Monthly Archives: December 2013

8 Key Design Considerations for Optimizing Your Demand Planning Process: Part I


Today’s guest post is from Josh Peacher, a Senior Consultant in the Operations Practice of Archstone Consulting, A Hackett Group Company.

Demand Planning was once an overlooked element of supply chain management. However, more and more companies are beginning to understand how essential this component is to overall operational well-being. After all, a demand forecast is the genesis of the supply chain process. If poor demand signals are being sent through the system, it becomes extremely difficult to manage raw material and finished goods inventories, execute an efficient manufacturing process, effectively service customers, and ultimately drive an accurate financial forecast. So if your organization hasn’t already taken a long, hard look at improving its demand planning process, it’s time to begin. As a starting point for your journey, let’s take a look at the 8 key design considerations for optimizing your demand planning process. In this first installment, we’ll focus on the 4 most basic design considerations and then move to more advanced principals in the second installment.

1. Start with Statistical Forecasting and Exception Management

  • Statistical forecasting should always drive the original forecast. A simple set of formulas such as exponential smoothing, weighted moving average, and Holt-Winters can deliver more accurate, reliable, and efficient forecasts across the entire sku base than manual forecasts. This can often be a change management challenge for many organizations as demand planners feel a pride of ownership over their forecast and have trouble with relinquishing control to a set of arithmetic functions. This is where exception reporting comes into play.
  • Exception reporting utilizes a set of pre-defined criteria to identify skus that are not ideal candidates for statistical forecasting. Since the strength of statistical forecasting comes from identifying patterns in demand history, highly erratic and/or variable skus are not good candidates and require manual intervention of the forecast. While exception criteria are customizable, common filters include frequent zero demand periods, high variance between last 6 months history and next 6 months forecast, high variance in month-over-month demand history, and frequent shortages. Exception reporting is also an excellent way for demand planners to prioritize their time across the sku set and focus their efforts on the skus that truly require attention.

2. Select the Right Software Tool

In today’s environment of sku proliferation and real time information, it’s become a necessity to utilize a demand planning tool to assist with the demand planning process. Software solutions such as Manugistics, SAP APO, and Logility all have their strengths and weaknesses. Key criteria to evaluate when selecting a solution include:

     
  • Customer service reputation of the provider
  • The tool’s ability to handle forecasting nuances (i.e., 5-4-4 calendar recognition and promotional forecasts)
  • Transparency and reliability of the generated statistical forecast
  • Forecast performance reporting and exception reporting capabilities
  • Flexibility to forecast at multiple levels (e.g., sku, customer, category, business unit)

3. Track the Right Metrics

Demand planning metrics should serve two purposes:

  1. Identify improvement opportunities and
  2. Drive accountability.

The appropriate metrics will vary based on the characteristics of the industry and company in question. However, a few core, agnostic metrics are routinely found in leading organizations. These include:

     
  • WAPE (Weighted Absolute Percent Error) – In my opinion, WAPE is the most balanced and telling measure of forecast error. Some professionals will advocate for MAPE. However, MAPE doesn’t effectively account for volume as the forecast error % for each period is treated equally.
  • BIAS – Bias is similar to forecast error. However, bias provides a measurement of whether your forecast tends to be above or below actual demand thus signaling a forecasting over/under “bias”.
  • Period-over-Period Error Trend – You’ll want to understand whether your demand planning process is improving or digressing. Measuring the forecast accuracy over time will also help to identify meaningful changes occurring in the business.

4. Leverage the Correct Data

Statistical forecasting and exception management will help to get a reasonably accurate forecast. However , to drive forecast error down to best-in-class levels, demand planners must leverage external information.

As the graphic above shows, there is an abundance of information that demand planners could call upon to help them adjust their forecast. The real art of demand planning is knowing which of these data sources to use and when. Over time, your organization will get a sense for which information streams are most relevant and can begin to build a rules-based process around the use of external information.

Thanks, Josh! We look forward to Part II.

The Manufacturing Labour Shortage Isn’t That Big of an Issue

when compared to the logistics labour shortage in the trucking industry.

The SCIDigest Editorial staff might have painted a grim picture in their recent article on how the labor shortage in manufacturing really is getting worse, but SI believes this grim picture is only temporary, whereas the logistics labour shortage is poised to continue getting worse for some time. Before SI explains why, let’s examine the current situation.

The SCDigest Editorial quoted a recent Fortune magazine article that said that companies that make tangible products are struggling to find candidates for about 237,000 job openings — a number that is 89,000 more than the total number of jobs created by the U.S. Economy in September. To make matters worse, nearly 80% of the manufacturing workforce is over the age of 45, and over 33% are over 55 and not far away from retirement — and the number of young workers (under 30) entering the sector is shrinking significantly, with one study reporting that only 5% are 25 or younger.

