The Smart Forecaster
Pursuing best practices in demand planning,
forecasting and inventory optimization
1. Built-in ERP functionality is baked into Order Management.
Consider what is meant by “demand management”, “demand planning”, and “forecasting”. These terms imply certain standard functionality for collaboration, statistical analysis, and reporting to support a professional demand planning process. However, in most ERP systems, “demand management” consists of executing MRP and reconciling demand and supply for the purpose of placing orders, i.e., “order management.” It has very little to do with demand planning which is discrete process focused on developing the best possible predictions of future demand by combining statistical analysis with business knowledge of events, promotions, and sales force intelligence. Most ERP systems offer little statistical capability and, when offered, the user is left with a choice of a few statistical methods that they either have to apply manually from a drop-down list or program themselves. It’s baked into the order management process enabling the user to possibly how the forecast might impact inventory. However, there isn’t any ability to manage the forecast, improve the quality of the forecast, apply and track management overrides, collaborate, measure forecast accuracy, and track “forecast value add.”
2. ERP planning methods are often based on simplistic rules of thumb.
ERP systems will always offer min, max, safety stock, reorder point, reorder quantity, and forecasts to drive replenishment decisions. But what about the underlying methods used to calculate these important drivers? In nearly every case, the methods provided are nothing more than rule-of-thumb approaches that don’t account for demand or supplier variability. Some do offer “service level targeting” but mistakenly rely on the assumption of a Normal distribution (“bell-shaped curve”) which means the required safety stocks and reorder points recommended by the system to achieve the service level target are going to be flat out wrong if your data doesn’t fit the ideal theoretical model, which is often gravely unrealistic. Such over-simplified calculations tend to do more harm than good.
3. You’ll probably still use spreadsheets for at least 2 years after purchase.
Most often, if you were to implement a new ERP solution, your old data would be stranded. So, any native ERP functionality for forecasting, setting stocking policy such as Min/Max, etc., cannot be used, and you will be forced to revert back to cumbersome and error-prone spreadsheets for at least two years (one year to implement at earliest and another year to collect at least 12 months of history). Hardly a digital transformation. Using a best-of-breed solution avoids this problem. You can load data from your legacy ERP system and not disrupt your ERP deployment. This means that on Day 1 of ERP go-live you can populate your new ERP system with better inputs for demand forecasts, safety stocks, reorder points, and Min/Max settings.
4. ERP isn’t designed to do everything
The “Do everything in ERP/One-Vendor” mindset was a marketing message promoted by ERP firms, particularly SAP, to get you, the customer, to spend 100% of your IT budget with them. That marketing message has been parroted back to users by analyst groups, IT firms, and systems integrators, drowning out rational voices who asked “Why do you want to be so dependent on one firm to the point of using inferior forecasting and inventory planning technology?” The sheer number of IT failures and huge implementation costs have caused many companies to rethink their approach to ERP. With the advent of specialized planning apps born in the cloud with no IT footprint, the way to go is a “thin” ERP focused on the fundamentals – accounting, order management, financials – but supported by specialized planning apps.
The expertise of ERP consultant’s lies in how their system is designed to automate certain business processes and how the system can be configured or customized. Their consultants are not specialists in on proper approaches to planning stock, forecasting, and inventory planning. So if you are trying to understand what demand planning approach is right for your business, how should you buffer properly, (e.g., “Should we do Min/Max or forecast-based replenishment?” “Should we use forecasting method X?”), you generally aren’t going to find it and if you do that resource will be spread quite thin.
Just-In-Time (JIT) ensures that a manufacturer produces only the necessary amount, and many companies ignore the risks inherent in reducing inventories. Combined with increased globalization and new risks of supply interruption, stock-outs have abounded. So how can you execute a real-world plan for JIT inventory amidst all this risk and uncertainty? The foundation of your response is your corporate data. Uncertainty has two sources: supply and demand. You need the facts for both.
Let’s start by recognizing that increased revenue is a good thing for you, and that increasing the availability of the spare parts you provide is a good thing for your customers. But let’s also recognize that increasing item availability will not necessarily lead to increased revenue. If you plan incorrectly and end up carrying excess inventory, the net effect may be good for your customers but will definitely be bad for you. There must be some right way to make this a win-win, if only it can be recognized.
If you both make and sell things, you own two inventory problems. Companies that sell things must focus relentlessly on having enough product inventory to meet customer demand. Manufacturers and asset intensive industries such as power generation, public transportation, mining, and refining, have an additional inventory concern: having enough spare parts to keep their machines running.
This technical brief reviews the basics of two probabilistic models of machine breakdown. It also relates machine uptime to the adequacy of spare parts inventory.