Supply Chain Archives | Prophit Systems

Statistical modelling and supply chain forecasting

When I was first getting started in this business a good friend and colleague who knows a thing or two about statistical modelling advised me; “you must understand your demand before you try to fit a statistical model to it”. This advice has served our team well over the years.

A number of statistical supply chain forecasting tools advocate that they will automatically forecast your demand for you. This is a very enticing sales pitch; it implies that the software will do all the work for you. But before you turn your back on the task of forecasting and leave the software to do its thing, a word of caution:

Statistics are a great tool for summarising and projecting subtle trends in market demand when there is continual sales history;
Statistical tools are poor at predicting demand when the demand is lumpy with periods of no sales. (Examples: Project work, promotions etc.); and
Statistics will not predict abrupt changes to demand such as a customer changing their artwork, or a customer moving production of a particular range of products offshore. By the time your statistical model is responding, your warehouse could already be full of items that particular customer will no longer take!

Scenario

One of our packaging clients had invested in a supply chain forecasting software solution that ‘automatically’ adjusted its forecast algorithms to seek the best fit. The sales team were delighted. They no longer had to spend their time creating forecasts. They no longer needed to talk to the customer about emerging trends or understanding the reasons for errors in previous forecasts. They now had more time to go out and sell more product.

Upon reviewing the plant performance, we found that there had been a significant increase in obsolete stock and key customer DIFOT was below expected levels.

When we attended the demand review the dynamic was interesting. Corrective actions that were assigned to resolve the stock outs, all focused on improving the statistics. Corrective actions to resolve slow moving and obsolete stock resulted in requests for the statistical algorithms to be tweaked. The business was allocating all responsibility for correct forecasts onto the systems statistical algorithms.

When we reviewed the new business, we found that sales had remained static. Some new customers had come on, but new sales to existing customers had declined. Perhaps lack of communication with existing customers was affecting repeat business.

Quick Fix

We continued the use of statistics, but we passed the ownership back to the key account managers.
Specifically we provided a portal where the account managers could adopt the statistical forecasts, or they could override them where they knew the statics were not correct, either way they had to choose the forecast they wanted. The ownership for slow moving and obsolete stock (SLOB) was again pushed back onto the account managers.

We coached the sales staff in conducting Business Review and Development (BRAD) reviews with their key customers to understand sales trends and prepare for future sales opportunities. These meetings were scheduled regularly for key accounts.

Information about pending artwork changes and promotions and other business changes that were identified from these BRAD reviews were utilised by the key account managers to override or correct the statistical forecasts as required.

SLOB dramatically reduced by adjusting the forecasts for known changes in products and lost work.

With increased customer contact, new business from existing customers increased.

The statistical tools continue to give a source of information to the key account managers , but responsibility is now on the account managers themselves to determine if it is correct.

Top TIPS

Forecasting should be owned by those who face the customers;
Statistics are of great assistance, if you understand their limitations; and
Sales can use forecasts to periodically talk to their customers. This builds market intelligence and seeds customer loyalty.

Tim Gray is a supply chain industry commentator and advises several businesses across APAC on supply chain systems. He is the managing director at Prophit Systems.

Mergers & Acquisitions and Sales Forecasting

When it comes to forecasting strategic acquisitions the need for containment can often result in an organisation’s planning functions not being directly involved in the processes. Initial scoping and feasibility is done at high level, and then project teams dive into risk assessments and due diligence functions.

At what point should the plans of these acquisitions be included into an existing planning system?

Scenario

Recently a Prophit Systems’ client successfully acquired a segment of a competitors business, thereby increasing their market share. To keep the details confidential, only a handful of people were involved in the financial modelling, and due diligence process.

When details of the acquisition became public knowledge the information provided was sparse and only available from the company’s senior management team. This created a number of costly problems that could have been easily avoided.

When Prophit Systems was asked to get involved, the client had realised that the sales figures that they had expected were not materialising.

