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About Demand Planning LLC

Demand Planning LLC, based in Boston MA, is a consulting boutique comprised of seasoned experts with real-world supply chain experience and subject-matter expertise in demand forecasting, S&OP, Customer planning, and supply chain strategy.

We provide process and solutions consulting, as well as customized training across a variety of industries.

Through our knowledge portal DemandPlanning.Net, we offer a full menu of training programs through in-person and online courses, as well as a variety of informational articles, downloadable calculation templates, and a unique Demand Planning discussion forum.

  • 11Jan

    As a consultant, I get asked this question a variety of times.  Why should we improve forecast accuracy?  What is it worth to us?  What is the business case to spending money to improve the process and implement some expensive demand planning software?

    There is value in this for sure – the evidence comes from the fact that year-after-year companies continue to hire more demand planners and invest millions of dollars in putting together processes and software packages.  Now to dissect this, let us look at the Service-Cost Balance model that we preach in our workshops.

    Improving the demand forecast directly gets to two things that are important to companies:

    1.  Increasing the top-line – Improve customer fulfillment and delivery and increase the level of Sales.  Nothing works like reputation or service performance.  People flock to your company products and services if they are satisfied with what you offer.  The converse will be painful – bad news spread like wild fire.

    2.  Improve the bottom-line through process efficiency – Optimize inventories by trimming the level of inventories, cutting obsolescence and producing just enough to meet your demand.

    3.  Eliminate process costs – Expediting, over-time that may otherwise be required to fulfill volatile and unpredicted customer demand.

    Now if we want to quantify the benefits from forecast accuracy, it is difficult to quantify the first item.  What is our lost sales?  What is the effect of one point reduction in fill rates?  Did it cause lost sales?  Did we lose a customer?

    It is a little easier to look at the savings in inventory through improvements in forecast accuracy.

    At any time, your inventory level is comprised of Safety Stock to meet demand and supply uncertainties and the inventory needed

    But your average inventory = Safety Stock + Lead time demand or (order quantity if you are using the min-max method to deploy).

    We can find a relationship between the amount of safety stock you can decrease for each percent point reduction in Forecast Error, since forecast error figures directly in the Safety Stock calculation.

    Safety Stock = Forecast Error * Service Level Quotient * Square Root (Lead Time)

    From simple calculus, the effect of one unit reduction in Forecast Error is just the product of Service Level Constant * Sqrt (Lead-time).  So at 98% service level with a lead time of say two months, the effect of one unit reduction in Forecast Error is 2.90 Units.

    What is the impact of a % point reduction in MAPE?  The answer depends on the level of your current forecast quality.

    However, the relationship of inventory reduction to your current MAPE Level is not linear.   The calculation on the reduction in inventory is a function of your current level of forecasting.  This determines the quantity of inventory reduced for each percent point improvement in Forecast error.

    If you are at 50% MAPE on average currently, the reduction would be 2% of inventories for each point improvement in forecast error.  IF you are at 25% MAPE currently, the improvement will be 4% for each point reduction.

    This is actually a conservative estimate since it considers only the safety stock component of the total inventory.

    If your forecast is an over-estimate of the Lead time demand, then you carry additional inventory not accounted for in the above calculation.  This will also introduce obsolescence risk.

    So a lower bound is to just use the reduction in safety stock but being aware that a better forecast will also allow you to deploy better to the lead time demand.  Better forecast can also help you reduce production and logistics costs such as over-time and expediting costs.

    So if you have $200MM in inventory and let us assume that 25% of that total is safety stock then you have $50MM in safety stock.

    A reduction of 10 point MAPE will result in a reduction of $10MM in safety stock.  10 point MAPE reduciton =20% reduction in Safety Stock or $10MM assuming currently your average MAPE is 50%.

    Again this does not consider any reductions in the inventory carried to cover lead time demand (particularly due to forecast bias) or obsolescence that results from unsold inventory.  Obsolescence is a bigger risk since you lose all of the inventory investment.

    Next question is what does the reduction of $10MM in inventory really mean to you?  This completely depends on the inventory carrying cost for your company.

    Inventory carrying cost can be any where between 12% to 40% per year.  So in financial terms, a $10MM reduction in inventory could result in bottom line improvement of $1.2MM (at 12% carrying cost) to $4MM.

    Now we are talking real dollars……………..

    Let us try to save some of those costs by bettering the demand plans!

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  • 23Mar

    Companies are rushing to upgrade from the APO 4.1 and 5.0 versions to the new SAP SCM 7.0  As the purse strings are loosening up this year, companies are spending more money in ECC and application upgrades.

