The Economy: Big Picture

In the last month, we’ve participated in at least six economic forecast presentations or discussions with experts (economic, banking, investment, manufacturing). Although they each had nuances, common themes emerged. Adding our expertise into the mix, we see volatility on the horizon. 

The bottom line is that inflation is likely to continue, interest rates are unlikely to decline near-term without creating additional down-the-line inflation, unemployment will have fits and starts and trend up slightly yet the labor participation rate will remain lower than pre-pandemic. From a jobs standpoint, low skilled jobs are being automated, yet high-skilled jobs are experiencing a severe skills gap. Overall, the economy will be slower than it has been. Last but not least, geopolitical risks are concerning every expert, leaving volatility the name of the game in the foreseeable future.

The Economy & The Data

A summary of findings from recent research on the state of the economy includes the following:

• Stimulus (COVID money flooded the economy): Inflation would need to rise by 30% to absorb the stimulus. So far, inflation has gone up around 20%. Thus, there is around 10% left to absorb to get supply and demand in alignment. If interest rates stay put, the COVID funds will run out in about a year. Otherwise, we will have spurts & starts.
• Government spending: Government spending has continued at historic levels. For example, in 2023, nominal GDP was up $1.5 trillion yet federal debt increased $2.5 trillion, leaving a gap. Debt is increasing at what some see as an alarming rate.
• Inflation rate: It has gone up by 20%, but to absorb the stimulus, there is still 10% to go. It is likely interest rates will remain flat to work through the COVID money. If not, there will be bursts of inflation and recession (volatility). Inflation is likely to stay higher than the goal around 3-3.5%.
• Unemployment rates & labor participation rates: Layoffs surged 136% in January to the second-highest level on record with financial companies, the technology sector, food production companies, and retail with the highest levels (in order of sequence). On the other hand, these findings led the experts to think employers would show the latest hiring at 180,000 workers yet the number came in double that amount (353,000). The unemployment rate stayed about the same at 3.7% with the labor force participation rate at 62.5% (which close to 1% lower than pre-pandemic, 63.4%). There is 1.3 jobs for every person looking for a job. From a client point-of-view, they simply do not have the high-skilled resources required although they are automating low-skilled jobs, and depending on the industry, they have put a pause on hiring.
• Wages: Workers’ wages are improving but they still have not caught up with inflation. In the last three years, real average hourly earnings are still down 2.4%. Thus, people are not feeling better.
• GDP (gross domestic product): Real gross domestic product has largely recovered. It increased 3.3% in the 4th quarter and consumer spending has remained relatively strong. It shifted from goods to services, but has held up overall even with the interest rate hikes thus far.
• Banking: There is concern about the regional banks. They hold most of the commercial real estate loans that will need to be refinanced at higher rates over the next few years. Also, bank’s liquidity requirements are driving concerns with the changing of bonds prices with the quick increase in interest rates.
• Geopolitical risk: Every expert mentioned concern around geopolitical risk. It will lead to inflationary pressures with reshoring, increased prices (for example, the Red Sea rates, diversion costs, and/or expedite costs), impact on energy prices, etc.

The bottom line can be summed up in with the misalignment of demand and supply, the shrinking workforce (with Baby Boomer retirements – by 2030, the youngest of the largest generation in history will be older than 65) combined with the divergent needs for high-skills vs low skills, and the emergence of high geopolitical risk. Thus, volatility and uncertainty will remain.

What Should We Take Away

Smart executives will take bold actions to ensure they can supply their key / ideal customers while pruning low margin/ non-value added customers. They are adding customer/ product profitability, pricing, and costing trends into their SIOP (Sales Inventory Operations Planning) processes to evaluate options, set strategy and make decisions.

They will invest in the best high-skilled resources, supplement with additional options (refer to our article, Where the Talent Has Gone, and create a high-performance culture. Strong leaders will be pivotal to ensuring success. People follow leaders; not companies.