In order to understand the cause of this discrepancy our team needed to compare the detailed sales to the expected sales. Unfortunately, the sales forecasting only existed at consolidated levels in balance sheets. The vendor had not provided detailed sales forecasts but rather historic sales figures.

To gain insight into where the problems were occurring, we built a forecast based on the historic sales. This forecast was detailed to the SKU, location and customer (SKULC) level. Having this level of granularity enabled us to slice the forecast vs. actual comparisons by item, by customer and by location to identify where underperformance was occurring.

It quickly became evident that the underperformance was localised to one account manager and another significant customer. Once the source of the issue was identified the Sales Manager was able to get to the root cause of the problems, and take appropriate action.

Now armed with a detailed forecast the Sales Manager was able to rapidly understand how the new business was performing, and where the hot spots were. Having a consolidated forecast of their finished goods requirements, they were also able to construct accurate projections of their raw material requirements.

The company’s acquisition also saw its total product volume increase by some 40%, and this led to an increase in the overall raw material required by the new-look business. Having detailed information about the consolidated material requirements our team leveraged this information to instigate a round of raw material price negotiations between the company and its suppliers.

Lessons Learnt

Obtain detailed forecasts as early as possible in your M&A transactions.
You will need this to build management targets, to help the transition and to facilitate the speed uptake of the management issues
Use these forecasts to chart your progress, and manage the transition of incorporating the new business. This is a risky time, where clients may jump ship. You need to manage the transition carefully.
Your raw material volume discounts thanks to the increased volume demand in an acquisition can be significant. The sooner the data is available to the various teams within the supply chain the earlier these discounts can be brought to bear.

Do you understand the weaknesses in your supply chain?

As a SCM solutions provider we understand that there are an infinite number of variables that influence a supply chain’s efficacy. This fact can make identifying the true culprit of a supply chain failure incredibly difficult. In many cases when there is a catastrophic failure within a supply chain managers tend to look for direct cause and this in most cases will be identified as one or two outside forces that were beyond their control. However, what these witch hunts fail to do is look at the bigger picture and identify all the factors that contributed to a supply chain disruption.

In John Manners-Bell’s book, Supply Chain Risk, he draws parallels between the Swiss Cheese Model and supply chain management. The Swiss Cheese Model was developed by academics in the risk analysis field. The gist of the model is that factors contributing to everyday operating procedures can be present for long periods of time without showing any symptoms of contributing to a potential adverse effect. It is only once a specific set of these dormant factors come together that operating conditions will see upheaval.

“All organisations have latent conditions – on their own they do not result in catastrophic failure. However, what is required is an ‘active failure’ which, when these latent conditions align across a network or organisation triggers a disastrous event.”

John goes on to provide an example which most people managing supply chains can relate to.

Imagine a carrier carrying key components to a factory is late with its delivery. Consequently, the factory has to shut down or 24 hours, which sees millions of dollars of production lost. The most obvious culprit to this scenario is the carrier itself.

However, what if the company in question whose factory is standing dormant waiting for the parts was actively pursuing leaner manufacturing, which in turn, had seen a minimisation of inventory and safety stock? What if procurement had also minimised their cost by sourcing parts from a foreign-based supplier and an earlier shipment had been rejected due to a failed quality inspection?

What if when appointing the new supplier the new lead-times had not been accurately accounted for and the potential for something going wrong along the new delivery route hadn’t been factored into planning and forecasting models?

Now all of a sudden the carrier (and the driver responsible for the delivery who was subsequently ‘let go’) aren’t solely responsible for the loss in revenue. In this case management and the relevant systems need to own a lion’s share of the responsibility for the failure.

This reality plays a major role in how we at Prophit Systems develop and implement our offering. We focus on making the input of variables as easy and error free as possible, while making sure that triggers are in place that will alert managers of any potential future anomalies that could impact any part of the supply chain. Furthermore, our reporting tools are designed to deliver transparent insights so that the combination of factors that led to a negative outcome can be identified and addressed.

info@ps.henrichan.com

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