Logistics Viewpoints

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Hormel is Winning with Trade Promotion Optimization

Consumer Packaged Goods (CPG) | By •

At Oracle OpenWorld last October, Kevin Kroymann, Director of Trade at Hormel Foods Corporation, gave a presentation about the company’s journey to improve the returns it gets from promotions with leading grocery retailers.

Based in Austin, Minnesota, Hormel Foods Corporation is a multinational manufacturer and marketer of high-quality, brand-name food and meat products for consumers. The company offers a wide variety of products, including hams, bacon, sausages, franks, canned luncheon meats, and shelf-stable microwaveable entrees and salsas. Hormel has 31 brands that are number one in their category. The company achieved sales of over $7 billion in 2010.

Hormel’s journey to improve its returns on promotions began back in 2004 when the company’s CEO challenged the sales and marketing group to grow sales at a faster rate than their budget for trade promotions.

Hormel started by implementing Siebel in 2005, a leading Customer Relationship Management (CRM) solution that included a trade promotion management application. Oracle acquired Siebel that same year. Around that time, Hormel also considered implementing Demantra (a best-of-breed demand management supplier) for trade optimization, but the company concluded that the software vendor was too small at the time to do business with.

Hormel’s experience with Siebel was that while the solution allows you to capture trade transactions and begin the process of better executing trade promotions, these capabilities can only take you so far. The company needed forward-looking analytics that could help it project the ROI of trade promotions before they occurred. Win/win, high-ROI promotions are the ones Hormel wants to pitch to the large grocery chains.

In 2006, Oracle acquired Demantra. This acquisition gave Hormel the confidence to go forward with implementing Demantra for trade optimization in 2009. Hormel was the first company to implement an integrated solution that included Demantra and Siebel to help enable an end-to-end trade management/optimization process.

Hormel has achieved a number of benefits from implementing Demantra:

  • Before the implementation, Hormel’s 60-day trade promotion forecast accuracy was 18 percent; today, it is greater than 50 percent (Account/Period/Product Group Level).
  • Prior to implementing Demantra, one third of Hormel’s promotions did not even cover the incremental spend associated with the promotion. For example, if the promotion cost $50,000, the company would not even garner $50,000 in extra sales. Further, half of its trade promotions generated no significant lift. The Demantra application allows Hormel to project much more accurately what a given promotion’s Gross Margin ROI is likely to be.
  • Based on the forward-looking analytics, Hormel has been able to improve the ROI of its promotions. While Mr. Kroymann was not willing to say how great these improvements have been, he did say the Demantra application has allowed the company to save millions of dollars, based on conservative estimates.
  • The cultural changes associated with effectively using Demantra have been difficult. Hormel has used the forward-looking analytics for 30 different promotions; 23 have been successful. Mr. Kroymann stated, “We are just getting started.” Based on this, the potential long-term savings are much greater than what the company has already achieved.
  • There is evidence that the relationship between Hormel’s trade account executives and selected retail account buyers is improving. Where the Demantra math has proved itself, the buyers are asking Hormel to come back and pitch other opportunities. This can help Hormel cement itself as a category captain.

Trade promotion optimization is at an early maturity level across the consumer products industry. This is because baseline applications or services (e.g., trade promotion management and store execution) must be in place before trade optimization can become fully effective. Further, any application that changes the way the sales force operates will always require more executive support to overcome cultural hurdles than is typical for other applications/process areas of coverage.

Finally, it is probably true that trade promotion optimization will have provide greater returns for category captains than for smaller manufacturers. A trade account executive needs to show a retail buyer that the promotion will generate significantly more in new sales for the retailer than it will cannibalize in sales of other brands in that category. The category captain has access to the sales and historical demand generated by competing brands. Smaller manufacturers will often not have access to such data.

(Note: Oracle is an ARC client)

Matching Demand Forecast Periods to Production Lead Times

Demand Signal Repositories | By •

It is common to speak of a “one number” demand forecast. And a “one number” forecast works fine if your demand forecast and your manufacturing planning are based on the same time period. For example, if you do a monthly forecast and the plant uses it to plan production for the entire month, then there is no inconsistency.

But a lot can happen in a month.

I was briefed a while back by Nitin Goyal, product manager for JDA Software’s Sales & Operational Planning (S&OP) solution. I asked him how his customers with long manufacturing lead times were dealing with this issue. He told me that some of JDA’s clients in the semiconductor industry are using banded forecasts that include a high-confidence forecast (for products the manufacturer is very certain the market will need); a medium-confidence forecast (often the “one number” consensus forecast); and a low-confidence forecast (a high demand scenario).

In this case, a manufacturer can safely make the high confidence forecast units early in the month, then produce the medium-confidence forecast units later in the month, unless they see a drastic drop-off in orders. Only if they see an unexpected surge of demand does the manufacturer ramp up the factory in the last week of the month. In the semiconductor industry, adding capacity is expensive. These banded probabilistic (“stochastic”) forecasts make a lot of sense and can improve the S&OP process.

Many companies have found that if they move from monthly to weekly forecasts their forecasts become more accurate. What if you take it a step further and produced forecasts every day? That is what Land O’Lakes did. The company moved to doing a daily rolling forecast that covers the next 28 days.

