Companies are overwhelmed with data. There is ERP related data, global supply chain data, world economic data, industry and market data, customer and supplier data, email communications, and the list goes on. Turning data into insights, decisions, and strategies to fuel profitable growth is no easy task yet is essential to success.
The Value of Data
Our most successful clients appreciate the value of data. They find ways to compile, analyze, and utilize data to set strategy and make key decisions. For example, from the sales side, proactive leaders analyze revenue, pricing, and profitability trends by customer, distribution channel, market, products, geographies, salesperson, etc. They utilize that information to focus sales efforts, find opportunities for expansion, and shore up weak spots. From a strategic standpoint, they utilize information to develop new strategies for markets, channels, new product development, and sales programs. Marketing will focus on market and customer preferences and insights. Inside Sales and Customer Service utilize data including lead times, service levels, order patterns, service policies, and customer scorecards to determine what to focus on, who to collaborate with etc.
From an operations standpoint, they utilize information to make strategic decisions such as make vs buy, diversifying sources of supply, investment in plant, equipment and facilities, product rationalization, R&D investment, strategic alliances to pursue, engineering resource allocation, and agreements to negotiate. They also utilize information to support SIOP (Sales Inventory Operations Planning) processes, review capacity bottlenecks, expansion opportunities, and for reallocation, training and development plans. From a planning and execution standpoint, data is integral to developing effective master plans, optimizing detailed production and labor schedules, digging into root causes, classifying items, maximizing efficiencies, reducing waste, ensuring quality, and end-to-end supply chain performance.
From a financial perspective, they utilize data to compare budgets to actuals to trends. There is a focus on department budgets, cost allocations and trends, to manufacturing and purchase price variances to spend trends. From an inventory standpoint, inventory accuracy, inventory variances, inventory reserves, inventory levels / turns are relevant. Financial ratios, balance sheets, income statements, cash flow projections are seen as basics. The most successful CFOs utilize data to become a strategic partner to the business.
Data Analysis, Business Intelligence & Big Data
No matter the label, the smart companies invest in their ability to access, consolidate, analyze, and perform what if analyses with data. From simple ERP reports to developing data clouds with slicing and dicing capabilities, all successful companies must perform data analysis. To read more on this topic, check out our article.
Every ERP system contains base reports. Typically, the majority are non-value added. They don’t allow you to search by transaction types, aren’t compiled by key customer, summarized by product line or geography, and don’t provide the answers to critical business questions. In fact, if taken at face value without ensuring the source of the data, how it was connected to related data, whether it “adds up”, and many other considerations, it could lead to disastrous decisions. Strong data analysts, sometimes called experts in data science are rare. Common sense analysts are even less likely to be found.
On the other hand, you must pursue developing the appropriate reports and analyses that allows you to separate the meaningful insights from the immaterial and the directionally correct from the misleading. There are several software tools that can aid in the process such as Microsoft Power BI and other business analytical tools. More and more, these tools have the potential to be powered by artificial intelligence (AI), yet be careful it doesn’t lead you to incorrect decisions quicker and buried in data.
Predictive Analytics
A step beyond data analysis is predictive analytics. Instead of analyzing the past, modern ERP and data analytics software options will provide predictive insights for enhanced decision making. For example, sales forecasting/ demand planning software can take demand patterns, external variables such as consumer spending and weather patterns and develop sophisticated sales forecasts. Staying ahead of forecast changes will improve customer value and profits. On the other hand, many clients are better off starting simple, gaining a solid footing and then expanding to the complex. 80% of clients are significantly more successful using this approach. Yet they do not get stuck in the mud. Instead, they look for opportunities that make practical sense that will support leaps forward. Continuous improvement is not always the best approach.
Predictive analytics can also be used for supply chain and inventory optimization. For example, if you incur a supply chain disruption in a global supply chain, you can follow up with each moving part (link) in your supply chain and address individually. However, this approach can be time consuming and ineffective and leave you exposed to the weakest link in your supply chain. On the other hand, if you are utilizing advanced forecasting, planning, logistics and supply chain systems with predictive capabilities, your system will “see” bottlenecks, re-route shipments taking delivery, cost and sustainability impacts into account, re-allocate capacity, recommend alternate suppliers and/or routes, etc. Our article on data analysis and predictive analytics provides additional insights.
An Industrial Equipment Manufacturer Case Study
An industrial equipment manufacturer was focused on growing the business during times of significant supply chain disruption. To transition from reactive to proactive, they built a data model to analyze complex data (engineer-to-order), synthesize insights, and to drive proactive decisions. Their core manufacturing facility was one of the best we’ve seen in terms of quick execution with forward-thinking foresight; however, it was limited by the information available. Prior to developing the data model, they met largely unachievable plans by brute force and resilient execution. After gaining insights to data, they doubled output, were ahead of the curve and purchased a new paint line to support seamless growth before they had to extend lead times, transitioned from daily execution meetings to “make it happen” to working exceptions ahead of time, increased margins by focusing in on pricing and profit opportunities, and insourced/ outsourced as needed to support aggressive growth plans profitably.
The Bottom Line
Start your data journey immediately. Use common sense. Hire experts to help navigate and think three steps ahead. Find the appropriate use for data analysis and predictive analytics, and build the capabilities ahead of time to transition from reactive to proactive.
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Achieving Customer Growth by Turning Data into Insights