Supply chains are full of surprises. As manufacturers face increased cost pressure in a fiercely competitive global marketplace, and as just-in-time models demand precision while raising the stakes, predicting and mitigating a potential crisis is key to maintaining a healthy business. Predictive analytics give you insight into what’s coming.
When you know what’s coming, you won’t get left behind.
Your Success Depends On Suppliers
Your suppliers are critical partners but relying on them exposes you to risk. Manufacturers are often so heavily reliant on just-in-time delivery that even a minor disruption, from the supplier’s perspective, can become a major crisis. A major disruption can be catastrophic for a business, but disruptions are shockingly common. Zurich Insurance Group found that over 85% of manufacturers report at least one supply chain disruption per year; more than 50% of manufacturers report two or more disruptions.
The top three impacts of supply chain disruption are loss of productivity, increased cost of working, and impaired service outcome. Delays can additionally erode your organization’s value by increasing costs or further financial ramifications, such as decreased sales, negative brand perception, or fines assessed by your customers. Further, if your customer even perceives a problem, it could lead to many of those same financial penalties, even when actual delivery remains on schedule. These not only impact your bottom line, they also affect employee morale.
Analytics Drive Actionable Intelligence
Traditionally, companies have made decisions based on historical data. With ever more data being generated across the supply chain, companies are looking for actionable intelligence to drive optimization, increase margins and avoid supply chain distributions. According to Hackett Group’s 2017 Supply Chain Resilience Report, 82% of supply chain professionals say analytics are needed to improve the visibility of the supply chain across the enterprise.
To derive actionable intelligence, manufacturers are implementing analytics solutions that can mine both the structured data across ERPs and other systems, IoT data, and the emails and spreadsheets dispersed throughout the organization. By leveraging the data across disparate systems, predictive analytics uncover insights and patterns in supplier behavior. This is allowing them to not just to see issues as they arise, but to see trends as they’re developing.
Predictive Analytics Empowers Proactive Decision Making
Predictive analytics empowers companies to be proactive: companies can discover quickly – even preemptively – who their best and worst suppliers are and flag potential threats for disruption. It improves decision making and forecasting based on both historical and real-time data, detecting patterns in supplier activities, and alert stakeholders to those suppliers who exhibit troubling trends.
For example, if a supplier’s defect level or missed shipments has increased recently, this could foreshadow a bigger problem that could cause a major disruption. Predictive analytics automates the detection of this and when flagged, corrective action can be taken, such as proactively awarding the business to an alternate supplier, mitigating future disruption.
So, what powers predictive analytics? Machine learning. This artificial intelligence (AI) that powers computers with the ability to learn without being explicitly programmed. It excels at finding anomalies, patterns and predictive insights in large data sets by reporting on historical data as well as deploying models built to forecast likely outcomes. In particular, machine learning automates “what if” analysis by modeling a range of scenarios and prescribing actions that can help the organization to achieve optimal results.
In production, analytics can detect variances, which should help minimize non-conformances that lead to late deliveries or returns. Additionally, predictive analytics can identify timing issues that may delay the launch of a program by identifying suppliers that historically take longer than scheduled to complete a product launch task. In this case, another supplier may be selected for the process or adjust the launch schedule based on the supplier demonstrated performance. Here, predictive analytics not only drives decision making but helps increase transparency while surfacing a significant issue in the supply chain.
Adoption of Analytics: A Strategy for Business Success
The industries seeing the highest penetration of predictive analytics include manufacturing, healthcare, transportation, and logistics. In fact, 87% of businesses are expected to adopt predictive analytics over the next five years, and about 80% are looking to adopt IoT sensors during that timeframe. Four-fifths of supply chain professionals say analytics will be important to reducing costs.
94% of organizations say digital transformation and analytics will fundamentally change supply chains, but only 44% have a strategy for getting there. So, how does an organization get started? Adopt an analytics solution – one that offers predictive analytics and a portal providing a single source of the truth – then focus on real-time transparency and collaboration. A key to success is mapping out the analytics vision that removes cultural readiness and data availability.
Predictive analytics uncover the truth related to internal and supplier performance, as well as potential threats for disruption. Companies are leveraging predictive analytics to better forecast demand, minimize program launch delays, discover opportunities for cost reductions or pre-emptively anticipate cost increases, and drive accurate, on-time shipments. With predictive analytics, you are equipped to prevent operational crises and reputational damage that could impact your company’s stock price, revenue, costs, and even employee morale.
Zurich Insurance Group, Strategic Risk: Do Not Forget Your Supply Chain!
MHI and Deloitte, 2019 MHI Annual Industry Report
Hackett Group, Laying the Foundation for Supply Chain Digital Transformation
As CEO of LiveSource, Bo brings nearly 30 years of manufacturing and supply chain business application experience to LiveSource. Previously the CEO/Founder of FBOS, an EAM solution, Bo sold the company to QAD in 2006. He then spent seven years at QAD in various leadership roles before becoming CEO of MFG.com. There he led the spin-out of the LiveSource product into a stand-alone company and completed the company’s Series A funding.