Proactive clients are upgrading ERP systems to ensure the basic processes (blocking and tackling) are in place. Additionally, they are rolling out advanced technologies including artificial intelligence (AI) to automate, digitize and thrive. To read more about these strategies, refer to our article, Automate, Digitize and Thrive in Supply Chain. It will be cornerstone to success in the next decade.

Additionally, smart clients are upgrading business processes, cleaning up their data to better utilize their ERP and peripheral systems, and building flexibility and scalability into their future thinking. The core processes include demand planning, production planning, engineering (for engineer-to-order ETO and configure-to-order CTO companies), production and inventory control, and replenishment/ distribution planning processes. From a data perspective, it is important to review bills of materials, routings, work centers, item masters, customer and supplier masters, and MRP parameters. Proactive clients are ensuring the basics are intact and they are focusing on the roadmap to stay at least a few steps ahead of changing conditions.

If you are interested in reading more on this topic:
Supply Chain Volatility, Risk & Capacity Remain Critical Priorities

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Lisa Anderson was quoted in the Snowflake blog about how data is critical to healthcare – especially in times of supply chain vulnerability as a result of natural and man-made disasters.

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The COVID-19 pandemic, coupled with increasingly common climate-based natural disasters, showed us how vulnerable global supply chains are. But while a broken supply chain in the automobile industry may mean a shortage of spark plugs at your local auto repair shop, the same situation in the healthcare industry can result in the inability to effectively treat illness or injury.

Many of us tend to be surprised when we realize healthcare is not immune to market forces. Medical logistics includes medical devices, from oxygen pumps to scalpels; prescription drugs and other medication; personal protective equipment (PPE); healthcare furniture; and disinfectants. These are channeled through almost 6,100 hospitals and 67,000 pharmacies handling about 6 billion prescriptions in the United States alone. Cast the definition of logistics a little wider and you have to account for the indispensable human equation: doctors, nurses, technologists, specialists, and administrators. 

By innovating data sharing and collaboration with partners and agencies, we may be able to better identify and more quickly analyze the most vulnerable links of the healthcare chain, predicting supply chain shortfalls and other issues before they happen

Data is critical to healthcare, said Lisa Anderson, President of the LMA Consulting Group. “Data can lead to figuring out better where your product is,” she said. “It allows you to track the demand for your product, to keep critical supplies in stock where they need to be. Everyone I’ve talked to believes data sharing is the future. The problem is, it is really quite hard to get competitors to agree to share data when you don’t know if you’re going to come out on the equal side of that situation.”

To read the full article, click here.

Originally published in Snowflake, April 11, 2023

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In working across company sizes, industries, and geographies, in almost 20 years of business consulting, every single client without exception needed to better utilize data to improve customer service and bottom line results. Smart clients are turning data into insights so that they can pinpoint where to focus quickly.

Data Analysis vs Predictive Analytics

There is a lot of conversations about data analysis and predictive analytics. What is the difference? Data analysis is using data (from your ERP system, related technologies such as CRM (customer relationship management) and MES (manufacturing execution system) along with manual inputs and spreadsheets to analyze data to understand past performance, see future requirements, and make decisions. Predictive analytics takes it a step further and uses data to predict future outcomes. Both are essential for survival, let alone for growth and success.

Data Analysis

If your business is growing and profitable, you are performing data analysis. Resources are limited, high-skilled talent is scarce. For that reason alone, smart executives are using data to make decisions. No one can afford to fumble their way through daily execution and expect to have happy customers. On the other hand, very few clients have this down to an art. Unfortunately, data integrity issues abound, data sources do not align (misleading teams of people to focus on incorrect priorities), and executives can focus on the wrong information and reports (therefore wasting precious resources and time).

