ERP in 2020: will there be an ERPII?
In the way that Materials Requirements Planning (MRP) had extra functionality added to transform it into Manufacturing Resource Planning (MRPII), and MRPII, in turn, had more functionality added to create Enterprise Resource Planning (ERP), that itself will develop further. But what should ERPII be and how should it differ from today's ERP?
Certainly, a lot of new things are arriving; from Artificial Intelligence (AI) to Big Data, and from the Internet of Things (IoT) to Blockchain, but at times these can still appear to be solutions in search of problems so perhaps a better approach is to identify problems that need to be solved before considering solutions. One thing that, if not on most companies ERP concern lists already certainly should be, is the ability to respond quickly to change and this is certainly something that some emerging technology should be helping with. Another way to look at responsiveness to change is to view it as a need to react to the unexpected and that leads us to consider unforeseen demand in the supply chain; be that sales spikes or supply disruption.
On the sales side, demand spikes can be caused by a number of things, few of which are under the control of the supplier. An obvious example would be unseasonal good weather causing a boost for ice cream sales and, sticking to a food theme, companies have also seen sales boom when a 'celebrity chef' uses a particular product or device on a TV cooking show. Quite simply; the faster they react, the more product they will sell.
With the technology now available, it is easy to pass data very quickly along the supply chain, even to link point-of-sale terminals in retail outlets directly to suppliers' ERP systems, but the problem is that transmitting raw data is rarely a good idea. Imagine a supermarket that has on its shelves a product that is just not selling, and perhaps 'sell by' dates are approaching fast. So local management slashes the price and, at give-away prices, the shelves start to clear. But that produces an apparent spike in demand and an 'unintelligent' system might see that and react by raising new orders on the supplier to prevent a stock-out.
Currently, with manual intervention, this can be avoided, so companies need to decide whether a delay in replenishment to allow manual verification is acceptable. If there are noticeable benefits to automating the process, it will be necessary to ensure that the system recognizes this unnatural demand and does not react to it. Perhaps this is a possible application of AI; especially if low demand at some sales points is balanced by high demand at others, raising the possibility of transferring stock between outlets.
Of course, there will be times when an increase in demand is genuine and again it may be the province of AI, and perhaps Big Data, to help identify if it is likely to be long term. If it is, then taking the time to renegotiate supplier discounts will be justified but, if it is not, then that time would better used in reducing the manual effort required to respond to it. If there is a need to order some more stock in the short term, the question becomes how to do that as efficiently as possible.
Decreasing demand also needs to be considered because, although there are opportunities for some companies, such as supermarkets, to get rid of excess stock via special offers and price reductions, others, for example, distributors of building materials or of high-value items, will have fewer options because purchases of this kind of items are rarely impulse buys. So raw demand data needs to be consolidated, whether at a regional level or at a central distribution point, in order to make proper sense of it. Companies just need to decide if the cost of using AI, Big Data and other tools is justified by the amount of money that they are likely to save.
Looking at supply disruption; the best way to fix a problem is to prevent it from happening in the first place. Supply disruption can be caused by a number of things, including customers launching un-forecast demand, but when suppliers let their customers down, it is normally because something has gone wrong, and the usual problems are:
- the supplier over-promised,
- there were quality problems, and
- the supplier was let down by its suppliers, for the same reasons.
All of these are things that today's ERP system can directly help with, but it might be worthwhile considering what it can do now and what ERPII might be able to do in the future.
Few things are as damaging as over-promising. It's bad enough when a supplier lets down a customer but even worse when that customer is in a supply chain because when Company D lets down Customer E, Customer E probably lets down their customer, F, who lets down their customer, G, and so on until the end of the chain. Clearly, the longer the supply chain and the earlier in it the first broken promise occurs, the worse the damage will be.
Over-promising can be deliberate and can be accidental. Deliberate over-promising can be caused by several things; one of which is over-optimism. Being optimistic is not in itself a bad thing but the problems come when that optimism causes orders to be accepted that should not be accepted because the company doesn't have the ability to produce what has been sold. Some companies can turn on overtime working but that can eat into profitability very quickly.
