One person managed 200+ Excel templates to store data and manually generate financial statements and reports. When outside investors had questions about company operations, manual reporting meant it often took days to answer. Reporting times concerned stakeholders and investors enough that future investments risked being delayed or even cancelled.

The finance team at ADAC Automotive had to manually consolidate and fix errors in budget submissions and monthly financials from departments across the company – leaving little time for higher value, insightful analysis. Finance director Kelly DeWispelaere and her team at ADAC oversee an annual budget of $300 million. But with budget owners spread across 30 departments and six plants, they couldn’t ensure that everyone worked from the same numbers. The team spent so much time manually consolidating each department’s figures, and troubleshooting errors, that they had little room for insightful analysis. They would begin the budget in August and complete it in January – if they were lucky. ADAC runs Microsoft Dynamics AX for its ERP and general ledger (GL), and relied on about 80 separate Excel templates for its budgeting process. Although employees were comfortable using Excel’s familiar interface, they’d hit the limits of what the software could do. For example, the finance team couldn’t automate the collection and consolidation of their budget input figures from each department. Employees would also export data from Dynamics into individual spreadsheets to analyze and report on their numbers. But without reliable version control or audit trails, this practice worked directly against the security, control, and visibility of an enterprise-grade system like Dynamics. ADAC’s finance team needed a more efficient system to end their troubleshooting headaches and free up time to analyze data and uncover new insights to help drive company-wide decisions. They decided to find an FP&A solution that offered the functions they needed, along with seamless and secure integration with their Microsoft ERP, GL and Excel templates.

AvalonBay assesses investment opportunities in the field but lacked a central tool to capture the high level of detail involved in each assessment. Development decisions were based on memory, incomplete information, and gut-feel, leading to lost opportunities and risky investments. Risky decisions could put the company’s share price at risk, along with affordable housing plans for those in need.

Data dispersed across numerous spreadsheets raised issues in efficiency and numerical accuracy. Frequent use of spreadsheets led to tedious and error-prone manual recreation of templates and reports. Uncertainty in financial data resulted in time spent on reconciling numbers, rather than analysis.

AEG faced significant challenges with their financial planning and analysis (FP&A) processes. The data was scattered across 60 different spreadsheet tabs, which compromised the accuracy of financials and the efficiency of report creation. Manual copying and pasting of data from various sources made ad hoc reporting difficult and time-consuming. Additionally, there was little time available for analysis and review, which hindered the ability to gain deeper insights and make informed decisions.

Pinnacle’s business and demands on finance were growing too fast for its existing FP&A software to handle. Ad hoc reporting and analyses were too cumbersome for the FP&A team, especially during audit season. Reconciling and re-reconciling accounts across 8 companies made the close process frustrating.

By 2013, when MicroVention opened its manufacturing facility in Costa Rica, unit sales and product-line growth had already begun to outstrip management’s ability to meet global demand cost effectively. The reason was simple. MicroVention was operating with inadequate technology and outdated processes for forecasting demand and planning operations. Prior to August of that year, when MicroVention implemented Vanguard Predictive Planning, sales and operations planners had to hustle through manual processes for gathering data and preparing forecasts each month. These processes were not only labor intensive, but rife with guesswork, input error and formatting inconsistencies. The result was unreliable sales forecasts, which led to faulty demand planning, ill-timed production scheduling, and unsustainably high operating costs. The company was not only wasting valuable human resources on the compilation and management of spreadsheets, the output of those spreadsheets was difficult to interpret and unfit for critical planning purposes. The situation had become untenable.

Through a series of acquisitions, Winzer had multiplied its number of stocking warehouses across multiple locations. The company struggled to reduce safety stock while maintaining its 99 percent order-fill-rate goal. Most of Winzer’s 165,000-plus SKUs experienced sporadic demand that could not be successfully forecast with the company’s in-house purchase recommendation system. To mitigate financial loss due to inaccuracy, Winzer carried $1.08M in inventory with low turn. Lack of trust in the system spurred Winzer’s purchasing managers to review and adjust each forecast, often using assumptions that were inconsistent from manager to manager. Winzer estimated this process cost each manager 2 to 3 days per month. Limited reporting features added to the difficulty of managing inventory. To reduce inventory and associated carrying costs, Winzer began looking for an automated inventory optimization solution that could accurately forecast demand.