Client Example Using Data Analysis

Before you can leap to predictive analytics, you have to ensure your data is directionally correct and would lead you to reasonable decisions. Take an 80/20 approach in reviewing, updating, and analyzing data. For example, we are working with an industrial manufacturer on a supply chain consulting project to develop a production schedule, evaluate capacity constraints and rapidly provide realistic projected ship dates to customers and resurrect service levels, and data was at its cornerstone. The client went live on a new ERP system a month before we started working with them and were struggling to sift through the data to provide answers on customer orders. Although they had several high-skilled resources working 24/7 to figure it out and determine order status and availability, they were drowning in data with no way out. They couldn’t work through the data integrity issues and lack of viable information to make sense of sales orders in order to schedule work orders, review capacity, and ship orders.

Thus, we had to jump into the abyss of data, determine which fields were directionally correct, which fields to prioritize, who owned which fields, and who knew how to address which issues. Also, we had to determine which sales orders reconciled to the ones on the schedule, in the production area, on the heijunka board, and which had materials available, which had shipped, and which orders had data issues and were stuck in the system so that we could determine where to start. If we had waited for each of these data integrity issues to be resolved, our customers would have left. Instead, we prioritized, made assumptions to refine the data, reconciled critical data, and addressed the largest issues quickly. Although it took longer than expected, we took a six month task, and completed it within a few weeks with this approach. Data analysis was paramount to getting to the point where we could communicate dates. Refer to our article, “Cleansing, Connecting & Consolidating Data to Support SIOP/ S&OP and a Superior Customer Experience” for additional examples and solutions.

Predictive Analytics

As your data starts to provide directionally correct information for decision-making, smart clients are moving towards predictive analytics. It makes sense to look forward instead of backwards when making decisions. In essence, transitioning from reactive to proactive from a data standpoint can provide valuable visibility for making strategic decisions for demand and supply. Data is often integral to the SIOP (Sales Inventory Operations Planning) processes as you forecast future customer needs, evaluate capacity and production sourcing options, and assess logistics capabilities to serve customers.

At its core, demand planning / sales forecasting uses predictive analytics. Depending on your process and technologies utilized, you can be performing simple forecasting or using advanced predictive analytics. The outcome is to forecast what your customers will need in the future. Listen to our podcast interview with WBS for more insights on this topic. From a capacity planning viewpoint, it makes the most sense to look forward and project capacity using predictive analytics. For example, if you can use recent performance of an item or work center to predict run rates and downtime, it will yield a far better result than referring to a standard set the year before. Depending on the sequence of items and the machine performance, the output can vary widely. This is especially relevant in engineer-to-order businesses as similar combinations and/or setups are difficult for a person to remember. Taking it a step further, as production starts running, it is better to look forward and predict machine breakdowns, thereby resolving those issues before they occur than incurring downtime with preventative maintenance.

Predictive analytics is gaining momentum in business circles as executives prefer to look forward instead of to the past. Thus, according to Statista, the predictive analytics market revenue worldwide will grow from $5.29 Billion in 2020 to $41.5 Billion in 2028.

The Bottom Line

Data can make or break your business. Clients that utilize data for strategic insights and to proactively manage the business achieve far higher customer performance with higher profitability. On the other hand, those clients that get caught up in their shorts can let data negatively impact the customer and bottom line. Which approach will you use?

Continue reading on this topic:
Achieving Customer Growth by Turning Data into Insights

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Should You Upgrade Your Systems?

It depends. Being the first to upgrade for each new fad is a waste of resources for little, if any, benefit. On the other hand, if the upgrade supports your ability to serve customers and grow profitably, it will become a “must”.

Inaction Isn’t an Option: The Southwest Airlines Debacle

Let’s take the Southwest Airlines debacle as an example. Although there were some extenuating circumstances (weather events and an infrastructure that supports point-to-point flights), the crux of the problem was that their systems failed. Weather caused delays and cancellations. Next, because Southwest doesn’t use the hub and spoke system, they do not have resources gathered in hubs throughout the country. Thus, when a flight was cancelled, they didn’t have their equipment (planes) and resources (pilots, crews, etc.) in the “right” place at the “right” time to quickly pivot.

