How manufacturers are driving growth and efficiency with advanced analytics (Part II)

Apr 12, 2021
  • IT
  • operations
  • SAP

In the first part of this blog series, we explained the challenges many manufacturing businesses face with the effectiveness of their analytics.

In this blog, we’ll explain how you can benefit from advanced analytics technology in improving supply chain planning and how this enables manufacturers to thrive, even amid global uncertainty.

A quick check list of data and analytics priorities for manufacturers

All manufacturing leaders must start with ensuring optimal operational reporting. Make sure that your business can answer the simple questions regarding your manufacturing processes, such as:

  1. How much are we producing?
  2. Are we keeping up with the demand?
  3. Do we have any common production issues?
  4. Are there specific products that take more time to produce?
  5. Can we detect failures and issues when they are happening?
  6. Can the business run supply and demand analysis: do you have a plant that tells you how much you need to produce for each product and how far ahead can you plan based on that?

Then, look at these points when reviewing your data priorities:

  1. Are there any data quality processes in place that can point to possible issues how data is entered to the system, both manually and automatic?
  2. Do you have sufficient data to evaluate if the business processes are working as expected?

As a result of implementing advanced analytics across all business functions, manufacturers are able to easily run reports that enable leaders to answer these key questions:

  • Which products are resulting in loss and which ones are driving profit?
  • Using various forecast methods: how will demand and sales look like in the next few months?
  • Do we have other options in case of changes in demand for some of our products?
  • What processes can be improved, what will be the cost of improving them and what is the expected ROI?

Achieving advanced analytics benefits, fast

To articulate the meaning of advanced analytics, you need to understand these two areas:

  • Predictive analytics: With Predictive analytics we can address questions that will help us to forecast what is going to happen or more accurately, what’s more likely to happen. We can create forecasts and scenarios based on machine learning models that will help us to determine what’s the likelihood of a new product to be sold, predict which suppliers are more trusted to deliver in time and which clients might not pay in time to have a better cash flow forecast.
  • Prescriptive analytics: With Prescriptive analytics we can address more advanced scenarios and answer the most complex business question: Based on historical and external data sources, probabilities and different scenario analysis what would be the best possible business outcome, and what needs to be done in order to achieve this?

To make this concept more tangible, let’s think of a use case where we need to evaluate what would be the most efficient route for a delivery track. This should take into consideration many different variables, such as track size, fuel price, length, different routes, local or regional restrictions, taxes and the type of the delivery. The question below is a real example that a well-known global retailer discovered using prescriptive analytics:

What is the least profitable shipment in our business? The answer, identified using prescriptive analytics, was when the content of the shipment is containing water bottles.

So that is another example that enables a business the flexibility to avoid scenarios which aren’t beneficial and get suggestions how can they replace it by using different factors.

Key benefits for manufacturers of implementing advanced analytics

  1. Resilience and flexibility to adopt to rapid changes, including dealing with spikes and dips in demand
  2. Get alerts on potential issues and risks before they become a real problem.
  3. Predict and score which suppliers, products and regulation may impact production and the supply chain
  4. Address complex cases such as stock allocation, delta quantity of unreturned pallets and actual maintenance cost analysis. This gives manufacturers a complete view on material and supply chain costs based on transfer rates and overall impact on the P&L.

Smart & Lean Quality Inspection use case

A leading international manufacturer of personal hygiene solutions needed to optimise the performance of product inspections in order to guarantee high quality products.

  • How did Delaware utilise advanced analytics to connect manufacturing to planning?

Delaware implemented a user-friendly interface and integration with quality measurement equipment, enabling automated quality inspections on production lines. This solution was based on the status of the machine and previous testing results, as well as the integration with testing equipment for automated quality recording.

As a result, the customer is now able to maximise the value of their production resources and they can guarantee delivery of high-quality products to their customers.

Advanced Analytics builds future-proofed manufacturing businesses

Data is waiting to be used to drive the development of new business processes and disruptive new business models. Your company’s ability to harness the power of data is the key to future-proof decision-making and the great differentiator in modern manufacturing.

Even more, data is used to power emerging machine-learning platforms that drive truly intelligent business processes with minimal input; saving time, and reducing waste in all areas of your business.

Delaware has an extensive experience with advanced analytics for supply chain and manufacturing, across SAP and other industry-leading technologies. We have strong experience with sales and demand planning optimisation, implementing embedded analytics solutions for real-time analysis of manufacturing operations, as well as working with a wide variety of ML and AI models to minimise risks and business disruptions.