Recently, Aurobindo’s U.S. market expansion began to outpace its internal planning capability. The company was using spreadsheets to forecast demand, which had become a major problem. The expanding portfolio overwhelmed their capacity to forecast demand. Sales and supply planners had to cobble together numerous, complex spreadsheets monthly to generate aggregate demand forecasts and corresponding supply plans. As the portfolio expanded, the spreadsheets became too cumbersome to manage and too error-prone to be used with confidence. The problem was exacerbated by the nature of the business, with recent consolidation among distributors and large wholesalers now controlling a big chunk of the market. This led to frequent and dramatic shifts in demand, making forecasting extremely difficult. The company’s forecasting process was essentially static, relying on a spreadsheet document that could not be updated mid-planning cycle to adjust for new sales orders, shipments, and other critical events.

The pharmaceutical development staff at Ethypharm often needs to compare finished product batches manufactured under different conditions, such as varying compression presses, manufacturing sites, device parameters, and dosages. Additionally, the analytical development staff must transfer analytical methods to different laboratories, including subcontractors, to ensure all labs control final products appropriately. This requires sufficient information to guarantee consistency and accuracy across different labs.

In agrobiological programs, new scientific results are regularly produced through field and laboratory experiments. SUA constantly publishes the findings of its data analyses in the areas of agrobiology, biotechnology, food production, and more. For this purpose, the faculty of agrobiology and food resources had primarily been using Microsoft® Excel as its analytical tool of choice, with a few exceptions, despite that Excel had become wholly inadequate for evaluation and publishing of research results. The university’s main goal was to improve the support of research and scientific activities conducted by its pedagogical and professional technical staff. Due to ever-increasing demands for improved publishing quality, SUA recognized the need to acquire more appropriate software, made possible through funding from the European Union structural funds project. SUA faculty commenced a search for more advanced tools that would enable processing and analysis of experimental data on a higher level than allowed by Excel. The software would need to fulfill dual roles as a suitable tool for academic research and as a standard learning tool in the classroom. Important factors in the software selection included (but were not limited to) an appropriately simple and user-friendly interface, integration of Slovak or Czech language, and purchase price. University representatives selected the STATISTICA software, available from StatSoft CR.

Folkia, a company providing short-term loans, faced the challenge of accurately selecting customers to minimize credit risk. The company needed a robust credit decision support system to improve its credit scoring model. The existing methods were not sufficient to discriminate effectively between good and bad customers, leading to potential financial risks. Additionally, maintaining and updating the system was cumbersome, requiring manual changes and extensive data structuring. Folkia aimed to develop a more efficient, accurate, and easy-to-maintain system to enhance its credit decision-making process.

In connection with an efficient and highly successful patient treatment process, the GUH performed a clinical research study. One aspect of the research was clinical pharmacology (pharmacogenetics, pharmacokinetics, pharmacodynamics) at intensive care units, whose practical use for patients entails drug dosages (doses plus intervals) individualized and optimized for specific patients. One such example would be a child with a specific pathological condition requiring an individualized treatment mode, e.g., whole-body hypothermic treatment, or extracorporeal membrane oxygenation (ECMO), or haemodiafiltration (HDF). As a practice, this facility performs individual pharmacokinetics/pharmacodynamics (PK/PD) and therapeutic drug monitoring (TDM) for patients in all cases of treatment where the effect of a drug depends on dose, plasmatic concentration, and therapeutic range. Processing of primary data and assessment of a given drug’s effect and movement within the organism for a larger group of patients—e.g., in a population of critically ill newborns and children—require not only multidisciplinary cooperation, but also suitable software. Aside from this purpose, this software is also used for post-graduate level instruction, lectures, and high-quality professional publications. In the past, the GUH addressed these needs via outsourcing, which was shown to be inefficient and unsuitable as it involved working with suppliers who do not understand this field completely.

In order to ensure manufacture of the highest quality product, the company engaged StatSoft Polska to develop and implement an integrated SPC system with STATISTICA software at its production plant in Gorlice. The SPC system’s main task is to conduct online monitoring of production process parameters.