Jeanine Viani, the Director of Supply Chain Planning for Dairy Foods at Land O’Lakes, spoke at Oracle OpenWorld last month about the company’s implementation of Oracle’s Demantra Advanced Forecasting and Demand Modeling application, an optional add-on to Oracle’s Demantra Demand Management solution. This application makes use of an individual customer’s order history and compares it to the orders that have dropped in the last few days. Land O’Lakes is the first Oracle customer to use Demantra for open order correction.

You might be wondering, why forecast daily unless the lead time for your factories is also just a day?

At Land O’Lakes, the production lead time to make butter is one week and the lead time for cheeses 3 to 4 weeks. The company’s traditional weekly forecast was 45 percent accurate, captured at the item/location level. But when they do daily forecasts, the following week’s accuracy is better than 70 percent. The forecast for two weeks out drops to about 55 percent, and the accuracy for weeks three and four remains at 45 percent. So, in effect, for the one-week lead time products, accuracy is now at 70 percent rather than 45 percent. For cheeses with a lead time of three weeks, the accuracy is now 56 percent (70 percent week one, plus 55 percent week two, plus 45 percent week three, divided by three weeks) rather than 45 percent.

So, why not forecast weekly? Because the plant can base its production schedule on the most current and accurate forecast. Demand is not jumping around so dramatically – partly because many customers want their shipments on the same day every week – that they are having a hard time balancing demand and supply.

Land O’Lakes is on track to receive the payback several months ahead of the original estimate, based on reductions in inventory, coupled with a reduced need to redeploy inventory from one distribution center to another.

Finally, some companies are using Demand Signal Repositories to take downstream data and create accurate, short time forecasts and in this way become “demand driven” (see, for example, “Del Monte Foods and Shelf-Level Collaboration”). But accomplishing what Del Monte did is difficult. For many companies, starting with stochastic forecasts, or moving to weekly or even daily forecasting, will be a better intermediate step.

Innovative Solutions for CPG Unveiled at Oracle OpenWorld

Consumer Packaged Goods (CPG) | By •

It is getting harder and harder for software vendors to innovate in supply chain management (SCM). I went to Oracle OpenWorld (OOW) last week expecting to see incremental improvements in many of its SCM applications (Oracle is an ARC client). Instead, I was blown away by what I saw and heard in one key area: Oracle’s innovative solutions for the consumer packaged goods (CPG) industry. 

For CPG companies, it is not unusual for promotions to drive 20 to 35 percent of their total revenues. A representative from Cadbury’s Asia business unit was one of the presenters at the conference and he mentioned that successful promotions can lift their demand by 300 to 400 percent. Without a good S&OP process and supporting tools, the supply chain organization could not cost effectively cope with these kinds of demand surges. 

Many supply chain executives believe they could run a more efficient operation if sales people were compensated, at least partially, on profitability. These high-level managers recognize the need for promotions, but they also believe that fewer, more profitable, and better forecasted promotions would lead to better financial results. Some of Oracle’s CPG customers are moving towards measuring their sales teams on the profitability of their promotions. One Oracle executive I spoke with estimated that sales VPs at over 10 percent of its CPG customers are beginning to entertain this idea. A few years ago the number would have been virtually zero.          

Oracle’s Siebel CRM suite includes a solution for Trade Promotions Management (TPM). Oracle has enhanced its end-to-end TPM/SCM solution, now referred to as Comprehensive Trade Management, by integrating Siebel TPM with Demantra Predictive Trade Planning and Optimization and Demand Management and Sales & Operations Planning suite; the release of a Demand Signal Repository (DSR); and by partnering with Frontline Consulting Services (FCS). 

For trade promotions, Demantra forecasts a time-phased net lift (sales are frequently depressed both pre- and post-event); a complementary product lift (e.g., increased sales of salsa can lead to increased sales of chips); and cannibalization (e.g., a promotion on a 24-oz. product will decrease sales of the 12 oz. product). The integration to TPM then allows Demantra to forecast net profit from the proposed promotion.

Companies should close the loop on their promotions by checking to see if the forecasted profits actually occurred. This is where the Demand Signal Repository plays a role. Clorox is the first customer to go live with Oracle’s DSR. Clorox is collecting near real time point-of-sale (POS) data from retailers that provide it, along with syndicated market data from IRI, shipment data, trade funds data, and other data elements. By correctly modeling this data in a DSR, Clorox can examine the effectiveness of a much greater proportion of its promotions, as well as measure the effectiveness of promotions much more quickly. Speed of analysis is important because it leads to better behavior shaping. 

Clorox used FCS to provide different views of the DSR data. For example, FCS built a promotional event library that provides an overview of all pending promotions. I visited the FCS booth at OpenWorld to view the new off-the-shelf product the company has developed to provide views into Oracle’s DSR. I won’t try to describe this product, but it was highly visual and intuitive with interesting drill downs. Further, receiving POS data from a retailer, cleaning it, and formatting it for a DSR is not an easy or straightforward process. FCS offers additional value to customers by providing them with managed services in this area.

In looking at this solution, I was struck by how it could affect providers of syndicated market data. Midsized CPG companies typically spend well over a million dollars a year for syndicated data. It is not unusual for large consumer goods companies to spend tens of millions of dollars. In addition to being costly, syndicated promotional data trickles in slowly over several months. It would be logical for CPG companies implementing a TPM/SCM solution to increase the number of promotions aimed at retailers capable of providing POS data. This would allow the manufacturer to improve its promotional forecasting models more quickly, as well as provide quicker feedback to sales on a promotion’s effectiveness.  More streamlined buying of syndicated data could go a long way toward paying for an innovative TPM/SCM solution.

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