One issue of significant importance during the current volatile times that was also key during previous periods (aggressive growth prior to the pandemic, the downturn of the Great Recession, etc.) is the critical importance of providing a superior customer experience. It is evergreen for companies that want to grow and succeed. This metric is measured in various ways including OTIF (on-time-in-full), OTD (on-time-delivery), lead times, perfect order (no errors/ quality issues from order to invoice), easy returns, and more.
During periods of volatility, customer service becomes more challenging because customer requirements constantly change and evolve, and supply can also change and evolve. Thus, a SIOP/ S&OP (Sales and Operations Planning) process becomes essential to keep up with these changes and proactively adjust sales forecasts, production strategies, supply plans, inventory strategies, etc. If SIOP/ S&OP were cookie cutter, every client would have it in place. Instead, it should be tailored to each company’s unique situation, objectives, people, processes, systems, and data.
Data is a particularly key element for SIOP/ S&OP because cleansing, connecting, consolidating, checking, and charting data is frequently not nearly as easy as it appears. If not done properly, you will make decisions based on incorrect data. Perfection isn’t required or even encouraged; however, directionally correct results are of paramount importance. For example, an industrial manufacturing client wanted to forecast long lead-time material purchases. Unfortunately, their sales quotes (forecasts) were in one system, and their sales orders, manufacturing, and engineering specs were in a different system. The two did not connect. Additionally, there were several data integrity issues and no field in common to connect the two systems. Yet, it was critical to look into the future to avoid critical supply disruptions. They quickly started down a path to get directionally correct data for analysis and decision-making.
Every client has an overload of data. The key is whether the data is directionally correct in supporting decision-making. One of the first steps is to cleanse the data. Every client believes their data is reasonably correct until the reports compiled show strange results. Unfortunately, the key is not to overreact and cleanse every piece of data 100% before proceeding as that will likely put you WAY behind your competition. Instead, use the pareto principle. Start with the data that is most meaningful to your decision. Work with broad groups of products and cleanse with directionally correct updates. This is where the saying “garbage in, garbage out” comes into play. If you don’t cleanse your data, all you will achieve is getting garbage quicker. For example, the industrial manufacturing client had to clean up their master records (starting by focusing on the 20% that drove 80% of the volume) to make sense of their data. It would have taken years to fully cleanse data, but it took weeks to start with a pilot set of items and cleanse critical fields.
Connecting & Consolidating Data
Once your data is cleansed, it is likely you’ll have to connect your data from multiple sources, tables, or databases. It is also far more complex than it appears. If you don’t join the data properly, it will appear correct but you’ll get inaccurate results. Bringing a data analyst or systems analyst into the mix to assist in connecting data properly will go a long way. This is often the single largest client issue. For example, the industrial manufacturer had to develop a link between the two data bases so that they could successfully connect the records.
Certainly, once you’ve consolidated data into one source or report, you would check and validate the accuracy. This is also an often overlooked yet critical step. Clients frequently spend days developing reports to get a result that doesn’t add up. Because they get so lost in the data and it took a long time to develop, they immediately share these incorrect results proudly. For example, the industrial manufacturing client spent many hours developing a purchase report. However, the results didn’t add up to a number that made sense. Upon further review, it turned out that one of the processes put into place to dig out the appropriate dollars didn’t add up in terms of the quantities. They changed the report design, and the results became directionally correct.
The final step is to turn an overload of data into an easy-to-understand chart that provides immediate insights. These charts are incorporated into a SIOP/ S&OP process to quickly assess the situation and trend, and the discussion will lead to the evaluation of options and decisions. For example, the industrial manufacturing client was able to make reasonable assumptions and purchase ahead for long lead-time materials months before getting the details squared away by seeing the percentages by material type in a pie chart, and then making educated assumptions when placing orders.
Turn Data Into Insights
By following the process of cleansing, connecting, consolidating, checking and charting data, the industrial manufacturing client was able to get ahead of its competition because it had materials available for production once the increased level of sales quotes were turned into sales orders and engineering specifications were completed. They were able to turn data into insights and achieve record sales during a time when companies struggled with supply chain disruptions because they placed orders for long lead-time materials proactively (in addition to other proactive steps).
When your data analysis is achieving directionally correct results, it will make sense to take it to the next level with dashboards and slicing and dicing ability with a business intelligence solution. Once you can slice and dice data and have a dashboard available with the push of a button, consider moving on to advanced data topics such as predictive analytics. Instead of solely using data for decision-making, you can take it to the next level and predict your future to get ahead of pack.
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Achieving Customer Growth by Turning Data into Insights