Category archives: AIMMS
In today’s business environment, you can’t remain competitive without mastering analytics. Still, most companies are “not very far” when it comes to implementing analytics and garnering benefits from data, as a recent survey from CSCMP suggests. In many cases, organizations haven’t succeeded in making the organizational changes required to become data-driven. Not enough managers are fluent in the language of analytics. Leveraging analytics at scale is hard. As the graph below shows, lack of talent, investment in hardware/software and siloed data are among the most common challenges.
According to a recent Wall Street Journal article, finance chiefs are cutting back on Excel because it hasn’t “kept up with the demands of contemporary corporate finance units.” “I don’t want financial planning people spending their time importing and exporting and manipulating data, I want them to focus on what is the data telling us” – Adobe Inc.’s finance chief Mark Garrett tells WSJ journalist Tatyana Shumsky. This mood prevails among supply chain executives as well.
Spreadsheets still dominate planning in the supply chain. We recently commissioned Supply Chain Insights to conduct independent research on supply chain network design, and 65% of the companies surveyed are reportedly using spreadsheets to support this process. Spreadsheets are familiar, inexpensive and convenient. But they pose serious setbacks for organizations:
- They are practically never integrated with other systems
- They are not updated automatically
- The logic behind them is often only clear to those who create them and often dies when somebody leaves, making collaboration difficult
- Analyses are often slow given that you can only work with a certain amount of data
- Version control is hard
Perhaps most importantly, spreadsheets are error prone. In fact, as Market Watch reports, close to 90% of spreadsheet documents contain errors. Even after careful development, spreadsheets “contain errors in 1% or more of all formula cells.” In large spreadsheets with thousands of formulas, there are often dozens of undetected errors.
The digital age has just begun. We haven’t seen the full force of disruptive business and operating models and there is no doubt that many more will keep emerging. We are only beginning to see the impact that digital transformation will have on our human resources as well. This will demand the creation of new and higher levels of personal development and organizational effectiveness to manage and sustain this culture transformation.
The next frontier: supply chain data architecture for your needs, not to feed the needs of numerous supply chain tech vendorsPosted on October 18, 2017 by Chris GordonLeave a reply
Data overload and quality issues are common problems faced by all organizations. This makes it really difficult to start getting value out of data with analytics. Inevitably, when you buy supply chain optimization software, you need to start hunting around for data to make the technology work – it can feel like you work for the technology vendor, not the other way around. That’s why 60% of the time spent in analytical supply chain projects is spent collecting data. The focus is on making the data work for the technology, rather than tackling your business need.
But today’s SC leaders don’t have weeks or months to solve pressing problems and the worse thing is, it doesn’t necessarily get easier once you’ve been through your first solution implementation. You may have deployed an out of the box Network Design solution. Implementing an Inventory Optimization App will take another tedious data integration process. In a supply chain context, that means it will take months before you can actually start improving margins, availability and service levels. To work proactively with analytics and truly embed them in your organization, data needs to be structured smarter and accessed for many needs.