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

Connecting the dots: why manufacturers need to build an integrated analytics strategy

Though the IT and engineering departments now typically work together better, they are still missing opportunities to capture production data and combine this with ERP data to discover actionable insights. With this in place, customers can drive action in both real-time and on a historical basis and enable access to the right KPIs, alerts and insights to maximise profits and minimise loss.

We find that many manufacturers lack an analytics and data strategy which can highlight where the business is having problems with production. It’s that 360 degree view provided by advanced analytics which can connect the missing dots into one comprehensive picture that drives actions. For example, if a customer needs to optimise data volumes produced to keep up with sales, they need to know what factors are impacting throughput (e.g. like breakdowns, scrap) and what can be done to address this.

Without a strong analytics capability and amid global uncertainty, manufacturers are facing significant risk

Machines could fail, products may not be delivered on time due to supply chain issues, or there could even be challenges with inventory due to sudden increases or dips of demand. Such issues risk impacting brand credibility, stock value and overall demand across products that prior to market uncertainty, were all functioning correctly.

Manufacturing and supply chain leaders need to know about issues before they create a negative impact on their work stream. For example, a machine which is beginning to indicate a malfunction needs to be flagged, or the introduction of a new regulation might impact whether a container can get to the warehouse in time or not. Leaders need this information as far in advance as possible to understand potential business impacts and be able to act on it.

How can manufacturers use advanced analytics to operate more efficiently?

Real-time analytics can detect quality defects and minimise scrap and possible machine failures that can result in major disruptions for production.

Some manufacturers are even harnessing social media data which helps them to predict if their suppliers will or not be able to meet the demand and source alternatives in case the suppliers are not capable of delivering.

Another example is where businesses are connecting customer behaviour insights to production and supply chain according to buying habits. This enables sales and operations planning to coordinate production based on forecasted customer behaviour, reducing waste and ensuring the business can maximise busy periods.

Automating Production Recording use case

One interesting project was delivered at a global chemicals company that produces a broad range of advanced materials, chemicals and fibers for everyday purposes. There was a need for automatically calculating produced amounts of bulk products based on hourly silo level/flow/weight measurements from the DCS system and to gain traceability of bulk stocks.

How did delaware utilise advanced analytics to connect manufacturing to planning? 

• Created a platform for tracking silo production levels, converting DCS production parameters and share these with the SAP solution through RFC.
• Built a bulk cockpit for monitoring issues with automatic production recordings.
• Applied SAP-based automation of production calculation and process order confirmation.

The result was a fully automated production calculation and order confirmation.

Improving data quality in manufacturing

Often there is a serious challenge in relation to how businesses process and manage customer data in their ERP system. This typically involves maintaining mandatory fields, validating new vendors and customers, maintaining master data, identifying mismatches, finding and fixing incorrect vendor and customer data and creating new vendor profiles. These are costly and time-consuming processes for manufacturers, and the risk is that they are unable to deliver products as fast as possible.

Poorly managed master data can lead to significant financial losses. Machine learning technology can address this by finding patterns in the master data and propose new rules based on these patterns. Data stewards can review and accept these rules which can then be automatically integrated into your master data process. Data can be validated quicker, thus enabling new vendors and clients to be added with minimum delay.

When it comes to the automatic data validation, wrong supplier or payment details will be prevented which can potentially save money and make the invoicing and billing process run with minimum flaws, resulting in better cashflow and with improved cashflow forecasts.

In the second part of this blog, we’ll explain how you can benefit from advanced analytics to improve supply chain planning even amid global uncertainty.