A second reason for over-selling is the mistaken belief that 'the more you ask for, the more you'll get' and that encourages some companies to over-promise to 'keep the pressure on'. They believe that disappointing some customers is a small price to pay in order to maximize output; especially if they have no need, or no expectation, of repeat business and all they have to do is to choose which customers to let down. People who understand manufacturing understand that over-loading a resource actually reduces its output because the inevitable lack of a realistic plan can do no other than to disrupt flow; especially if priorities change frequently to satisfy disgruntled customers.
Turning to accidental over-selling; that frequently happens when companies don't know their true capacity or when the production department is as over-confident of what can be produced as their sales colleagues. Even when they do know their actual capacity, they can be tempted, or feel pressured, to plan to 100% of that capacity. That's fine. Until something goes wrong; until items fail inspection and have to be reworked or remade; until a machine breaks down, and then things deteriorate rapidly and output falls because of logjams in the factory.
ERP can help with all of these problems through its capacity planning and scheduling modules. Capacity planning (also known as Rough Cut Capacity Planning or RCCP) can be run against sales forecasts to identify capacity restrictions before they become a problem and capacity scheduling can be run against new orders, or order inquiries, to calculate realistic delivery dates. One possible application of Big Data is to help identify a resource's true capacity: i.e. how much resource is actually available after typical breakdowns, rework etc have taken their share.
Most companies recognized many years ago that quality is important. Making substandard or reject product is not a good way to make money (or retain customers) but totally eradicating quality problems is a continuing battle. Poor product quality can be caused by poor quality materials or by problems with the production process, and ERP can help with both.
Firstly, ERP systems hold information on supplier receipts and rejections. They also hold data on items that had to be switched out during the production process and, using batch and serial number tracking, items that were found to be faulty after they had reached the customer. All this data, correctly analyzed, helps identify problem suppliers, problem items and problem processes so that appropriate action can be taken.
Suppliers let down by their suppliers
Acknowledging from the previous two points that, whatever companies do, Murphy's Law will inevitably kick in occasionally, the challenge is to identify and put in place actions that can mitigate its effect. This is especially important in long or complex supply chains where the effect of problems can ripple a long way.
In an ideal world, the whole supply chain would be planned together, as one interconnected set of resources, and to some extent, this is already happening today. There are suppliers who dedicate resources to specific customers because they have contracts that enable them to do so. When suppliers are offered enough work to fill a machine long-term, it is easy for them to reserve that machine's capacity but, when a particular customer only takes a percentage of its output, there will be occasions when other customers have a prior claim on that capacity. Whether companies can negotiate with suppliers to reserve capacity will depend on circumstances, and suppliers may wish to be compensated when reservations are not taken up and plant stands idle.
So, given that ERP can already help with the above by, for example, enabling buffer or safety stocks to be held at strategic locations, what should be expected of ERPII? One view would that it should do these things quicker and with less effort; and that is perhaps where RPS (Robotic Process Automation) can help. To use a very simple scenario as an example, imagine that a company has an item under ROP (re-order point) control and that this item has both a preferred supplier, an agreed price and a preferred order quantity (possibly but not necessarily an EOQ).
That being the case, there is no reason for any human involvement to be required in getting that demand to the supplier. The only exceptions would be cases when delivery is required within the normal lead time or when the required order quantity is exceptional. Under what other circumstances could the order not be automatically relayed to the supplier? All of this can be achieved by RPA.
A step beyond that, but a step that can only be taken when all customers and suppliers fully trust each other, is for the demand to flow all the way along the supply chain automatically. That would potentially take several days out of the overall supply lead time but requires a 'global' MRP run that can seek potentially 'spare' stock at each point so that only netted-off demand is passed back. If customers and suppliers agree on allowing capacity and material reservations, this all becomes possible and, looking at the example of the automobile industry, the benefits for both customers and suppliers could be enormous.
What else could RPA be used for to increase efficiency and to take cost out of the supply chain? Beginning with sales order entry; why can't the customer's system enter an order directly into the supplier's system? And why can't the supplier's invoice be entered directly into the customer's system (where, of course, delivery quantity and purchase price can be automatically checked before payment is automatically processed)?
Taking cost out of the supply chain is always a worthwhile endeavor but, at this stage and until emerging technology finds its niche, the likely advantages of an ERPII will be to take time out of the supply chain and, if anything, that will have a more profound effect than just targeting cost.
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