Basically, the majority of young people just don’t see manufacturing work as an attractive option — which it isn’t if you are talking about old-school 1980’s shop floor manufacturing which was hard work for low blue-collar pay.

Turning our attention to logistics and trucking, new estimates put the driver shortage at 240,000 drivers, as SI reported back in March. With 100+% turnover a year, one third of drivers reaching retirement age this decade, and an average graduate age from driver training schools of 54, the trucking industry is in dire straits!

In comparison, manufacturing has it easy. Young professionals enter an industry in which they see opportunity, typically defined as a mix of growth potential in their career and their salary, and given two equal options, many will choose the industry with the higher starting salary. Taking this into account, we see that manufacturing is in much better shape.

First of all, factory jobs are not what they were in the old days. Most of the tedious, menial labour has been replaced by automation and the only manual labour done by shop floor workers are high-end speciality tasks as most of the work on the shop floor is focussed on maintaining the robots on the automated assembly lines. In comparison, in trucking, you’re still driving a truck. The only difference is instead of driving an old pollution producing rig, you might get to drive a new hybrid that uses electricity and biofuel or clean diesel and is equipped with enhanced catalytic converters.

Secondly, the opportunity for advancement is great. Factories need senior engineers for each task, floor managers, and plant managers — there is a career path for a bright engineer. In comparison, in trucking, unless you can be a dispatcher, you’re still driving that truck in 20 years.

Thirdly, due to the sophisticated high-end nature of the work in manufacturing, most of the jobs are for skilled engineers who will often start at 50K to 60K a year, and have the potential to climb to 100K a year or more as an engineer progresses, whereas the trucking jobs require one skill — the ability to drive a truck — and salaries, adjusting for inflation, have not increased and typically don’t increase much more than inflation on an annual basis (if the driver is lucky).

Manufacturing can easily solve their labour shortage by

  1. enhancing their image and
    which could be as easy as the NAM producing the right PR campaign (with prime-time airings on traditional and online media); a
    manufacturing equivalent of the “Got Milk” campaign could rejuvenate the industry
  2. implementing their own apprentice-type programs
    which take community college graduates (for the more traditional jobs in welding, machining, etc) and even university graduates (for the newer jobs in robot maintenance, etc.) and teach them the skills that colleges and universities don’t

In comparison, logistics is out of the frying pan and into the fire between a rock and a hard place. With little advancement opportunity and limited earning potential, how do you make trucking advantage to anyone who has other options? Unless you’re targeting fast food workers (tired of asking “would you like fries with that”), interest is going to continue to wane.

You Need Analytics. You Need Intelligence. But Do You Need Visualization?

Probably not. Especially if all that visualization does is significantly jack up the cost of your analytics solution.

As a Procurement Pro, you need to know who is buying what, from which supplier, at what price, and in what quantity, and you need to present the relevant aggregate summaries to department managers and overall summaries to the CPO and the C-Suite — who will want to see the data graphically. Since even Excel has a plethora of charts and graphs at your disposal, it’s a safe bet that any decent spend analytics platform is going to have just about every standard graph and chart imaginable.

But just because you need charts and graphs, does this mean you also need interactive graphics that form the cornerstone of modern data visualization solutions? Kind of yes but mostly no. Interactive tree-maps, that are already built into leading spend analysis solutions, that allow a user to click on a quadrant that corresponds to a category and drill down into sub-category spend to understand why a certain category represents such a significant percentage of spend are quite useful, but 3-D rotating graphs, hyperbolic category hierarchies, network models, etc. are not that much use when it comes to understanding spend.

SI really likes the straight-forward answer to the question of does your company actually need data visualization given by Bill Shander over on the HBR Blog Network.

Given that data visualization can be expensive, especially if it involves large amounts of data and complex algorithms or deep interactive experiences, you need to decide if it’s worth the investment. If you’re selling straightforward solutions to simple problems, data visualization is probably not worth the money. Consumer packaged goods firms Coca Cola and Nestle don’t need interactive graphics to explain their products, just as Playboy and Playgirl don’t need to educate the opposite sex much about their centerfolds.

If a vendor is trying to convince you of the value of their analytics solution, they shouldn’t need rotating cubes to demonstrate that they can do sophisticated analysis, just like you shouldn’t need 3-D graphs to point out that a certain department’s refusal to adopt the corporate contract is increasing costs 15% and costing the organization 1 Million a year.

Simply put, if the analytic solution at your disposal has some advanced data visualization capability that is useful, use it, but don’t spend a lot of money for fancy graphics that don’t convey any more information than you can package in an Excel bar chart!