Category Archives: Forecasts

Achieving Supply Chain Visibility

Supply & Demand Executive recently ran an article by Aatish Goel & Murali Krishnan Sundararajan on “The Flat Supply Chain” that noted that globalization has created a massive increase in supply chain complexity and that supply chain visibility is emerging as a critical differentiator for companies to stay ahead of the competition.

The article also suggested that companies should manage supply chain visibility according to the process-technology-organization framework, and recommended the following from the process angle.

  • Effective S&OP Process Deployment
    An effective S&OP process brings the right information in front of all stakeholders in a timely manner.
  • Internal & External Inventory Turns Review
    Use a multi-echelon inventory visibility system that allows for regular review of inventory status inside the company and inside the supply chain as a whole.
  • Alerts & Exception Management
    The amount of data produced by a well-run supply chain at any point in time is huge and almost impossible for anyone to review manually (even with great analysis and reporting tools). Thus, it is important to have exception reporting and alerts to bring critical incidents and issues to the immediate attention of the right person.
  • Alignment of Supply Chain Metrics with Business Goals
    Use process-based metrics that complement business goals to monitor and improve the process.

This was a good starting list, but I’d also add at least the following:

  • Integrate PoS data and forecasting across the supply chain
    A supply chain view of inventory is not very useful if you do not know how much product you should have at any given location at any given time, and given that demand can fluctuate, using static forecasts to plan inventory is not optimal.
  • Use collaborative issue resolution processes
    When a critical incident occurs upstream in your supply chain that effects you, it’s important not just to insure your supplier, or your supplier’s supplier, starts working on a resolution immediately, but that you work with the supplier to not only insure a quick resolution, but to understand why the critical incident happened in the first place. Then, you can pass that knowledge onto your other suppliers and make sure it doesn’t happen to them.
  • Regular Review of Transportation Timeframes
    When calculating inventory requirements, it’s vital to understand how long, on average, it will take to restock a location in order to insure that you can handle demand spikes and not lose sales. If it takes 7 days, but a demand spike due to an upcoming promotion could wipe out inventory in 3 days, then it might be wise to temporarily increase the inventory requirements of that remote location.
  • Use Process Models
    Standardized, Integrated, S&OP processes are good – but streamlined process that are sound and complete are better. A process model will help you analyze your processes to make sure they are of just the right complexity. If your process is too simple, you could miss critical incidents or key data that could significantly change your forecasts and inventory requirements. Too complex, and you could lose the ability to react quickly.

From an organizational technology angle, the article had the following to offer:

  • Use an extended enterprise system.
    This will allow you to create a centralized data store, on which you can execute the data mining applications needed to detect exceptions and critical incidents, the analytic applications need to determine required inventory levels and transportation timeframes, and the reporting applications necessary to insure key information gets to key stake-holders in a timely and comprehensible manner.
  • Extend it with specific business solutions.
    The article recommends inventory optimization tools, business intelligence tools, and master data management tools.
  • Look at Service-Oriented Architectures (SOA)
    This can enable flexible collaboration at a lower cost.

This is a great start, but you should also consider the following:

  • Look at On-Demand SaaS
    Building a completely integrated supply chain management framework, even using SOA, will be a very time-consuming and costly endeavor even for the most technologically sophisticated organization. Starting with on-demand SaaS can allow an organization to get a fairly sophisticated system of supply chain management tools up-and-running quickly and cost-effectively.

In conclusion, the authors have it right – with supply chain complexity having increased exponentially in the last decade thanks to globalization and increased outsourcing, visibility is becoming key to managing risk and total cost and the time to do something about it is now.

“Demand Shaping” or “Demand Sensing”?

The EE Times ran a great article by Romit Dey and Manoj K. Singh last month on “Demand Shaping” and how it aligns customer trends with supply. But I have to ask, is it really “demand shaping” or is it more “demand sensing”. Is not “demand shaping” what marketing and advertising does? It’s true that supply chain has a supporting role, in terms of letting marketing know how much a product can be produced for, how many units can be produced, and how fast the units can be in consumers hands. However, what supply chain really does, in a company that runs like a well-oiled machine, is sense the demand that has been created, and the demand that is in flux, and adapts to the situation.

So what is “demand sensing”? According to the article, which calls it “demand shaping”, it is a demand-driven, supply-constraining customer-centric approach to planning and execution that aligns process with customer demand at strategic and tactical levels and with an organization’s capabilities which helps optimize use of resources, reducing excess inventory and improving inventory turns. More specifically, at the strategic level, the emphasis is on aligning customers’ long-term demand patterns to long-term resource and capacity constraints and at he tactical level, the focus is on understanding demand patterns and then influencing customers’ demand toward available supply, using the levers of price, promotion and products/services bundling.

