Operations Analytics Training

Although it doesn't have a formal definition, the term Operations Analytics is commonly referred to as a type of Business Analytics specifically focused on improving existing operations. It involves a wide spectrum of functions in a business ranging from supply-chain management to manufacturing optimization. As with all analytics capabilities involving disparate functions within a business, the main challenge facing Operations Analytics is the variety of the data collected. Since Operations Analytics also includes optimizing management infrastructures with the help of behavioral data as well as designing physical systems, the resources from which the data is collected differ greatly. 

Some of the applications of Operational Analytics include: 

• Supply Chain Management: The term Supply Chain is defined as "the sequence of processes involved in the production and distribution of a commodity". For large organizations with high production capacities, multiple production facilities and complex distribution networks, the complexity of the Supply Chain requires organizations to adopt novel methods to optimize their supply chain operations. This is where Supply Chain Management comes in. Supply Chain Management is the process of representing business requirements analytically, and taking actions to optimize various processes found in the Supply Chain. 
• Procurement: Procurement has always been an important percentage of overall revenues. Every company has to spend significant amount of money to buy products or services ranging from legal advice to office equipment. If an organization can evaluate vendors, optimize contracts with vendors, forecast demand and run analytics on spending, it can cut a significant portion of its expenses on procurement. 
• Manufacturing: To improve manufacturing processes, managers can take a look at historical process data to identify patterns and relationships between different process steps and inputs. They can then optimize the vairables that have the greatest impact on production yield (amount of output per unit of input). In complex manufacturing processes that have high variability, traditional methods such as Six Sigma and Lean may prove to be insufficient. In that case, for example, a good solution may be to cluster closely-related production steps together, collect all types of data related to each and every individual process (usually with the help of sensors), and create a central repository for this data. Stiatistical and Machine Learning methods can then be applied to this data to uncover interdependencies among different process parameters and parameters that have a significant impact on yield can be optmized.

Experfy's courses on Operations Analytics cover each of the subtopics extensively, and will provide you with case studies and demos to make sure that you gain the skills to apply what you've learned in real-life problems. Whether you're a manager looking to get up to speed with different methods of improving operations or a field engineer working at the manufacturing facility, Experfy's courses will enable you to gain the skills that will accelerate your career.

According to Indeed, the average salary for a Process Engineer with data science skills is $112,000/year.