Interested in how DecisionNext is making a difference for customers in the meat industry? 

Check out the story below of Joe and how he used DecisionNext in his meat processing company.

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Introduction

Meet Joe.Screen Shot 2018-10-30 at 2.01.41 PM

Joe manages a value added meat processing company and, like most agriculture & food companies, has always wrestled with how to manage market volatility as his team makes decisions about raw materials purchasing, product mix, and pricing and selling finished goods. 

 
In this use case, see how Joe applied the DecisionNext platform to put rigorous analytics and machine learning to work addressing these challenges. We’ll look at everything from the people, organizational, market, and profit impact.

 

 

The Background

Joe's company is a value-added meat processor. They purchase boxed beef from the large meatpackers who slaughter cattle and further process it to produce products for foodservice businesses and retailers. The boxed beef that value-added meat processors purchase contains large chunks of meat called primals (e.g. the flank or chuck) or subprimals (e.g. top rounds and whole tenderloins).

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 As their name implies, value-added meat processors create value by transforming boxed beef into products that are ready for the deli counter or a restaurant kitchen and selling them for a profit. At Joe’s company, they produce whole and sliced brisket, marinated cuts from various primals and subprimals, ground beef from all different parts of the cow, and a range of fully cooked roast beef products.

The Challenge

Joe has decided to use the DecisionNext software to plan and execute his company’s purchases of boxed beef and other raw materials. Maintaining a reputation as a reliable supplier is essential so value-added meat processors must avoid shortfalls in raw materials at all costs.

Purchasing raw materials is far from simple in Joe’s industry, and the choices value-added meat processors make about raw material mix, contract type, and the timing of purchases dramatically affect their bottom lines.

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The number of products a value-added meat processor produces and sells can vary widely, but it normally falls within the range of 20-2,000 different SKUs, each of which can each be made from several inputs. In most cases, Joe is able to choose between multiple inputs to create a given product. For instance, a processor producing London broil might purchase deboned rounds one week and determine that it is more profitable to purchase bone-in rounds and do the deboning themselves the next.

 

Another source of complexity and profit opportunity for value-added meat processors arises in contract selection. Joe currently uses a mix of fixed price contracts, formula pricing, and spot contracts to cover his delivery commitments. Contract decisions are inextricably related to timing decisions and choices about contract type and purchase timing represent implicit market decisions.

 


 

Consider when Joe is deciding how to purchase the raw materials required for a product to be delivered in 16-weeks. Joe has 2 options:

(A) He can purchase the raw materials for a delivery immediately and hold them because he believes that the input’s price will rise.

(B) He decides to purchase meat on formula for delivery in 15 weeks because he believes that prices will fall.

Whether intentionally speculating or not, all value-added meat processors are taking a market position each time they sign a contract.

 


 

Comparing prices at different points in time is further complicated by the costs of storing (and sometimes freezing and thawing) raw materials. In reality, Joe and other value-added meat processors tend to use a mix of contract types and purchase raw materials at several different points prior to delivery, creating a natural hedge against price volatility. Optimizing the types of contracts used and the point at which the processor enters these agreements is extremely important for maximizing profits while consistently honoring commitments to customers.

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 In other words, the purchasing decisions that are integral to the performance of value-added meat processors are affected by a huge number of variables – a number that can get Joe’s head spinning. Making the most profitable purchasing decision requires a forecast of the price for each product and each of its possible inputs at every point between the present and delivery and a system to synthesize this vast amount of information into actionable insights. 

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There is only so much information the human brain can process at one time before a person deemphasizes or even abandons rational, systematic thinking. Humans struggle to synthesize multiple channels of information and large data sets. 

However, some amount of valuable market information always escapes collection in a database, necessitating human interactions with models. Humans can add value to automated models by applying their qualitative views of market sentiment, recognizing one-off events, and applying lessons from their own expertise.

As a Harvard Business Review study noted, combining human decision-making with algorithmic decision support software leads to better results than purely human-made or computer-made decisions.

That's where DecisionNext comes in.

The Solution: The DecisionNext Difference

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