Investments made in enterprise software are usually not in the range of neglectable, so buying decisions are quite a process that requires thorough deliberation. For Retail companies even more so as margins tend to be significantly smaller than in other industries. Take the average profit margin of food retailers in Germany: A highly concentrated and competitive market, it leads to margins between 1 – 3 % only. Consequently, investments are intensely scrutinized with respect to the value they will generate for the company.
Today, I wanted to give a brief overview on what investment discussions typically revolve around when it comes to SAP Forecasting and Replenishment: What elements do our customers consider before making their decision, and how can they determine quantifiable cost and benefits prior to investing in the solution?
Let’s start with the low hanging fruit:
The easiest part is the determination of the direct cost that goes along with the purchase of software, such as cost for licensing, maintenance, implementation, change management, and possibly additional hardware cost.
Another aspect is opportunity cost: – how much would it cost the company to stay on their current system versus changing to another one? This is something I will address at the end of this blog post.
More complicated deliberations are needed to grasp the benefit side of a cost/benefit analysis. Especially as they include cost as well: Many benefits will appear to you in a decrease of cost rather than in an increase of revenue, like cost for stock overages.
Often, the real objectives and the actual needs of a company are not directly addressed in the RFI we receive very early in the engagement process, for example: “The system should have different forecasting methods to cover different selling pattern.” (A very popular one.)
The actual need is to generate a more accurate forecast. The means to get there is not important at that stage of decision making. The mere look at features and functions will not tell you to which amount the system is able to contribute to your objective - which is what you ultimately want to know.
So rather than limiting your thinking to features and functions - think big! Before getting into the details of an RFI:
describe actual needs and overall objectives
derive measurable KPI that go along with it – like KPI which describe increase in forecast quality, reduction of out of stocks, increase in revenue, reduction of necessary manual interaction
and then assess the value the new solution is going to deliver to your objectives. (How to do this? - see end of this blog post)
You also want to understand whether the processes covered by our solution are a good fit for the company? Fitting is not solely used in terms of covering a retailer’s current processes but also regarding whether the software supports the evolution of its future business.
A simple example: We have multiple customers who addressed us from a position of very manual and gut feeling based forecasting and replenishment procedures. Their processes require too many resources, lead to overstocks for some items and stock outs for others, eventually resulting in less cash flow, poorer margins and hence a stall in growth. A fit then doesn’t mean to mimic their current operations or just cover their current business. It is going beyond that, helping them to get to the next sophistication level of F&R processes that scales with the company’s growth while delivering better KPI. This could result, for example, in a solution in which
sales forecasting and replenishment is widely automated (thereby speeding up processes and freeing up resources for tasks that actually require human interaction)
sales forecasting yields significantly higher accuracy while it relies on robust statistical methods and facts rather than on intuitiveness of individuals (thereby providing the ground work for optimized stock levels)
other information available to the company are integrated. For example, factors which influence future sales, such as promotions, are automatically taken into account whilst the forecast is calculated (thereby resulting in better sales forecasting and a higher degree of automation)
the replenishment into distribution centers is not guess-work based on distorted past goods issues, but relies on the aggregated store order forecasts (thereby optimizing the stock in distribution centers as well)
the solution scales with the business growth of the company (thereby ensuring its ability to generate revenue)
it is possible to gradually evolve from more manual to more sophisticated procedures
Certain aspects of this fit cannot be quantified reliably. How could you possibly number the contribution of a solution’s capability to scale to the amount of revenue generated?
However, other items can be made quite tangible: For example, the cost of labor, cost of stock overages, cost of revenue loss due to stock outs - these are measurable items that can be determined to provide for a clear decision. Which leads us to the last item:
How to measure the value SAP Forecasting and Replenishment might create for a company?
While it is a feasible task to assess cost and benefits of an already implemented solution, coming up with those numbers for a solution that a company doesn’t operate (yet) seems to be an impossible undertaking. Yet, to take a sound investment decision this is exactly what you’d need to do.
This is why the SAP Center of Excellence Forecasting and Replenishment offers a service named “Proof of Value”: It allows companies to estimate the possible benefits SAP Forecasting and Replenishment would yield for them. For that purpose they simulate an implementation with the customer’s own historical data and compare the results SAP Forecasting and Replenishment would have produced to the actual results obtained with the current solution.
How does it work?
First, the SAP Services team analyzes real historical sales, stock data, and all further data relevant to order processes, such as order / delivery schedules, supplier information etc.
Second, data quality is assessed, and missing or inconsistent data uncovered and shared with the customer.
Third, the team will model scenarios to simulate SAP forecasting and ordering processes for a period between 6 and 12 months.
Finally, on basis of resulting data KPI are generated, which allow assessing the results, for instance:
Reduction of inventory carrying costs achieved
Average out-of-stock rate
Once you put all this together, it is almost easy to calculate return of investment of the solution.
Here’s where also opportunity cost come back into play: You can now compare the cost of continuing to run your current system, including cost such as lost revenues incurred, to the cost of switching to another, and hopefully better, solution.