They could have moved resources to the correct places; however, their systems were not upgraded to handle these ever-changing conditions and so flights came to a screeching halt. Southwest had insurmountable challenges resulting in cancellations to OVER 90% of their flights and countless unhappy passengers. Clearly, in hindsight, a systems upgrade would have been worth it!

How to Know If You Should Upgrade Your ERP System

How do you know if you should upgrade your ERP system? Of course, there is not a magical answer; however, there are several questions to consider:

• Does your ERP system support your customer needs? Customer needs are constantly evolving. Do you have high levels of service? OTIF (on-time-in-full) or OTD (on-time-delivery). Can you quickly pivot to changing customer conditions? Are you aware of these changes? Can you provide the visibility customers need?
• Will your ERP system support your FUTURE customer needs? Too many clients are running on the hamster wheel to keep up with current conditions and don’t have time to look to the future. Focus on where your customers are going and whether your systems will support their future needs.
• Does your ERP system support process upgrades? For example, if you would like to have inventory in the “right” place at the “right” time to achieve high service levels yet you could reduce your inventory levels to free up cash flow, does your system support this type of upgrade?
• Will your system support automation and digitization? For example, could you collect information about your machines with IoT capabilities and feed back to your systems so that you can use AI (artificial intelligence) to predict your machinery needs instead of focusing on preventative maintenance? Or could you simply attach a robot or automate a mundane process?
• Will your system connect with customers & suppliers? Viewing just one link of a supply chain is useless. You are only as strong as your weakest link. For example, to provide visibility of order status, you should connect with your suppliers, your suppliers’ suppliers and so on.
• Does your ERP system support your data needs? The only route to success is to be able to collect and analyze data so that you can stay ahead of changing conditions to make directionally correct strategic decisions. Business intelligence, predictive analytics, what if scenarios and more.
• Is your ERP system a hodge-podge of functionality or will it work like a well-oiled machine? It is fine to have add-on software so long as it works seamless together and as issues occur, the parts work together to resolve rapidly without finger pointing and wasted time. If not, you need to upgrade your system.
• Will your system keep up with modern functionality needs? For example, if your system doesn’t have a strong CRM (customer relationship management) system, it is doubtful you will keep up with customer needs. Does it have a forecasting system, an e-commerce system, an MES (manufacturing execution) system, and a WMS system (warehousing management) that acts as part of your ERP system? These are often separate systems or modules but they must seamlessly integrate.

If Your Answer is Yes, Perform a Selection Process

Clients frequently call to ask for help in selecting an ERP system. Unfortunately, it is not for the faint of heart. If there is one thing that is a certainty, it is that ERP salespeople are sharks. Some are likeable, friendly sharks, but sharks nonetheless. They easily sell ice to an eskimo.

Review our ERP category on our blog for several articles on ERP selection, but a few of the key steps include:

• Assess business requirements: Determine what your system needs to do in detail to support your needs.
• Document business requirements: Although we’ve tried to find a way to minimize the workload associated with this step, it is vital to keeping the sharks at bay.
• Source potential ERP suppliers: Narrow the list to ones that support your type of business and customer needs effectively.
• Develop an RFP & Scoring Method: Even for small selection projects, it is good to document at least the key points to further narrow your list of potential ERP suppliers.
• Demo the software: It is important to visualize how you can use the software.
• Ask second-level questions &/or perform deep dive demos: The devil is in the details. The software can look amazing yet not support your business. It happens frequently. The key is in asking the underlying questions and seeing how the software handles your specific needs. Will it handle the 80/20 or only 70/30? It will make a huge difference to your success – and resources invested to achieve success.
• Pricing comparisons: There is no doubt that software suppliers must sit at a bar and find ways to make pricing apples to oranges so they are impossible to compare because after 100+ projects, there are still curve balls to work through every time. You must get it to “apples to apples” to understand if your likely investment and comparison.
• Partner comparisons: The 80/20 of success will be implementation (assuming you get a decent fit for functionality). This will boil down to your partner and the capabilities of the specific resources you will work with on the project. Even with the best of partners, you can go down the “wrong” path easily and waste countless hours before pivoting. In fact, it is only the better partners that will tell you when they discover it is the wrong path because they don’t want to tell you that time has been wasted. The better your partner, the better your success.
• Summary comparison of functionality, partner and implementation which results in a final selection.

