Boost Supply Chain Performance
FinanceNovember 18, 2021

Boost Supply Chain Performance. How? With Advanced Supply Chain Analytics

Read this blog to learn how to boost Supply Chain Performance with Advanced Supply Chain Analytics

Data is the currency of business success. Regardless of your business. And when you gather data about your company’s products, sales history, suppliers, and more, then apply that data to supply chain analytics you're in the position to make better decisions, reduce costs, and improve your bottom line. 

Advanced Analytics

Advanced analytics – sophisticated techniques and tools for deeper insight into data – are used in all aspects of supply chain management (SCM). They enable organizations to respond quicker, increase efficiency, and gain greater integration across the supply chain. Below we will go into detail on the four types of advanced analytics – descriptive, diagnostic, predictive, and prescriptive – you can use to gather the insights necessary for your business. 

Descriptive Analytics 

Descriptive analytics is a look at the past through consolidated, classified data based on similarities and differentiation. With descriptive analytics, you can see what has happened in your supply chain. There is no uncertainty in descriptive analytics, which reveals reasons behind past successes and failures. Descriptive analytics only looks backwards. However, your business is moving forward. Therefore, while descriptive analytics can tell you where you have been, it cannot inform you on where your business is going. 

Diagnostic Analytics 

Diagnostic analytics infers the causes of outcomes and compares the effect of different variables on them. Due to inconsistencies, data scarcity, unknown factors, data sampling, and preparation techniques, the diagnostic approach has an inherent level of uncertainty. Although it does not predict future events, finding the cause of a specific occurrence means you have the information to make improvements and prepare for similar occurrences in your supply chain. 

Predictive Analytics 

Unlike descriptive and diagnostic analytics, which look at the past, predictive analytics enables a look forward. This approach helps forecast future events, quantities, or times at which events might happen based on diverse variables. It offers you great insights by relating contextual data such as pricing, promotional calendars, macro-economic indicators, weather, and social media. 

Predictive analytics is the route to anticipating events, enabling you to be proactive through the evaluation of multiple scenarios. You can optimize your supply chain using these advanced techniques: 

  • Data mining: Which data are connected? 
  • Pattern identification: What pattern, or lack thereof, deserves an action/correction/adjustment? 
  • Monte Carlo simulation: What could happen? 
  • Forecasting: What if the trends persist?
  • Root cause analysis: Why did this happen?
  • Predictive modeling: What might happen next with various variables? 

Prescriptive Analytics 

With prescriptive analytics, you can optimize decisions and understand what to do next. This approach enables you to run thousands of “what if” scenarios in a split second to determine optimal inventory levels to inform better decision making. It is used when there are many options, variables, constraints, and data points in play.  

Prescriptive analytics can be complex and difficult to manage, unless you have purpose-built software. You must use advanced models, scenarios, and simulations with known and random variables to understand the range of possible outcomes. When used well, it can help you optimize inventory, production, and customer experience. 

Connecting Operations and Finance

Data is vital to supply chain success. For smart, trusted supply chain planning, the data used should come from all aspects of the company. While it is common sense that data from cash flow should affect how much inventory companies buy, and when, the truth of the story is that decision makers in operations and finance communicate less and less as companies grow, reducing responsiveness to changing business environments. 

An integrated supply chain that connects operations and finance not only makes your company more efficient, but also strengthens connections along the supply chain. This efficiency can only happen when supply chain plans are made collaboratively within planning teams, across teams within the same organization, and with entities outside the organization, such as suppliers and customers. 

The aftermath of the 2008 recession shows how the connection between operations and finance works well in practice. As industries began rebounding from the downturn, the construction equipment manufacturer, Caterpillar, realized that their suppliers could not handle increased demand due to scaling back to ride out the recession. Caterpillar reacted by helping its suppliers finance to build capacity ahead of time, so those suppliers could fill orders when they came. This reaction helped everyone along the supply chain, but could not have happened without an integrated supply chain that takes financial flow into account. 

As history is repeating itself in the aftermath of the COVID-19 pandemic and the disruptions that continue, the ability to collect, normalize and validate data from a variety of sources has become increasingly valuable. CCH® Tagetik Supply Chain Planning expert solution from Wolters Kluwer offers high levels of integration across operational, financial, and third-party data flows. Our customers leverage previously siloed data to enable holistic budgeting and forecasting. Gain perspective on what you could achieve with CCH® Tagetik in this customer-based report from Nucleus Research. 

Brian Lewis
VP, Product Management - CCH Tagetik

Brian has over 15 years of experience in predictive planning, advanced analytics, and supply chain optimization. Prior to joining CCH Tagetik, Brian worked at Ford Motor Company and UPS. He has a PhD in Industrial and Systems Engineering from the Georgia Institute of Technology, a MSE in Industrial and Operations Engineering from the University of Michigan, and a BS in Industrial Engineering and Operations Research from the University of California, Berkeley.

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