INNOVATION IS moving along rapidly in supply chain these days. Users and solution providers are learning together how to apply more advanced analytics and data. But so many organisations are still relying on past philosophies of demand and supply. Too often, price elasticity curves (and the accompanying promotions) drive forecasts for production.

We all know price is not the only factor that influences demand. However, in the past, with fixed concepts of data, this was all many forecasters could analyse and calculate. Even if your organisation has more nuanced forecasting systems, most companies have basically ignored many important inputs in their forecasting systems, for example:

Marketing input: Marketing people often chafed at price elasticity as the method, instead presenting other views and market analyses of demographics and other methods for defining customer grouping, as well as the customer’s desire for/need for a certain product or service. In fashion, ‘prestige’ or ‘budget shopper’ laces marketers’ language. Since they include income data in their spreadsheets, these various views allow them to conjecture pricing and demand for certain groupings of customers.

Problem. It’s all in the spreadsheet! And even if marketing does have a system, the credibility of the data is often not validated. It becomes an artifact of the recent past as production gets rolling.

Product design/engineering input: Product people also chafed at the demand/supply elasticity curve as it did not take into consideration product attributes and features and their utilitarian and sustaining value. Importantly, price elasticity does not take into consideration the customer’s ability to actually evaluate the increased value they receive and, thus, their justification of an increased price.

Product designers often supported their argument with competitive analysis based on feature/ function. Again, this data does not fit neatly into the forecasting system your company may have purchased as recently as two or three years ago.

Logistics input: Then we have the issue of availability. Currently, consumers in more ‘developed’ economic sectors have access to lots of home shopping/delivery options to get deliveries. Delivery today plays a big role in demand. And one way or the other, consumers are paying. We must find a way to fit this data into the forecasting system.

Historically, we could look at some of these factors and attempt to glean whether our strategy and investments in customer segments or product development were effective or not. Fact is, in the very recent past there was no consistent way to validate that the results could actually be tied to the causals. Many organisations today still don’t practice building smarter models based on these and other demand-factor inputs that they may even know about through experience.

Widening the horizons in demand

So, let’s dive deeper into this discussion about widening our ability to understand demand and supply. Simply, products and pricing strategies can be looked at in two fundamental ways, assuming the necessity of the product: commodity, highly available from multiple sources (often cheap sources of production); or limited supply, often single source (where often production cost is higher, with expensive material, but not always).

• Commodities

A popular commodity like soap is a necessity. But it is also a highly competitive market. As well, there is abundant supply. Thus, buyers often feel little pressure to buy without some incentive at a given time. Therefore, the amount of soap you sell, to a great extent, can be gauged on price. This is why brand companies and their channels expend so much effort on promotions. However, promotional management comes at a huge price. Commodities products can, at times, have so much availability that, in essence, they are dumped in the market.

Yet we can still spend a fortune on the associated information, relationship management and logistics costs and coordination.

• Single source/limited supply

These are products for which there is only one source, or limited sources, of supply. Generally, sellers in these markets can claim higher prices for their products. These conditions may be a permanent factor of supply markets, or temporary. For example, some products, such as lifesaving medications, will be bought at any price. On the other hand, the condition could be temporary, such as recently when we went through a significant supply shortage of toilet paper. Though traditionally this would be a commodity product, toilet paper, hand sanitizers and disinfectants recently moved into the category of short supply, and prices moved up accordingly.

Fixed forecasts are out. Flexibility in forecasts is in. And in reality, that kind of always was the truth. Products, and the calculating approach used to plan them, may not be fixed values with a fixed relationship between demand/supply and price.

Whether a regular part of the landscape, the normal process within the cycle of product introduction and fulfillment, or emergency market issues, grappling with these issues requires a widened horizon in looking at, understanding and, therefore, forecasting and managing demand and supply. The impact of weather, a social trend, or some kind of national emergency can be understood and expressed as part of an analytical model, i.e., as fields, parameters, values and equations, to capture, calculate and communicate said impact and its resultant values.

Enter the non-fixed parameter

So, let’s get back to toilet paper. What changed here? If we think about traditional forecasting systems, the forecasting parameters are fixed – in the toilet paper example, we use history + some safety stock value and demand flows by channel/ customer/location. Within safety stock, there is a value that either a planner or the system set based on history. In this example, my field has a fixed parameter/a fixed forecast method. This is the way we did things. Now, however, history, channel and safety may not mean much with a range of factors influencing how we plan even the simplest things. History can tell us a lot, but not maybe in the fixed way it did before.

Grocers and distributors know, either by history or intuitively, the dynamics that previously impacted actual sales or shortages. For example, severe weather warnings will create a run on food, water, and toilet paper. Hence, the lower limit on safety may no longer apply, since demand will spike and we don’t want to run out.

How would this flexibility in parameters, methods and data work? If I have an ability to flex or change my parameter for toilet paper for each storm, say based on category of the storm, I can select a time period that contains the last major storm of this magnitude. I can select with the sales history date-range and leverage that forecast. I can also change the actual way I utilise those specific values, for example time/data range, smoothing and best fit algorithms.

In essence, most of the fields, ranges and the actual forecast method used can change for each product at each location at any time. In theory, we could continuously tune our parameters every time we forecast. In practice, we might not want to do that since our mind can’t grasp all these dynamics, and too many changes to tasks and processes challenge the auditability of what we are doing.

Certainly, when we have major changes, we will want to tune or change our parameters. And we would want this to occur automatically. Whether the systems are autonomous or just send alerts to the user, we do need the system kind of running on some level of autopilot since this is just too much for a human to figure out.

Conclusions – dynamic, expanded, continuous and autonomous

We know that the idea of using history alone in forecasting the future was somewhat questionable, at times. While that approach may not be obsolete, it is limiting, since now we can look at an expanded view of our markets, customers, channels, suppliers, carriers and so on, and see the many demand-impacting factors as well as the dynamic interrelationships between them that we never saw before. And if we are customers of a large supply chain provider, we can now gain the benefits of an aggregated view of history and multiple in-process logistics flows to monitor and react to changes. We can see that by the SKU for each time increment, allowing constant tuning and optimising of many of the factors where the money gets spent and made – price, order quantities, supply volumes, inventory investments, safety stock, on and on.

We now have the ability to capture all that data, have a machine digest and continuously learn from it, and produce some insights. Using formulas, the system can pick the right forecast method based on all of the factors.

Why would we want to do this?

We now have the ability to dynamically change plans, safety stock, production values, warehouse stores and so on based on the variety of demand impacts and their interrelationship at a specific timeline and location, giving us much better perceptions and actions. Based on this, we can improve sales and profits by producing at the right time, reducing logistics costs or excesses; and at the right price, optimising prices across the life cycle of a product and with more dynamic pricing schemes based on market characteristics.

This is increasing sales, increasing profit. A real win. But we do have to make a lot of changes to get to this point. And that means upgrading the data and technology. If the recent past has taught us anything, those who had a more robust and smarter supply chain operating model or had the smarts to react early are doing okay. •