If Your Answer is No, Don’t Do It!

It is often easier to blame process issues on the system. Or, it could be for a lack of knowledge of how to expand the use of your system. Refer to our recent article, The MacGyver Approach: Leveraging Your Under Utilized Asset to find ways to upgrade your results without upgrading your system. Or ask experts to evaluate your situation. Don’t pursue an upgrade if it isn’t needed! Upgrading your system unnecessarily could be the quickest route to insolvency or customer debacles.

If you are interested in discussing whether you need an ERP upgrade, review our ERP resources and/or contact us to discuss further.

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Managing Increased Complexity with High OTIF & Efficiencies Using Technology

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Improved supplier data will give your supply chain the competitive advantage to drive customer loyalty.

Netstock recently collaborated with leading supply chain transformation expert, Lisa Anderson, Founder & President of LMA Consulting, to discuss how businesses can improve supply chain processes and reduce supplier risk to ensure they meet demand.

According to Zippia.com, only 6% of companies report having complete supply chain visibility. With access to improved supplier data, you’ll better understand your supplier network and utilize these insights to ensure you make the best decisions to optimize your planning. Investing in a supply chain planning solution that monitors and measures suppliers’ reliability will increase supplier visibility across your supply chain. 

Use these five steps to manage and identify supplier risk across your supply chain.

Step one: Classify your suppliers. 

To prioritize your efforts, you should know the following information about your suppliers: 

1. Which suppliers deliver on time and in full
2. How many items each supplier provides
3. The average lead time per item
4. What stock items come from which locations

External disruptions also impact suppliers. By increasing the frequency of communication with your suppliers, you’ll know if they are experiencing any challenges in sourcing materials or potential delays in delivering your products. This information will help your business quickly adapt to any increased lead times so you can find an alternative solution.  

Step two: Use a dashboard to help disseminate the data. 

Work with improved real-time supplier data across your supply chain. During these volatile times, having a Sales & Operations Planning solution in place can help manage the data and provide visibility to make the best choices to help your business minimize supplier risk.

Click here for the full story.

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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.

Cleansing Data

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.

Checking

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.

Charting

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

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As companies do their best to navigate the significant disruptions in the supply chain, they have realized they have to integrate technology if they want to survive, let alone thrive post pandemic. In fact, there has been a dramatic acceleration of the adoption of technology and the digital transformation. What would have taken 5 years is happening imminently. Thus, executives realize they must integrate technology to decrease labor costs and minimize resource requirements especially as reshoring gains steam, and they need to implement technology to keep up with changing customer expectations and to create predictable revenue forecasts. What are the most successful companies doing to successfully integrate technology successfully? Our expert panel of manufacturing and supply chain consultants will weigh in on the challenges of automation and the use of technology and will provide insights and solutions to achieve success.

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Every successful client is experiencing rapid growth and changing customer requirements. To support this growth in a scalable, profitable manner, turning data into insights is of paramount importance.

Data Overload

Every client has an overload of data. Whether using a modern ERP system or struggling with a complex maze of manual spreadsheets, the one item in common is an abundance of data. We have worked with clients with archaic ERP systems, but so long as we can extract data, progress can be made. On the other hand, we have worked with clients with Tier 1 ERP systems such as SAP or Oracle, but if they cannot get data out of their systems, the client is in a worse situation than the one with an archaic ERP system. Every manufacturer has products, defines how to produce those products (whether captured in someone’s head, in spreadsheets, or via an ERP system), has customers, receives orders, purchases from suppliers and so on. Each of those transactions and associated data is stored somewhere.

