Posts Tagged ‘Forecasting’

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IndustryWeek’s Manufacturing Business Challenge this month is entitled “Unreliable sales projections ripple through company.”  This month’s challenge focuses on a company struggling with faulty forecasts.  The challenge is:

Since I’ve been the CFO at Sodtt Ceramics the last two-plus years, I have worked to develop a thorough sales and operations planning process that monthly works through plans for sales, production capacities, inventory, lead times, investments, finances, etc. Sodtt makes insulators and substrates for the electronics industry as well as orifices and nozzles for industrial uses. With such a range of customers, it’s important that our planning works as planned. Occasionally, everything hums along just fine from month to month, but inevitably we are blindsided.

I believe good organizational planning really starts and ends with our customers and sales forecasting, and too often this data turns out to be very unreliable. The director of sales and I have tried to install some standardization to the sales function, from the way salespeople define “a sale” to the manner by which they develop their individual forecasts. But most of the sales staff are a bunch of cowboys, shooting from the hip and grabbing their commissions (forecasts be damned). And despite some investments in demand planning and supply-chain planning tools, as a company we still rely heavily on a variety of manual techniques and legacy spreadsheets to pull our forecasts together and share. And these homegrown tools make it difficult to update the many day-to-day changes that occur.

I believe the sales forecasting component of our planning process has turned into Sodtt’s Achilles heel. With the rapid ups and downs of our markets today, my operations and supply chain is constantly being whipped back and forth: either piling up unnecessary inventories (ours and suppliers’), overtime, and expediting costs to hit sales targets that eventually don’t materialize, or straining to satisfy unexpected orders and racking up every conceivable quality and delivery error in the rush. I don’t want a crystal ball, but I do need to reorganize our sales forecasting before it damages the company. Where to begin?

Solutions to this challenge are provided by Deloitte and Kinaxis.

I found some great background on forecasting concepts and also some good suggestions in the white paper “Demand Planning:How to reduce the risk and impact of inaccurate demand forecasts“. Regarding the suggestion of using collaborative forecasting techniques to improve the accuracy of forecasts, this is something that I’ve seen with the customers I’ve worked with (mostly in the high tech/electronics sector). Most companies of any size do some type of collaboration to develop a forecast. At a bare minimum, sales forecast data is collected on a monthly basis and marketing and product management are consulted regarding new product introductions, product end-of-lifes, promotions, discounting, competitor activity and industry trends. As the author points out, one of the real challenges related to this activity is trying to minimize the time collecting and rationalizing the data as this takes time away from the type of analysis that adds real value. This can usually be solved by choosing a suitable, scalable tool that all users will accept (which, as the author points out, is not Excel).

It’s also important to measure forecast accuracy properly. On a recent visit, I noticed that once of our customers is using a model that looks at a weighted composite of bias (actuals consistently higher or lower than forecast), error (the size of the delta between actuals and forecast) and stability (how much is the forecast changing over time) which should be applied separately to each of the collaborative inputs. Generally, collaborating with customers on forecasting is something that is considered to be difficult to pull off. As the paper points out, efforts in this area should be focused on your small set of ‘A’ customers. Also, if attempting this, you need to be sure you have a forecasting model you believe in and that has shown to work because you’ll often be pitching this process to a customer that is still in the dark ages of forecasting.

Regarding the last point about forecasting more, the theory may makes sense but I think in practice, it would be very difficult to pull off. Forecasting is inherently a process of gathering multiple points of information and finding patterns that, when compelling enough, cause a change to occur. I think rather the key is to communicate changes to the forecast to all affected parties as soon as they occur and only when they occur rather than necessarily forecasting more often.

Kinaxis has just released a new white paper entitled “Demand planning: how to reduce the risk and impact of inaccurate demand forecasts.”

As a general rule, forecasts are always inaccurate. This paper discusses two approaches which can be used to reduce the risk and impact of inaccurate demand forecasts:

1. Collaboration between customers and suppliers to improve the accuracy of the forecast.
2. Quicker response to demand changes to reduce the cost of forecast error.

By improving your demand planning and demand management processes, your organization can improve customer service (by being more responsive) as well as operations performance (by achieving inventory reductions).

You can download the paper here: “Demand planning: how to reduce the risk and impact of inaccurate demand forecasts.”

Infosys has a compelling post entitled “What’s the right forecasting approach in the current business environment?“  This is a great question in light of the volatility most businesses face today.  I posted my comment on their site, but have also included it below as well.

### My comment ###

I have a few comments about the original post as well some of the follow on entries.

I do agree overall with the approaches recommended by Aatish. I think to sum it up, demand planners need to ensure that they are not relying on simply one input (the quantitative forecast driven from demand history) but rather to consider (and challenge) inputs from multiple providers including sales, marketing, product management and key customers in order to make more informed decisions. Sales forecasts tend to be more accurate in the short term while marketing forecasts tend to be more accurate further out along the curve. Working with key customers on collaborative forecasting programs can help improve the quality of the forecast because you are getting closer to the ultimate source of the demand. From experience, it’s also a great way to improve overall customer relationships because it’s often the case that the customer sees this as a value-added service that can distinguish you from other vendors.