How do you sense demand? As the article points out, you need three key capabilities:

  • demand pattern recognition
    who is buying what, when, and in what quantity
  • supply supportability analysis
    how much can be made, when, and how fast can it be delivered
  • optimal demand steering
    if demand patterns suddenly change, and you do not have enough of product A, can product B be used as a substitute and can customers be steered to that product instead

The first skill is obvious – you need to manage inventory appropriately so you aren’t holding too much, and generating excessive inventory carrying charges, or holding too little, and selling out before supply can be replenished. The second skill is less obvious, but easily understood – you need to know how much you can make, and how fast it can be made, to appropriately plan your inventory level.

The third skill is what takes “demand sensing” to a whole new level, to the point that it is almost “demand shaping”, but not quite, and hence the source of confusion. It is, as it’s called, “demand steering”. The Dell example the authors use is the best. By maintaining real-time visibility into its supply chains, Dell knows its inventory levels now and in the immediate future on an hourly basis. If a customer configures an order for a 60GB drive on their web-site, and Dell knows they don’t have enough stock to configure the system immediately, then Dell informs the user of a delayed ship date and presents the customer with an opportunity to replace it with an 80GB drive at a discount – steering the customer towards another product that can meet their needs, even if it is more expensive, but Dell takes a discount on margin to make the sale and keep the customer.

The key to success, as the article points out, is to make sure that all three processes are part of a single, integrated loop. A supply supportability analysis is run on a regular, automated, basis; inventory is updated on a near real-time basis; and short-term forecasts are updated at least daily. Each of these numbers is compared on an automated basis, and as soon as forecasts exceed inventory and obtainable supply, an alert is sent to a planner who determines whether there are alternative products that can be used to meet the need or if marketing and sales needs to be informed that they need to take actions to steer demand on their end. Then, customers are steered towards the alternative products through the appropriate channels – in real-time.

The article also does a good job at overviewing what is required for a demand sensing framework. The elements it outlines are:

  • inter and intra organizational connectivity
  • the ability to capture, structure, and comprehend data from customers and channels
  • advanced business intelligence to identify demand patterns
  • optimization
  • common processes
  • a common data model
  • common performance metrics
  • available-to-process capabilities
  • exception management
  • electronic negotiation and collaboration

The best thing about the framework is that these are basic capabilities and processes a good organization should already have in place. It’s just a matter of tying them together and using them wisely!

Forecasting, Part III

In Forecasting, Part I, I pointed out that accurate forecasting is a complex and challenging problem but that it is generally still possible to create good forecasts through the proper combination of judgmental and statistical methodologies. Specifically, manually adjusted statistical forecasts by an expert who has “inside” information, is aware of “one-time” events, and / or who is responsive to the latest environmental changes can often (dramatically) improve forecast accuracy, provided human bias does not creep in. (Thus, only practitioners with domain knowledge should adjust statistical forecasts using a structured process and only do so when there are known changes in the environment that the statistical model wasn’t really built to handle.)

Then in Forecasting, Part II, I pointed out an article in Purchasing that noted that when it comes to commodity forecasting, judgmental forecasts by experts have the best accuracy on record, demonstrating that expert human judgment applied to good statistical models with solid historical data that also take into account market intelligence and global economic trends are the way to go.

Now I am going to draw your attention to a recent white paper, sponsored by Supply Chain Consultants, by Tom Wallace and Bob Stahl titled Forecast Less and Get Better Results that demonstrates that the conventional wisdom that companies need to project forecasts and plans far into the future at a highly granular level is not necessarily right. Specifically, it points out that detailed forecasts and plans are normally only needed inside of what the authors call the Planning Time Fence or the point in the future when the cumulative lead time to acquire the material and build the product is only a short time away. They argue that outside of this planning time fence, you should only be concerned with aggregate volumes.

Specifically, they argue that up until it’s time to plan a production run, you should only be concerned about forecasting the aggregate volumes required for raw materials beyond the average planning time fence. After all, if you’re a large fast food chain, chances are you can predict with a fairly high degree of accuracy how many burger patties you are going to need over the next year, even if you can’t predict exactly how many Big Burgers, Bob Burgers, or Bo Burgers you are going to sell in any specific week. And if you are a toy manufacturer, chances are you can predict roughly how much plastic you are going to require over the next quarter, even if you don’t know precisely how many units of Dolly House or Trixie Truck you are going to be asked to manufacture. Attempting to forecast to a granular level too far in advance will just mean you’ll constantly be revising your forecasts and wasting time and resources, instead of focussing on what’s truly important for sourcing – the raw material aggregate volume, since that’s where your leverage is.

I’m sorry, but “I’m not omnipotent” just doesn’t cut it anymore!