    What are the big differences between the 4.0/5.0 vs. 7.0?  There are some marginal improvements that the tech shop may admire but anything for the planning community?!

    We also hear that the planners have not been using the Statistical modeling features in APO.  Will upgrading to 7.0 persuade the planners to use the Stat Models more?  Not just more, just even barely?

    IT implementation teams say that Stat models are not a priority given the budget constraints they have.  So more millions before and no stat models.  Now five years and many millions later, we have a shiny new upgrade and again the stat models are not a priority.

    I have been preaching Usability for the past few years.

    Put together fine tools  – But help the users in making the transition to the tool – give them better understanding – Make the new tool more usable!

    Give them the reports they need.  Provide them an exception based workflow!

    APO has good statistical models.  They will help you move the peanut forward but only if these models are better understood and leveraged.

    We just re-launched the marketing campaign for our Usability Consulting – Model tuning and model matching to product profiles are important elements of the Usability training. 

    Once implemented the Usability project will harmonize the use of models across planners from various geographies for the same business/product family.  There will be streamlined work flow.

    We help you answer the following questions:

    1. Am I using a Pareto Approach in my APO planning process?
    2. How can I leverage APO DP to improve our forecast accuracy?
    3. Why does APO mostly give me flat forecasts? How do I fix this?
    4. What are Alpha, Beta, Gamma, Sigma and Theta? How do I leverage these parameters?
    5. What is the correct level to model so as to improve the overall accuracy at the SKU level?
    6. What are weighting profiles? How does it affect my final forecast?
    7. How can I control time trend using trend dampening profiles?
    8. Are there products and customers that are better left to APO’s automated modeling strategy?
    9. Which models to choose for what family of SKUs?
    10. What are custom modeling profiles?
    11. How is APO helping us simplify and improve the promotional planning process?
    12. How do I create Multiple Linear Regression Models in APO?
    13. Are we using the system defined error metrics in APO? Why are they different from the classic MAPE calculations?
    14. How do you conduct phase-in/phase-out of products?
    15. When should I not use the Croston’s Model?
    16. Why am I getting 9,000+ alerts every morning?

    Perhaps the stress test of your new implementation or upgrade will be to ask the team if they can answer the above questions.

    Our Model tuning and training project can be launched any time -but it is better to launch it sometime during the middle of the configuration calendar before the go-live.  This way the training to the planners can be combined with the Navigation training.

    The benefits will be substantial:  improved forecasting process, workflow and significant supply chain benefits and cost savings.

    What does it cost you?  Not much – perhaps one-fiftieth of what you paid for the APO DP implementation or some where in that ball park.  We finish everything in ten to twelve calendar weeks.

    The detailed brochure on this Model Tuning and Training service is available for download at http://demandplanning.net/documents/SAP-APO-DP_ModelTuning_v2.pdf.

    Happy Forecasting!

    Mark Chockalingam

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  • 06Mar

    If all works well, then it is a perfect world.  You carry just the right amount of inventory to service your customers at 99% and get away with very minimal working capital.  Obviously, your low cash-to-cash cycle should result in larger portion of your Gross Margins go to your Net Margins………..

    Excess inventories happen as a matter of fact:

    • Forecasting problems – not knowing what the customers need.  This may also result in some obsolescence.
    • Forecast Bias – Just keeping the forecasts high generally on everything.
    • Sudden Demand reduction due to market place volatility or losing a key customer.
    • Economies of Scale in production – Higher lot sizes are way too attractive to resist.

    Excessive inventory can also be carried as a price Hedge.  Steel prices are expected to rise and quantities may even be in short supply. So you buy up and keep more of it.

    Life time Buys – Rare earth materials or a supplier that is close to a single source is facing financial difficulties.

    Utilities some times carry spare parts inventory for the next 100 years. Perhaps one or two ancient grids use these parts. If we stock out of these parts, the Utility has no choice but to scrap the old grid and build a new one. The opportunity cost may actually outweigh many times over the cost of carrying these parts.
    Excess inventory can also result from supplier uncertainty. If supplier does not meet schedule or if the lead time is time varying over a period, you have to carry more inventory to meet the uncertainty in supply.

    There is perhaps another reason but really a different version of the price hedge. IF suppliers offer a quantity discount, then that ends up lowering your cost of production with the consequent higher price to pay on the inventory carrying cost.

    The punch here is the lowered cost per unit from the discount that applies to your consumption as well. This may result in ordering and carrying a quantity much higher than the dictated EOQ.  Now here comes the distinction between items above the COGS line and items below.  If a quantity discount is offered, this lowers the COGS and boosts the Gross Margin.   You may have to order more than your calculated EOQ to avail the discount.

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