Advanced Bionics regularly conducts clinical studies to improve cochlear implant systems. The challenge was to find an easy-to-use analytics solution that could seamlessly resume analyses, share results among team members, and produce high-quality, customizable graphs for presentations and reports. Before adopting STATISTICA, the research staff used various software packages like SPSS, Excel, SPAD, XlStat, SAS, and Matlab. However, these tools did not meet all their needs, particularly in terms of graph quality and ease of use. The need for a more efficient and user-friendly analytics platform was critical to support continuous improvements and scientific progress.

TAURON Dystrybucja S.A. has ongoing obligations to forecast electric energy supplies to serve a wide consumer base across a broad geographic territory. These forecasts must account for such factors as multiple supply voltage levels, contractual power amounts, etc., and they are to be produced monthly for a varying range of contractual periods. The provision of accurate and timely forecasting had grown beyond the practical capabilities of over-the-counter software products used in the past.

Surgeons at the University of Iowa Hospitals and Clinics needed to know if patients were susceptible to infections in order to make critical treatment decisions in the operating room. Reducing the infection rate has major implications for overall patient health and cost savings. In the United States, roughly one out of every 20 patients admitted to a hospital will acquire an infection. Knowing if the patient is vulnerable can help doctors make critical decisions about treatment. According to the U.S. Centers for Disease Control and Prevention, surgical site infections are the most common, accounting for more than 30 percent of occurrences, and putting patients at risk of illness and prolonged hospitalization. Sometimes, people die. The total cost of hospital-acquired infections to the healthcare industry is estimated at $10 billion per year.

Due to the high-precision nature of the semiconductor industry, it is important that Photronics controls their products and processes to ensure that quality photomasks are shipped to their customers. Photronics management recognized the need for a well thought out enterprise-wide SPC System and conducted a thorough investigation of several SPC packages.

This project began by evaluating the state of three existing paint lines, two powder coat operations and a liquid line, which are used to apply paint from a widely varying palette of colors to the Office Systems. Steelcase customers require that millage be enough to provide sufficient coverage of the systems. Traditionally, reports on the paint millage had summarized the performance of these lines with the average of the paint thickness, and the variation in this process was not largely considered. As such, using the prior SPC and monitoring tools, the average millage was consistently within customer specifications. However, commonly more paint was applied than necessary to satisfy customer requirements, at significant material expense to Steelcase.

Aluminerie Alouette needed to continuously improve its production processes to stay among the worldwide leaders in aluminum manufacturing. The company faced the challenge of understanding the influence of several hundred inputs on the aluminum manufacturing output. Some inputs could be controlled, such as the dosage of additives and energy management, while others, like outside temperature and raw material composition, could not. To address this, Aluminerie Alouette required a solution that could identify significant inputs and develop multivariate models to monitor key performance indicators.

Borden Chemical faced the challenge of managing and analyzing vast amounts of data generated from their production processes and research studies. With over 30 sites worldwide and more than 150 researchers, Quality Control engineers, and technical consultants, the company needed a robust system to automate data analysis and report generation. The manual process was time-consuming and prone to errors, leading to inefficiencies and delays in delivering critical insights to both internal stakeholders and customers. Additionally, Borden Chemical required a solution that could seamlessly integrate with their existing data repositories, such as SAP and their LIMS system, to ensure accurate and timely data access for analysis.

3M France faced the challenge of maintaining the progress achieved through numerous Six Sigma projects and keeping critical process variables under control. The company needed a robust system to monitor and adjust manufacturing processes in real-time to ensure quality and performance. Additionally, they required a method to continuously improve their processes and track various quality metrics, such as the percentage of acceptable incoming material and the number of defects by type and week.

argonauten360° faced the challenge of needing advanced analytic tools for client scoring, clustering, and life-time-value computations in the telecommunications industry. Each project presented unique data scenarios and analytic challenges, requiring a flexible and powerful toolset. Additionally, the solution needed to provide quick ROI, be easy to apply, and have a fast learning curve for analysts to quickly take ownership of advanced analytic procedures.

