The Smart Forecaster
Pursuing best practices in demand planning,
forecasting and inventory optimization
1. The setup will be straightforward.
We just need to feed our demand histories into our new statistical methods, and we can start planning more effectively. Not quite: it’s about the technology and the process. You are investing in a new business process to develop forecasts for driving business strategy and inventory planning decisions. It will take time to get all stakeholders involved: sales, marketing, procurement, operations, and maintenance/technicians (for spare parts inventory). Who owns the forecast? What will your items’ forecast hierarchy look like? Where will the most business knowledge come from? Is there a consensus process that will use the business knowledge to customize the forecasts to your particular situation? Does everyone understand the statistical methods? Is there agreement on the underlying values that balance holding, ordering and (especially) shortage costs? Are you prepared to make choices along the crucial tradeoff curve relating inventory costs to customer service levels? How do you plan on measuring forecasting accuracy/error? Does management understand the concept of “forecast value add” whereby you track the error with each version of the forecast (statistical error vs. sales forecast error vs. consensus error). Without this context and agreed upon participation from key stake holders, the system will still be implemented but used in silo
2. All I need is historical demand data, and then I can start forecasting.
Almost. Getting good data isn’t easy. Are your demand history data complete and correct? Are your supplier data (e.g., lead times) also complete and correct? Have you recognized the special needs of new and end-of-life items? Sure, IT could export a file of aggregated demand data (weekly or monthly), but how do you know it is correct? When orders and shipments are booked, they fall under a variety of different transactions codes. You have to be able to know how to compose your demand signal. Orders or shipments? Include or exclude returns? What about warehouse transfers? What about returns that occur many periods after initial shipment? How will my ERP interpret the forecast? But wait…we are using a solution with an ERP connector that promises data will flow back and forth seamlessly. An ERP connector will certainly cover the transfer of historical data and forecast results between systems but it won’t improve bad data quality. You also have to make sure the ERP connector has the flexibility of determining how to compose your demand history. For example, if it is hard coded to pull certain transactions types that you may not want or require different transactions it doesn’t include, you’ll need customizations. There is also the problem of product supersession and/or location changes – i.e., Product A gets phased out and becomes Product B, or now Product A ships from a different warehouse. Sounds simple, but if this happens often across thousands of items then it must be accounted for as part of an automatic forecasting process. Otherwise, your users are required to manually manage this constant updating. Then you lose economies of scale. More “data wrangling” means more hassle, more errors, and missed decision deadlines. Less frequent updates can mean less accurate forecasts, which leads to excess inventory for some items and insufficient inventory for others
3. If we get a better forecast, we’ll have the right inventory, reduce stockouts, and increase service.
The demand forecast is one component of a larger process. If you have another department that applies incorrect buffers (too much or too little safety stock), then a lot of the benefit of a more accurate forecast goes out the window. You have to look holistically at forecasting within the context of inventory management. You can’t get maximum benefit (and in some cases, any benefit) unless you account for all components including buffer levels such as safety stock and reorder points, ordering rules, and managing supplier/internal lead times. It is not uncommon for buyers to implement rules of thumb inventory policies such as ordering early or inflating the forecast to reduce the risk of running out. The opposite behavior where an order signal triggered by the forecast is deferred to a later date to prevent an order from being placed “too early” is equally prevalent. This type of behavior is based on a pain avoidance response that occurs within companies that have an ad-hoc inventory planning process that doesn’t holistically connect the forecast to inventory strategy.
4. The more forecasting models the better.
This is true in some cases. In an ironic twist, the more models to choose from sometimes means you’ll have a greater chance of picking the wrong one. This occurs even when there is an automated system selecting the right method. This is because most automated forecasting systems still make the mistake of selecting methods based on best fit to past demand. This backward-looking approach usually results in poor performance when looking forward in time; this can be tested by waiting a bit and then comparing forecasted versus actual demand (or, if you don’t want to wait, by hiding some of the recent data and forecasting it, in which case the actuals are already in hand). In principle, having more models might be useful, but what is important is understanding the approach for model selection. Furthermore, most forecasting models produce a single-number forecast (“Demand for Product A will be 17 units next month”) without any indication of the forecast uncertainty or margin of error. Without knowing the margin of error, you cannot appreciate and rationally manage forecast risk.
In our software, we offer automated time series selection that chooses from dozens of proven techniques on the basis of estimated future performance, not fit to past data. We also go beyond single-number forecasting using probabilistic methods to generate thousands of forecast scenarios to assess forecast uncertainty. We’ve found that this approach is considerably more accurate for certain types of data than the traditional tournament selection. So, in these situations the number of models we’d recommend using is “One!” Does that it make inferior? Of course not. Take the time to fine-tune your models in order to see what works best for your business.
5. With the right software, anybody can do the job well.
Would that it were so. However, after our involvement in decades of implementations, it is clear that not everybody should be at the demand planning keyboard. The job doesn’t need a super-hero, but certain traits make for success:
- Having a company-wide perspective. So many problems in demand planning stem from stove-piped thinking. A proper planning process surfaces the need for all stakeholders’ involvement, so a user unable to think beyond his or her previous fiefdom can be a liability.
- Being innumerate. A user who is not comfortable with numbers will struggle.
- Appreciating randomness. This is similar to innumeracy but goes beyond. Most of the friction in demand planning and inventory optimization derives from randomness: in product demand, in supplier lead time, etc. Without a good feel for how randomness causes trouble, a user will often be puzzled at how poorly his or her decisions turn out
- Being incurious. Top-flight software encourages users to game out “what if?” scenarios to see how to tweak automatically computed solutions to get even better results. If the user never gets into a “what if?” mentality, they will under perform. Furthermore, playing with alternative scenarios is one of the best ways to build an instinctive feel for the randomness in the system.
The five reasons outlined here show why implementing a forecasting, demand planning, or inventory optimization system isn’t as simple as turning on the software, importing your historical data, and getting some user training on how to operate the software. You are implementing a new process for planning your business and determining stocking policy that will drive spending on inventory and impact your ability to capture sales. However, the effort is well worth it. Per an Institute of Business Forecasting (IBF) blog, a 1% reduction in under-forecast error at a $50 Million company yields a savings as much as $1.52M. Conversely, the benefits of a 1% reduction in over-forecast error were $1.28M yielding an average benefit of $1.4M. This means you stand to save your business $260,000 annually for every $10 Million in revenue!
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.