The demand planner is the individual in the organization that is responsible for providing sober, second-thought to all of the inputs gather during the forecasting process. They typically are the only role that brings a historical perspective to the process because everyone else either has a short memory or would prefer to forget past predictions. How often has this customer backed out at the last minute or overstated the business that they thought they were going to do? What happened the last three times we ran this kind of promotion in the spring? How quickly did demand materialize the last time we introduced a new model of this product? These are all the classic types of questions that, if given the opportunity, the demand planner needs to ask when driving to a consensus of future demand and that’s just to get to ‘square-one’ i.e. what is demand going to be given what sales, marketing and customers are planning to do. The next step is to then use that information to try and shape demand in order to align it with what the business wants to do (represented by high-level management forecasts propagated down to the SKU or product level). This might mean shifting marketing focus to higher-margin products or regions, implementing specific add-on or upgrade incentives with key customers, or adding key features to a new product.

Just two final points. Aatish started off his post by saying ‘Now, when the consumer demand is extremely volatile and sentiments are down across almost all the industry segments… ‘. I would argue that the comment about extreme volatility has been the norm for quite a while now at least in high-tech and consumer electronics segments. The approaches being suggested are really becoming the minimum mandatory for companies that want to survive the operational and margin pressures faced by a lot of today manufacturers. The other key point is related to Vikram’s comment about demand planners not having the time to attend review meetings. I believe this is a symptom of the process and tools used to support the demand planning process (i.e. planners are spending too much time collecting and rationalizing data). There are tools available now that provide the ability for all participants in the process including sales, marketing, finance and even customers to simultaneously input their information into a single data model that is ‘pre-rationalized’ so that demand planners can spend more of their time actually planning.

I wrote an earlier blog post titled “Supply chain lessons learned from the credit crisis” which looked at the importance of cash management and the need to understand the inventory liability in the channel in the context of cash management. The Wall Street Journal reports that Circuit City may be closing 150 stores and liquidating the inventory in an attempt to avoid bankruptcy. While the seeds of Circuit City’s lackluster performance are not rooted in the credit crisis, the Wall Street Journal does report that getting short term loans to pay for operations is proving difficult, hence their need to cut costs by closing 150 stores and liquidating up to $350M in inventory to pay for real-estate costs, such as abandoned leases.

While the fate of Circuit City is interesting, what does this mean for the Consumer Electronics companies which are Circuit City’s suppliers? How are they going to reduce their exposure not only to finished goods inventory liabilities but also to raw material purchase commitments made to satisfy anticipated demand from Circuit City? I think traditional forecasting and supply chain planning systems that rely on historical sales data are not the right solution. And speed is imperative. You can bet that all the other Circuit City suppliers are trying to solve the same problem, and many of them, especially the Consumer Electronics companies share common contract manufacturer’s and component suppliers. I suspect that customer relations principles and sound financial management, not to mention human behaviour, will mean that the first few brand owners that request a large change to supply commitments from the contact manufacturers may get a favourable response, but the ones last in line might get a different response.

What the brand owners need is a system that provides:

• multi-tier visibility to all inventory and commitments
• a collaborative environment that includes suppliers and customers
• rapid what-if capabilities, shared with their customers and suppliers, in which they can evaluate alternatives

An optimization engine run by the brand owner is not the solution. First of all the optimization engine will not be able to incorporate the compromises needed in such a situation. Second, a collaborative environment which allows all participants to understand their shared liability is likely to achieve a speedier and more widely accepted resolution than one in which only the brand owner’s needs are satisfied. They need a system in which any suggested changes can be evaluated immediately by other participants in the supply chain. Using a collaborative system as described above, the brand owner can also work with other retailers to understand how much of their finished goods they can divert from Circuit City, perhaps even running promotions to increase demand. Again speed in this context is imperative. Retailers will only be willing to run a few promotions at any one time, so being first to the table is very important in being able to shift the inventory.

Using RapidResponse, all parties along the supply chain can collaborate and immediately respond to the event and eliminate the unwanted inventory, thus cutting shipping costs, manufacturing costs, storage costs, etc.

Last week, I visited both Japan and Hong Kong. I’d been long looking forward to this visit, but dreading the 20+ hours of travel both ways. I finally figured out the best way to beat the jet lag…do not succumb to the urge to pull out the laptop during the first half of the flight – sleep, then work. I was surprisingly well adjusted upon arrival for a change.

Several things struck me during my week long voyage. The economic meltdown was front-page-top news story every single day. In Japan, there was talk of their own “lost decade” in the 90’s, largely caused by the same challenges being felt in the US – poor loan performance. One major difference between the challenges experienced by Japan in the 90’s, and the US today – The Japanese financial crisis was contained within the banking industry. The US induced challenges have spilled onto other financial industries and spread to other countries making it more difficult to contain.

I couldn’t help but wonder how “ready” the large and well known Japanese electronics manufacturers are given the lack of global economic stability.  How would companies like Hitachi, Pioneer, Sony, Sharp, or Nikon manage their pervasively outsourced supply chain given the lack of confidence in future demand? Surely their forecasting systems would not have predicted the current conditions, nor can they assist in determine what will happen in the coming weeks/months.

Some companies are responding by setting in place gross assumptions – such as an overall reduction in demand of 20%. I believe, however, the leaders (and winners) will not make such macro assumptions, and rather draw their attention to learning how to listen and respond quickly to what actually happens with demand. Time will tell as we watch these global electronics giants weather the storm.