SAS stands for Statistical Analysis. It was developed for multivariate analytics, data management, advanced analytics, business intelligence and predictive analytics. The software can be used through SAS programming language, Base SAS and graphical interface. SAS is the only vendor for leading data warehousing, traditional BI and analytics applications to develop intelligence from massive amounts of data. For every organization even though it is gaining huge profits, there still is a scope for improvement. SAP software strives on higher inventory and reduced costs. This case study focusses on Asia’s biggest food and beverage firm’s usage of SAP software, to overcome financial

SAS stands for Statistical Analysis. It was developed for multivariate analytics, data management, advanced analytics, business intelligence and predictive analytics. The software can be used through SAS programming language, Base SAS and graphical interface. SAS is the only vendor for leading data warehousing, traditional BI and analytics applications to develop intelligence from massive amounts of data. For every organization even though it is gaining huge profits, there still is a scope for improvement. SAP software strives on higher inventory and reduced costs. This case study focusses on Asia’s biggest food and beverage firm’s usage of SAP software, to overcome financial downfall.

SAS is an integrated software suite for business intelligence, advanced analytics, data management, and predictive analytics. The software can be used through SAS programming language, Base SAS and graphical interface.

With SAS software, a company can do following tasks:

• Access data in almost every format, including database files, SAS tables and Microsoft Excel tables.
• Manipulate and manage company’s existing data to fetch the data that is required. For instance, company’s get choice to subset their data, mix it with other data, and create new columns.
• Analyze company’s data with help of statistical techniques which cover wide range of techniques starting from descriptive measures such as correlations to mixed models and logistic regression to end with sophisticated methods like Bayesian hierarchical models and modern model selection.
• Present the outcomes of company’s analysis in more purposeful report that is exchangeable or shareable with others too. The reports that are created can be saved in a different formats, including RTF, HTML and PDF.

With the taken case study, it is shown that using SAS software, how a company can overcome downfall in terms of finance.

Problem Statement:

Even a well-run A business has scope for improvement. For instance, a business gaining $ 100 million in sales, if strived upon improvement of four to six inventory, may turn yields of $6 million in cash; from six to eight turn the releases into $4 million; and if extended to eight to ten then frees up approximately $2 million.

Challenges an organization faces while not able to achieve the desired goal includes:

• Inaccurate Forecasting: If the forecasts are inaccurately estimated, that may turn to overstocks or stock-outs, destroying or spoiling customer satisfaction.
• Low Inventory Turnover: Definition of low inventory turnover is, storage intakes a huge capital along with greater interest for expenses whereas very low cash tied up in unproductive working capital.
• Long cash-to-cash cycle time: Cycle times that take too long tie up cash inclines to losses as those tied up cash are needed for development, marketing and capital investment.
• Inadequate Systems: Instead of opting for analytics, most of the companies impart resource-intensive and time-consuming solutions driven by biased judgment and domain knowledge, results in inaccurate forecasts.
• Pessimism over long-term results: When an issue encounters in inventory control and if solved with quick-fix approaches, leaves management concerned that revenue streams and customers may have negative affect. Management has impression to adopt permanent changes of their forecasts, cash on hand and processes for lasting longer in market.

Goal : A company is required to meet customer requirements along with lesser working capital. Under any economic conditions, top priorities of a company needs to be minimizing debt and preserve cash. When operating and margin cash flows are stagnant or finished, the next most promising and reliable source of potential cash is definitely working capital. Most of the companies keep restricted or confined reigns on working capital and are not left with option to utilize payable or receivables. Eventually, one crucial component of working capital which remains reliable for scope of improvement is inventory. When industries analyzed, it was deduced that higher demand forecast accuracy helps to lower inventories approximately 15 percent, 35 percent shorter cash-to-cash cycle times and 17 percent durable perfect-order fulfillment. All the industry stalwarts always realize that accurate forecast lead not only to increased revenues and higher customer satisfaction, but more importantly reduces working capital and lowers inventories – which infers to higher available cash.

Approach by SAS:

Seeking optimal inventory levels is sometimes diligent task. The efforts needs to be applied on utilizing customer demand and other upstream data to build the unbiased and most accurate forecasts at all levels of product hierarchy. SAS troubleshoot the issues or problem by delivering services and software to help a user.

• Companies should fetch data that is complete, correct and available when they need it with data management tools that is able to access data in any format and from any system, then automatically cleanse and transform the data to get it ready for analysis.
• Generate demand-driven and accurate forecasts at any hierarchical level with flexible data modeling capabilities and patented forecasting engine that make sure the firm uses the right models and methods.
• Identifying optimal demand-shaping actions with help of advanced analytics via a user-friendly interface that allow the company to test different scenarios and conduct what-if analyses to predict or calculate the financial impact pf changes in their product mix or marketing.
• Predict or calculate forecast performance against KPIs with an interactive dashboard that allow the company to track, report, monitor and surface alerts on forecast performance metrics.
• Proactively drive S&OP processes with help of advanced consensus forecasting planner workbench that delivers statistically driven, automated, weighted consensus forecasts and workflows that enable the consensus forecasting process.
• Exchange information companywide through self-service Web interface that allow users interact with and drill down through reports as required.

SAS offers advanced forecasting processes and technology that can improve S&OP address and processes associated challenges for greatly enhanced demand and supply alignment. No other vendor provides event modeling that has as much breadth and depth for anticipating incremental lifts in sales volume related with marketing events and activities, sales promotion and other irregular events that have impact on sales demand. SAS is at top in advanced analytics, offering the companies with:

• It offers statistical forecasting engine that automates creation of business/product hierarchy on the fly and evaluate every level of the hierarchy to find the accurate statistical model which includes input for POS data and casual factors.
• SAS offers the scalable statistical forecasting which allow to run through a GUI or batch mode.
• A model custom and repository algorithms for forecasting within a hierarchy that aid all model families (like ARIMA, regression, intermittent demand function, time series, ARIMAX and UCM), personalized algorithms and infinite methods.
• Scenario modeling and what-if analysis for learning the financial impact of changes in marketing strategies and product mix so that the company can respond proactively to alter demand signals. Only SAS offer a demand-forecasting solution that allow permanent improvements in inventory turns that the company can depend on for future plans and budgets.

Example:

One of Asia’s largest beverage companies is distributing and manufacturing the accurate amount of quantity and mix of beverages to fulfill the consumer demand which was quite challenging. However, the company’s production plants at different location is estimating their own forecasts with the help of different processes and tools that is entirely dependent on historical data. This resulted in stock-outs and overstocks, high inventory levels along with high redistribution ad distribution costs.

With help of SAS, the company got a solution that allowed the company to mix demand data with the replenishment planning process to determine production levels, generate sales forecasts and creation of distribution plans for 100 percent of its products at different locations.

a result, the company was able to reduce stock-outs, escalate margins and lower inventory levels coupled with improved production turn upto 50%. Besides that distribution and carrying costs was reduced by replenishing the first time.