chain design. By changing to an assemble-to-order strategy, they could bring down the lead time from 90 days to 21 days. This was a significant change with severe implications for their whole supply chain.
Planning for better results
Ahlers was asked to build a demand planning model of this assemble-to-order strategy and run it through different scenarios to see the exact impact on their supply chain. Ahlers investigated different scenarios on inventory levels to see how those would change when sub-assemblies were pre-built and put in stock. Also, how would these sub-assemblies be distributed globally?
First, we analysed the historical data for the last three years of sales. Then we created a first rough demand model. We organised workshops with the manufacturer’s sales experts to discuss potential forecasting models, and these were updated and combined with historical data. We ran different scenarios: historical demand, double demand, and extreme demand. We studied outcomes and determined stock levels for all parts and subassemblies.
When the manufacturer operated under the build-to-order model, they needed a lot of WIP stock. Large quantities of partially assembled machines were waiting for other parts during production. With assembly-to-order, the manufacturer can ship out devices to customers more than four times faster, with lower WIP stock levels. Total savings on total working capital were 11 percent and the lead time went from 90 to 21 days.
Ask an Expert
Whether you want to redesign your whole supply chain or just tweak parts of it within specific parameters, you always need proper demand planning. It is quick and easy to run based on aggregated historical data. Still, if you want to be accurate, you need to add external data and use demand modelling tools like Tangent Works and get expert help from the Ahlers Data Analytics team. Contact one of our experts if you have any questions or want to drive down costs and increase efficiency in your supply chain.
Author Bio
Miguel van Asch is Head of Data Analytics Services at Ahlers. Ahlers provides state-of-the-art logistics support in sustainable supply chain management, warehousing, projects & machinery logistics, secured transport, trade logistics, after-sales services, and data analytics. Their extensive experience and knowledge of local markets make Ahlers your ideal partner for business in countries like China or Russia.
This blogpost is sponsored by Ahlers
