Evolving to Intelligent Order Promising – No Time like the Present

    

As we start the new year, many people often reflect on the past year. What went well, what do we need to do different.

2020 has been an incredibly unique year for many, especially for consumer products companies. Demand for some products is going through the roof while others are looking to squeak out growth. Add the challenge and complexity created in the supply chain with limitations of materials and manufacturing capacity constraints, many CPG companies are facing order fill challenges unlike they have ever seen before, leading to unhappy retail partners and consumers. And for some, significantly increased OTIF penalties that are starting to have a real impact on internal and external partnerships.

At times like these, CPG companies often fall back on their Available-to-Promise (ATP) processes to kick in and help smooth things out. But as my colleague, Mohit Dubey, recently wrote in an article on the evolution of Available to Promise (ATP), most CPG companies are still relying on systems that are still too manual and/or cannot react quickly enough to the current reality. So, the Sales team is getting chewed out by their retail partners and in turn, chewing out their Supply Chain and Logistics department, and so on it goes.

CPG companies need to make a change – NOW – rather than wait and hope that “this too shall pass”. They need much smarter systems that can handle the volume of decisions that need to be made – Which orders should be filled? In what priority? How much should we allocate? What orders are likely to come in beyond what I have as firm orders? How much inventory do I reserve for the orders that haven’t come in? How do I balance keeping all of my retailer partners happy while minimizing OTIF fees? If I can’t satisfy everybody, how should I define a customer segmentation strategy? They need a solution that can do a better job of anticipating demand and then optimizing order priority in the best way possible to control fill rate decisions. And they need that solution NOW.

Intelligent Order Promising (IOP) is that solution.

IOP is the evolution in the machine learning world of what we know today as ATP. Using a combination of demand sensing, to anticipate the near-term demand from each retailer’s ship-to location, and AI/ML modeling, that incorporates customer attributes. For example, service level targets, profitability, volume growth, and OTIF fines levied. These attributes define the significance of each retailer partner to the CPG company, providing the ability to optimize for two key outcomes: 1) Allocated Inventory – determining what is the ideal fixed quantity that should be apportioned to customers based on the segmentation strategy in times of a SKUs scarity and 2) Unallocated Inventory – at those times when supply and demand vary to such a degree that it creates an inability to fulfill all quantities requested across the firm orders for that SKU, then you need to automate the prioritization of filling the orders by promising inventory (on-hand and inbound supply), that respects the customer segmentation strategy that best represents the business KPIs.

So, what is the value creation of IOP? The impact is broad, both in hard and soft benefits for a CPG company:

Examples of hard benefits:

  • Ability to fill based on customer segments to support growth and profitability – PRICELESS!
  • Reduction in OTIF fines in the millions – Estimated at $3M-$5M per year
  • Reduction in customer service/DC/planning personnel administrative tasks – Estimated at $0.5M per year

Examples of soft benefits:

  • A common data and decision making process utilized by sales, supply chain and customer service departments to help efficiently and intelligently manage demand and supply variability, all the way through to order execution
  • Improved customer relations including more productive joint business planning initiatives
  • Taking noise out of the process due to the lack of standardization and misaligned priorities

 

How do you get started? By taking a pragmatic approach to validate the efficacy of the solution. Here are some key steps:

  1. Organize a SWAT team of key stakeholders to identify the key attributes for defining the customer segmentation drivers and the outcomes to be achieved with the new process.
  2. Identify the potential integration points between the legacy systems (SAP, Kinaxis, Oracle, Blue Yonder) and the processes for IOP.
  3. Stage gate 1: Backcast – Benchmark ‘what we did’ versus ‘what IOP would have done’ to validate the output against the past, and to extrapolate the impact on the future.
  4. Stage gate 2: Pilot – Run IOP in parallel for 1 DC to validate the findings in a real environment, perform acceptance testing, and battle test the entire process under pressure.
  5. Go-live! Cut-over to production and rollout across the enterprise, making sure to measure the business impact.

Pandemic or no pandemic, there will always be a challenge of managing order fill variability at the time of execution; the issue does not go away. So, when we talk about resiliency in planning, Intelligent Order Promising effectively manages uncertainty where it matters most, customer experience and shareholder value. It provides a quick time-to-value and short payback opportunity that has an exponential positive impact on the business and customer relations.    

To learn more, feel free to reach out to me at pat.smith@antuit.ai or visit us a www.antuit.ai.