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3 Ways for Manufacturers to Maximize the Value of Financial Data Analysis

For many firms, greater data collaboration unlocks new opportunities,

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Manufacturers are collecting more data than ever before, and in parallel are becoming increasingly proficient at deriving data-driven insights. However, as manufacturing becomes digitized, companies are running into self-imposed ceilings. Limiting data analytics within business lines or departments, instead of sharing data, is preventing manufacturers from optimizing operations with business objectives in mind.

A recent survey by Boston Consulting Group revealed that nearly 75% of manufacturing firm managers see data sharing as the key to capturing higher efficiency levels. Financial data remains highly undervalued from an operations perspective, as typically, operations and finance teams work in separate worlds. 

However, financial data analysis can unlock new opportunities in manufacturing once companies encourage greater data sharing and collaboration. Here are types of value that manufacturers can see by making more of their financial data. 

Reduce Cycle Times and Unlock Greater Efficiency

Finance teams routinely run cost analysis reports and project future expenses. However, combining metrics that speak to operational parameters, such as assembly line wear and tear and part lifecycle data will give cost analysis a new dimension. Finance teams, especially FP&A units, can predict future costs with greater accuracy. 

They can extend these conclusions to increase the accuracy of operational data such as plant capacity reports. Typically, these reports have a limited focus. As FP&A solution provider DataRails notes in its recent report on manufacturing KPIs, "Benchmarking actual production against full capacity is an opportunity to get out on the floor and ask questions: Why is there so much variability? What options are there to smooth this out?" 

By adding a financial perspective, leaders at manufacturing firms can ask better questions to help unearth the best business growth strategies. When correlated to costs, metrics such as equipment effectiveness can inform procurement times, creating a seamless supply chain. Data sharing and collaboration are key. 

Michael Lock, Senior Vice President and Principal Analyst at Aberdeen Research, puts it aptly. "Where companies once had to prioritize and choose the data most relevant to their analyses, the tools now exist to help lessen this burden," he says. "Companies can now capture, integrate, and prepare a larger volume of data from a greater number of sources, thus making it easier to expose it to the users who can get the most value from it." 

Support Agile Manufacturing

Just-in-time or agile manufacturing is the norm these days, and data plays a huge role in minimizing disruptions to the value chain. What if companies could leverage financial data to create more efficient procurement and maintenance processes? 

Financial datasets such as inventory costs and procurement expense trends can add immense value to inventory optimization processes. Finance teams are accustomed to stress testing their projections and can bring these skills to procurement cycles. 

For instance, finance teams can add costs to procurement models and stress them via various scenarios. Adding financial data, or incorporating it through KPIs, also helps companies become flexible. Additionally, integrated data analysis of this kind reduces costs. 

One of the biggest obstacles to engineering such processes is inertia. Manufacturing resisted leveraging IIoT data to automate maintenance for a long while. Moving forward, flexible manufacturers that can rely on data to cater to omnichannel demand from consumers are the ones that will prosper. 

"It’s just easier to slip back into old ways of working, siloed thinking, and a command-and-control culture — easier but not better,” explains Paul Miller, a Forrester analyst. “Rigid hierarchies, ossified processes, and an iron grip are the antithesis of the flexible and adaptive organizations that will successfully navigate tomorrow’s challenges." 

Eliminate Unnecessary Costs

Manufacturing is an amalgam of several sub-processes, which is part of what makes a manufacturing execution system (MES) so useful. And yet, tiny inefficiencies in these processes create outsized effects on the entire chain. For instance, faulty measurement in one part of the process creates scrap or rework later in the line. 

Manufacturers generally account for scrap and rework through two separate analyses. Operations teams track inefficiencies throughout the assembly line and procurement, while financial teams track rework costs and the impact on margins. What if these teams could collaborate to unearth inefficiencies hidden from the individual teams? 

For instance, poor raw material quality might create rework issues in automobile production. Raw material sourcing is usually price-centric, a financial decision. Thanks to exposure to rework rates and line processes that create rework, procurement teams can evaluate alternate suppliers. They can deprioritize low costs thanks to data-backed evidence of such material creating costly rework. 

Thanks to such end-to-end visibility, manufacturers can connect the dots better. They also give room for predictive analytics to make an impact, since these systems have more data to work with. 

"Mature analytics systems can today process large quantities of data and offer various analysis methods, thus identifying potential faults in advance and providing opportunities to develop suitable counteractive repairs," notes Deloitte in a report on analytics in vehicle manufacturing. "The introduction of such predictive quality analytics systems will dramatically alter recall management, leading to considerable increases in efficiencies, and result in massive savings in warranty costs." 

Better Insights and Transparency

Incorporating financial data into strategic planning and installing data sharing processes can benefit manufacturers immensely. Not only can they maximize asset ROI, but they can also unearth better opportunities. For instance, combining capacity utilization with cost data can give finance teams better context when examining ERP data. 

Firms that embrace such end-to-end connectivity will prosper in the challenging manufacturing environments of the future.

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