Instrumentation Laboratory faced challenges integrating and accessing data from various systems for quality control of its manufacturing process. The complexity of medical device manufacturing and testing involves large amounts of data from suppliers, manufacturing processes, product testing, and field performance. The data are stored in different file formats and use different terminology, making it difficult and time-consuming for engineers and management to access and analyze the data. To address these challenges, Instrumentation Laboratory investigated the utility of a Web browser-based reporting tool and portal with links to static and interactive reports analyzing data from disparate databases.

In the past, UCM’s Faculty of Natural Sciences (mainly the Department of Chemistry) used a run-of-the mill software application such as Microsoft® Excel®. In order for the university to become more competitive and increase its visibility through scientific publications, however, it would need to process experimental results using suitable statistical software, the standing of which would impact the scientific community’s perception of the value of UCM’s publications and related activity. The university sought an advanced tool that not only would permit thorough analysis of scientific data but would also be suitable for use in education. It needed software to analyze clinical and physical data, test results, substance behaviour and separation (chromatography) in model systems, drugs, or food samples. First-rate scientific work helps UCM obtain state funding and increase its rating. The school’s management selected STATISTICA Base and STATISTICA Data Miner through StatSoft CR in Prague.

For planning needs and quality control processes monitoring, Cˇeská rafinérská previously used traditional tools included with commonly available computer software programs. For superior prediction of process changes—both undesirable and desirable—it became necessary to acquire much more sophisticated software. Cˇeská rafinérská thus started searching for a suitable solution and, through recommendations, it found the right one in STATISTICA software from StatSoft CR. The main requirement of the company was to acquire a tool for observing the quality of products in production units. In practice, this meant observing the quality of input values so that the quality of the output values would meet specified requirements. Among the observed variables are temperature, which can influence the properties of raw materials during manufacturing, as well as the density and viscosity of the material. The goal is to monitor a massive volume of measurements with the help of appropriate software in order to achieve maximum quality according to spec, as well as to ensure optimum production scheduling.

GfK Market Measures needed to automate the analysis and production of their market research reports. These reports are formatted documents that draw upon GfK Market Measures’ data repositories of prescribing behaviors and include the results from statistical summaries and comparisons. The manual process was time-consuming and prone to errors, necessitating a more efficient and reliable solution.

Oklahoma State University (OSU) faced the challenge of equipping both traditional and non-traditional students with the tools necessary to solve a variety of classification and prediction problems. These problems ranged from predicting diabetic illnesses based on demographic data to forecasting financial indicators like the S&P 500 and foreign exchange rates. The university needed a comprehensive data mining tool that could handle these diverse requirements while being user-friendly and cost-effective. After evaluating several leading data mining tools, OSU chose STATISTICA Data Miner for its impressive array of algorithms, graphical interface, and web-based accessibility. The tool's ability to be launched as a web application was particularly appealing, as it allowed students to access it from any web browser without needing to install client-side components.

Shire’s Rare Diseases Business Unit faced significant challenges in managing vast amounts of manufacturing data using a complex network of heterogeneous systems, including LIMS, Excel, and JMP. The manual collection and reporting of key findings were extremely time-consuming and prone to errors. The division needed a validated data capture and analysis tool to conduct statistical process control, monitor processes, and identify areas of improvement. The key requirement was for an off-the-shelf application with validatable output and demonstrated use in the biopharma industry.

EOS sought user-friendly software that would “score” debts—in other words, a tool to assess the success of each step of the collection process. The required tool would need to serve internal decision-making processes, e.g., in the sense of deciding whether to go to court over debtor disputes, or whether it should collect a debt during a specific phase of the process, or skip a step, etc. In light of changes in the global—and, thus, also the Czech—market that are driving companies to retain their customers, a new system of procedures is coming about that brings with it the need to keep a debt out of the courts for as long as possible. This requires an advanced modeling tool that is capable of automating required processes in a suitable manner according to company needs, one that is quick, efficient, and reduces workload. EOS has found that all of these needs are met by a combination of STATISTICA Data Miner, STATISTICA SAL (Sequence, Association and Link Analysis), and STATISTICA Standard software. EOS has found that these tools from StatSoft CR together provide a suitable solution due to a favorable combination of user-friendly environment, acquisition cost, and requirements for implementation into its existing system.