Whilst the quotes in the below picture may be more familiar as the heated courtroom exchange between Tom Cruise’s Lt. Kaffee and Jack Nicolson’s Col. Jessup at the denouement of the movie A Few Good Men, it will also resonate with NAMs, Demand Managers, Brand Managers and Category Teams who have tried to make sense of the various drivers affecting the demand for consumer goods.
What do I mean? Well, hopefully, most demand reviews are not quite as tense as the confrontation between Tom and Jack (they might come close…), but the reality is that most consumer goods organisations do not have an enterprise platform for integrating all relevant demand signals (internal and external) into a single source of truth; far less the ability to interpret those demand signals and take real-time decisions to enhance volumes and profitability. In short, most consumer goods organisations simply can’t handle that amount of data and make sense of it.
Why is this important?
Say for example you are an alcoholic drinks manufacturer and you have a strategic imperative to deliver NPD to offset volume decline in a mature category. The Category Team has identified a trend amongst younger consumers for sweeter products with a fruit mix (think fruit cider). The NPD team have developed and tested the liquid, Marketing has defined the 5 P’s, and Sales have sold the proposition to the customer as the best thing since sliced bread. The first deliveries to customers have been shipped and field sales are busy securing display space and running consumer sampling events.
But how are consumers responding – both in store and online? Just how much distribution did you achieve? How many costly out-of-stocks were you able to identify in those precious early weeks post-launch? What happened to the recommended selling price (RSP) after the introductory promotion period ended, and how does the RSP compare to that of your competitors? What were the top 10 and bottom 10 performing stores, and why? How were sales affected by the weather? What category volume / value share did you achieve one month after launch?
The answers to some of these questions, or the ability to find the answers, might already lie somewhere within the organisation – most probably within an offline spreadsheet on a NAM’s hard disk drive or within somebody’s head, or they might be available as a piece of commissioned work via a Market Research partner. Most probably it will be prove very difficult to access the information which is likely to be incomplete and subject to time delay in retrieving EPOS and syndicated data, and it certainly won’t be conveniently contained within a single solution. This is where SAP DSiM comes into its own.
Step in Demand Signal 2.0 (DSiM)
SAP has just released Demand Signal Management 2.0 (DSiM) which is designed to help manufacturers track sales of goods and respond faster to demand fluctuations. The true power of SAP DSiM lies in the fact that it enables a wide spectrum of commercial processes, non-exhaustive examples being demand planning and demand sensing, promotion optimisation, NPD launch, and consolidated market share reporting, but more of that later.
But what exactly is SAP DSiM?
For a non-techie like myself, SAP DSIM 2.0 is essentially a solution for housing and analysing data gathered from a plethora of sources in the Macro environment (retailer EPOS, distributor data, market research data, social media, weather) and the Micro environment (shipments to customers).
This data can then be integrated into the SAP Business Suite on HANA, and consumed by business users via applications when they want to perform analytics. Speaking of analytics, we all know that Sales people hate nothing more than the frustration of waiting in front of their screens for a report to run, watching the clock whilst that annoying blue circle wheels around. However, because DSiM 2.0 is powered by SAP HANA (SAP’s in-memory database) these analytics are performed quickly, really really quickly.
Enhanced reporting performance is not the only advantage that SAP HANA brings to the table
- Consider the huge volumes of data generated by EPOS feeds (hours x days x number of SKUs x number of stores) – this can mean up to billions of rows of data. Previously, data analysis with these volumes wouldn’t have been possible or much harder to do
- Integration of data is also extremely fast compared to BW / data warehousing on disk. Loading high volumes of data traditionally takes a long time and removing a lengthy processing window means that data can be transformed and ready for reporting much faster.
What’s the downside of SAP DSiM?
Identifying relevant data is, in theory, the easy part - but it may be harder to acquire in practice, particularly accessing daily EPOS data necessary for enabling real-time response, or social media scanning in product categories lacking an online buzz.
Further hard yards come in uploading and validating data in differing formats. However, this is more of a one-off effort, and is where a partner earns its corn. Once the data has been mapped and cleansed, then any future alterations (such as new SKUs and store locations) can be handled by business analysts within the application. After the pain comes the glory, and once these hard yards have been overcome then business users are able to perform real-time analytics using a user-friendly interface which is equipped to work on a mobile device.
It’s still early days for SAP DSiM 2.0, and there are many possible usage cases to be explored and reported upon as uptake extends beyond the early adopters. The key thing is to start by really understanding what it is that you want to achieve or measure, invest appropriately to acquire the right level of data, and then deliver an application that enables business users to make decisions in real time.
As Col. Jessup might say, “you want answers?”
If so, then SAP DSiM 2.0 is a good place to start looking.