Forefront delivers supply chain optimisation with Process Analytics

It all started with an innocuous conversation last year with life long friend and CIO Brendan...and a beer of course. He explained the transformation he was going through and the vision he had for optimising operations at his company. It was ambitious to say the least. Leveraging real time data coupled with the theory of constraints, Brendan implemented niche technology that sought to improve customer satisfaction, quality of service and increase throughput at the same time...

Sandra Cutic
Analytics Roadmap

Situated in Canberra, Forefront Analytics were fortunate enough to work with one of the lead innovative government agencies to develop a Roadmap for the build and maintenance of a Business Intelligence and Analytics (BI and A) solution to meet its strategic data and information management needs. They work with Australian businesses, individuals and hold many strategic relationships locally and globally.

Jennifer Chandler
Putting the Fizz into Business Intelligence Analytics

One of Australia’s largest beverage companies originally called upon the Forefront Analytics team to add value to their investment in SAP HANA. From there we helped them bridge the gaps between their business customers, project and innovation teams, and the IT support organisation with a Business Intelligence Strategy.

Jennifer Chandler
Part 2 – Anomaly Detection on Twitter Sentiment Stream

In Part 1, I introduced the topic of anomaly detection in streaming analytics context. In this part, I will briefly describe and apply three simple anomaly detection tools to find unusual single records within a stream. First let’s dig into the dataset we will be using.

Sandra Cutic
Part 1 – Introduction to Real-Time Anomaly and Change Point Detection in Big Data Streams

So, you’ve jumped on the IoT bandwagon. You are streaming data left, right and centre. In fact, you may be streaming data from thousands of different data sources at once, be they ISP servers, sensors on manufacturing conveyor belts, transactional systems or people. The big question, is what do we do with all that live incoming data? That’s going to depend on why you collected the data in the first place. You do know, right?  One thing we do know, is that while companies are scrambling to collect “big data”, they ultimately need to get some value and insight from this investment. 

Sandra Cutic
Calculating Moving Annual Target (MAT) with SAP BusinessObjects 4.x

Moving Annual Target or the Moving Annual Total (MAT) is calculated as the Actual value of the variable based on the running sum of the previous 12 months. Each month (or end of the month) is an indicator of adjusting the MAT value to include a new month and exclude the last month from the previous 12 months.

Sandra Cutic
Build on you good thing, build on!

Rebecca is a competent and enthusiastic field service technician. Her work includes reviewing information about the design of a part or specifications for a piece of equipment they’re in the midst of repairing. William is a face-to-face sales associate and requires access to critical client data when meeting with customers or prospects. Both of them spend time in the office and in the field/on the road, and have a number of colleagues who perform similar roles...

Sandra Cutic
The universe is a big place… perhaps the biggest

SO, IN SAP LAND, WHY USE A SEMANTIC LAYER ON TOP OF A CUBE?

That’s pretty much the main question I’d like to answer, or better, discussion I’d like to start. I don’t think there’s a single answer to it, nor am I trying to promote the blanket use of semantic layers on top of any multi-dimensional cube. Some prefer iPhone, some prefer Android; both achieve the same thing (mostly).

Sandra Cutic
Order Picking in a Large-Scale Modern Warehouse – A HANA/R Use Case

Warehouse optimisation has always been critical to the success of retailers, but as retailers grow to export goods globally and expand sales through e-commerce, it becomes even more critical. There are many operational processes within a warehouse that can be optimised, including batching, zoning and warehouse layout. One area of importance to large warehouses, is warehouse picking efficiency – that is, how quickly one or more orders can be filled by a person or automated machine physically traversing the warehouse and picking items off the shelves.

Sandra Cutic
Learnings from early adoption of BW7.4 with BOBJ 4.x

Working with the latest BW7.4 on HANA comes with its challenges. Thinking progressively and adopting the new data source types of Composite Providers, Advanced DSO and Open ODS Views is the way to go. However, you would think that SAP would update their code base to allow their BI Suite to connect to those objects. Wouldn’t you want the likes of Webi and Lumira to take full advantage of those sources in an enterprise environment?

Sandra Cutic