Data and related transactions adds up quickly. If you think about your typical day, you receive an overload of data before you even arrive at work. Your phone gives you the latest news and alerts. The news provides additional insights into key events. You drive by billboards and listen to the radio or talk with people on your way to work. You receive automated reports in your inbox. You most likely receive thousands of inputs before even arriving at work. Its no wonder we are overloaded! Since the pandemic might have cut out your drive, you might have replaced it with a walk or workout although you are still receiving data via the radio, a podcast or on the phone. To make matters worse, we employ people to run reports and track metrics on spreadsheets. Every time corporate, a Board member or a customer asks a question, another metric is created. Soon, we employ loads of people to publish data that no one uses to make decisions. How do we throw a line to save ourselves from drowning in data?

Take Stock of Your Data

Collect your data. It is likely to be an eye opening exercise. Take stock of standard reports your ERP system provides. Find out which reports are run frequently, and ask those people what they do with the information. Find out if the reports have to be massaged or adjusted to provide useful information. What has to be changed and why? Take a survey of the metrics your organization is collecting, tracking, or publishing. Ask who is using the metrics and what decisions are made based on the information.

In addition to understanding the volume and use of data, check into your data accuracy. Can folks make directionally correct decisions with your data? Or are there several exceptions that have to be discussed before taking action? If someone not intimately familiar with the data evaluated the report or dashboard, could he/she make a decision?

Reduce the Number of Reports to the Meaningful

We have not yet met a client that wasn’t collecting too much data. It is easy to get too ambitious and lose track of what is meaningful. Ask who would notice if the reports weren’t completed for a week? Go talk to that person and find out why. If no one knows or has a compelling reason to review the report, turn it off or tell the person compiling the information to stop compiling it for the next week or month. See if anyone asks about it.

Attend the meetings where data is discussed. Are people arguing about data or having meaningful discussions about what to do based on the data? If they are debating the accuracy, get the group together to develop ONE report that everyone agrees upon to illustrate the data. Eliminate the rest. For example, a building products manufacturer had several nuances to how they calculated OTIF (on-time-in-full), and so the team would debate the way the data was captured at each meeting instead of acting upon the data. We took a step back and aligned on the meaning of fields, how the data was captured and used, and we consolidated several reports into one cross-functional process to collect and analyze a single source of data. Although it certainly wasn’t the only reason, having meaningful reporting supported key decisions and actions which led to an increase in OTIF by 30%.

Turn Data Into Insights

Simply reducing the number of reports isn’t enough. It is essential to make the information meaningful for decision-making. Understand your sources of data. Connect the data. Cleanse the data. And turn data into insights with dashboards, data models or simply by retrieving the appropriate data for decision-making. It sounds much easier to do than it is in reality. For example, an aerospace client captured open orders and shipment data. Because they selected the data by the order creation date instead of the shipped date, the reports didn’t contain the correct information and didn’t reconcile to the invoicing data. Additionally, because they didn’t have their customer segregated in a key field they used to pull the report, the records that were blank didn’t get captured. Both were easy fixes but poor decisions were being made until the data was reconciled and cleansed. On the other hand, once the data was directionally-correct, they used it to determine the machine capabilities, capacities and staffing required to support their growth plans so they could get in front of their demand.

In another client, they had a proactive executive team for a small, rapidly growing building products company. They tracked potential sales leads, probability of success, sales history by geography and much more in their CRM system. Thus, they knew they needed to focus efforts on a key product line in the northeast because sales were lower than expected. By having this key information at their fingertips, they could target their sales efforts on the customers, products, and locations that would make a meaningful difference to their growth plans. They were recognized as the fastest growing company for several years in a row due to the expert ability to turn data into insights and take appropriate action.

You can turn data into insights no matter what systems you have supporting your business. It is far more important to have the appropriate processes and to focus people’s attention on how to dig into data and use it to make meaningful decisions. With that said, if you can support your data efforts with a business intelligence solution, you will gain a leg up on the competition. The great news is that BI tools are scalable; start small and scale up with complexity as it makes sense to support your business. Once you can slice and dice data and have 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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Thriving Post-Pandemic: Turning Data into Insights

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